Developing an effective evaluation metric is crucial for accurately and swiftly measuring LiDAR perception performance. One major issue is the lack of metrics that can simultaneously generate fast and accurate evaluations based on either object detection or point cloud data. In this study, we propose a novel LiDAR perception entropy metric based on the probability of vehicle grid occupancy. This metric reflects the influence of point cloud distribution on vehicle detection performance. Based on this, we develop a LiDAR deployment optimization model, which is solved using a differential evolution-based particle swarm optimization algorithm. A comparative experiment demonstrated that the proposed PE-VGOP offers a correlation of more than 0.98 with the vehicle detection results in evaluating LiDAR perception performance. Furthermore, compared to base deployments, field experiments indicate that the proposed optimization model can significantly enhance the perception performance of various types of LiDARs, including RS-16, RS-32, and RS-80. Notably, it achieves a 25% increase in detection Recall for the RS-32 LiDAR. Additionally, sensitivity analysis under varying traffic densities further verifies the robustness of the proposed model. This study provides a practical and generalizable framework for enhancing roadside LiDAR deployment in diverse traffic environments.

This paper presents a traffic oscillation mitigation-oriented optimal control framework for connected and automated vehicles (CAVs) in a mixed traffic environment where the behavior of human-driven vehicles (HVs) is unknown. The primary objective of this framework is to alleviate traffic oscillations, thereby improving overall traffic flow. To achieve this, we introduce a novel total equilibrium spacing estimation method, incorporating stochastic parameters into a car-following model and quantifying the deviation between the mean and equilibrium spacing. This estimation, integrated with a jam-absorption driving strategy, is embedded into a Model Predictive Control (MPC) model for the objective of mitigating traffic oscillations. The efficacy of the proposed control method is evaluated through two experiments utilizing real vehicle trajectory datasets. The first experiment focuses on a single CAV, exploring the impact of key controller parameters on oscillation mitigation. Results demonstrate the optimal performance of the proposed Oscillation Mitigation-based Model Predictive Control (OM-MPC) model, even with a shorter CAV distance (e.g., 100 m), revealing a positive correlation between CAV distance and suitable preset oscillation duration. The second experiment extends the investigation to multiple stop-and-go shockwaves and varying CAV penetration rates. A comparative analysis of control models, including OM-MPC, regular MPC, and proportional-integral with saturation, is conducted based on velocity mean (VM), road segment congestion index (RI), and vehicle stop times (VST). The findings underscore the effectiveness of the proposed control method in mitigating traffic oscillations and enhancing overall traffic efficiency, establishing it as the optimal choice among the three approaches.

Bus bunching, a pervasive phenomenon in public transit systems, significantly undermines passenger satisfaction and operational efficiency. Recent studies on mitigating bus bunching have turned to model-free reinforcement learning (RL)-based methods for developing holding control strategies, demonstrating superior performance over traditional model-based approaches. However, a systematic evaluation of how different learning configurations affect the overall performance of these methods remains unexplored. To this end, this study develops an open-source framework to systematically examine the control performance of various RL approaches, including both value-based and policy-based algorithms, under different configurations using real-world bus data. Our findings reveal that: 1) simpler state representations (e.g., considering only forward and backward spacings between adjacent buses) may outperform more complex ones that additionally incorporate stop-specific information; 2) adopting actions with discrete holding time achieve comparable results to their continuous counterparts, offering practical advantages for implementation; 3) while multi-agent methods can slightly improve performance, their computational costs can outweigh benefits in certain scenarios; and 4) RL approaches demonstrate strong generalizability across diverse routes and fleet sizes, suggesting potential for widespread application with minimal retraining. These insights could guide the selection and implementation of practical RL deployment for public transit agencies.

This paper presents a hierarchical tactical merging optimization (HTMO) approach for connected and automated vehicles (CAV) at freeway merging segments. The proposed approach comprises two layers: a merging sequencing layer and a trajectory optimization layer, which are coupled by a hierarchical model that utilizes sequence set and vehicle state variables. In the merging sequencing layer, a sequence set variable is introduced to simplify the sequence space and identify the optimal merging sequence using a customized tabu search algorithm. In the trajectory planning layer, we formulate a two-point-boundary optimal control model for CAV trajectory planning, incorporating a platoon formation strategy to further enhance travel efficiency. To handle outliers caused by variations in the preceding vehicle’s trajectory, we have developed a heuristic trajectory optimization algorithm to ensure the generation of feasible trajectories with predetermined optimal acceleration values as proposed. Numerical experiments conducted demonstrate the robust convergence performance and computational efficiency of the HTMO approach across different arrival flow scenarios and parameters setting, thanks to its utilization of a rolling horizon strategy. Additionally, when combined with the platoon formation strategy, the schedule produced by HTMO significantly reduces total travel time and delay, as evidenced by our findings.

