Accurate and timely detection of incident-induced congestion (IIC) is essential for mitigating its negative impact on traffic efficiency. Existing studies on IIC detection mainly focus on traffic flow on freeways and face challenges on urban roads due to the impacts of signal lights at intersections and diverse road networks. Additionally, the low penetration rate of probe vehicle trajectories poses another challenge. This study proposes a probe-vehicle-trajectory-based algorithm for IIC detection on urban roads at the movement and cycle levels. Two critical features (i.e., the average speed and the entrance time into the road segment) are defined to capture the characteristics of trajectory segments. A Vehicle Trajectory Polar Coordinate Transformation (VTPCT) method is proposed to differentiate anomalous trajectory segments (ATS) affected by IIC from normal ones, considering the periodicity of fixed signal timing at the intersections. Anomaly rates calculated from the identified ATS within a spatiotemporal window are introduced to reflect the movement-cycle-level traffic states. A two-stage algorithm framework is designed to enhance the algorithm’s adaptability to spatially sparse trajectories and diverse road networks. Experimental studies show that the proposed algorithm is applicable to trajectory data with low penetration rates and outperforms benchmarks of typical statistical and AI-based algorithms.

Queue length is one of the most commonly used indicators to evaluate traffic operation at signalized intersections. Many studies aim to estimate queue length using trajectory data, but this remains a challenge with spatially sparse trajectories. This paper introduces a probabilistic-based method for cycle-by-cycle estimation of queue length distribution and the point estimate in closed form using connected vehicle (CV) trajectories. The method is applicable to isolated signalized intersections with under-saturated traffic. It works well in a low CV penetration rate environment by exploiting the trajectories of both queued and non-queued CVs. Vehicle arrival rates and CV penetration rates are first estimated to capture the vehicle arrival pattern within a time of day (TOD) using the maximum likelihood estimation approach. The closed forms of the probability distribution of queue length are derived for each cycle, which are attractive for applications such as adaptive signal timing considering traffic uncertainty. The queue length with the highest probability is taken as the point estimate. Numerical and empirical studies demonstrate that the proposed method outperforms the benchmark method in estimating CV penetration rates, particularly at low penetration rates. In term of queue length estimation, the proposed method is also superior to existing methods in most scenarios and is especially effective for cycles with observed trajectories. Sensitivity analysis reveals that the method is robust to different demand levels, signal timings, and variability in arrival patterns with under-saturated traffic. Additionally, the suggested requirements for the number of collected trajectories are investigated to provide practical guidance.

Appropriate deployment of autonomous-vehicle-dedicated lanes (AVDLs) is crucial for optimizing transportation system with mixed human-driven and autonomous vehicles. Existing studies mainly focus on matching the capacity and traffic flow volume to maximize throughput under different AV penetration rate, while lane changing scenarios and network geometry impacts were ignored. Therefore, the lateral locations in optimal deployment on expressway cannot be determined. This study developed an analysis framework to jointly optimize the longitudinal and lateral locations of AVDLs on expressways. The problem is formulated as a bi-level optimization model. In the lower level, a lane-level multiclass equilibrium assignment model is built to predict traffic flow distribution among lanes on both longitudinal and lateral locations regarding AVDL deployment plan. In the upper level, a 0–1 integer programming model is established to obtain the optimal deployment plan of the AVDL with minimal total travel time (TTT). Additionally, a problem-specific heuristic-based adaptive large neighborhood search algorithm is developed to solve the problem. The advantages of the proposed model are validated in North–South Elevated Expressway, Shanghai, China. Results indicate that the proposed framework outperforms existing methods in terms of TTT reduction. Finally, sensitivity analysis demonstrates the robustness of the developed method in reducing the TTT under different headways, demand levels, and OD distributions. The proposed framework enables the optimal deployment of AVDLs regarding both longitudinal and lateral locations in urban expressways containing multiple on- and off-ramps, which supports decision-making for managing expressway with mixed HV and AV traffic flow.

As an emerging data source, the trajectories of probe vehicles can compensate for the deficiencies of high maintenance costs and low coverage ranges of infrastructure-based detectors (e.g., loop detectors). However, existing arterial signal coordination studies typically assume high-penetration-rate trajectories, which are difficult to achieve in reality. Utilizing low-penetration-rate vehicle trajectories for arterial signal timing with cyclic stochastic traffic demand remains a significant challenge. To address this issue, this study developed a nonlinear optimization model for arterial signal coordination that is applicable to low-penetration-rate vehicle trajectories. Offsets and green splits were optimized to minimize the average delay of probe vehicles on both major and minor roads. Probe vehicle trajectories across cycles were aggregated into one cycle to compensate for the low penetration rate of the trajectory data. The concepts and estimation of the sampled arrival pattern, sampled departure pattern, and transition period were proposed to capture the spatiotemporal progression of probe vehicles along the arterial with varying signal timings. A genetic algorithm (GA)-based solution algorithm was designed to solve the proposed model. Simulation studies validated the advantages of the proposed model over the models in Synchro Studio, MULTIBAND, and the simplified model without considering the transition period. The sensitivity analysis showed that: 1) number of sampled trajectories matters instead of the penetration rate; 2) required number of sampled trajectories increases approximately linearly with the number of intersections and the demand factor; and 3) proposed model is robust to the sampling interval that is no longer than 7 s.

Stochastic traffic demands and turning ratios are critical factors in coordinated signal control. However, existing studies ignore the spatial-temporal dependencies of traffic flows between adjacent intersections and signal cycles. Turning ratios are usually assumed to be deterministic. This study develops a two-stage stochastic programming model for two-way coordinated adaptive signal control under stochastic traffic demands and turning ratios. A hierarchical multi-objective function is developed for overflow management and operational efficiency under both over- and under-saturated traffic. The primary and secondary objective functions minimize residual queue lengths and average vehicle delays, respectively, which are formulated considering spatial-temporal dependencies for the coordinated traffic flow. In stage one, a base coordinated signal timing plan is optimized to maximize the expected performance under stochastic scenarios. In stage two, adaptive cycle lengths and green times are determined by setting the tolerance factor for the base green times to maintain the stable traffic flow. The concept of Phase Clearance Reliability (PCR) is extended to decouple the interaction between the two stages. The deterministic equivalent problem of the proposed model in one signal cycle is modified to optimize the base signal timing plan for serving the stochastic exogenous and endogenous traffic demands up to certain PCR values. A PCR-based gradient algorithm is designed for solutions. The experimental results demonstrate that the proposed model can significantly improve traffic operation compared to six benchmarks.

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. Theoretical analysis suggests that the proposed framework can obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed method. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.

Testing and evaluation are critical steps in the development of connected and automated vehicle (CAV) technology. One limitation of closed CAV testing facilities is that they merely provide empty roadways, in which testing CAVs can only interact with a limited number of other CAVs and infrastructure. This paper presents an augmented reality environment for CAV testing and evaluation. A real-world testing facility and a simulation platform are combined together. Movements of testing CAVs in the real world are synchronized with simulation and information of background traffic is fed back to testing CAVs. Testing CAVs can interact with virtual background traffic as if in a realistic traffic environment. The proposed system mainly consists of three components: a simulation platform, testing CAVs, and a communication network. Testing scenarios that have safety concerns and/or require interactions with other vehicles can be performed. Two exemplary test scenarios are designed and implemented to demonstrate the capabilities of the system.