Finishing

 

Transportation Security Proposal
A Master Thesis
Submitted to the Faculty
of
American Public University
by

In Partial Fulfillment of the
Requirements for the Degree
of
Master of Arts
American Public University
Charles Town, WV

Abstract
To maintain social and economic equilibrium, day-to-day operations must make use of other modes of transportation, such as buses, trains, and ferries. These intricate and interconnected transit systems need to make use of the proper technology to avoid disruptions in their operations, which would be detrimental to the inhabitants, users, data, and assets they serve. At every stage of the journey, the passengers, the crew, the equipment, and the cargo all need to be safeguarded and protected. This is because of the very complicated and frequently vast landscapes. Transportation routes, such as those found in airports and seaports, need to be routinely examined to verify that they are not only free of obstructions but also that they are operating as smoothly as possible. In addition to the risks posed by other dangers and vulnerabilities, the potential for terrorist attacks is an inherent risk at these transportation facilities; as a result, they require the necessary measures to protect what is most important. In addition to issues regarding the environment and the structure, maintaining the integrity of information technology systems is a consistent focus. In each of these situations, a comprehensive and well-thought-out security solution is necessary, even though the particular needs may vary.

 

 

 

Transportation Security Proposal
Introduction
One of the organizations that have successfully ensured the safety of the nation’s transportation system is the Transportation Security Administration (TSA). The transportation business is unique in that it has an impact not only on the specifics of daily living but also on the state of the economy as a whole. When it comes to providing transportation services in a fast and effective manner, the dependability and safety of the organizations providing those services are quite necessary. The transportation industry faces a wide range of issues that get more complex as the market matures. These challenges are exacerbated by the large number of people who use transit stations and the high value of the items that are transported on ships and ferries. Transportation Security in the United States employs a variety of strategies to defend the country, some of which may be covert while others may be obvious to the general public. These strategies are implemented in varying degrees depending on the severity of the threats. This division makes use of tactics such as canine-team airport searches and randomized data collecting and analysis to accomplish its goals. In addition, it works in conjunction with the United States Federal Air Marshals and checks passenger manifests to watch lists. According to Palmer (2020), the Transportation Security Administration (TSA) uses a variety of screening measures to ensure the safety of American citizens. These screening methods might range from the use of technology to direct questioning of potential terrorists. Both a photo identification and a passport are required to verify a passenger’s identity. The passenger will be allowed to continue on their journey once the facial recognition process has been finished. In addition, the TSA makes use of very advanced imaging technology, which has been linked to elements that increase the risk of cancer as well as privacy violations. A thorough search of the passenger’s person is performed to look for any metals or explosives. Another technique that needs the suspect to step aside to ensure their safety throughout the threat detection process is the use of pat-down searches. Travelers are required to adhere to the procedures and justifications established by the TSA for their safety. This assures that the process of checking passengers for security at the airport will go without a hitch. Because travelers’ shoes will be examined during security checks, they should put on footwear that is simple to take off. When travelling with liquids, such as gels, lotions, and sprays, you are required to carry each item separately, and the total weight of all liquids in a single bag cannot exceed 3.4 ounces. At this point, passengers must put away all electronic devices, including cell phones, laptops, tablets, and so on. However, the administrative division must contend with several obstacles to guarantee the citizens of the area are kept secure regardless of the mode of transportation they choose—driving, flying, sailing, or using the train.
Protecting customers and employees, cutting down on crime and vandalism in transportation corridors, promptly settling liability claims, reacting to crises, and ensuring compliance with privacy regulations and local ordinances are the principal challenges. Operators may be able to avoid these obstacles if they invest in a scalable enterprise video management system that supports both mobile and fixed video monitoring under one roof and with the same user interface. Now, operators can keep tabs on all of their assets as well as the locations of those assets. A management system that incorporates well-known data components such as GPS data (speed, location, and direction) and vehicle data alarms and parameters (such as bus number, operator I.D., impact, hard brake, and door open/closed) provides drivers with a comprehensive view of what is occurring on board. In addition, a management system that incorporates well-known data components such as GPS data (speed, location, and direction) provides drivers with a comprehensive view of what is occurring on board. Because the future is equally as vital as the present in all investments and collaborations, it is essential to make certain that solutions incorporate A.I. that is both transformative and adaptive, cloud storage, and remote services that can be accessed on demand.
Because of the constant flow of traffic, the transportation sector is one of a kind. There are a great number of routes you can take to get from point A to point B, regardless of the mode of transportation you choose (metro, bus, train, or aeroplane). In addition, because there are several teams accountable for the safety of operations, it may be difficult to prove who was responsible for what and when it was done. Any failure might potentially have catastrophic repercussions for the safety and security of the system. A malfunction can present itself in a variety of ways, including a fault with the software, a physical defect, or a failure to maintain a safe and secure environment. The problem is not a lack of technical data; rather, the problem is a lack of correct, up-to-date physical data. On the physical side, our continuous reliance on manual, paper-based, and outmoded procedures puts us at an increased danger of a safety and security catastrophe. This puts the transportation business at an increased risk.
A further key obstacle faced by the transportation industry is the prevalence of one-of-a-kind analogue video recording equipment, systems, and software. Even if they have access to intellectual property, the flexibility, growth capacity, and integration potential of proprietary solutions are limited. Buses and light rail are classic examples of proprietary systems that need employees to swap hard drives from cars manually. These systems also require frequent maintenance. Only if there is no video proof will a camera or recorder be considered to be broken. In light of the abundance of data that is currently accessible, linking video to events that are produced by CAN bus and MIB systems is more significant than it has ever been. The best solution should provide support for increasing automation and integration, with highlighted data and events being promptly offloaded through 5G/4G and the system and device status being accessible in real-time.
Purpose Statement
In this essay, we will look at the various types of criminal activity that are related to transportation infrastructure. It examines the outcomes of the investigation and pinpoints important approaches to make the transportation system more secure. When talking about the safety of transportation in the United States, it is necessary to analyze the role that the economy plays, the problems that currently exist, and the potential solutions. The Transportation Security Administration (TSA) was established by the government of the United States to strengthen safety measures at airports, seaports, and train stations. The Transportation Security Administration (TSA) can be thought of as a nation’s first line of defence due to the responsibilities that it performs. When compared to other law enforcement officers, they stand out because they work together and exchange information to strengthen national security. The provision of security by the TSA, which in turn results in the creation of jobs and the attraction of investment, is one factor that contributes to the economic development of a nation. Despite this, the transportation security system in the United States still has several significant flaws that need to be corrected, leaving it susceptible to Attack.

Research Question
The following research questions have emerged
1. What are the possible security threats that exist in the transportation sector
2. What measures have been put by the relevant administrations to address the security concerns

