TRANSIMS is a disaggregate, behavioral, regional transportation planning package developed by Los Alamos National Laboratory (LANL) under funding from US DOT, EPA, and Department of Energy. It is an integrated system of travel forecasting models designed to give transportation planners accurate, complete information on traffic impacts, congestion, and pollution by simulating on a second-by-second basis the movements of every person and every vehicle through the transportation network of a large metropolitan area. This regional microsimulation approach provides a better assessment of the performance of a large network than the current link performance functions utilized in the current planning procedures. On the other hand, their microsimulation approach on a regional scale requires a lot of data that may not be readily available, and utilize a low fidelity microsimulation in order to make it operational. Some agencies may be interested in performing a more detailed investigation of traffic patterns within a sub area, such as the downtown area. The author implemented a subarea focusing methodology within TRANSIMS and also developed an interface that allows the investigator to use a high-fidelity, small-size network efficient traffic operational software package — CORSIM to perform sub area traffic operational analysis with demand and network extracted from applications of TRANSIMS. This methodology will allow TRANSIMS to take advantage of higher fidelity models for sub-network analysis and allow CORSIM to use planning inputs such as individual 24 hour travel activities and trip chains. An evacuation model is also built and applied to Virginia Tech main campus, Blacksburg, VA to evaluate this sub area focusing methodology. / Master of Science

In this thesis is performed the risk analysis, harms and injuries for the delivery equipment operation area. On the basis of these analysis was work out the proposal of changes for the traffic accident, injuries in the Czech Republic.

Continuously monitoring and automatically identifying existing problems in traffic signal operation is a challenging and time-consuming task. Although data are becoming available due to the adoption of emerging detection technologies, efforts on utilizing the data to diagnose signal control are limited. The current practices of retiming signals are still periodic and based on several days of aggregated turning movement counts. This dissertation developed a framework of automatic signal operation diagnosis with the aim to support decision-making processes by assessing the signal control and identifying the signal retiming needs. The developed framework used a combination of relatively low-cost data from Wi-Fi sensors and historical signal timing records from existing signal controllers. The development involved applying multiple data matching and filtering algorithms to allow the estimation of travel times of vehicular traversals. The Travel Time Index (TTI) was then used as a measure to assess the traffic conditions of various movements. Historical signal timing records were also analyzed, and an additional signal-timing measure, referred to as the Max-out Ratio (MR), was proposed to evaluate the frequency in which the green time demand of a phase exceeded its preset value. Thresholds for the TTI and MR variables were used as a basis for the diagnosis. This diagnosis first identified the needs for assigning additional green times for individual signal phases. Further assessments were then made to determine whether or not the cycle length for the entire intersection or capacity was sufficient. The developed framework was implemented in a real-world signalized intersection and proved to be capable of identifying retiming needs, as well as providing support for the retiming process. Compared to field observations, the diagnosis results were able to reflect the signal operations of most of the movements during various time periods. Moreover, the flexibility of the developed framework allows users to select different thresholds for various movements and times of day, and thus customize the analysis to agency needs.

The landscape of vehicular transportation is quickly shifting as emerging technologies continue to increase in intelligence and complexity. From the introduction of Intelligent Transportation Systems (ITS) to the quickly developing field of Connected and Autonomous Vehicles (CAVs), the transportation industry is experiencing a shift in focus. A move to more autonomous and intelligent transportation systems brings with it a promise of increased equity, efficiency, and safety. However, one aspect that is overlooked in this shift is cybersecurity. As intelligent systems and vehicles have been introduced, a large amount of research has been conducted showing vulnerabilities in them. With a new connected transportation system emerging, a multidisciplinary approach will be required to develop a cyber-resilient network. Ensuring protection against cyberattacks and developing a system that can handle their consequences is a key objective moving forward. The first step to developing this system is understanding how different cyberattacks can negatively impact the operations of the transportation system. This research aimed to quantify the safety and efficiency impacts of an attack on the transportation network. To do so, a simulation was developed using Veins software to model a network of intelligent intersections in an urban environment. Vehicles communicated with Road-Side Units (RSUs) to make intersection reservations – effectively simulating CAV vehicle network. Denial of Service (DoS) and Man in the Middle (MITM) attacks were simulated by dropping and delaying vehicle's intersection reservation requests, respectively. Attacks were modeled with varying degrees of severity by changing the number of infected RSUs in the system and their attack success rates. Data analysis showed that severe attacks, either from a DoS or MITM attack, can have significant impact on the transportation network's operations. The worst-case scenario for each introduced an over 20% increase in delay per vehicle. The simulation showed also that increasing the number of compromised RSUs directly related to decreased safety and operational efficiency. Successful attacks also produced a high level of variance in their impact. One other key finding was that a single compromised RSU had very limited impact on the transportation network. These findings highlight the importance of developing security and resilience in a connected vehicle environment. Building a network that can respond to an initial attack and prevent an attack's dissemination through the network is crucial in limiting the negative effects of the attack. If proper resilience planning is not implemented for the next generation of transportation, adversaries could cause great harm to safety and efficiency with relative ease. The next generation of vehicular transportation must be able to withstand cyberattacks to function. Understanding their impact is a key first step for engineers and planners on the long road to ensuring a secure transportation network. / Master of Science / The landscape of transportation is quickly shifting as transportation technologies continue to increase in intelligence and complexity. The transportation industry is shifting its focus to Connected and Autonomous Vehicles (CAVs). The move to more autonomous and intelligent transportation systems brings with it a promise of increased transportation equity, efficiency, and safety. However, one aspect that is often overlooked in this shift is cybersecurity. As intelligent systems and vehicles have been introduced, a large amount of research has been conducted showing cyber vulnerabilities in them. With a new connected transportation system emerging, a multidisciplinary approach will be required to prevent and handle attacks. Ensuring protection against cyberattacks is a key objective moving forward. The first step to developing this system is understanding how different cyberattacks can negatively impact the operations of the transportation system. This research aimed to measure the safety and efficiency impacts of an attack on the transportation network. To do so, a simulation was developed to model an intelligent urban road network. Vehicles made reservations at each intersection they passed – effectively simulating an autonomous vehicle network. Denial of Service (DoS) and Man in the Middle (MITM) attacks were simulated by dropping, and delaying vehicle's intersection reservation requests, respectively. These cyberattacks were modeled with varying degrees of severity to test the different impacts on the transportation network. Analysis showed that severe attacks can have significant impact on the transportation network's operations. The worst-case scenario for each attack introduced an over 20% increase in delay per vehicle. The simulation showed also that increasing the number of attacked intersections directly related to decreased safety and operational efficiency. Successful attacks also produced a high level of variance in their impact. One other key finding was that a single compromised RSU had very limited impact on the transportation network. These findings highlight the importance of developing security and resilience in a connected vehicle environment. Building a transportation network that can respond to an initial attack and prevent it from impacting the entire network is crucial in limiting the negative effects of the attack. If proper resilience planning is not implemented for CAVs, hackers could cause great harm to safety and efficiency with relative ease. The next generation of vehicular transportation must be able to withstand cyberattacks to function. Understanding their impact is a key first step for engineers and planners on the long road to ensuring a secure transportation network.

科学研究費補助金 研究種目:基盤研究(B) 課題番号:17360245 研究代表者:中村 英樹 研究期間:2005-2007年度