Battery electric vehicles (BEVs) are known as a sustainable solution for decarbonizing the transportation system and attaining the objectives of Paris Agreement. The main challenge issues for the widespread adoption of BEVs are battery capacity and driving range limitations. Signalized intersections are one of the places where a considerable amount of the total trip energy is consumed due to their stop‐and‐go nature. One efficient solution to deal with range limitations in BEVs is eco‐driving. In this study, an eco‐cooperative driving strategy was developed for BEVs at back‐to‐back signalized intersections in free‐flow traffic. The optimal trip time and acceleration and deceleration levels are calculated to enable the vehicle to pass through signalized intersections without stopping. The proposed eco‐cooperative driving optimizes a cost function that considers both energy consumption and travel time. The optimization problem was solved using an approach based on the particle swarm optimization technique. The results show that the proposed eco‐cooperative driving strategy could save energy consumption and trip time up to 26.61% and 44.29%, respectively.

In urban rail transit networks, transfer connections between lines during off‐peak hours significantly impact passenger experience. Our study introduces a novel approach to optimize these connections based on perceived waiting time. First, we established a functional relationship between actual transfer waiting time and transfer perceived waiting time (TWT–TPWT) across various scenarios, based on passenger arrival sequences at transfer platforms. We then developed an optimization model that minimizes total TPWT network wide by weighting transfer passenger volumes in each direction, while incorporating constraints related to train arrival/departure time at transfer stations. Using the Shenzhen Metro network’s off‐peak period as a case study, we implemented an improved simulated annealing algorithm to solve the model. Results demonstrate that our TWT–TPWT approach substantially enhances transfer connection efficiency: “just in time” connecting trains increased by 19, long‐waiting connections decreased by 16, and both total and average TPWTs decreased by 28.38%. Furthermore, we conducted sensitivity analyses on key model and algorithmic factors, including station dwelling time, train departure intervals, transfer walking time, and the algorithm’s reheating mechanism, providing insights for practical implementation.

The swift improvement of wireless communication technology has aided the creation of vehicular ad hoc networks (VANETs) as a revolutionary option for the realization of intelligent transportation systems (ITSs). VANETs facilitate seamless communication between vehicles and infrastructure (V2I) and among the vehicles themselves (V2V). These networks can enhance safety and traffic control while providing various infotainment options. To ensure stable and efficient communication in highly dynamic and quickly changing vehicular contexts, effective data packet routing is a crucial component of VANETs. This survey delves further into the VANET routing algorithms for ITS. The study aims to evaluate the effectiveness and acceptability of various routing protocols suggested for VANETs while considering their unique requirements and difficulties. The review covers ad hoc on‐demand distance vector (AODV) and dynamic source routing (DSR), as well as more current strategies designed specifically for VANETs, such as geographic routing, cluster‐based routing, and hybrid routing protocols. The review assesses the routing algorithms based on several vital parameters, including packet delivery ratio (PDR), end‐to‐end delay, throughput, network overhead, scalability, and robustness. This detailed assessment supports aspiring researchers in acquiring a better knowledge of the benefits and drawbacks of the routing algorithms currently used in VANETs.

The purpose of this study was to explore the risk factors for autonomous vehicle (AV) crashes and their interdependencies. A total of 659 AV crash data were collected between 2018 and July 2024 from AV crash reports published by the California Department of Motor Vehicles. Characteristics such as crash location and time, driving patterns, vehicle motion, crash type and vehicle damage, and traffic conditions were considered as potential risk factors in the study. Considering the multilevel and multidimensional nature of the crash data, the study adopted an association rule mining (ARM) approach to identify the risk factors that frequently occur in AV crashes. By improving the Apriori algorithm, based on the traditional Apriori algorithm, the association rule judgment index is added, and the accuracy and mining efficiency of association rules are improved. The results show that rear‐end collisions in autonomous driving mode are more serious, especially when stopping at intersections, while the rear vehicle chooses to continue driving or slow down. Accident risk is higher at night, with on‐street parking and 2‐lane conditions in both directions. The occurrence of no‐damage and minor crashes is more likely to be influenced by roadway characteristics and traffic conditions, and nonmotorized lanes, on‐street parking, and median strips on the roadway play a key role in reducing crash damage. The results of the study inform relevant policies to improve road safety and the efficiency of AVs to enhance the overall safety of road traffic.

This study tackles the increasing complexity of passenger travel patterns in urban rail transit systems by utilizing Automated Fare Collection (AFC) data and an enhanced GraphSAGE model. We construct detailed spatiotemporal travel graphs and introduce a weighted aggregation strategy to capture intricate network relationships. By clustering passengers into four distinct groups based on their temporal and spatial travel behaviors, we successfully identify regular passengers and other key travel patterns. A case study conducted at Anting Station in Shanghai demonstrates the method’s effectiveness in real‐world applications. Our approach not only advances theoretical understanding of passenger behavior but also provides practical insights for metro operators, helping optimize operational efficiency and service quality in urban transit systems.