Literature Review
Transport Security
The Transportation Security Administration (TSA), which was established after the attacks of September 11, 2001, is responsible for the development of various transportation security technologies. The Transportation Security Administration (TSA) was established to improve the security of the country’s transportation infrastructure and to ensure that people and goods can move freely throughout the country (Homeland Security, 2021). In less than a year, the Transportation Security Administration (TSA) took over responsibility for airport security on a national scale and deployed government officers to examine all passengers and luggage on commercial airlines. At this time, the Transportation Security Administration (TSA) conducts screenings on all arriving, departing, and remaining visitors to the United States. This is accomplished through the utilization of Secure Flight. These programs make use of the most cutting-edge technologies available to stay one step ahead of the ever-evolving dangers. In addition, the most cutting-edge technology is used to conduct security checks on every shipment, irrespective of its dimensions, method of transport, or country of origin (Homeland Security, 2021).
In order to protect the country’s transportation infrastructure, the United States adopts a risk-based and layered strategy for transportation security. The Transportation Security Administration (TSA) is continuously working to improve the level of transportation security by utilizing a variety of its resources, including its technologies, processes, staff, and intelligence. The Transportation Security Administration (TSA) is responsible for operating many checkpoints, which all contribute to the enhancement of the overall safety of the American transportation system. In addition to this, there are components of its security that are both visible and invisible to the general public. There are also canine teams that randomly search airports, federal air marshals, federal flight deck officers, and intelligence gathering and analytic teams. Passenger names are checked against watch lists (Homeland Security, 2021). As a result, the Transportation Security Administration (TSA), which is dedicated to system evolution, improves the transportation security system. The general populace, as well as the passengers, in particular, will be safer as a result of this.
Current Challenges in Transport Security
Physical passenger Protection
For decades, airports have been the primary topic of conversation on issues about security. There have been a large number of cases in which airport systems have been vulnerable to control by terrorists (Gillen & Morrison, 2015). In addition to this, there are indications that a great deal of aircraft was taken by force throughout the 1970s (Slack & Rodrigue, 2020). In addition, terrorists have taken advantage of the absence of protection to hijack aeroplanes to gain publicity and extract ransom (Slack & Rodrigue, 2020). In addition, theft is one of the major problems that has been hurting all kinds of freight facilities. Theft problems are especially dangerous in settings where expensive commodities are being handled (Slack & Rodrigue, 2020). In certain locations, such as the docks, the local labour unions have been given the impression of being under the authority of organized crime. With time, access to freight terminals has been more restricted, and the deployment of security officers has contributed, at least to some extent, to the prevention of theft. As a solution to this issue, multiple precautions against potential danger have been put into place. For instance, the airline industry and a global regulatory agency created methods for examining both people and their luggage. The number of instances of hijacking has dropped, which indicates that these methods are working well. On the other hand, terrorist organizations started using new strategies, such as hiding explosives in unattended baggage (Gillen & Morrison, 2015).
Concerns about national security have had a significant impact on the aviation industry in a variety of ways, including the raising of prices, delaying of flights, and other types of inconveniences. Additionally, as a direct consequence of the COVID-19 pandemic, a new component of passenger transportation security known as epidemiological security has emerged (Slack & Rodrigue, 2020). This has proven to be particularly disastrous for modes of transportation that have a high passenger density, such as train travel, air travel, and cruise ships (Sivak & Flannagan, 2004). It is quite evident that many locations, like airports, ports, and terminals, among others, need to beef up their security procedures. By doing so, both the tourists and their goods will be protected from harm and kept safe from theft.
Protection During Transportation
The freight sector has long recognized the need for security as a primary priority. Illegal immigration, dodging of customs charges, drug trafficking, the use of vessels that do not meet safety standards, and piracy are some of the most important challenges (Gillen & Morrison, 2015). The process of securing freight transit is undergoing a steady transformation that emphasizes developing a plan that is more extensive and complicated per global supply networks (Friedrich & Balster, 2013). It is exceedingly difficult for a variety of different reasons to find a solution to the problem of insufficient security in the maritime industry. These include the difficulty of detection, the wide diversity of commodities transported on board, the scale of the global shipping fleet, and the sheer number of ports across the world (Slack & Rodrigue, 2020). In addition to this, the vulnerabilities of ports are susceptible to attack from both land and ocean (Arway, 2013).
Cyberattacks
Chief executive officers in the transportation sector are the most likely to face cyber threats. Hackers have extra access points since fleet telematics systems are being used increasingly frequently to monitor the location, condition, and status of physical assets. Despite the fact that breaching a private network exposes sensitive customer information, the greatest concern for companies in the transportation industry is the possibility that cybercriminals will cause actual damage to a truck or its expensive cargo. This is the case even though the breaching of a private network will reveal this information. Cybercriminals can take control of the digital dashboard of a truck that is hauling fresh food and change the temperature measurement that is displayed there so that it is accurate even when the cooling systems are turned off. The result is rotten food worth thousands of dollars, which the driver might not see until he or she stops to make a delivery. However, the driver could be unaware of the situation until this point. Automation, network technology, and electronic sensors are all heavily utilized in the train business as well. Power distribution, communications, train track signalling, and positive train control all necessitate the utilization of these technological advancements. Products and commodities that are very popular Rail companies may be particularly vulnerable to cyberattacks launched by political actors because of the greater possibility that supply chain disruptions may occur.
Advanced Technology
It is anticipated that the introduction of novel technologies such as semi- and fully-automated cars, as well as the utilization of sensors connected to the Internet of Things, would increase the cyber security concerns associated with transportation. In addition to reducing the risk of cyberattacks, the technology in question can also make driving more secure and help alleviate the current driver shortage. Companies, on the other hand, face the risk of slipping behind their competitors and missing out on big opportunities if they choose to ignore these advancements or fail to adapt to them. As operative technology progresses, operators of critical infrastructure must ensure that new architecture is not installed until it can be regulated and protected. This must be done before the new architecture can be implemented. As businesses grow and become more technologically advanced, they will make themselves more vulnerable to cyberattacks. Security can be compromised by efforts such as updating obsolete computer systems.
Lack of Drivers
The American Trucking Association predicts that by the year 2022, there will be almost 100,000 fewer truck drivers operating on the nation’s roadways. The primary factors contributing to the ongoing labour shortage are the retirement of older workers, the difficulty of replacing vacant positions with new hires, and the increased delivery demand that is a direct result of the growth of e-commerce. It is estimated that truck drivers in the United States reach the age of 56 on average. Younger folks have difficulties accepting the long, alone, inactive hours that are a part of the trucking lifestyle, even though incomes are rising and signing bonuses are being offered to new drivers. Furthermore, an older workforce is associated with a higher prevalence of health conditions as well as a larger consumption of employee benefits, all of which contribute to a rise in these expenses for firms. Because there is a lack of drivers, senior drivers are under increased pressure to make additional trips and spend more time behind the wheel each day to meet delivery deadlines. This contributes to increased levels of driver tiredness and the risk of being involved in an accident.
Destroyed Infrastructure
Ground transportation in the United States will continue to have issues, and there will be more people using the roads and rails as a result of the ageing and, in some places, decaying infrastructure in the country. Even short delays can result in significant financial losses for the proprietors and operators of rail and transportation businesses. According to Dwyer, in 2014, there was a total of 6.9 billion vehicle hours worth of road delays created as a result of traffic congestion in 470 urban areas (2018). Because of these delays, the United States was forced to throw away 3.1 billion gallons of fuel, which resulted in a total cost of 160 billion dollars. According to projections made by the ASCE, the total amount of time lost due to delays will increase by 20% by the year 2020, reaching 8.3 billion hours. Poorly maintained roads increase the likelihood of vehicle damage, which results in the need for either more frequent maintenance or more expensive repairs. This is in addition to higher fuel prices and delivery delays. In addition to this, it is highly unlikely that things will get better any time soon. According to projections provided by the ASCE infrastructure research, the investment shortfall will amount to $1.1 trillion between now and 2025 and an additional $3.2 trillion between 2026 and 2040.
Moreover, the price of gasoline has a significant impact on the profit margins of businesses involved in the transportation industry. A decrease in the price of oil leads to a decrease in the price of gasoline, which puts more money in the pockets of drivers. Because of the terrible inconsistency of oil prices, the trucking, rail, and aviation businesses are forced to contend with unpredictable price swings, which have a disproportionately negative impact on each industry. When gasoline prices are low, a greater number of shippers choose to deliver products via truck rather than rail because trucks are typically faster than rail (Dwyer, 2018). When oil prices are higher and freight trains offer a greater value than gas-guzzling tractor-trailers, railways may see an increase in business. This is because freight trains use far less fuel than tractor-trailers. In addition to shifts in the cost of oil, factors such as international trade conflicts and political unrest can impact shipping volumes and disrupt supply networks around the world. According to research conducted by Fitch Ratings, the transportation industry in the United States may be impacted by taxes on steel and aluminium as well as levies placed on automobiles. If auto tariffs dramatically increased the cost of buying a vehicle, it is possible that they would have the effect of taxing drivers by increasing the cost of driving and, as a result, reducing demand (Dwyer,2018).
Strategies to Improve the Safety Of The Transport System
The Transportation Security Administration has been the subject of a wealth of writing. These resources talk about the strategies that the TSA uses to increase security, the security standards that they have developed, the effects that those strategies have, and some suggested solutions for making those strategies more effective. Among the strategies that are used include the application of various forms of technology, the study of human behaviour, and the employment of other animals, such as dogs. Every traveller is required to behave by the procedures that have been outlined by the TSA. A nation will experience both positive and negative effects as a result of the TSA. The implementation of TSA has led to a variety of positive outcomes, including the generation of new job possibilities, the decline in the dependency rates of a nation, the growth of the economy of that nation, and many more. On the other hand, it has led to an increased risk of cancer as well as invasions of privacy.
Using Technology in Improving the Safety
Technology plays an increasingly important role in the operation of our transportation and logistics networks, from the high-tech traffic signals and speed limits that are being implemented in our smart cities to the robots and sensors that make “just in time” delivery possible in the modern era. Because of this, transportation networks must continue their digitization efforts and incorporate cutting-edge tools and analytics to raise their level of efficiency. The very same technologies that make it possible for people and commodities to move around also cause problems that need to be understood and managed as population concentrations continue to rise. Cybersecurity concerns must first be identified, then anticipated, and then avoided if at all possible (Bouhdada, 2019).
It is difficult to modernize the transportation company because, like many other businesses, it relies on established techniques and systems that have never been forced to consider cybersecurity. This makes the process tough. As a result of this lack of awareness, industry professionals have an immediate obligation to take action and create industry standards for new large- and small-scale solutions (Bouhdada, 2019). The increased possibility of something unfavourable taking place as a result of increased connectedness.
There are numerous examples of the impact that technology is having on transportation and logistics, such as the following: haulage fleets can increase their fuel efficiency by using real-time telematics and GPS data; ports are digitizing processes to streamline the flow of goods, and Alibaba and Amazon are pushing shipping companies to adopt Internet of Things (IoT) technologies to provide more visibility into their cargoes. Several governments are already investigating the possibility of installing infrastructure along railroad tracks to provide 5G internet connectivity to train passengers and on-board systems while the passengers are travelling to and from work (Bouhdada, 2019). All of these interconnected technologies hold a great deal of potential, but at the same time, they present serious safety concerns. Systems that were once protected from network attacks are now vulnerable to internet-related dangers, and potential attackers are persistent in their search for vulnerabilities to exploit across any attack surface that is accessible to them.
Transport Security of America Strategies
Accelerating the screening and searching processes to cut down on wait times is one of the numerous ways that the Transportation Security Administration (TSA) works to ensure the nation’s safety (Narh, 2021). In addition, the Transportation Security Administration (TSA) established the Screening Partnership Program (SPP), under which the TSA awards contracts to qualify organizations and people to assist with screening and search activities. For the purpose of screening passengers, the Transportation Security Administration has access to the most advanced technological equipment. In 2017, electronic devices that were larger than a typical mobile phone were required to be thrown into a bin before being x-rayed to identify any contraband materials or items. (Fischer& Walters 2019). These technologies and machines have an efficiency of up to 99 per cent and produce flawless results. In addition to screening passengers, they also directly question them about whether or not they appear suspicious based on their behaviour or appearance. Since the terrorist attacks of September 11, 2001, the United States has been protected against potential dangers by the Transportation Security Administration’s (TSA) aviation security measures, which are regarded as the toughest in the world.
The first thing that happens as you get through airport security is an identification check. Airport personnel check passengers’ identification cards before they proceed through the security checkpoints. After the member of the staff has verified your identity, you will proceed to the TSA agents, who will scan both your boarding pass and your I.D., as well as perform a face recognition check to guarantee that you are the legitimate owner of the I.D. After that, you will proceed to the security checkpoint, where both you and your belongings will be subjected to screenings. TSO is committed to utilizing what is widely regarded as one of the safest ways available. In addition, TSA agents will execute pat-down searches on passengers who have the appearance of being suspicious or who have violated security laws. Some of these justifications include the usage of bulky jewellery, the donning of a coat, or the donning of a sweater. If the TSA has reasonable grounds to suspect that you are acting suspiciously, you will be subjected to a pat-down search, which may be uncomfortable for you (U.S. Department of Homeland Security).
In addition, the transportation industry is anticipated to confront many issues in the not-too-distant future which may result in a shift in the priorities of the Transportation Security Administration (TSA) to handle the security issue (Kim et al., 2017). He examined earlier studies, discovered significant factors that can cause changes, constructed multi-driver scenarios, categorized the scenarios, identified techniques for constructing transportation, and, as a final step, established essential strategies for addressing the security issue. Because fewer people in the United States are taking advantage of the nation’s rail network, the federal government has increased spending on highways and airports.
Behaviour Detection
This technique is utilized frequently by the TSA to identify individuals whose behaviour raises questions. According to Holmes (2011), the face is the single most reliable indicator of whether or not a person is telling the truth when they are being interviewed. This is because of the ongoing interaction that takes place between the neural viewpoint of the face and the brain. Since the relevant neurons and facial muscles move involuntarily in response to emotional states, the face can portray either the truth or a lie (Hill & Craig, 2002). Officials at the TSA can tell the difference between voluntary expressions and those that are involuntary, which enables them to spot lies (Ekman, 2009). According to Holmes (2011), a standard polygraph machine can also be used to detect dishonesty by applying infrared technology, remote sensors, and other approaches. This can be done using a variety of different methods. These sensors measure physiological reactions like temperature, respiration, and heart rate, which can be used to determine with a high degree of accuracy whether something is genuine or not. The neuroscientific technique also incorporates neuroimaging to examine and decode subjective experiences (Davatzikos, 2005). This method yields trustworthy data that can be put to use in studies investigating the intricate relationship that exists between the mind and the brain (Holmes, 2011).
Program Implementation
The mission and purpose of the TSA, which is to strengthen security, are reflected in the programs that they offer. According to Tarpey (2016), the Foreign Airport Assessment Program (FAAP) provides the Transportation Security Administration with the ability to conduct inspections at international airports, as well as U.S. air carriers and international air carriers that provide services that are comparable to those provided in the U.S. This agency evaluates the effectiveness of the safety precautions taken at national airports and by air carriers by referring to both its regulations and those that have been imposed by the United Nations. According to Tarpey (2016), in 2016, the Transportation Security Administration carried out 1,665 carrier inspections and 139 assessments at airports (2013). In addition, the TSA offers training on developing capacity that addresses the flaws identified by the FAAP, as well as a comprehensive risk analysis that includes information regarding threats as well as information regarding flights. The Transportation Security Administration (TSA) is responsible for providing training in capacity building and concentrating on security training, cargo inspections, crisis management, and preventative safety measures. Tarpey (2016) suggests that the Transportation Security Administration (TSA) carry out this training by utilizing its Aviation Security Sustainable International Standards Team Program.
Methods and Procedures for Screening
The Transportation Security Administration (TSA) uses a variety of screening techniques on passengers to bolster the country’s overall security. For full-body imaging, they use a technology called advanced imaging technology (AIT), which can detect metallic dangers like bombs and other types of weapons (Palmer, 2020). The potential for cancer-causing X-rays and invasions of passenger privacy were two of the issues that led to the development of the millimetre advanced imaging technology (AIT) by the TSA. The passengers’ privacy is maintained because the screen is seen in a different room, and the millimetre AIT does not produce graphic images of the passengers (Palmer, 2020).
Some of the Security Measures Employed by the TSA
By using technology
By utilizing cutting-edge technology, the Transportation Security Administration (TSA) was able to expedite the screening process for carry-on luggage. The Transportation Security Administration uses dual-energy, multi-view x-ray scanners to inspect baggage. These scanners display non-intrusive views of the interiors of the bags (Liang et al., 2018). This renders any potentially dangerous objects or equipment visible, enabling them to be isolated and investigated in greater detail. The Transportation Security Administration (TSA) has introduced newly built and improved scanners. They can scan, analyze, and utilize the characteristics of a particular item to determine whether or not a material contains any potentially illegal compounds. The TSA is working to improve the capability of its automatic detection systems so that they can identify contraband items like firearms. This can be accomplished with the help of a swift algorithm that is accurate enough to do away with any misconceptions and quick enough to guarantee much shorter lines of people waiting their turn.
The scanner contains several x-ray detectors, which allow for the generation of images from a variety of perspectives. X-ray technology is a major contributor to the success of the Transportation Security Administration (TSA). Moreover, the Transportation Security Administration has implemented cutting-edge imaging technology (AIT). Jackson (2015) This technical advancement includes gadgets that image the entirety of the human body and screen passengers for metallic and nonmetallic dangers such as bombs and weapons. Any object that can be considered illegal that was photographed on the person will be shown on the screen (U.S. Department of Homeland Security).
The backscatter AIT was utilized in the past, but it has since been superseded by the millimetre AIT, which is still used today. Backscatter put the safety of the passengers in jeopardy by compromising their right to privacy and putting them at risk for a variety of possible health problems. Before it was put out of use permanently, it had only been in use for three years. Because millimetre AIT makes use of radio waves, it can perform the same function as backscatter while not compromising the user’s privacy or causing any risks to their health.
Need for Safety in the Transportation System
It is necessary to make a significant increase in the amount of work devoted to awareness campaigns and education to assist personnel at all levels of the transportation company, from track engineers to senior executives, in understanding the threat that is posed by cybersecurity. When discussing effective security measures, it is essential to take into account not just the people involved but also the processes and the technology. In order to address this issue, training programs that are not a part of a one-time induction procedure need to be designed, and key performance indicators for the employees need to include more frequent performance monitoring.
In addition to this, it is of the utmost importance that the idea of “security by design” be broadly accepted. When implementing new strategies, it is imperative to conduct risk assessments and implement mitigation strategies at each stage of the design pipeline. In order to accomplish this, it is necessary to take into account how new systems interact with existing ones. It was not until researchers discovered flaws in systems used in the real world that the practice of preventing in-vehicle telematics from connecting with control systems became a widespread practice.
The industry as a whole needs to enhance its ability to foresee and plan for risks of this nature, and it also needs to acknowledge that potential flaws can be introduced at any point along the supply chain. Only then can a new solution be developed. To address these problems, a greater number of companies should hire professionals to carry out audits that are vendor-independent when the system is still in the design phase. The goal of these audits is to detect and address any cyber security vulnerabilities. After networks and solutions have been established, security by design also needs frequent security audits and penetration tests. Even though the transportation industry has particular issues, long-term system support strategies must be prepared ahead.
The good news is that there are benefits to codifying best practices, which is especially encouraging when considering how significant transportation is. Communication channels that are already in place between asset owners and intelligence and security services are ideally suited for close coordination of cybersecurity measures. It is possible to increase awareness and give warnings about potential threats to digital security using the same mechanisms that are used to communicate information about threats presented by international terrorists, for example. Establishing worldwide organizations that can assist industries in directing their activities and fighting for changes to existing channels should be a priority for the industry as soon as it is practically possible to do so.
In order to overcome many of the difficulties that are now facing the international transportation business, there needs to be more collaboration between governments, operators, and security specialists. As additional systems are brought online and as hostile actors develop their toolkits, we can be confident that the number of assaults in the industry will increase. This is something that we can look forward to. Unavoidably, more security flaws will be found in extensively implemented systems, which will lead to a rise in the number of online attacks that are carried out with the assistance of automated software.
Together, we can reduce the likelihood of security incidents by bringing attention to the problems associated with cybersecurity, improving the quality of conversations that take place regarding these issues, and implementing training programs that raise the level of cyber security awareness among key personnel and operators. Because of this, transportation and logistics companies will be able to keep implementing innovative digital solutions to improve the productivity of their field workers safely.

