Electric vehicle timeshare rentals (EVTRs) have been recognized as promising solutions to growingly severe problems of traffic congestion, air pollution, and insufficient parking spaces. This study aims to explore the factors that affect the adoption of EVTRs. To achieve the research objective, the household survey is conducted to obtain the travelers’ attitudes towards their travel. Ten latent attitudinal factors are extracted based on the technology acceptance model (TAM) and the theory of planned behavior (TPB). The multi-index and multi-cause (MIMIC) method simultaneously estimates the correlations between the attitudinal factors. Two logit models with attitudinal factors or not are constructed to estimate the quantitative relationship between various factors and EVTR adoption. The results show that the accuracy of the mixed logit model with latent attitude variables is better than the binary logit model without attitude latent variables. This indicates that attitude latent factors could be well matched with the traveler’s travel behavior and could better reflect travelers’ travel demand. Perceived comfort, perceived efficient, subjective evaluation, use preference, and use willingness significantly impact EVTR use frequency. The inconvenience in travelers, rental stations, shared vehicles, and use modes have significant negative impacts. Finally, social pressure has no significant impact. Findings provide valuable insights regarding the efficient planning of the EVTR system and allow decision-makers to develop scientific and practical measures of EVTRs.

Coordinated ramp metering (CRM) is one effective measure to alleviate urban expressway congestion. Traditional model-based methods generally concentrate on single-bottleneck scenarios, while ignoring the case of multiple bottlenecks. In addition, the fixed-sensor fails to fully capture the dynamic traffic characteristics. The rapid development of traffic detection technology has made available a large amount of automatic vehicle identification (AVI) data, which can record detailed individual trajectories. Taking advantage of the AVI data, CRM can be improved. Besides, multi-agent deep reinforcement learning (MADRL) and game theory have been proven to be effective for traffic signal control. These methods can address the challenges faced by CRM, such as solving nonlinear and high-dimensional optimization problems. This paper proposes a distributed CRM strategy with multi-bottleneck to minimize the total travel time and balance the multiple on-ramps equity, using the individual trajectory information from AVI data. Firstly, the paper defines road segment units, road segment groups, and bottlenecks. Next, the problem is formulated as a potential game that captures the interaction among multiple bottlenecks. The controllers utilize the MADDPG algorithm to determine the green duration of the on-ramps. Finally, the proposed strategy is tested on a real-world urban expressway in a microsimulation platform SUMO. Experimental results demonstrate that the proposed strategy performs better than the baseline methods in eliminating mainline congestion and improving the multiple on-ramps equity. Compared to the no-control scenario, the proposed strategy has improved the performance of the system throughput, average travel time, and average mainline speed by 1.31%, 44.36%, and 115.23%.

It is important to achieve the real-time and reliable vehicular ad hoc networks (VANETs) for the safety and efficiency of the autonomous intersection management (AIM) system. However, there are challenges in establishing the low-delay and robustness VANETs topology in a horizon due to the high mobility, intersection shelters and malicious attacks. This paper investigates the long-horizon VANETs networking problem with the objective of producing the robust topology in the process of routing paths planning. To achieve this goal, a centralized periodic intervention networking (CPIN) approach is proposed, which consists of three main modules: long-horizon information mapping, full-path routing modeling and proactive optimization mechanism. The first module aims to provide a spatio-temporal delay tensor, considering the mobility and the quality of service (QoS). The second module converts routing path planning into a combinatorial optimization problem to achieve long-horizon full-path routing. The third module introduces a proactive optimization mechanism based on node degree centrality to evolve the VANETs networking process toward high robustness. To find the optimal solution efficiently, we propose an improved Monte Carlo Tree Search (MCTS) algorithm based on the operational characteristics of the AIM system. A series of simulation experiments are carried out to verify the effectiveness of CPIN. The results show that the CPIN outperforms the benchmark method in terms of time delay, wireless hops, and network robustness. Specifically, the CPIN reduces the average time delay by at most 44.4%-48.3% while ensuring relative high robustness.

Combined bridge-tunnel scenarios of driving on mountainous city expressways occur when bridges and tunnels frequently alternate during driving. The complex nature of these driving scenarios imposes crucial requirements on the drivers’ eye movement characteristics. This paper attempts to clarify these characteristics using descriptive statistics and the box graph method, registering the pupil diameter, blink duration, fixation, saccades, and fixation loci at different tunnel locations, bridges, and ramps. Realistic driving experiments were performed on the road segment spanning from the Nanchang tunnel to the Liujiatai tunnel freeway in Chongqing, China. Eye movement data were collected for 21 drivers. The experimental results showed that, while driving in the tunnel, the maximal pupil diameter of the participating drivers was approximately 4.0 mm as the driving mileage and the number of tunnels increased, and the maximal visual load on the drivers in the tunnel tended to be stable. At the second tunnel exit, the ramp, the middle section of the first bridge, and the third tunnel exit, the driving load was the highest, while the fixation duration was shorter for nighttime driving. The fixation duration was the longest for the diversion road of bridge B1 to the ramp during the day, and the fixation times were the longest at the beginning and end of the test road. The drivers more often paid attention to the speed dashboard while entering tunnels during daytime driving (compared with nighttime driving).

Signal timing parameters are essential components in traffic signal control (TSC). It affects not only traffic management, but also traffic safety. However, due to the confidential issues of the traffic management department, or lack of data integration from different signal manufacturers, it is intractable to obtain the city‐scale signal timing data. In the previous studies, some existing estimation methods focused on a single parameter and fixed‐timing scheme. To tackle this issue, this study attempts to develop an integrated parameters inference method based on license plate recognition (LPR) data, considering phase weight, average phase duration information and the overall phases of the intersections. In particular, the proposed method includes phase sequence inference model, cycle length inference model and phase duration inference model. To testify the performance of the proposed method, a real‐world LPR dataset from Guangzhou, China, is applied. Numerical results show that the proposed method performs more efficiently on parameter inferences than the state‐of‐the‐art (SOTA) approach. For instance, in the given research time period, the mean absolute error (MAE) of each phase duration is 2 s averagely (6.32 s in the SOTA approach), and the mean relative error (MRE) of cycle length is 0.91% (11.67% in the SOTA approach).