As one of the most important traffic signal coordination models, the multi-path progression model can provide dedicated green bands for multiple critical path-flows in an artery. However, as the number of intersections and critical paths in the system increases, the bandwidth often becomes narrower or even ineffective. In this paper, a multi-path progression model for long arterial signal coordination is proposed. The proposed method permits the progression bands to be broken if necessary and optimizes system partition and signal coordination within a unified framework. To ensure the continuity of the progression band, the bandwidth loss caused by partitioning is taken into consideration. Furthermore, to obtain a more feasible solution, we optimize offsets and phase sequences at each intersection simultaneously. The proposed model is formulated as a mixed-integer linear program, which can be precisely solved by the standard branch-and-bound technique. A case study was conducted to prove the effectiveness and advantages of the proposed model. The results show that the proposed model can generate more ideal green bandwidths for critical paths compared with the benchmark. Further simulation demonstrates that the proposed model can outperform the benchmark model in reducing average path-flow delay and average number of stops, and improving average travel speed.

Passive transit signal priority (TSP) controls have long been recognized as a viable method for reducing bus delays on urban arterial roads. This paper introduces a partition-enabled multi-path model designed to address the problems of categorizing progression and coordinating signals for multiple bus paths on urban arterials. By incorporating the spatial distribution of bus stations, the proposed approach allows for the subdivision of progressions into multiple sub-progressions, thereby expanding the feasible region. Additionally, this approach considers the potential overlaps or conflicts between different bus paths. The diverse stopping demands of multiple bus paths are also taken into consideration. Furthermore, to get a more feasible solution, we optimize offset and phase sequences at each intersection simultaneously. A binary mixed-integer linear program (BMILP) is used to formulate the proposed models, which can be solved precisely through mathematical solvers or the standard branch-and-bound technique. The numerical examples and simulation experiment results presented in this paper demonstrate that compared with the BUSBAND and INTEBAND, the proposed models can significantly reduce average delays, average stop times, and average travel time for buses while having almost no impact on passenger cars. Furthermore, Model-2, which includes phase sequence optimization, shows higher levels of reliability and effectiveness.

Efficient public transportation systems are fundamental to achieving sustainable urban development. As the backbone of urban mobility, the coordinated development of rail transit and bus systems is crucial. The opening of a new rail transit line inevitably reshapes urban travel patterns, posing significant challenges to the existing bus network. Understanding passenger switch behavior is key to optimizing the competition and cooperation between these two modes. However, existing methods on the switch behavior of bus passengers along the newly opened rail transit line cannot balance the predictive accuracy and model interpretability. To bridge this gap, we propose a CART (classification and regression tree) decision tree-based switch behavior model that incorporates both predictive and interpretive abilities. This paper uses the massive passenger swiping-card data before and after the opening of the rail transit to construct the switch dataset of bus passengers. Subsequently, a data-driven predictive model of passenger switch behavior was established based on a CART decision tree. The experimental findings demonstrate the superiority of the proposed method, with the CART model achieving an overall prediction accuracy of 85%, outperforming traditional logit and other machine learning benchmarks. Moreover, the analysis of factor significance reveals that ‘Transfer times needed after switch’ is the dominant feature (importance: 0.52), and the extracted decision rules provide clear insights into the decision-making mechanisms of bus passengers.

Vehicle trajectories display several problems such as multi-source heterogeneity, space–time-limited fragmentation, and low-penetration-rate sparsity, failing to support traffic state estimation and traffic collaborative control. Fully sampled vehicle trajectory reconstruction has received increasing attention. However, existing approaches to trajectory reconstruction focus on simple scenarios with single lanes, short distances, and non-interference, ignoring driving dynamics such as overtaking, resulting in limited applicability. To improve the utilization of sparse trajectories, a novel trajectory reconstruction framework is tailored by incorporating car-following and overtaking behaviors. First, an improved method is proposed to recognize driving behavior patterns, and the optimal number of the cluster-based patterns are determined. Then, an improved piecewise cubic Hermite interpolating polynomial algorithm is designed to reconstruct pattern-recognized probe vehicle (PV) trajectories, by integrating local weighted regression. Second, a piecewise order-changing model is proposed to capture overtaking dynamics and arrival stochasticity. Third, by incorporating the K -dimensional tree algorithm, a novel algorithm is developed to draw a speed contour map, providing regional speed baselines for estimating unknown non-probe vehicle (NPV) trajectories. Finally, an integrated approach combining the car-following (CF) and inverse car-following (ICF) models is tailored to avoid the accumulation of errors and address overlapping problems in trajectories reconstructed by the CF model. A weighted fusion model is tailored to fuse the two NPV trajectory candidates estimated by the CF and ICF models. A model comparison proves the superiority of the combined approach over the CF model (41.39%–51.33%) and reveals that the higher the penetration rate of PVs, the better the reconstruction outcomes.