Theoretical Framework
The theoretical framework covers the gaps that the literature will cover. Also, the section discusses the crime pattern theory as the theory that will be used. Lastly, the paper will state a statement of hypotheses to be tested.
Since we know little about transportation and logistics, authors frequently write about them. Logistics in transportation refers to the management of the systems that transport people and goods. Logistics is the study of how objects are moved from one point to another, including how they are packaged, moved, and stored. Transportation is a key component of the supply chain since it enables the movement of goods, people, information, and even services via water, land, air, and digital networks. The goal of transportation security is to reduce risk during the transit phase of the supply chain. Additionally, the essay will discuss cybersecurity from the perspective of security. Frequently, the first line of defence for transportation systems is cybersecurity. Modern technology has made it easier for hackers to identify weaknesses that were previously unknown. These cybercriminals are attempting to acquire access to sensitive data that might be used to carry out terrorist activities (Fan et al., 2018). A virus that spread after servers were compromised cost roughly $10 billion. The literature review will also consider the relationship between passenger safety and overall transportation safety. To flee, individuals such as refugees can hijack aeroplanes. At airports, individuals are anxious about their physical safety (Taylor et al., 2020). Screening methods are an effective method for addressing these problems. Thanks to screening, terrorists now have a new way to conceal explosives in their bags. In 1985, a plane crashed in India. This is an excellent illustration. The published works will also provide information on various forms of safety measures. A company’s security system comprises the measures used to protect its assets against theft, fraud, sabotage, and other forms of harm. Thefts and acts of violence occur more frequently outside of protected zones, according to the data. Since the transportation company cannot guarantee a safe parking lot for its drivers, it is in their best interests to identify and utilize safe parking sites (Joewono & Kubota, 2018). In the long run, dealing with cargo theft will be more expensive than paying a nominal fee at packing stations. Consequently, the textual contents will describe how scanners operate as a security precaution. As part of the security protocol for vehicle transportation, scanners must be utilized. A scanner may swiftly detect potentially dangerous objects outside. Consequently, security measures can be adopted to eliminate the threats permanently (Dvorak et al., 2018). Guards at entry points can detect danger if they are equipped with the proper equipment. In the event that the cargo is stolen, the passengers are protected by a license reader and an automatic under-vehicle inspection system. Even while it is always a good idea to be prepared for the worst, occasionally, the issue is a fallen tree on the road. It is essential to maintain vigilance, recognize anything out of the ordinary, and know how to respond correctly.
Crime pattern theory is the criminology notion most similar to how public transit operates. Below is an illustration of feedback in criminal pattern theory.

This theory is based on three fundamental concepts: nodes, paths, and edges. A node is a place where people carry out their primary activities and spend the majority of their time, such as at school, work, or in their free time (Newton & Ceccato, 2015). Around these activity nodes, users are supposed to establish “consciousness spaces” or familiar settings. Nodes are the stops, stations, and points in a transportation system where separate lines meet. Paths are the frequent routes people take between “nodes” following criminal pattern theory. These are the travel routes utilized by travellers. On a bus, train, or tram, they are the features of transit settings that occur throughout transportation excursions. The final component of the crime pattern theory is the concept of edges, which are unknown regions to individuals. Consider the edges of the public transportation system to represent the transportation environment’s limits, where one environment ends, and another begins (Newton & Ceccato, 2015). From the perspective of the entire voyage, however, these margins may become less apparent, particularly when walking.
Around these nodes, pathways, and edges arises a concept applicable to both lawbreakers and law-abiding individuals. To explain crime at nodes, according to the crime pattern theory, criminals would engage in activities within or near their awareness space. Transportation hubs can play a significant role in a person’s daily life; therefore, it stands to reason that they could also serve as meeting places for potential criminals and their victims. This makes me question if the offender’s activity spaces consist just of transportation hubs and the surrounding area or whether they also include transportation routes. Outside of the context of transportation, movement between nodes is typically straightforward, with the possible exception of natural obstacles such as rivers. These routes are typically travelled on foot or by car. On a transportation network, a victim’s options are primarily limited by the network’s configuration and a set of predefined routes (Zhang et al., 2022; Shiri et al., 2020; Twumasi et al., 2019). In other words, the activity space of a criminal may extend from nodes to routes, stops, and stations to buses and trains. Consequently, the transit system may be an indication of greater awareness, making it simpler to commit crimes in more locations (Oliveira et al., 2019; Barabino et al., 2020).
Utilizing the concept that best illustrates how theft might occur in road transportation, we gathered data on transportation security challenges. Given that road transport is the most common means of transporting people and goods, the concept appeared reasonable. It was determined that driving is the least expensive way of transportation.
Research Design/ Findings/Results/Discussion
This research project’s objective is to investigate potential threats to transportation security and develop measures to make transportation safer. The information was drawn from books and publications that provided extensive coverage on transportation security. In addition to seaports and airports, surveys are used to collect data in other sites, such as ports and cities. For data collection, both open and closed questionnaires are utilized.
There are additional ways to acquire information. A specific transportation company gave statistics and dealer feedback for this concept. There will be an examination of the data to discover whether there are any feasible explanations for the method in which replies were obtained. The findings are then analyzed to address the research questions.
Model of Transportation Network Growth
The model of network dynamics that is being examined in this section brings together all of the relevant agents and describes how they interact to reproduce the expansion and contraction of road networks. This part of the discussion will continue in the next section. The model that has been offered for expanding the transportation network has significant flaws since it is unable to represent the complexity of the transportation system that is present over an entire region. This presents several challenges to the expansion of the transportation network. It is essential to consider economic growth to be an exogenous variable because the transportation infrastructure is not the only factor that affects the rate at which the economy expands, and this is because transportation infrastructure is not the only factor that affects the rate at which the economy expands. During the process of analyzing a network, external factors like patterns of population and land use are also taken into consideration. These problems may be solved at some point in the future. The expansion of the road network has recently attracted much attention because it is a procedure that is already tough to understand and intricate. The model takes into account a variety of elements, including the amount that people desire to travel and how they move, as well as the amount of money that can be earned from prices, how investments are made, and the rate at which roads continue to deteriorate over time.
Figure 1 explains how the various parts of the model should be put together and provides an overview of the model’s constituent parts. A travel demand model projects link-level flows by factoring in the road network, how land is utilized, as well as socioeconomic and demographic data. The demand projections are used to figure out both the amount of money that will be made and the amount of money that will be spent in the future. When an investment module is put to use, the supply changes on an annual basis, and the network can keep becoming better (Namasudra et al., 2021). It is not necessary to perform annual iterations at every point in time for the modelling process to be successful. There is far more scope for development in this area (Gyalai-Korpos et al., 2020). However, budgets are often established at the beginning of each fiscal year, and this is also the time when any necessary adjustments to supply are carried out. The road network is illustrated as a directed graph with curved lines connecting the many nodes that make up the network (links). In the following part of this article, I will go into the particulars of how the submodels are described in mathematics by utilizing the conventional notation for directed graphs.