This paper extends the share‐a‐ride problem (SARP) by incorporating electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions, thus addressing environmental concerns. We introduce this new extension as the electric share‐a‐ride problem (E‐SARP). We aim to generate E‐SARP routing plans where EVs serve all passenger and parcel requests while visiting charging stations (CSs) as necessary for recharging. The objective is to maximize total profit from fulfilling passenger and parcel requests. We develop a mixed‐integer programming (MIP) model and propose a hybrid algorithm based on the variable neighborhood search (VNS) framework, integrated with a simulated annealing (SA) acceptance criterion (HVNS). The MIP model provides optimal solutions for small E‐SARP instances using the CPLEX solver, while the HVNS algorithm is designed to solve larger E‐SARP instances. Numerical experiments are conducted to assess the performance of the proposed HVNS and to provide managerial insights, demonstrating that the use of EVs can effectively address environmental concerns without significantly compromising the profitability of the transportation network.

This paper develops a method for estimating carbon‐emission specific road networks, considering the presence of electric vehicles (EVs). A mixed equilibrium traffic assignment model is set up to obtain the traffic volume for each link in the network, where oil‐fueled vehicles (OFVs) prioritizing travel time minimization, while EVs also consider charging station locations and battery charge state in route selection. A carbon‐emission estimation method is then developed, which is calculated by three parameters of traffic volume, average speed, and the road category. A case study is carried out using two networks. It is found that the travel time of the road network has increased by 27%, because EVs tend to choose paths containing charging stations. The route selection of EVs is affected by perceived risk, safe electric quantity, and expected charging electricity. EVs can reduce carbon dioxide emissions but not energy consumption for road network. In addition, it was found that the location of charging stations has a significant impact on traffic flow. After optimizing the location of charging stations, the total travel time, total carbon emissions, and balance of charging station utilization indicators in the transportation network have all relatively decreased. Among them, the total travel time has decreased by 0.2%, the total carbon emissions have decreased by 1.85%, and the balance of charging station utilization has decreased by 0.95%. The research is helpful for determining the locations of charging piles and designing road networks, and it is also helpful for estimating the traffic flow and carbon emissions.

In satellite video vehicle tracking, due to the tracking failure and tracking loss caused by similar characteristics of the target and obstacle occlusion, respectively, the traffic flow extraction accuracy is reduced. To address these issues, an improved traffic flow extraction method for satellite video based on kernelized correlation filter (KCF) was proposed. First, we introduced a multifeature fusion strategy into the KCF based on the discrete Fourier transform (DFT) framework to enhance vehicle tracking accuracy and reduce tracking drift and jumps. Second, we utilized the Kalman filter for trajectory prediction to reduce the loss of target during vehicle tracking. Compared with other mainstream algorithms on the satellite video dataset, the results showed that the tracking accuracy and success rate of the proposed method reached 86.74% and 79.96%, respectively. Finally, the virtual detection line method was used to extract the traffic flow. The experimental results showed that compared with the real traffic flow data obtained by visual method, the accuracy of satellite video traffic flow extraction by virtual detection line was 98.48% under noncongestion condition and 90.18% under congestion condition.

This study explores transit‐oriented development (TOD) in Dhaka City using optimization algorithms to provide urban planning and policy‐making insights. The analysis examined the distribution of the TOD index values across the city and identified areas with varying levels of TOD potential. Two optimization algorithms, ant colony optimization (ACO) and particle swarm optimization (PSO), were employed to assess and compare the TOD index values. The results highlight the significance of transit infrastructure in promoting sustainable urban development, particularly in proximity to existing mass rapid transit (MRT) lines. PSO is more suitable for this study among the optimization algorithms because it offers a more precise TOD potential assessment. The findings suggest prioritizing investments in transit infrastructure and implementing TOD‐friendly policies to foster sustainable urban growth and improve residents’ quality of life. Future studies can benefit from optimizing the algorithm parameters and incorporating real‐world data to improve the accuracy of the TOD assessments.

Autonomous valet parking has drawn wide attention these years. The k‐stacks layout, known for its ability to increase parking capacity by stacking vehicles more compactly, is of great practicality among all possible layout patterns. Although this layout can increase the capacity of a parking lot, it generates relocations, which let vehicles move additional distances and influence the lot’s peak hour service ability. For the sake of optimizing them all simultaneously, we propose a simulation‐based multiple‐objective optimization (SMOO) and use NSGA II to solve the problem, obtaining candidate solutions. Then, a nondominated sorting based on cumulative advantages (NSCA) method is put forward to select the most robust solution from all candidates, considering different demand scenarios. K‐stacks parking lots optimized by the SMOO can provide 36%–59% more parking spaces than a traditional parking lot while keeping other evaluations fine. In addition, we specify high‐demand and low‐demand scenarios and discuss the impact of different aspect ratios. It is recommended to use k‐stacks layouts when a lot’s length is close to its width.