This paper proposes a hierarchical model for cooperative on-ramp merging control (CORMC) in mixed traffic with both connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). The upper-layer of the CORMC model employs an anticipatory position searching (APS) algorithm to determine the anticipatory positions at which merging vehicles (MVs) should merge from the on-ramp lane to the adjacent mainline lane, and to assign cooperative vehicles (CVs) for each MV. A collaborative utility choice (CUC) model is presented to determine the optimal maneuver of CVs to create proper gaps for MVs. The driver compliance rate is introduced to account for CHVs’ unwillingness to follow the instructions given by the CUC model. The lower-layer comprises a cooperative merging control (CMC) model that ensures safe and smooth merging execution for MVs. Longitudinal and lane changing models are developed for mainline vehicles to facilitate an efficient and safe merging process. Simulation results show that the performance benefits of the CUC model are marginal when the CHV compliance rate is relatively low. However, the performance improvement is significant at higher compliance rates ($>$50%). Furthermore, the CORMC model has the potential to increase merging and mainline throughput by over 75% at sufficiently high CAV penetration rates. Comparison of three control strategies shows that the APS algorithm plays an important role in the CORMC model. A comparison with the Simulation of Urban Mobility (SUMO) indicates that the CORMC model significantly mitigates the propagation of congestion waves across varying levels of CAV penetration and on-ramp flow rates.

In the foreseeable future, a coexistence of human-driven vehicles (HVs) and connected automated vehicles (CAVs) is expected in traffic flow systems. Effectively controlling CAVs to improve overall traffic performance and stability is a crucial yet challenging issue, especially when considering the uncertain behavior of HVs. This study proposes an optimal CAV controller for mixed connected traffic based on the adaptive model predictive control (AMPC) method. To mitigate the instability of the mixed platoon, the concept of loose disturbance string stability (LDSS) is introduced and integrated into the controller. Instead of using a fixed platoon size, a platoon formation criterion is established to dynamically calculate the number of vehicles in a platoon, taking into account the uncertain behavior of HVs, such as sudden deceleration and lane changes. An adaptive parameter tuning approach is incorporated into the MPC-based control framework to enhance the control performance in terms of stability, accuracy and disturbance recovery ability. The effects of the proposed CAV controller on overall traffic performance are investigated through two simulation experiments. In the first experiment, the performance of the proposed controller and LDSS are verified under three scenarios: sudden deceleration, vehicle leaving, and vehicle cut-in. In the second experiment, the proposed AMPC method is compared with two existing models: the linear quadratic regulation -based and acceleration-based connected cruise control models. The comparison results demonstrate that the proposed control model ensures LDSS and outperforms the other two approaches in terms of disturbance mitigation, albeit with a slight sacrifice of traffic efficiency.

The rapid development of self-driving cars and breakthroughs in key technologies have made the truck platoon possible. In addition to reducing truck fuel consumption and air pollution by reducing air resistance, effective platoon strategies can also maximize highway throughput while improving driving safety. However, the truck platoon strategy’s current resource allocation model is still in the preliminary research stage. Therefore, inspired by the successful experience of deep reinforcement learning (DRL) in solving resource allocation problems, this article proposes a dynamic resource allocation model for the truck platoon based on the semi-Markov decision process (SMDP) and DRL, which is used to maximize system revenue when considering the resource cost and income balance of the transportation system. Precisely, the proposed method first models the process of controlling the dynamic in and out of the truck platoon as SMDP. The action value in a specific state obtained by the planning algorithm is used as a DRL sample for model training. Finally, the SMDP is optimized through the trained model to obtain a truck platoon resource that approximates the optimal strategy distribution plan. The experimental results show that compared with the traditional greedy algorithm, value iteration, and${Q}$-learning scheme concerning solving the dynamic resource allocation model of the truck platoon, the Deep${Q}$-Network (DQN) used in this article can reduce the probability of request processing delay while causing the system to obtain higher rewards.

It is widely accepted that travelers value both the reliability of travel time and its mean or expected value. Strategies for traffic signal control typically seek to optimize average travel times, although reliability is in general not explicitly taken into account. In this paper, we propose a new framework for evaluating the consequences of signal-control tactics on both reliability and expected values of travel time, based on an analytic model of travel time distribution. A genetic-algorithm-based approach is then employed to identify optimal multicriteria signal control strategies, including sensitivity analysis, to the relative weighting between reliability and expected value. We expose the properties of the proposed framework via an empirical case study of four alternative optimization approaches (the signal setting optimized with the traditional Webster's method, TRANSYT model, and the newly proposed model) under various traffic conditions. Results indicate that the newly proposed framework outperforms the alternative signal control strategies in terms of both travel-time variability and expected travel time.