Figure 1: Model of Network Expansion Flowchart (U.E. = User Equilibrium)
Travel Demand
The typical four-step forecasting model that is used to anticipate travel demand at the link level takes into consideration exogenous variables, including land use, socioeconomic conditions, and the existing network. This allows the model to predict travel demand at the link level accurately. A framework that is based on zone regression is applied whenever travel plans are being produced. The origin-destination (O-D) cost table that was developed from the traffic assignment for the previous year is used for trip distribution in the current year. This is the result of deriving this from a model of gravity that takes into account two separate sets of limitations. The point is further demonstrated by Equation 1, which can be found in the appendix.
After it has finished its work, the origin-based user equilibrium traffic assignment algorithm that Bar-Gera and Boyce devised will install the resulting O-D table onto the transportation network for the current year (Ru et al., 2019). The amount of time it takes to complete the journey and the amount of money that must be paid in tolls are the two components that combine to generate what is known as the generalized link cost function. For the answer to equation 2, please look at the appendix that has been provided below.
The component of travel time is calculated by making use of the functional form that is made available by the Bureau of Public Roads (BPR) (Huo et al., 2022). The traffic assignment phase concludes that the Wardrop user equilibrium has been reached when the relative excess trip cost is less than 0.001.
Price and Revenue
At the link level, a cost is applied to automobiles that are in the process of moving through the system. The annual flow and the toll are the sole components of the annual income that are taken into consideration. The pricing strategy will be used to establish the amount of money that is required to be paid for the toll. The functioning of the fuel tax system in the United States is depicted by Equation 4 below. If regulations for fuel efficiency in automobiles are not taken into mind, gasoline taxes are, in essence, a form of tolling that also takes into account distance travelled (Fridstrøm & Østli, 2021). People mistakenly believe that the money that is collected from the gasoline tax is administered by a central organization, which also selects where the money should be invested (Carlan et al., 2019). This is a common misperception. This is made abundantly clear by equations number 3 and 4, which can be found in the appendix.
Maintenance Cost
The empirical evidence reveals that the costs of operating and maintaining roadways are only moderately connected (about 30% and 46%, respectively) to the volume of traffic that is present on the road at any given time (11; W. D. O. Paterson and R. Archondo-Callao, Estimating Road Use Cost, unpublished document, World Bank, Washington, D.C., Sept. 1991). Some maintenance charges remain consistent regardless of the number of customers who walk through the establishment each day. There is no guarantee that there will be a linear increase in the expenses of maintenance as a result of an increase in the volume of traffic. The number of vehicles that utilize the road daily has an additional influence on the amount of money that will be needed to resurface it (Madhav & Tyagi, 2022). In a streamlined version of the function for determining the cost of link maintenance, the three factors that are considered are the link’s length, capacity, and volume (Hamim et al., 2020). Utilizing this function allows one to calculate the cost of keeping the link up and running. Therefore, according to this principle, capacity is liable for absorbing any maintenance costs, regardless of how much traffic there is. The appendix contains Equation 5, which has extra information regarding this subject.
Construction Cost
The cost of building a road can be affected by several factors, including the road hierarchy (what kind of road it is, such as an Interstate highway or a state highway), the cost of purchasing land, the level of urbanization in the area, the terrain, and any elevated parts that may be included in the road. Other considerations include any high portions of the road that may be present (e.g., interchanges and bridges). Only the number of construction lanes and the level of urbanization were required for Keeler and Small to develop their estimate of how much it costs to construct something. Their methodology is capable of explaining around 52 per cent of the total price range’s volatility. A regression model for construction projects in the Twin Cities of Minnesota can be improved by including two new variables—namely, road hierarchy and work duration—in the equation. This enables the formulation of a more accurate model (Wu et al., 2019). As a consequence of this, we will now have an explanation for 77% of the variability in the costs (Nikolić & Cerić, 2022). When estimating how much it will cost to build a road, it is common practice to make use of a function that contains three different variables. In a prior study, the lane mile variable was applied, and the product of the first and third components of the cost function has an appearance that is comparable to that variable. The second statement may lead one to believe that the ranks of the various highways are distinct from one another. According to this cost function, the construction of a road that is longer, that has a higher capacity, and that is positioned higher in the road hierarchy will cost more money than the construction of a road that requires a larger capacity increase. Make sure you look in the appendix for equation 6, which you can find there. Others believe that investments in building roads are distinct, very lumpy, and difficult to split, but others believe that expenditures in extending capacity are often less lumpy than they appear to be. Both of these schools of thought are correct. In this argument, there is evidence to support both of the opposing perspectives that were adopted. Either you can only enlarge a road by a specified amount by adding a specific number of lanes, or you can only build a road with a specific number of lanes by yourself (Hymel, 2019). Neither option is available to you until both of these conditions are met. However, incremental capacity gains can also be obtained by making managers more effective, broadening shoulders, and repainting pavement (Badue et al., 2021). These three methods are listed in the previous paragraph. All of these are examples of several approaches that can be taken to obtain incremental capacity gains. Equation 6 is an illustration of a function that does not terminate. It is also feasible to use it to indicate how much it costs to construct a new road by including an additional limit. You may do this by following the steps in the previous sentence. This objective can be accomplished in a couple of different ways, the first of which is to keep a record of the number of lanes that are present on each road in the network, and the second is to determine a capacity-to-number-of-lane function. The capacity-number-of-lane function determines the maximum number of vehicles that may be accommodated on a specific stretch of road by factoring in the total number of lanes that comprise that stretch (L). You will find additional information on equation 7 in the appendix of this document. The number of lanes that were present in link a during the year I am represented by the symbol Li an, and the coefficients 0 through 2 are established by paying attention to what goes on. The inclusion of the second-order element is done to account for the likelihood that capacity increases will not develop at a linear pace as time goes on. This function could compute the sum of money that would be necessary to widen a roadway by one or two travel lanes to accommodate additional traffic. Because the expansion cost model was intended to be as easy to understand as possible, it does not take into account the level of urbanization that exists in a region. The level of urbanization can be directly correlated to the population of a region or the distance from the region to the nearest downtown.
Investment Rule
The investment model compares the amount of money that each link produces to the amount of money that it costs to maintain that link and uses this comparison to determine how the money should be spent to keep the network running and develop it. In the case that the funds that have been allotted to a connection are not adequate to sustain the ongoing maintenance requirements for that connection, the capacity of that link will be decreased in the round that follows. It is also possible that this money may be used to add a few links to the network; however, this will depend on how the money is invested and how the organization is formed. Another possibility is that this money will be used to purchase equipment. You are about to receive an in-depth education on two of the most frequent ways that individuals invest their money, and we could not be more excited to share it with you! Depending on what the user prefers, the construction cost function can do the capacity change calculation in either a discrete or continuous manner (Merkert et al., 2021). This flexibility is provided to accommodate the user’s needs (Courchelle et al., 20219). When the capacity of a link is increased or lowered, there is a possibility that the link’s free-flow speed will also vary simultaneously. When compared to narrower roads, wider highways often enable faster travel times for autos. A log-linear function is shown below as a means of illustrating how changes in capacity and free-flow speed occur with time. Equation 8 contains more information. There will be changes to several factors that influence how people move, such as the amount of time it takes to get from one link to another and the price that they are required to pay. When the capacity of a link is increased, and the free-flow speed is increased, there will also be changes to several other factors that influence how people move (Leong et al., 2020; Ostrowski & Budzynski, 2021). A new pattern of consumer demand will emerge in the succeeding round as a direct consequence of the aforementioned shifts in supply, as well as shifts in preferences, economic growth, and population. This will occur as a direct consequence of the previous shifts in supply.
Estimation of Model Parameters
The road network that is being modelled will cause the precise values of the parameters in the network dynamics model to take on a variety of different forms since this will affect how the model behaves. In the following section of the piece, we will go over the steps that need to be taken to generate a full set of parameter estimates for the road network in the metropolitan area that surrounds the Twin Cities. In addition to this, we talk about the empirical data that is necessary for model estimation as well as the potential sources of data of this kind. The process of assessing the road network in the Twin Cities had previously utilized the network dynamics model, which had been applied to the process (L. Zhang and D. Levinson, A model of the rise and fall of roads, unpublished paper, 2008) (Shahat et al., 2021). For the network dynamics model to be successfully executed, the very first piece of data that the model requires is the current status of the network. This covers the capacity, length, and free-flow speed of any lines, as well as any tolls that may be required for them or their connections. It is vital to use information from forecasting models that consider these changes because it is presumed that nothing within the area is generating changes in the land use, population, or economics of the area (Obeidat & Al-Kofahi, 2020). The practice of making an educated guess as to the number of people who could be interested in going on a trip has become a fairly standard component of the planning process over time. Travel demand models are typically four-step models, and they are routinely updated every five to ten years. The majority of large cities utilize these models to predict future travel demand. It is essential for organizations that deal with urban planning to have access to the travel demand parameters, such as those that may be found in the gravity model (Equation 1). In the general vicinity of the Twin Cities, the value is roughly 0.1. The pricing policy ought to be applied in the process of creating the formula for the process of determining the toll fee on a roadway, as was just discussed. This was just mentioned. The collection of income from taxes on fuel has long been one of the primary methods used in American cities like the Twin Cities and other American cities to pay for the construction and maintenance of roadways. Putting aside the fact that different autos achieve different levels of gas mileage (see Equations 4; and 3), the fuel tax is only a user fee that is based on the distance travelled. In order to calculate the quantity of the constant term, you will need to know the amount of the additional charge that is imposed per gallon. Following that, the value of the constant term is normalized to 1 by using the constant terms that are associated with the cost functions. Using data from several projects, Keeler and Small developed an easy linear model to estimate the costs involved with road repair (Qiao et al., 2021). The total number of miles that have been driven in a vehicle can have an impact on the expenditures that are associated with maintaining that vehicle. When Heggie was estimating how much it would cost to maintain the roads, he took into account both the order in which the highways were travelled and the overall number of vehicle kilometres driven. Paterson and Archondo-Callao state that only about 46% of the total expenditures associated with maintenance are related to the circulation of people and vehicles (Keller et al., 2020). The remainder, which accounts for 54%, is made up of fixed costs. Utilizing data that is unique to the road in question is going to be the most effective strategy for arriving at an accurate estimate of how much it will cost to maintain the road. As the amount of traffic that is being handled increases, there is some evidence to suggest that as a result of economies of scale, maintenance costs are becoming reduced. There is a direct correlation between the number of vehicles on the road and the rate at which the cost of maintenance per vehicle kilometre drops (Jones et al., 2020; Wróblewski & Lewicki, 2021). This suggests that the value of 3 lies somewhere between 0 and 1, about in the middle. (Equation 5). It is likely that higher-level roads, such as Interstate highways, have a maintenance cost that is higher than lower-level roads, such as arterial streets. This is something that should be taken into consideration when comparing the two types of roads. Even if both highways carry the same amount of traffic, which is some number greater than zero, this statement is still true. To this day, however, no research has yet been able to identify with absolute certainty how much 2. There is a significant probability that there will be scale-related concerns with the way the road is maintained once its capacity is increased from one to two vehicles per hour. In order to reiterate, the most reliable method for estimating how much it will cost to widen a road is to make use of the facts that are collected at the project level (Equation 6). In earlier studies on the topic of economies of scale in road construction, one of the predictors that were used was the number of additional lanes that