Individual variable speed limit trajectory planning considering stochastic arriving patterns

Connected vehicles enabled by communication technologies have the potential to improve trafc mobility and enhance roadway safety such that trafc information can be shared among vehicles and infrastructure.Fruitful speed advisory strategies have been proposed to smooth connected vehicle trajectories for better system performance with the help of diferent carfollowing models.Yet,there has been no such comparison about the impacts of various car-following models on the advisory strategies.Further,most of the existing studies consider a deterministic vehicle arriving pattern.The resulting model is easy to approach yet not realistic in representing realistic trafc patterns.This study proposes an Individual Variable Speed Limit(IVSL)trajectory planning problem at a signalized intersection and investigates the impacts of three popular car-following models on the IVSL.Both deterministic and stochastic IVSL models are formulated,and their performance is tested with numerical experiments.The results show that,compared to the benchmark(i.e.,without speed control),the proposed IVSL strategy with a deterministic arriving pattern achieves signifcant improvements in both mobility and fuel efciency across diferent trafc levels with all three car-following models.The improvement of the IVSL with the Gipps’model is the most remarkable.When the vehicle arriving patterns are stochastic,the IVSL improves travel time,fuel consumption,and system cost by 8.95%,19.11%,and 11.37%,respectively,compared to the benchmark without speed control.

Empowering highway network:Optimal deployment and strategy for dynamic wireless charging lanes

Amid escalating energy crises and environmental pressures,electric vehicles(EVs)have emerged as an effective measure to reduce reliance on fossil fuels,combat climate change,uphold sustainable energy and environmental development,and strive towards carbon peaking and neutrality goals.This study introduces a nonlinear integer programming model for the deployment of dynamic wireless charging lanes(DWCLs)and EV charging strategy joint optimization in highway networks.Taking into account established charging resources in highway service areas(HSAs),the nonlinear charging characteristics of EV batteries,and the traffic capacity constraints of DWCLs.The model identifies the deployment of charging facilities and the EV charging strategy as the decision-making variables and aims to minimize both the DWCL construction and user charging costs.By ensuring that EVs maintain an acceptable state of charge(SoC),the model combines highway EV charging demand and highway EV charging strategy to optimize the DWCL deployment,thus reducing the construction cost of wireless charging facilities and user charging expenses.The efficacy and universality of the model are demonstrated using the classical Nguyen-Dupius network as a numerical example and a real-world highway network in Guangdong Province,China.Finally,a sensitivity analysis is conducted to corroborate the stability of the model.The results show that the operating speed of EVs on DWCLs has the largest impact on total cost,while battery capacity has the smallest.This comprehensive study offers vital insights into the strategic deployment of DWCLs,promoting the sustainable and efficient use of EVs in highway networks.

Online learning-based model predictive trajectory control for connected and autonomous vehicles: Modeling and physical tests

Motivated by the promising benefits of connected and autonomous vehicles (CAVs) in improving fuelefficiency, mitigating congestion, and enhancing safety, numerous theoretical models have been proposed to plan CAVmultiple-step trajectories (time–specific speed/location trajectories) to accomplish various operations. However, limitedefforts have been made to develop proper trajectory control techniques to regulate vehicle movements to follow multiplesteptrajectories and test the performance of theoretical trajectory planning models with field experiments. Without aneffective control method, the benefits of theoretical models for CAV trajectory planning can be difficult to harvest. This studyproposes an online learning-based model predictive vehicle trajectory control structure to follow time–specific speed andlocation profiles. Unlike single-step controllers that are dominantly used in the literature, a multiple-step model predictivecontroller is adopted to control the vehicle’s longitudinal movements for higher accuracy. The model predictive controlleroutput (speed) cannot be interpreted by vehicles. A reinforcement learning agent is used to convert the speed value to thevehicle’s direct control variable (i.e., throttle/brake). The reinforcement learning agent captures real-time changes in theoperating environment. This is valuable in saving parameter calibration resources and improving trajectory control accuracy.A line tracking controller keeps vehicles on track. The proposed control structure is tested using reduced-scale robot cars.The adaptivity of the proposed control structure is demonstrated by changing the vehicle load. Then, experiments on twofundamental CAV platoon operations (i.e., platooning and split) show the effectiveness of the proposed trajectory controlstructure in regulating robot movements to follow time-specific reference trajectories.

Video-based trajectory extraction with deep learning for High-Granularity Highway Simulation(HIGH-SIM)

High-granularity vehicle trajectory data can help researchers develop traffic simulation models,understand traffic flow characteristics,and thus propose insightful strategies for road traffic management.This paper proposes a novel vehicle trajectory extraction method that can extract high-granularity vehicle trajectories from aerial videos.The proposed method includes video calibration,vehicle detection and tracking,lane marking identification,and vehicle motion characteristics calculation.In particular,the authors propose a Monte-Carlo-based lane marking identification approach to identify each vehicle's lane.This is a challenging problem for vehicle trajectory extraction,especially when the aerial videos are taken from a high altitude.The authors applied the proposed method to extract vehicle trajectories from several high-resolution aerial videos recorded from helicopters.The extracted dataset is named by the High-Granularity Highway Simulation(HIGH-SIM)vehicle trajectory dataset.To demonstrate the effectiveness of the proposed method and understand the quality of the HIGHSIM dataset,we compared the HIGH-SIM dataset with a well-known dataset,the NGSIM US-101 dataset,regarding the accuracy and consistency aspects.The comparison results showed that the HIGH-SIM dataset has more reasonable speed and acceleration distributions than the NGSIM US-101 dataset.Also,the internal and platoon consistencies of the HIGH-SIM dataset give lower errors compared to the NGSIM US-101 dataset.To benefit future research,the authors have published the HIGH-SIM dataset online for public use.