Journal of Transportation Engineering, Part A: Systems

Runaway accidents caused by vehicle braking have been extensively studied from the perspective of passenger safety. The traditional escape ramp alleviates such conditions to some extent, which places higher demands on human protection and more complex application scenarios (slope and long-distance site requirements). This paper proposes a new type of escape ramp arresting system (ERAS) and developed a novel type of variable cable force damper. The combination of the damper and the arresting net in the ERAS could more effectively restrict the speed of the runaway vehicle and ensure the safety of the occupants, while being able to adapt to more practical scenarios (flat slopes and short-distance site requirements). Six sets of real low- and high-speed intercept tests with different models were conducted to obtain some key data, such as the acceleration of the vehicle’s center of gravity, the impact velocity of occupants, and the sequence diagram of the vehicle during the impact. The vehicle impact force at low speed and the crash safety performance were effectively evaluated. The results indicated ERAS was applicable to runaway vehicles including sedans and large buses ($60–120\mathrm{km}/\mathrm{h}$), but further optimization was needed for overweight trucks. In addition, the paper provides valuable experimental data for the future study of real vehicle crash tests and the calibration of the finite-element model.

The principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago’s O’Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area.

Within the urban road system, traffic conflicts most frequently are concentrated at road intersections, and left-turning traffic is a prominent and pivotal factor leading to intersection conflicts. The scarcity of applied models that can comprehensively elucidate the shifting trajectory dynamics and exit lane selection behavior of left-turning vehicles has posed challenges to the accurate analysis and prediction of intersection vehicle conflict patterns. This study extracted trajectories of left-turning vehicles at intersections from video data. A vehicle left-turn trajectory model was formulated, hinging on changes in trajectory curvature. Moreover, leveraging geometric intersection parameters and video data, a vehicle exit lane selection model was devised using random utility theory. This model integrates variables such as intersection turning angle, vehicle type, entrance lane position, vehicle speed, following clearance, and the lead vehicle’s position in the exit lane. The outcomes indicate that left-turn trajectories can be approximated well with a flat curve, and the trajectory’s curvature has a quadratic parabolic correlation with the intersection’s aspect ratio. The trajectory model presented in this paper adeptly characterizes left-turning vehicle movement across diverse intersections. Similarly, the exit lane selection model adeptly forecasts the distribution of left-turning vehicles in exit lane positions. This predictive efficacy stems from the recognition that turning angle, entrance lane vehicle position, and vehicle speed profoundly impact the exit lane choice for left-turning vehicles. This research sheds light on the intricacies of driving behavior and exit lane selection of left-turning vehicles at signalized intersections. By focusing on these critical aspects, this study offers valuable insights that contribute to the improvement of intersection design and overall traffic safety.

Reservation-based intersection control for connected and automated vehicles (CAVs) is promising to improve travel mobility, safety, energy consumption, and reducing emissions. Though the first-come-first-served (FCFS) policy is proven to be effective in increasing traffic capacity, the fairness attribute of FCFS policy could cause higher levels of delay in asymmetric intersections (e.g., intersections of major and minor roads) compared with signal control. The reason is that the signal control gives priority to high-demand road vehicles, whereas the FCFS policy neglects road types and assigns the same priority to all vehicles. To resolve this paradox, we propose a heuristic platoon control method. Based on the real-time traffic states, vehicles adaptively form platoons to reduce the frequency of right of way (ROW) exchanges in the FCFS method and to allow continuous traffic flow on the high-demand road. A heuristic scheduling policy evaluates different policies and selects the passing mode with the minimum delay to adjust vehicles’ service sequences. By implementing a reservation cancellation mechanism, vehicles’ service sequences are updated in response to new arrivals. The simulation results demonstrate that the proposed heuristic platoon control method has a higher traffic capacity compared with the traditional FCFS method and the FCFS-based platoon method. The improvement is more significant when the minor road is two-way, the traffic is heavy, and the traffic flow difference between the major and minor roads is large. The proposed method can also reserve longer safety buffers for vehicles. Furthermore, the heuristic platoon control method is verified to be applicable to multiple intersections.

Abstract

This work investigates commercial vehicle platoon predictive cruise control for highways. We propose a cloud-based platoon predictive cruise control method (CPPCC). A two-layered control architecture of the CPPCC is proposed as a platoon predictive cruise speed planning layer in the cloud and a platoon stabilization control layer. The CPPCC communication topology is proposed to achieve coupled control of the hierarchical architecture. The speed planning layer is a dynamic planning (DP) algorithm based on road slope in the rolling distance domain. The lower layer is a stability control algorithm to meet the stability requirements of vehicle platoon driving; the vehicle side is distributed model predictive control (DMPC). The CPPCC is validated by real road and vehicle data models, and comparative experiments with the traditional predecessor-leader following–cruise control (PLF-CC) platoon and predecessor following–cruise control (PF-CC) platoon. The speed error of the vehicle platoon was maintained at [$-0.25$, 0.30] ($\mathrm{m}/\mathrm{s}$) and the space error at [$-0.13$, 0.66] (m) in platoon stability. Against the comparison method, the CPPCC saved fuel by over 5.13% and achieved an overall operational efficiency improvement of 5.71%.