were added every mile (Mahdavian et al., 2021; Kedarisetty et al., 2022). The researchers operate under the hypothesis that 1 equals 3. It is not surprising that the majority of building projects have virtually the same returns to scale (1 = 3 = 1), given that construction projects of all sizes are combined together. This is because building projects of all sizes are joined together. A recent study that used data from 110 projects in the Twin Cities indicated that the returns to scale (1 = 3 = 0.5) were significantly going up after it was taken into account that the sequence of the roadways was taken into consideration. The data for the study came from a recent study that used data from the Twin Cities. Without taking into consideration the sequence in which the roads were identified, the authors were able to find consistent returns to scale by using the same data set. It makes sense that the cost of widening a high-capacity road would go up in tandem with the rising cost of buying land. Because we do not possess any other information, it seems reasonable to assume that two is superior to 1. According to the results of these studies, the procedure that yields the most precise estimate of the cost function of a road expansion is to take the entire cost and divide it by three, which yields the number 1. In addition to this, they illustrate the need for additional research into other cost functions (e.g., a function that considers second-order effects, factor prices, and land acquisition costs). Utilizing the Metropolitan Council’s Transportation Improvement Program allowed for the gathering of information regarding the prices of goods and services in the Twin Cities. This was performed by the Metropolitan Council. In addition, the Metropolitan Council’s database for the regional transportation planning model contains information regarding the capacity the free-flow speed, and the number of lanes for over 10,000 different road segments in the Twin Cities (Nasim Khan et al., 2020). This information was compiled as part of the project to develop the model (Vishnu et al., 2018). Using these data, it is possible to estimate the capacity-number-of-lanes function found in Equation 7 as well as the capacity-speed function found in Equation 8. (Equation 8). Each coefficient (1 = 341, 1 = 162, 2 = 273, 1 = 30.6, 2 = 9.8) is statistically significant when analyzed at the.05 threshold of statistical significance. The expected values of the dependent variables are compared to the actual values in Figure 2. You may find this comparison in the figure. R2 values of 0.6 and 0.7, respectively, were calculated for the two different models.
Figure 2: There are Capacity-Speed (vph= number of vehicles per hour) and Capacity-Number-of-Lanes functions
It is recommended that the investment rule be formulated based on the investment policy, which is now being evaluated for normative applicability (such as policy evaluation). Calibration and validation of the investment model are not at all required in this hypothetical situation.
Investment Policies
When the cost of adding a new capacity unit is precisely equal to the amount that it adds to the total benefit, we say that a road is operating at its maximum capacity and that it is functioning at its full potential. Even though it creates a goal for long-term investments, this optimality condition does not provide you with any information that will tell you how to make decisions on long-term investments. The benefit-cost analysis has evolved to become the first stage in the decision-making process for many different types of investments. A variety of engineering best practices has been utilized to make certain that sufficient financing will be allotted for the road-building projects that are most appropriate (Ahmadabadi & Heravi, 2019). One of these procedures is the removal of any bottlenecks that may exist (Alsuliman, 2019). In this section, we will analyze the two investment rules that were brought up earlier and explain how they can be applied to regular road networks.
Bottleneck Removal
The strategy for eliminating bottlenecks is often applied to stretches of roadways that experience the most severe levels of traffic congestion, and the policy frequently favours expansion projects as a means of achieving its goals. This form of generic rule of thumb for investments is illustrated through the use of a mathematical model. The management of all network revenue (also known as E.T.) from all roadways falls under the purview of a single, centralized organization (Kashani et al., 2022). In addition to that, this entity is in charge of the distribution of the E.T. If you need any further explanation, please look at Equation 9.
At this point in time, it is absolutely necessary to keep the state of all of the roadways in good repair (Li et al., 2020). You can figure out how much the overall cost of maintenance will be by utilizing Equation 10, which can be found over here (M.T.). The money assists in paying for some of the costs that are associated with maintaining the property and helps pay for some of those costs. The volume-to-capacity ratio, denoted by the letter S, indicates how busy each road currently is. The capacity is raised dependent on the amount of money that is still available as well as the current level of congestion on each route (E.D.). Road A will be the first road in the network to have extensive work done to it because it has the highest S value out of all the roads in the network. The maximum amount of capacity that can be raised is determined by the level of service that is sought, marked by the symbol S*, or by the goal value, signified simply by the letter S. the value of S* must be reliant on the overall level of effectiveness achieved by the network. The value of S can be determined by locating the average of all of the roads, which can then be used in the calculation. If you have not done so already, you should look at equations 11 and 12. Equations 13 and 14 detail, respectively, the expenditures associated with the expansion as well as the rise in revenue that may be attributed to the expansion. The subsequent section of the highway has the second-highest volume of traffic of all of them. Calculations quite similar to these can be carried out to ascertain the amount of money that the firm is still making as well as the amount of money that it will cost for the business to expand. The cycle will keep going on and on forever, right up until the point where there is no more money to be made. The method that was just outlined is built on the idea that the money that is invested on roads can be invested in more than one location. The value of Fa i+1 in Equation 11 can be anything that has a positive sign in front of it. Only a small number of locations would be allowed for the building of road extensions if the model more properly mirrored reality. Because of Equation 7, we have a little bit more leeway with this assumption. In order to establish the number of lanes that will be added to the extension as well as the costs that are involved with the expansion, the anticipated new capacity is compared to the actual capacity in the discrete scenario (Marques et al., 2021; Shayanfar & Schonfeld, 2019). This is carried out regardless of the number of lanes that are created, be it one, two, or three.
Benefit-Cost Analysis
A benefit-cost analysis, on the other hand, takes into account both the benefits and the expenses of a potential course of action, in contrast to the bottleneck reduction approach. Before we can move on to examine the costs and benefits of the situation, we first need to reach an agreement on the point of view that everyone subscribes to. Likely, a company will not regard the costs to society to be “real costs” until after they have been factored in some way. This is because society’s costs are sometimes difficult to quantify. Again, a public organization is taken into consideration, and it is considered that it was founded to assist the greatest number of people who can receive it. The table below and equation 15 can be used to estimate the life-cycle cost (Ca) of a road extension project. This can be done by basing the estimate on how much it will cost to build and maintain the road. The majority of road widening projects offer several long-term benefits, some of which are challenging to quantify (Johnsson et a., 2020). These benefits include better traffic flow and increased capacity. When a road is widened, there will be less traffic passing along that route, at least temporarily. This will be the case whenever the road is widened (Meža et al., 2021). This will make transport faster and more dependable, cut down on the number of accidents, cut down on the amount of energy that is used, and cut down on the amount of pollution that is emitted into the air. It is more difficult to assess exactly how advantageous the extension will be to the system as a whole as a result of the fact that expanding a road has a propensity to make traffic worse on roads that connect to it and takes traffic away from routes that compete with it (Li et al., 2018; Elvik, 2021). The benefit (Ba) estimation method that we present simply takes into consideration the amount of time that will be saved by taking the shorter route because we are attempting to keep things as simple as possible. It is not taken into account how this will influence the total number of accidents, the total amount of pollution, or the total quantity of fuel that is used. Nor is it taken into account whether or not there will be any benefits that extend beyond the local area. Another issue that is not taken into consideration in this approach is induced demand. Have a look at the solution to equation 16 in the appendix, which can be found further down this page.
You can discover the ideal amount of growth for each road by first determining how to acquire the optimal benefit-to-cost ratio for that specific path (Li et al., 2021; Henke et al., 2021). This will allow you to establish the optimal amount of growth that should be pursued along each path. This non-linear programming problem contains a target function that is quite hard to comprehend, and it may be one of the most challenging aspects of the problem. The fact that there is a limit to the number of lanes that may be added to a roadway, on the other hand, makes it a great deal less difficult to understand the optimization problem. Calculating the benefit-to-cost ratios of adding one, two, or three lanes, as well as the route that delivers the best benefit-to-cost ratio (B.C. *) and the best number of extra lanes to add (F*), is not a difficult task at all. It is recommended that the funds be allocated to the course that has earned the greatest B.C.* by F* score and that this process is repeated until all of the available funds have been used.
The emergence of the Road Hierarchy
The network growth model can be applied to any existing highway network in the real world; the amount of time it takes to run is primarily determined by how quickly the traffic assignment algorithm converges on a solution. In the real world, the network growth model has the potential to predict future traffic patterns. In this section of the article, a 10-by-10 grid network with 100 nodes and 360 links is used to analyze the two investment plans. The evaluation has a special emphasis on how each of the plans affects the reliability of the network. Both of these possible policy settings start with the same situation as a baseline. The initial capacity of the grid network’s links, each of which is 4 kilometres long, is 735 vehicles per hour (this value corresponds to a one-lane road, according to the regression analysis with the capacity and number-of-lane data in the Twin Cities; see Equation 7). With an average S value of 0.8 and a speed of around 10 kilometres per hour on average, the initial network is very congested. The initial use of land in every one of the 100 network zones is the same. This is since each network zone serves as the starting and terminating point for 10,000 journeys. O-D demand is prone to fluctuations throughout time because link capacity and actual trip prices are prone to change. The point at which the simulation model converges is directly proportional to the total number of activities that can be taken to increase the reach of a network. When the network is managed from a central location, it is possible to achieve supply and demand equilibrium over the long run; however, this requires that the total income be exactly sufficient to cover the whole cost of maintenance (i.e., there are no more road expansions or contractions) (Huang & Kockelman, 2020; Wen et al., 2018). One can improve the effectiveness of both long-term and short-term road networks by making investments in a road network based on benefit-cost analysis. This will allow for the investments to have a positive impact (Figure 3). The discovery that the total number of vehicle hours of travel (VHT) in both situations appears to be coming closer together with time is an intriguing observation. Even though the most important shift that occurred over the 50 years was 20%, the difference in VHT when equilibrium is established is less than 3% (benefit-cost analysis = 0.31 million hours; bottleneck reduction = 0.32 million hours). Even though this technique does not take into account all of the potential benefits and drawbacks, the strategy for removing bottlenecks often prioritizes rerouting traffic onto the busiest highways first as the primary strategy for removing bottlenecks. On the other hand, a process that is sometimes referred to as the “iron law of traffic congestion” begins to take effect, and the newly created space is quickly occupied by a greater number of vehicles (Lee et al., 2020). This is because of the “iron rule of traffic congestion” Along with this, there have been further accounts of similar occurring in the “real world.” It is feasible that the volume of traffic on a route that has just been widened will return to what it was before the extension within a short period (Félix et al., 2020; Yu et al., 2020). As a direct result of the bottleneck reduction program, the possibility that highways that had their lanes widened the year before will have their lanes widened once again has grown. This is because the likelihood that bottlenecks will be reduced as a direct result of the program has increased. This self-perpetuating pattern of “the affluent getting richer” leads to the construction of road networks that have an increasing number of levels over time (Figure 4). It would appear that there is a power law at work in the way that the capacity of the route is distributed. When selecting how to spend money based on benefit-cost analysis, you will not observe the same tendencies that you do when making financial decisions based on intuition (Christie et al., 2022; Hannay, 2021; Pannell, 2018). Even though there is not enough evidence to prove it, it is still possible that a normal distribution is a good approximation of the capacity distribution. This is even though there is a lack of evidence to support it (or Poisson distribution, due to nonnegativity). Because all of the roads that comprise the Twin Cities network actually functioned as intended in the year 1998, the investment strategy that has typically propelled network growth in the region has probably been one that emphasizes eliminating bottlenecks (Oleśków-Szłapka et al., 2020). This is because of the fact that all of the roads that comprise the network actually functioned as intended in that year. Figure 5 depicts how the growth rates of the two alternative investment programs are different from one another and show how these differences compare.

