Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters’ route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

Transit signal priority (TSP) has become an increasingly popular way to tackle bus operation issues. Many efforts have been conducted to design TSP strategies, yet challenges still exists in real-life applications due to the difficulty in realizing the control logics and the limitation of road infrastructures. This paper proposes a novel TSP strategy considering lane sharing and real-time bus arrival time prediction. First, a right-turn lane sharing method is presented, it shares the right-of-way of a dedicated right-turn lane with through buses at the approaches of a signalized intersection. Next, a control logic based on phase insertion is proposed, and the new signal plan for next signal cycle is generated by judging the requirement of an exclusive bus phase based on predicted bus arrivals. Finally, the Kalman filter is used to establish a bus arrival time prediction model by using RFID and GPS data. The proposed method was implemented on an arterial road of real-life traffic network in Kunshan, China. Test results show that the proposed TSP strategy can achieve satisfactory performances. Bus delay decreases significantly compared to the general traffic delay, especially in peak hours. Further investigation shows that the findings of sensitivity analysis can provide beneficial guidance for practical applications.

Origin–destination (OD) passenger flow is a critical variable for metro system planning and operation. While numerous studies have investigated the influence of the built environment on passenger flow, most have focused on ingress or egress flows at metro stations. The impact of the built environment on OD flow dynamics, particularly the differences between origin-side and destination-side effects, remains poorly understood. This study proposes a novel method for exploring the non-linear effects of station-area built environments on OD flow in large-scale metro networks. First, hourly OD flows and station-area built environment features were extracted from multi-source data. Next, an analytical framework was developed to model the built environment–OD flow relationship using a gradient boosting decision tree model. Finally, the contributions of built environment variables and their non-linear effects on OD flows were systematically investigated. The proposed method was implemented on the Suzhou metro network in China. Test results show that most built environment variables exhibit time-varying, non-linear correlations with OD flows. Even the same variable demonstrates notable differences in its effect between the origin and destination sides. The findings of this study provide valuable guidance for metro planning and station-area urban development.

Enhancing bus movements along urban arterial roads to reduce waiting times and stops at intersections is critical for improving overall bus operational efficiency. Although extensive research has investigated transit signal priority (TSP) and bus guidance to achieve these objectives, mixed traffic conditions without dedicated bus lanes remain constrained due to inefficient traffic organization and underutilized intersection lanes. To address these limitations, this study proposes a multi-strategy bus guidance approach for connected environments that maximizes intersection lane utilization through right-turn lane sharing (RTS), particularly in China where dedicated and uncontrolled right-turn lanes are common. The approach is implemented via a dual-stage hybrid Bayesian network (HBN): Stage I forecasts bus travel times and queuing states using real-time bus positions and signal states data; Stage II determines the combination of speed guidance, dwell-time management, and RTS by accounting for passenger car speed distributions and allowable dwell times at stops. We evaluated the approach in a SUMO simulation environment of a real-world urban arterial in Kunshan, China, under four demand scenarios with varying volume-to-capacity (V/C) ratios of total traffic flow. Simulation results demonstrated that the proposed approach significantly reduced bus waiting times and stops at intersections. Under low-to-moderate demand situations, the combination of three strategies outperformed other strategies, and RTS-incorporated strategies achieved the greatest benefits under high-demand conditions.

Eco-driving at signalized intersections uses signal timing to plan vehicle arrivals, smooth speed changes, and reduce CO₂ emissions. However, the effectiveness of eco-driving policies may be limited without considering queue discharge, because queues ahead can delay vehicle passage even during a green phase. This study proposes a queue-aware multi-agent eco-driving model for connected and automated vehicles (CAVs) in mixed traffic. First, an effective green window is estimated for each CAV by accounting for queue discharge. Second, a local-emission reward combines emissions from the controlled CAV and nearby same-lane followers. Finally, these components are integrated into a proximal policy optimization controller that generates continuous acceleration commands. The proposed model is evaluated in SUMO across varying traffic demands and CAV penetration rates. Results show that CO₂ emissions reduce in most tested conditions and the reduction rates grow with CAV penetration at each demand level. At 1200 veh/h, the reduction rises from 7.0% to 25.6% as CAV penetration increases from 10% to 70%. Ablation results further reveal that the effective green window and local-emission reward jointly contribute to the CO₂ reductions. These findings highlight the potential of the proposed method for eco-driving in mixed traffic.