The development of the Internet of Vehicles (IoV) offers significant opportunities to enhance the traffic management system based on connected vehicles (CVs), while accurate traffic arrival rate estimation is critical for the dynamic evaluation and optimization of signalized intersections. Existing CV-based methods, however, are constrained to single-stream estimation that assumes first-in-first-out (FIFO) discipline, overlook initial queues, and deteriorate sharply when CV penetration is low or data are spoofed. To address these limitations, this study proposes a JO-MAP (JOint Maximum A Posteriori) method that jointly estimates cycle-based arrival rates of multiple traffic streams under both undersaturated and oversaturated conditions. The key innovations include a joint weighted likelihood function that treats each queued CV as an independent observation, eliminates FIFO assumptions, and explicitly accounts for the initial queues, and a joint Bayesian prior that embeds historical CV sample-size information for enhanced accuracy even with sparse real-time CV data. Comprehensive simulation and field experiments show that JO-MAP produces reliable estimates under different penetration rates, arrival patterns, and traffic volume levels, achieving 100% estimation success and less-than 4 veh cycle-level error with only 5% CV penetration. The feature of joint estimation makes the method less demanding for the penetration rate of CVs and more robust to noisy/spoofing data compared to baseline methods, limiting the error increase to 1.2 veh under deliberate spoofing attacks. Besides, JO-MAP reduces average vehicle delay by 12%–20% when integrated into adaptive signal control, demonstrating its potential for IoV-enabled traffic management.

With the emerging Internet of Things (IoT) and Vehicle-Road-Cloud Integration System (VRCIS) technologies, coordinating Connected and Automated Vehicles (CAVs) and traffic signal is becoming a practical solution to further enhance traffic efficiency. However, current studies still have limitations. Firstly, there is a domain mismatch between CAV trajectory planning (temporal domain) and signal optimization (spatial domain). This mismatch requires separate modeling of trajectory planning and signal optimization, which greatly reduces global optimality. Secondly, previous studies are not applicable to actual mixed traffic environment, since they mostly simplify Human-driven Vehicle’s (HV) behavior without considering queuing and stop-and-go maneuvers. Therefore, we propose a novel Multi-Vehicles and Signal Cooperation (MVSC) planner to solve the limitations via following designs. (i) Joint optimization is achieved via formulating in the spatial domain, unifying CAV’s planning domain with traffic signal optimizing domain. (ii) A parallel algorithm is designed for the adaptation to numbers of CAVs. This algorithm is based on Alternating Direction Method of Multipliers (ADMM), making full use of IoT and VRCIS. (iii) HV queuing and stop-and-go behaviors are considered in our modeling. Simulation results show that the proposed MVSC planner can enhance efficiency and ecology by 23.60% and 15.63%. At CAV’s penetration rate of 40% and V/C ratio of 0.75, the proposed planner shows its full potential in performance enhancement. The average computation time of parallel computing approach is only within 10 milliseconds, which confirms the real-time implementation capability.

A critical aspect of safe and efficient motion planning for autonomous vehicles (AVs) is to handle the complex and uncertain behavior of surrounding human-driven vehicles (HDVs). Despite intensive research on driver behavior prediction, existing approaches often overlook the interactions between AVs and HDVs, assuming that HDV trajectories are not influenced by AV actions. To address this gap, we present a transformer-transfer learning-based interaction-aware trajectory predictor for safe motion planning in autonomous driving, focusing on a vehicle-to-vehicle (V2V) interaction scenario involving an AV and an HDV. Specifically, we construct a transformer-based interaction-aware trajectory predictor using widely available datasets of HDV trajectory data and further transfer the learned predictor using a small set of AV-HDV interaction data. Then, to better incorporate the proposed trajectory predictor into the motion planning module of AVs, we introduce an uncertainty quantification method to characterize the predictor’s errors, which are integrated into the path-planning process. Our experimental results demonstrate the value of explicitly considering interactions and handling uncertainties.

High-speed cruising scenarios with mixed traffic greatly challenge the road safety of autonomous vehicles (AVs). Unlike existing works that only look at fundamental modules in isolation, this work enhances AV safety in mixed-traffic high-speed cruising scenarios by proposing an integrated framework that synthesizes three fundamental modules, i.e., behavioral decision-making, path-planning, and motion-control modules. Considering that the integrated framework would increase the system complexity, a bootstrapped deep Q-Network (DQN) is employed to enhance the deep exploration of the reinforcement learning method and achieve adaptive decision making of AVs. Moreover, to make AV behavior understandable by surrounding HDVs to prevent unexpected operations caused by misinterpretations, we derive an inverse reinforcement learning (IRL) approach to learn the reward function of skilled drivers for the path planning of lane-changing maneuvers. Such a design enables AVs to achieve a human-like tradeoff between multi-performance requirements. Simulations demonstrate that the proposed integrated framework can guide AVs to take safe actions while guaranteeing high-speed cruising performance.