Figure 3: Effectiveness of Networks with Varying Investment Strategies
In the vast majority of instances, the sole factor that a network hierarchy takes into consideration is how the various nodes are connected to one another (Jiang et al., 2020; Riolo & Newman, 2020; Alzahrani et al., 2020). It does not take into account the characteristics of the connection, such as its capacity or the cost of maintaining the connection. Because the nodes in some networks, such as social networks and the Internet, are the most significant parts of those networks, it is feasible that overlooking these characteristics will not be a huge worry for those particular networks.

Figure 4: The Capacity Distribution of Roads
On the other hand, connection characteristics are especially important in transportation networks, particularly road networks, because they have a substantial impact on how efficiently the network performs. This is particularly the case with road networks. This is especially true for systems that involve roadways. As a result, the hierarchy that is present in road networks has at least two components: namely, the connectivity hierarchy and the capacity hierarchy. The most efficient type of link is a star network, which may be thought of as an ideal hub-and-spoke structure (Wang et al., 2021). This type of link has a ring topology (Bangjun et al., 2022; Hu., 2020). One kind of distribution curve is called a power-law curve, and it shows how the capacity of each link in a network is distributed in a certain way. This particular network is the one that possesses the hierarchy that has the most capacity available. The discussion on how to establish a road hierarchy in the hypothetical experiment that was detailed above focused solely on the capacity hierarchy. This is a result of the connectivity hierarchy being built to be the same for both investment plans, which has brought about the aforementioned result.

Figure5: Changes in Networks Caused by Diversified Investment Strategies
Network Fragility and Vulnerability
The process of simulating the growth of a network through simulation entails several fascinating sub-steps that each has their unique appeal. However, the primary focus of this inquiry is centred on the relationship that exists between investment strategies, road hierarchy, network fragility, and vulnerability (Lu et al., 2021; Galiano & Moretti, 2021; Thees, 2020). This is because of fact that these four factors are interrelated. Does the more hierarchical road layout suggested by the bottleneck elimination policy allow for a greater number of failures than the flatter network that was presented in the benefit-cost analysis? A Monte Carlo simulation was run to acquire greater insight into this topic of reliability so that further action can be taken. After each iteration of the simulation, the functionality of the networks that have been degraded is evaluated. During each iteration, one of three criteria is chosen to remove a predetermined quantity of links from the equilibrium networks representing the two investment strategies. These networks are then compared to each other. The travel demand has changed to use the other connections, but not the connections between O and D pairs. This argument is based on the central premise that the reduction in capacity that will occur as a result of the various types of road failures that are described in the following paragraphs will not last for a period that is sufficient to result in significant changes in demand other than rerouting. This argument is based on the central premise that the reduction in capacity that will occur as a result of the various types of road failures that are described in the following paragraphs will not last for an extended period of There are three distinct circumstances that could lead to the collapse of a network: (a) a random link failure, in which the possibility of a link losing its capacity to carry traffic is completely random (Scenario 1), (b) a volume-dependent failure, in which a link carrying more traffic is more likely to fail than its peers carrying less traffic, and the failure rate is proportional to traffic volume, and (c) a scenario in which the most important links, those with the highest criticality, are the ones Each (Scenario 3) (Sohouenou et al., 2020; Li & Zhou, 2020; Ganin et al., 2019; Almotahari & Yazici, 2021). The first scenario is an appropriate fit for many road problems that are not caused by traffic, such as those that are caused by natural disasters.
This is because traffic is not the root cause of these difficulties. The second scenario makes an effort to take into account the effects of connection failures brought on by occurrences related to traffic, such as accidents and routine maintenance. The idea that the number of accidents and the volume of traffic are closely related to one another in a linear way has been supported by some empirical studies that used gathered traffic statistics as their primary source of information. These studies used a correlational approach to examine the relationship between the two variables. Recent studies have indicated that the underlying links between accident rates and traffic flow are more complex than previously thought (Retallack & Ostendorf, 2019; & Yamamoto, 2018; Miglani & Kumar, 2019). These studies took into account a variety of parameters, including speed, weaving movement, weather, and lighting conditions, among others. There is a correlation between the changes in the accident rate and the degree of traffic congestion, which is frequently determined by the computation of S, but this correlation does not exist in a manner that is directly proportional to the changes. In the following inquiry, there is a chance that improved models of connection failure induced by traffic will be implemented. In Scenario 3, the capability of a road network to be employed in the event of targeted attacks is put through its paces. A Monte Carlo simulation is not essential for the analysis of Scenario 3, and the reasons for this are clearly evident to the reader. It is said that a network is fragile if it experiences problems even with a relatively low number of connection failures that are either random or dependent on the amount of traffic passing through it (Yu et al., 2022; Shen et al., 2021; Shang et al., 2022). On the other hand, we refer to a network as being vulnerable when the disruption of one or more of its most critical connections has the potential to bring it down in a very short amount of time. The road network that has flatter topography is superior in all three of the conceivable scenarios in which a link breaks according to the benefit-cost investment criterion (Figure 6).

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Figure 6: Fragility and Vulnerability of Networks: Cross-Network Comparison (a) Random Link Failure (a), (b) Volume-Dependent Failure (b), and (c) Targeted Attack.
On the graph, the dots represent the outcomes of the Monte Carlo simulation run thirty times, with the averages of those runs being displayed. Because the Markovian volatility of the average performance was quite low, it was decided that 30 iterations would be sufficient to meet the requirements. On the other hand, random failure rates reveal the least variance in performance between the two networks, but directed attacks demonstrate considerable differences in performance between the two networks. It is becoming more and more apparent, with each of the three different possibilities, that the network that has fewer nodes and fewer connections is the preferable option. The bottleneck elimination policy tries to construct an extremely fragile network, and it does so by establishing a very hierarchical structure as its foundation. The overall travel time will quickly lengthen as more of the top links sustain damage. Even if only 4% of the most important connections are removed, the total time it takes to complete the journey will rise by a factor of two (14 out of 360). The difficulties that have been found in other scale-free or power law networks are consistent with the findings presented here. When the performance indicator for each network is given on its own, the network with a less hierarchical structure exhibits a gradual increase in journey costs for all types of network deterioration (Figure 7). The relationship that exists between the percentage of removed links and the percentage of an increase in the overall journey duration is best represented by a linear function. This is because this relationship is linear in nature. There is no correlation between the number of tiers in a road hierarchy and a linear rise in the amount of time required for travel (Tang et al., 2018; Goel et al., 2022; Lin et al., 2020). Both of these networks are susceptible to a type of network deterioration known as a random link failure, which is considered to be the mildest of the two. On the other hand, it is extremely clear that targeted attacks have the greatest impact on the effectiveness of the network.

Figure 7: Identifying and Eliminating Bottlenecks and Addressing Fragility and Vulnerability in Networks (a) Through Comparative Internal Analysis and (b) By Changing Current Practices.
Conclusion
Depending on how other transportation investments are made, the future forms of networks may differ greatly from one another. The study findings confirm the hypothesis that road transport is the most affordable way of transport. The safety and dependability of a transportation system must be considered alongside its potential to generate revenue. This is due to the fact that even a tiny amount of unanticipated capacity loss in a transportation system incurs substantial expenses. Researchers investigate the resilience of networked systems against targeted attacks, traffic accidents, and natural calamities. They also investigate how investment policies and the hierarchical arrangement of highways interact. The equilibrium road network for two distinct policy scenarios can be estimated using a microscopic network growth model. The first category includes investments based on a cost-benefit analysis and the elimination of bottlenecks. In order to determine how various types of network degradation would affect the stability of road networks, a series of Monte Carlo simulations with varying numbers of removed links were conducted. Hierarchies may be implemented in road networks for pragmatic reasons, such as enhancing the system’s cost-efficiency. However, it has been discovered that too many degrees of hierarchy make the system less trustworthy. During the phase of equilibrating or evolution, the cost-benefit analysis revealed that the grid network was more efficient and less prone to failure. This study demonstrates how security and safety may be incorporated into the design of transportation networks.

 

 

 

 