Practical Applications

This research contributes to solving the problem of energy-efficient driving in vehicle platoons. Based on the cloud control system (CCS), cloud-based platoon predictive cruise control (CPPCC) is proposed, which is a layered structure. The upper layer is the platoon speed planning layer in the cloud and the lower layer is the platoon stability control layer. By adding cloud nodes and changing the structure of the existing platoon predictive cruise control (PPCC) and communication topology, CPPCC is able to achieve the goals of platoon economy and stability. Compared with PF-CC and PLF-CC, it is able to achieve fuel savings of more than 5.13% and efficiency improvements of 5.71% while ensuring stable platoon operation. Deploying the vehicle-side platoon stabilization controller in a commercial vehicle platoon can provide a solution to existing PPCC for energy saving and stability control. Combined with cloud-based speed planning, this enables commercial vehicle platoon PCC. Solving the problem of energy consumption of existing commercial vehicles and thus reducing environmental pollution from logistics transport.

With the development of autonomous driving, a mixed traffic flow state composed of connected automated vehicles (CAVs) and human-driven vehicles (HVs) will last for an extended period. The abundant computing resources and CAVs with high compliance in the intelligent connected environment provide a good situation for variable speed limit control on highways, which helps even the traffic flow and improves traffic efficiency and safety. In this paper, we propose a variable speed limit control method for mixed traffic flow based on deep-reinforcement learning. First, the variable speed limit control problem is abstracted into a Markov decision process and the factors of real-time CAV penetration rates and predictions are considered in the state description. Different from variable message signs (VMS), CAVs are taken as the executive objects of the controller so that the variable speed limit control for mixed traffic flow is realized indirectly through the interaction with HVs. Next, double deep Q network (DDQN) is introduced to calculate the optimal speed limit in different states. Finally, the empirical study on US101-S proves the effectiveness of the proposed model. The results show that the variable speed limit control model based on the DDQN algorithm can effectively improve the efficiency and environmental benefits of mixed traffic flow. Moreover, the multi-objective reward function can achieve a better control effect than the single objective. Besides, the proposed model outperforms other models in this paper and predictive factors can further improve proactive control performance. In addition, with the increasing penetration of CAV, the proposed model achieves a better control effect.

This paper proposes a reinforcement learning (RL)-based traffic control strategy integrated with attention mechanism for large-scale adaptive traffic signal control (ATSC) system. The proposed attention RL integrates attention mechanism into a multiagent RL model, namely multiagent proximal policy optimization (MAPPO), so as to enable more effective, scalable, and stable learning in complex ATSC environments. In the attention RL, decentralized policies are trained using a centrally computed critic that shares an attention model, while the attention model selects relevant intersections for each agent to estimate the global critic. This framework effectively reduces the computational complexity and stabilizes the training process, enhancing the ability of RL agents to control large-scale traffic networks. The proposed control strategy is tested in both a large synthetic traffic grid and a large real-world traffic network of Yangzhou city using the microscopic traffic simulation tool, SUMO. Experimental results demonstrate that the proposed approach learns stable and sustainable policies that achieve lower congestion level and faster recovery, which outperforms other state-of-art RL-based approaches, as well as a gap-based actuated controller.

The current study proposes a modified reliability theory and methodology for the speed-based evaluation of successive geometric elements such as a long tangent followed by a curve. A formulation based on lower limit and upper limit speed adjustments, $S_L$ and $S_U$, respectively, between the successive geometric elements is developed for range-based reliability evaluation. To understand the sensitivity of reliability, graphical representations for the variations in reliability due to variations in speed difference distributions, ${\mu }_G$ and ${\sigma }_G$, are explained. It is observed that the reliability of highway geometric elements changes with variations in ${\mu }_G$ and ${\sigma }_G$. For fixed values of ${\sigma }_G$, $S_L$, and $S_U$, the reliability of highway geometric elements can increase or decrease with increases in ${\mu }_G$. However, for fixed values of ${\mu }_G$, $S_L$, and $S_U$, the reliability of highway geometric elements increases with decreases in ${\sigma }_G$. A typical example using this method has also been discussed in this paper to provide an idea on the practical application of the method. Analysis of the geometric effects on reliability values elucidates an inverse relationship between reliability ratio ($RR$) and deflection angle. Conversely, a direct relationship between radius and curve length was observed. The observed negative relationship of deflection angle and degree of curvature with $RR$ is in compliance with AASHTO’s recommendation of designing for smooth curve appearance. Design engineers can suitably use the proposed methodology for a targeted reliability(Re) and/or $RR$ value. These findings can help to generate a reliable design of successive geometric elements at the design stage and recommend suitable low-cost safety measures to mitigate the geometric design shortcomings in the operational stage of highways.

In the application of battery electric buses, cruising range anxiety, high charging cost, and unreasonable allocation of charging resources are essential factors hindering their promotion. This study aims to identify an overnight charging schedule for battery electric buses to minimize battery charging–related operation costs. Among them, the impact of battery degradation cost and nonlinear charging function on the charging schedule is mainly considered. One novel feature of the considered problem is a controllable charging completion level. Therefore, we describe the overnight charging of battery electric buses as a parallel machine scheduling problem with a controllable task completion level. To solve this problem, mixed-integer linear programming formulations are developed. An efficient heuristic algorithm is proposed for solving large-scale instances, and numerical experiments verify the effectiveness of the proposed algorithm. A case study further reveals that the optimal charging schedule can save at least 10% of operation costs and has the potential to be applied in the operation of large-scale bus fleets.