Recent advancements in Connected Vehicle (CV) technology have prompted research on leveraging CV data for more effective traffic management. However, existing studies on CV-based signal control share a common shortcoming in that they all ignore traffic flow estimation errors in their modeling process, which is inevitable due to the sampling observation nature of CVs. This study proposes a CV data-driven robust optimization framework for traffic signal timing, accounting for both traffic flow variability and estimation errors. First, we propose a general CV data-driven deterministic optimization model (CV-DO) that can be widely applied to various scenarios, including under-/over-saturated and fixed-/real-time signalized intersections. Then, we propose a novel CV data-driven uncertainty set of arrival rates, circumventing the error-prone estimation process and accounting for both traffic flow variability errors. Finally, a CV data-driven robust optimization model (CV-RO) is formulated to explicitly handle arrival rate uncertainties. Employing the robust counterpart approach, this robust optimization problem can be converted to deterministic mixed-integer linear programming problems that can be solved efficiently with exact solutions. The evaluation results at a real-world intersection highlight the superior performance of the CV-RO model compared to the deterministic model and traditional methods across various scenarios. At different levels of traffic flow fluctuations, CV-RO can reduce delays by 5-26% compared to CV-DO at fixed-time signalized intersections with 0.1 CV penetration rate. The results on a real-time signalized network show that CV-RO can reduce 5% delays compared to the CV-DO model and 35.5% delays compared to actuated control at a 0.3 penetration rate.

Network signal coordination control is a crucial means to improve the traffic operation efficiency of the overall roadway network. Accurate identification of critical paths does play an important role in determining the scope of network coordination control. Therefore, this paper proposed the definition of critical path from the perspective of traffic control and management. Under the detection environment of connected vehicle (CV), a comprehensive quantitative indicator system for path criticality evaluation from three aspects, supply side, demand side and operation side, which are arranged in the form of a tower structure. A critical path identification method (CPIM) was then proposed based on the analytic hierarchy process (AHP) theory, which was hereinafter referred to as AHP-CPIM. In order to evaluate the feasibility and effectiveness of the proposed method, a case study set in an urban network in Tongxiang, Zhejiang Province in China, is conducted through simulation models built through VISSIM and Synchro. Two scenarios were set, one is coordination control based on the coordination subarea obtained from Synchro (namely without critical path identification), and another one is coordination control with critical paths obtained from AHP-CPIM. Results showed that, compared with the control of Synchro and Multiband method under the scenario of coordination control without critical path identification, network signal coordination control optimization based on AHP-CPIM improved about 37.9% and 35.9% in average delay, respectively, justifying the effectiveness of CV-driven critical path identification for network signal coordination control.

Fixed-time control is a widely adopted and cost-effective method for signalized intersections. However, existing studies utilizing connected vehicle (CV) data have not effectively addressed fixed-time control due to their reliance on specific vehicle arrival assumptions. To overcome this limitation, this study presents a novel traffic control approach for fixed-time signalized intersections based on a cumulative flow diagram (CFD) framework. The proposed method comprises a CFD model and a multi-objective optimization model. The CFD model establishes analytical relationships between traffic flow operations and varying signal timing parameters, with intersection demand estimated using a novel weighted maximum likelihood estimation method. A multi-objective optimization model based on CFD is formulated to minimize exceeded queue dissipation time as the primary objective and average delay as the secondary objective, which is applicable under both undersaturated and oversaturated traffic conditions. Leveraging the data-driven nature of the CFD model, a specially designed bi-level particle swarm optimization-based algorithm is employed to determine optimal cycle length (and offset if applicable) and green ratios separately. Evaluation results demonstrate that the proposed method outperforms Synchro, a conventional approach, in terms of average delay and queue under various traffic conditions. Moreover, the proposed method exhibits the capability to handle specialized scenarios involving spillbacks.

Although considerable methods have been developed for the performance evaluation of signalized intersections using sampled vehicle trajectories, most of them aim at estimating a single parameter and cannot describe the entire arrival–departure process of the traffic flow. This significantly constrains the application of these methods for a comprehensive evaluation and efficient optimization of signalized intersections. In this paper, we propose a cumulative flow diagram (CFD) estimation and prediction method using sampled vehicle trajectories. It can be used to calculate multiple performance measures—traffic volume, queue length, average delay, and total delay—based on the estimated CFD for the current signal timing plan. Concurrently, it can be further employed for signal control optimization based on the predicted CFDs for candidate signal timing plans. The core idea of the proposed method is to generate the cumulative arrival curve based on the arrival characteristics of the sampled vehicles, and then fit the queue leaving points to obtain the cumulative departure curve. Thereby, given the current or any candidate signal timing plan, we can estimate or predict the CFDs by updating the sampled vehicle arrivals. The proposed method is evaluated using both simulation and empirical data. The simulation results yield that the average estimation error of the four performance measures is 10.3% under a real-world level penetration rate of 10%. Meanwhile, similar accuracies are achieved for the CFD prediction. The empirical results show that under a penetration rate of 8.6%, the estimation errors of the traffic volume and queue length are 2.7% and 3.3%, respectively.

Intersection queue length estimation using high-resolution probe vehicle trajectory data has received increasing attentions in recent years. Existing methods for cycle-based queue length estimation still face the challenge of low and/or unstable estimation accuracies under the condition of sparse vehicle trajectory data, i.e., there is no greater than one vehicle trajectory per cycle on average. To address this challenge, this study proposed a novel approach for cycle-based queue length estimation by fusing real-time and historical probe vehicle trajectory data, through a statistical parameter estimation method, i.e., maximum likelihood estimation (MLE). With known signal timing information, firstly, the historical probe trajectory data are used to acquire the arrival flow rate distribution over the entire study period. Then, a likelihood function of queue length is derived by fully exploiting real-time traffic flow information provided by the queued and non-queued probe vehicles. Finally, the MLE method is adopted to estimate the cycle-based queue lengths with the maximum probability. The proposed approach is verified using both simulation and empirical data. Results indicate that precise estimation for cycle-based queue lengths can be realized based on sparse vehicle trajectory data, while showing superiority to a representative existing method. The proposed method is basically an offline method, but it can also work in an online manner if provided a priori arrival distribution either acquired from historical probe vehicle trajectory data or a theoretical assumption.