References
Namasudra, S., Deka, G. C., Johri, P., Hosseinpour, M., & Gandomi, A. H. (2021). The revolution of blockchain: State-of-the-art and research challenges. Archives of Computational Methods in Engineering, 28(3), 1497-1515. https://doi.org/10.1007/s11831-020-09426-0
Gyalai-Korpos, M., Zentkó, L., Hegyfalvi, C., Detzky, G., Tildy, P., Hegedűsné Baranyai, N., … & Zsiborács, H. (2020). The role of electricity balancing and storage: developing input parameters for the European calculator for concept modelling. Sustainability, 12(3), 811. https://doi.org/10.3390/su12030811
Huo, J., Wu, X., Lyu, C., Zhang, W., & Liu, Z. (2022). Quantify the Road Link Performance and Capacity Using Deep Learning Models. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2022.3153397
Ru, H., Xu, J., Duan, Z., Liu, X., & Gao, C. (2019). Modelling Travel Time After Incidents on Freeway Segments in China. IEEE Access, 7, 162465-162475. https://doi.org/10.1109/ACCESS.2019.2951792
Fridstrøm, L., & Østli, V. (2021). Direct and cross-price elasticities of demand for gasoline, diesel, hybrid and battery electric cars: the case of Norway. European Transport Research Review, 13(1), 1-24. https://doi.org/10.1186/s12544-020-00454-2
Carlan, V., Sys, C., & Vanelslander, T. (2019). Innovation in road freight transport: quantifying the environmental performance of operational cost-reducing practices. Sustainability, 11(8), 2212. https://doi.org/10.3390/su11082212
Madhav, A. V., & Tyagi, A. K. (2022). The world with future technologies (Post-COVID-19): open issues, challenges, and the road ahead. In Intelligent Interactive Multimedia Systems for e-Healthcare Applications (pp. 411-452). Springer, Singapore. DOI: 10.1007/978-981-16-6542-4_22
Hamim, O. F., Hoque, M. S., McIlroy, R. C., Plant, K. L., & Stanton, N. A. (2020). A socio-technical approach to accident analysis in a low-income setting: Using Accimaps to guide road safety recommendations in Bangladesh. Safety Science, 124, 104589. https://doi.org/10.1016/j.ssci.2019.104589
Wu, G., Hu, Z., & Zheng, J. (2019). Role stress, job burnout, and job performance in construction project managers: the moderating role of career calling. International journal of environmental research and public health, 16(13), 2394. https://doi.org/10.3390/ijerph16132394
Nikolić, M., & Cerić, A. (2022). Classification of Key Elements of Construction Project Complexity from the Contractor Perspective. Buildings, 12(5), 696. https://doi.org/10.3390/buildings12050696
Hymel, K. (2019). If you build it, they will drive: Measuring induced demand for vehicle travel in urban areas. Transport policy, 76, 57-66. https://doi.org/10.1016/j.tranpol.2018.12.006
Badue, C., Guidolini, R., Carneiro, R. V., Azevedo, P., Cardoso, V. B., Forechi, A., … & De Souza, A. F. (2021). Self-driving cars: A survey. Expert Systems with Applications, 165, 113816. https://doi.org/10.1016/j.eswa.2020.113816
Merkert, R., Beck, M. J., & Bushell, J. (2021). Will It Fly? Adoption of the road pricing framework to manage drone use of airspace. Transportation Research Part A: Policy and Practice, 150, 156-170. https://doi.org/10.1016/j.tra.2021.06.001
Courchelle, V., Soler, M., González-Arribas, D., & Delahaye, D. (2019). A simulated annealing approach to 3D strategic aircraft deconfliction based on en-route speed changes under wind and temperature uncertainties. Transportation research part C: emerging technologies, 103, 194-210. https://doi.org/10.1016/j.trc.2019.03.024
Leong, L. V., Azai, T. A., Goh, W. C., & Mahdi, M. B. (2020). The development and assessment of free-flow speed models under heterogeneous traffic in facilitating sustainable inter-urban multilane highways. Sustainability, 12(8), 3445. https://doi.org/10.3390/su12083445
Ostrowski, K., & Budzynski, M. (2021). Measures of functional reliability of two-lane highways. Energies, 14(15), 4577. https://doi.org/10.3390/en14154577
Shahat, E., Hyun, C. T., & Yeom, C. (2021). City digital twin potentials: A review and research agenda. Sustainability, 13(6), 3386. https://doi.org/10.3390/su13063386 Gharaibeh, A., Shaamala, A., Obeidat, R., & Al-Kofahi, S. (2020). Improving land-use change modelling by integrating ANN with the Cellular Automata-Markov Chain model. Heliyon, 6(9), e05092. https://doi.org/10.1016/j.heliyon.2020.e05092
Qiao, Y., Labi, S., & Fricker, J. D. (2021). Does highway project bundling policy affect bidding competition? Insights from a mixed ordinal logistic model. Transportation Research Part A: Policy and Practice, 145, 228-242. https://doi.org/10.1016/j.tra.2021.01.006
Keller, S., Gabriel, R., & Guth, J. (2020). Machine learning framework for the estimation of average speed in rural road networks with OpenStreetMap data. ISPRS International Journal of Geo-Information, 9(11), 638. https://doi.org/10.3390/ijgi9110638
Jones, J., Genovese, A., & Tob-Ogu, A. (2020). Hydrogen vehicles in urban logistics: a total cost of ownership analysis and some policy implications. Renewable and Sustainable Energy Reviews, 119, 109595. https://doi.org/10.1016/j.rser.2019.109595
Wróblewski, P., & Lewicki, W. (2021). A method of analyzing the residual values of low-emission vehicles based on a selected expert method taking into account stochastic operational parameters. Energies, 14(21), 6859. https://doi.org/10.3390/en14216859
Mahdavian, A., Shojaei, A., Salem, M., Laman, H., Eluru, N., & Oloufa, A. A. (2021). A Universal Automated Data-Driven Modeling Framework for Truck Traffic Volume Prediction. IEEE Access, 9, 105341-105356. https://doi.org/10.1109/ACCESS.2021.3099029
Kedarisetty, S., Kim, C., & Harvey, J. T. (2022). Regression Models of Road User Cost Prediction for Highway Maintenance and Rehabilitation for Life Cycle Planning in California. Transportation Research Record, 2676(1), 18-29. https://doi.org/10.1177%2F03611981211027153
Nasim Khan, M., Das, A., & Ahmed, M. M. (2020). Non-parametric association rules mining and parametric ordinal logistic regression for an in-depth investigation of driver speed selection behaviour in adverse weather using SHRP2 naturalistic driving study data. Transportation research record, 2674(11), 101-119. https://doi.org/10.1177%2F0361198120941509
Vishnu, N., Kameshwar, S., & Padgett, J. E. (2018). A framework for resilience assessment of highway transportation networks. In Routledge handbook of sustainable and resilient infrastructure (pp. 216-238). Routledge. A framework for resilience assessment of highway transportation networks (taylorfrancis.com)
Ahmadabadi, A. A., & Heravi, G. (2019). The effect of critical success factors on project success in Public-Private Partnership projects: A case study of highway projects in Iran. Transport Policy, 73, 152-161. https://doi.org/10.1016/j.tranpol.2018.07.004
Alsuliman, J. A. (2019). Causes of delay in Saudi public construction projects. Alexandria Engineering Journal, 58(2), 801-808. https://doi.org/10.1016/j.aej.2019.07.002
Kashani, K., Sabet, M. S., Jalali Javaran, M., & Moieni, A. (2022). Bottleneck removal of paclitaxel biosynthetic pathway by overexpression of DBTNBT gene under methyl-β-cyclodextrin and coronatine elicitation in Taxus baccata L. Plant Cell, Tissue and Organ Culture (PCTOC), 149(1), 485-495. https://doi.org/10.1007/s11240-022-02279-4
Li, C., Yue, W., Mao, G., & Xu, Z. (2020). Congestion propagation based bottleneck identification in urban road networks. IEEE Transactions on Vehicular Technology, 69(5), 4827-4841. https://doi.org/10.1109/TVT.2020.2973404
Marques, N. L., Bastian-Pinto, C. D. L., & Brandão, L. E. T. (2021). Crossing the Brownian Bridge: valuing infrastructure capacity expansion policies as real options. Construction Management and Economics, 39(3), 261-276. https://doi.org/10.1080/01446193.2020.1863439
Shayanfar, E., & Schonfeld, P. (2019). Selecting and scheduling interrelated road projects with uncertain demand. Transportmetrica A: Transport Science, 15(2), 1712-1733. https://doi.org/10.1080/23249935.2019.1639083
Johnsson, F., Karlsson, I., Rootzén, J., Ahlbäck, A., & Gustavsson, M. (2020). The framing of a sustainable development goals assessment in decarbonizing the construction industry–Avoiding “Greenwashing”. Renewable and sustainable energy reviews, 131, 110029. https://doi.org/10.1016/j.rser.2020.110029
Meža, S., Mauko Pranjić, A., Vezočnik, R., Osmokrović, I., & Lenart, S. (2021). Digital twins and road construction using secondary raw materials. Journal of Advanced Transportation, 2021. https://doi.org/10.1155/2021/8833058
Li, L., Zheng, K., Wang, S., Hua, W., & Zhou, X. (2018). Go slow to go fast: minimal on-road time route scheduling with parking facilities using historical trajectory. The VLDB Journal, 27(3), 321-345. https://doi.org/10.1007/s00778-018-0499-4
Elvik, R. (2021). Why are there so few experimental road safety evaluation studies: Could their findings explain it? Accident Analysis & Prevention, 163, 106467. https://doi.org/10.1016/j.aap.2021.106467
Li, S., Shu, K., Chen, C., & Cao, D. (2021). Planning and decision-making for connected autonomous vehicles at road intersections: A review. Chinese Journal of Mechanical Engineering, 34(1), 1-18. https://doi.org/10.1186/s10033-021-00639-3
Henke, I., Cartenì, A., Molitierno, C., & Errico, A. (2020). Decision-Making in the transport sector: A sustainable evaluation method for road infrastructure. Sustainability, 12(3), 764. https://doi.org/10.3390/su12030764
Huang, Y., & Kockelman, K. M. (2020). Electric vehicle charging station locations: Elastic demand, station congestion, and network equilibrium. Transportation Research Part D: Transport and Environment, 78, 102179. https://doi.org/10.1016/j.trd.2019.11.008
Wen, J., Chen, Y. X., Nassir, N., & Zhao, J. (2018). Transit-oriented autonomous vehicle operation with integrated demand-supply interaction. Transportation Research Part C: Emerging Technologies, 97, 216-234. https://doi.org/10.1016/j.trc.2018.10.018
Félix, R., Cambra, P., & Moura, F. (2020). Build it and give them bikes, and they will come: The effects of cycling infrastructure and bike-sharing system in Lisbon. Case studies on transport policy, 8(2), 672-682. https://doi.org/10.1016/j.cstp.2020.03.002
Yu, B., Lee, Y., & Sohn, K. (2020). Forecasting road traffic speeds by considering area-wide Spatio-temporal dependencies based on a graph convolutional neural network (GCN). Transportation research part C: emerging technologies, 114, 189-204. https://doi.org/10.1016/j.trc.2020.02.013
Lee, H. L., & Shen, Z. J. M. (2020). Supply chain and logistics innovations with the Belt and Road Initiative. Journal of Management Science and Engineering, 5(2), 77-86. https://doi.org/10.1016/j.jmse.2020.05.001
Christie, A. P., Downey, H., Frick, W. F., Grainger, M., O’Brien, D., Tinsley‐Marshall, P., … & Sutherland, W. J. (2022). A practical conservation tool to combine diverse types of evidence for transparent, evidence‐based decision‐making. Conservation Science and Practice, 4(1), e579. https://doi.org/10.1111/csp2.579
Hannay, J. E. (2021). Benefit Points for the Project. In Benefit/Cost-Driven Software Development (pp. 21-48). Springer, Cham. https://doi.org/10.1007/978-3-030-74218-8_3
Pannell, D. (2018). Prioritizing environmental investments. Editor: David Salt Website: decision-point. com. au, 20.
