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Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

Reduction of the transportation sector's air emissions has been an essential goal for decreasing the emissions' externalities and slowing climate change. This study evaluated the emission impacts of low-level automated vehicles (LAVs) in 86 metropolitan areas in the United States, based on county-specific emission costs of morbidity, mortality, and environmental damages. Twelve LAV technologies were investigated to estimate their impacts on CO2, CO, NOx, PM2.5, SOx, NH3, and VOC. The study analyzed the LAV technologies' direct and indirect emission impacts, including accident-related and non-accident-related congestion reductions, aerodynamic force reduction, the operational load of equipment, and traffic rebound. The results show that the LAV technologies could reduce the annual social costs of emissions by between 9 and 20 billion USD2019 in these metropolitan areas. This is equal to approximately 20% of the vehicles' emissions. A sensitivity analysis was performed on the technologies' market penetration rates (MPRs), demonstrating that the rate strongly influences the emission savings. This study revealed that LAVs' savings per vehicle-miles traveled (VMT) vary between areas, with the technologies performing differently in different areas due to both geographical factors (e.g., population) and technological factors (e.g., internal combustion engine performance). The study provides extensive resources for policymakers to develop targeted incentives and maximize the social benefits of the LAV technologies.

Gasoline is the main source of energy used for surface transportation in the United States. Reducing fuel consumption in light-duty vehicles can significantly reduce the transportation sector’s impact on the environment. Implementation of emerging automated technologies in vehicles could result in fuel savings. This study examines the effect of automated vehicle systems on fuel consumption using stochastic modeling. Automated vehicle systems examined in this study include warning systems such as blind spot warning, control systems such as lane keeping assistance, and information systems such as dynamic route guidance. We have estimated fuel savings associated with reduction of accident and non-accident-related congestion, aerodynamic force reduction, operation load, and traffic rebound. Results of this study show that automated technologies could reduce light-duty vehicle fuel consumption in the U.S. by 6% to 23%. This reduction could save $60 to $266 annually for the owners of vehicles equipped with automated technologies. Also, adoption of automated vehicles could benefit all road users (i.e., conventional vehicle drivers) up to $35 per vehicle annually (up to $6.2 billion per year).

The transportation sector is the largest producer of greenhouse gas (GHG) emissions in the United States. Energy-optimal algorithms are proposed to reduce the transportation sector’s fuel consumption and emissions. These algorithms optimize vehicles’ speed to lower energy consumption and emissions. However, recent studies argued that these algorithms could negatively impact traffic flow, create traffic congestions, and increase fuel consumption on the network-level. To overcome this problem, we propose a collective-energy-optimal adaptive cruise control (collective-ACC). Collective-ACC reduces fuel consumption and emissions by directly optimizing vehicles’ trajectories and indirectly by improving traffic flow. Collective-ACC is a bi-objective non-linear integer optimization. This optimization was solved by the Non-dominated Sorting Genetic Algorithm (NSGA-II). Collective-ACC was compared with manual driving and self-centered adaptive cruise control (i.e., conventional energy-optimal adaptive cruise controls (self-centered-ACC)) in a traffic simulation. We found that collective-ACC reduced fuel consumption by up to 49% and 42% compared with manual driving and self-centered-ACC, respectively. Collective-ACC also lowered CO2, CO, NOX, and PMX by up to 54%, 70%, 58%, and 64% from manual driving, respectively. Game theory analyses were conducted to investigate how adopting collective-ACC could impact automakers, consumers, and government agencies. We propose policy and business recommendations to accelerate adopting collective-ACC and maximize its environmental benefits.

This article addresses the impacts of flood events on urban street networks. Macro-traffic simulation techniques were used on disrupted and undisrupted scenarios to assess the increase on the network’s mobility and accessibility. Local topographical aspects of the terrain were analyzed to identify portions of the network more prone to disruption. Flood maps were used to systematically remove links from the network, generating its disrupted state for different scenarios. The traffic assignment model generated routes using k-shortest path methods with link impedance penalty functions, selecting them based on user equilibrium assumption. Simulation results indicated the viability of the method to analyze the impacts of flood events of different severity and duration. The successful validation of this method indicated its viability as a tool for benefit cost analysis of urban improvement projects including resilience plans for high risk cities. The analysis was validated using the City of Hoboken, New Jersey’s transportation network and flood models. Results can be applied to cities with a high chance of flooding and should help authorities to effectively review their infrastructure strategic plans as well as their short and long-term urban mobility plans.