Oleśków-Szłapka, J., Pawłyszyn, I., & Przybylska, J. (2020). Sustainable urban mobility in Poznan and Oslo-Actual State and development perspectives. Sustainability, 12(16), 6510. https://doi.org/10.3390/su12166510
Jiang, K., Wang, Z., Yi, P., & Jiang, J. (2020). Hierarchical dense recursive network for image super-resolution. Pattern Recognition, 107, 107475. https://doi.org/10.1016/j.patcog.2020.107475
Riolo, M. A., & Newman, M. (2020). Consistency of community structure in complex networks. Physical Review E, 101(5), 052306. https://doi.org/10.1103/PhysRevE.101.052306
Alzahrani, B., Oubbati, O. S., Barnawi, A., Atiquzzaman, M., & Alghazzawi, D. (2020). UAV assistance paradigm: State-of-the-art in applications and challenges. Journal of Network and Computer Applications, 166, 102706. https://doi.org/10.1016/j.jnca.2020.102706
Wang, M., Cheng, Q., Huang, J., & Cheng, G. (2021). Research on optimal hub location of agricultural product transportation network based on a hierarchical hub-and-spoke network model. Physica A: Statistical Mechanics and its Applications, 566, 125412. https://doi.org/10.1016/j.physa.2020.125412
Bangjun, W., Quoqiang, S., Long, Z., & Wei, Z. (2022). Designing Optimal Hub-and-Spoke Network Considering Flow Delay Cost: The Case of Urban Express Delivery in Eastern China. https://orcid.org/0000-0003-1372-2486
Hu, W., Dong, J., Hwang, B. G., Ren, R., & Chen, Z. (2020). Network planning of urban underground logistics system with hub-and-spoke layout: two-phase cluster-based approach. Engineering, Construction and Architectural Management. https://doi.org/10.1108/ECAM-06-2019-0296
Lu, L., Fang, K., Liu, C. M., & Sun, C. (2021). The Spatial Network Contagion of Environmental Risks Among Countries Along the Belt and Road Initiative. Frontiers in Environmental Science, 9, 721408. Frontiers | The Spatial Network Contagion of Environmental Risks Among Countries Along the Belt and Road Initiative (frontiersin.org)
Galiano, G., & Moretti, L. (2021). Consistency of urban roads to manage emergencies: Methodology to identify the minimum network with total connectivity at maximum availability. Sustainability, 13(20), 11151. https://doi.org/10.3390/su132011151
These, H. (2020). Towards local sustainability of mega infrastructure: Reviewing research on the New Silk Road. Sustainability, 12(24), 10612. https://doi.org/10.3390/su122410612
Sohouenou, P. Y., Christidis, P., Christodoulou, A., Neves, L. A., & Presti, D. L. (2020). Using a random road graph model to understand road networks’ robustness to link failures. International Journal of Critical Infrastructure Protection, 29, 100353. https://doi.org/10.1016/j.ijcip.2020.100353
Li, J., & Zhou, Y. (2020). Optimizing risk mitigation investment strategies for improving post-earthquake road network resilience. International Journal of transportation science and technology, 9(4), 277-286. https://doi.org/10.1016/j.ijtst.2020.01.005
Ganin, A. A., Mersky, A. C., Jin, A. S., Kitsak, M., Keisler, J. M., & Linkov, I. (2019). Resilience in intelligent transportation systems (ITS). Transportation Research Part C: Emerging Technologies, 100, 318-329. https://doi.org/10.1016/j.trc.2019.01.014
Almotahari, A., & Yazici, A. (2021). A computationally efficient metric for the identification of critical links in large transportation networks. Reliability Engineering & System Safety, 209, 107458. https://doi.org/10.1016/j.ress.2021.107458
Retallack, A. E., & Ostendorf, B. (2019). Current understanding of the effects of congestion on traffic accidents. International journal of environmental research and public health, 16(18), 3400. https://doi.org/10.3390/ijerph16183400
Ye, L., & Yamamoto, T. (2018). Impact of dedicated lanes for a connected and autonomous vehicle on traffic flow throughput. Physica A: Statistical Mechanics and its Applications, 512, 588-597. https://doi.org/10.1016/j.physa.2018.08.083
Miglani, A., & Kumar, N. (2019). Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges. Vehicular Communications, 20, 100184. https://doi.org/10.1016/j.vehcom.2019.100184
Yu, J., Xiao, B., & Cui, Y. (2022). Cascading failure model and robustness of heterogeneous interdependent combat network. arXiv preprint arXiv:2205.04099. https://doi.org/10.48550/arXiv.2205.04099
Shen, H., Liang, Y., & Shen, Z. J. M. (2021). Reliable hub location model for air transportation networks under random disruptions. Manufacturing & Service Operations Management, 23(2), 388-406. https://doi.org/10.1287/msom.2019.0845
Shang, W. L., Gao, Z., Daina, N., Zhang, H., Long, Y., Guo, Z., & Ochieng, W. Y. (2022). Benchmark analysis for the robustness of multi-scale urban road networks under global disruptions. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2022.3149969
Tang, K., Chen, S., Liu, Z., & Khattak, A. J. (2018). A tensor-based Bayesian probabilistic model for citywide personalized travel time estimation. Transportation Research Part C: Emerging Technologies, 90, 260-280. https://doi.org/10.1016/j.trc.2018.03.004
Goel, R., Goodman, A., Aldred, R., Nakamura, R., Tatah, L., Garcia, L. M. T., … & Woodcock, J. (2022). Cycling behaviour in 17 countries across six continents: levels of cycling, who cycles, for what purpose, and how far? Transport reviews, 42(1), 58-81. https://doi.org/10.1080/01441647.2021.1915898
Lin, Y., & Li, R. (2020). Real-time traffic accidents post-impact prediction: Based on crowdsourcing data. Accident Analysis & Prevention, 145, 105696. https://doi.org/10.1016/j.aap.2020.105696
Annur, R., & Ponnusamy, V. (2020). Information and communication technology (ICT) for intelligent transportation systems (ITS). Advances in Electronic Government, Digital Divide, and Regional Development, 164-194. https://doi.org/10.4018/978-1-7998-1851-9.ch009
Arway, A. G. (2013). Government and industry supply chain security programs. Supply Chain Security, 99-112. doi:10.1201/b13871-8
Davatzikos C. (2005) Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Neuroimage 28(3):663–668
Ekman P. 2009 (1985) Telling lies: clues to deceit in the marketplace, politics, and marriage.
Fischer, R. J., Halibozek, E. P., & Walters, D. C. (2019). Transportation security issues and regulation. Introduction to Security, 463-486. doi:10.1016/b978-0-12-805310-2.00018-4
Friedrich, H., & Balster, A. (2013). Supply-chain risk analysis with extended freight transportation models. Freight Transport Modelling, 217-232. doi:10.1108/9781781902868-011
George, B., & Shekhar, S. (2017). Road maps, digital. Encyclopedia of GIS, 1780-1786. https://doi.org/10.1007/978-3-319-17885-1_1138
Gillen, D., & Morrison, W. G. (2015). Aviation security: Costing, pricing, finance and performance. Journal of Air Transport Management, 48, 1-12. doi:10.1016/j.jairtraman.2014.12.005
Hill M., L. & Craig K., D. (2002) Detecting deception in pain expressions: the structure of genuine and deceptive
Holmes, M. (2011). National security behavioral detection: A typography of strategies, costs, and benefits. Journal of Transportation Security, 4(4), 361- 374. doi:10.1007/s12198-011-0076-1
Homeland Securiy. (2021). Transportation security overview. Retrieved from https://www.dhs.gov/transportation-security-overview
I. Alghamdi, M. (2021). Digital forensics in cyber security—Recent trends, threats, and opportunities. Cybersecurity Threats with New Perspectives. https://doi.org/10.5772/intechopen.94452
Jackson, B. A., & LaTourrette, T. (2015). Assessing the effectiveness of layered security for protecting the aviation system against adaptive adversaries. Journal of Air Transport Management, 48, 26-33. doi:10.1016/j.jairtraman.2015.06.009
Kim, A. A., Sadatsafavi, H., Anderson, S. D., & Bishop, P. (2017). Preparing for the future of transportation construction: Strategies for state transportation agencies. Journal of Management in Engineering, 33(3), 04016045. doi:10.1061/(ASCE)me.1943-5479.0000494
Kolesnikov, N., & Danilina, E. (2022). Transport security and digitalization: Trends and perspectives. https://doi.org/10.31222/osf.io/9264c
Kudinov, V. V., & Mukhina, E. G. (2020). Some features of the legal regulation of ensuring economic security in the United States. Mezhdunarodnaja jekonomika (The World Economics), (12), 42-55. https://doi.org/10.33920/vne-04-2012-05
Lez’er, V., Muratova, I., & Korpusova, N. (2019). Issues of transport security and the human factor. E3S Web of Conferences, 91, 08062. https://doi.org/10.1051/e3sconf/20199108062
Liang, K., Gregory, C., Diallo, S. O., Roe, K., Heilmann, G., Carin, L., … Sigman, J. (2018). Automatic threat recognition of prohibited items at aviation checkpoint with X-ray imaging: A deep learning approach. Anomaly Detection and Imaging with X-Rays (ADIX) III. doi:10.1117/12.2309484
Novozhilov, A. (2022). Eco-friendly transport: New trends and perspectives. https://doi.org/10.31222/osf.io/7gh4e
Ovcharenko, O. (2021). Analysis of existing approaches to assessing the economic security of the region. Science, technologies, innovation, (№3(19)), 20-29. https://doi.org/10.35668/2520-6524-2021-4-03
Palmer, M. (2020). Transportation Security Administration (TSA): Issues, Successes, and Solutions. Retrieved from https://www.rieas.gr/images/terrorismstudies/usatsa9.pdf
Philipova, M. (2022). Transport security – new threats or new opportunities. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4152751
Shvetsov, A., & Kokieva, G. (2020). Transport security: Analysis and comparison of existing approaches. IOP Conference Series: Materials Science and Engineering, 953(1), 012076. https://doi.org/10.1088/1757-899x/953/1/012076
Tarpey, D. (2016). NO NATION IS HOME ALONE: UNDERSTANDING THE INTERNATIONAL DIMENSION OF HOMELAND SECURITY THROUGH GLOBAL TRANSPORTATION SECURITY PROGRAMS.
Toro, E. E., Van der Krogt, A., & Vallejo, J. S. (2020). Challenges to the mobility and integration of public transport systems in Latin America. Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing. https://doi.org/10.1145/3448823.3448860
Zamparini, L. (2022). A review of models for transport security and their relevance for supply chains. Transport Reviews, 1-17. https://doi.org/10.1080/01441647.2022.2056255
Zhang, G., Jia, N., Zhu, N., Adulyasak, Y., & Ma, S. (2022). Robust Drone Selective Routing in Humanitarian Transportation Network Assessment. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2022.05.046
Shiri, D., Akbari, V., & Salman, F. S. (2020). Online routing and scheduling of search-and-rescue teams. OR Spectrum, 42(3), 755-784. https://doi.org/10.1007/s00291-020-00594-w
Twumasi-Boakye, R., & Sobanjo, J. (2019). Civil infrastructure resilience: state-of-the-art on transportation network systems. Transportmetrica A: transport science, 15(2), 455-484. https://doi.org/10.1080/23249935.2018.1504832
Oliveira, E., Natarajan, M., & da Silva, B. (2019). Bus robberies in Belo Horizonte, Brazil: solutions for safe travel. Crime & Delinquency, 0011128719871547. https://doi.org/10.1177%2F0011128719871547
Barabino, B., Lai, C., & Olivo, A. (2020). Fare evasion in public transport systems: A review of the literature. Public Transport, 12(1), 27-88. https://doi.org/10.1007/s12469-019-00225-w

 

 

 

 

 

 

 

 

 

Appendix
Appendix A

Appendix b

Table: Coefficients in Network Growth Model