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Highlights • A mixed integer linear model is developed to optimize multi-period tram timetables. • Priorities of improving tram operational efficiency and timetable adherence are considered. • Arterial signal timings are resynchronized when optimizing scheduled tram trajectories. • Tram performance is improved without sacrificing vehicular mobility. Abstract Modern trams run on exclusive rail lanes along urban streets, but they usually share the right of way with general traffic at intersections and often get interrupted by traffic signals. To improve tram operation reliability, this paper develops a methodology to optimize a multi-period tram timetable by simultaneously adjusting bidirectional scheduled tram trajectories and traffic signal timings. The objective balances the operational priorities of minimizing tram running time and maximizing timetable adherence. The scheduled trajectories depict tram movements along the roads and dwell processes at stations. Arterial signal timings are resynchronized to favor tram movements. The proposed methodology was evaluated in a simulation of a real-world tram line. Compared with a traditional approach, the proposed methodology reduced tram running time, number of stops at intersections and schedule delay by 11.1%, 82.4% and 37.5%, respectively. The impact on general traffic could be assumed neutral since cycle lengths, green splits and vehicular bandwidths were kept unchanged. [ABSTRACT FROM AUTHOR]

As transit agencies increasingly adopt the use of Automatic Passenger Count (APC) technologies, a large amount of boarding and alighting data are being amassed on an ongoing basis. These datasets offer opportunities to infer good estimates of passenger origin–destination (OD) flows. In this study, a method is proposed to estimate transit route passenger OD flow matrices for time-of-day periods based on OD flow information derived from labor-intensive onboard surveys and the large quantities of APC data that are becoming available. The computational feasibility of the proposed method is established and its accuracy is empirically evaluated using differences between the estimated OD flows and ground-truth observations on an operational bus route. To interpret the empirical differences from the ground-truth estimates, differences are also computed when using the state-of-the-practice Iterative Proportional Fitting (IPF) method to estimate the OD flows. The empirical results show that when using sufficient quantities of boarding and alighting data that can be readily obtained from APC-equipped buses, the estimates determined by the proposed method are better than those determined by the IPF method when no or a small sample sized onboard OD flow survey dataset is available and of similar quality to those determined by the IPF method when a large sample sized onboard OD flow survey dataset is available. Therefore, the proposed method offers the opportunity to forgo conducting costly onboard surveys for the purpose of OD flow estimation. [ABSTRACT FROM AUTHOR]

Bus bridging has been widely used to connect stations affected by metro disruptions such that stranded passengers could resume their journeys. Previous studies generally assumed that a bus operates exclusively on one bridging route with given frequency, which limits the service flexibility and reduce the operational efficiency. We propose a strategy to instruct buses to operate on predefined bridging routes once they are dispatched from depots. Buses are allowed to flexibly serve different bridging routes. Each bus operates based on a bridging plan that lists the stations to serve in sequence instead of route frequencies. A two-stage model is developed to optimize the bridging plans and their assignments to buses with the objectives that balance the operational priorities between minimizing bus bridging time and reducing passenger delay. A Weight Shortest Processing Time first (WSPT) rule based heuristic algorithm is developed to solve the proposed model. The developed model is further incorporated in a rolling horizon framework to handle dynamic passenger arrivals during the disruption period. The effectiveness of the proposed strategy is demonstrated in comparison with alternative strategies in real-world case studies. [ABSTRACT FROM AUTHOR]

Data-driven traffic management and control has attracted much attention recently. This paper conducts a series of coherent analyses based on geocoded data to understand the distribution characteristics of bus operational speed and to explore the potential applications of speed distributions. First, an original bipartite model is adopted for capturing instantaneous speed where the suspended and moving states are considered separately and a two-component mixed Weibull distribution is used to model the speed distribution in moving states. The mixed Gaussian distribution with variable components is found to be capable of expressing the speed distribution patterns of different road sections. Second, elaborate analyses on the basis of speed distribution modelling are conducted: (i) regression analyses are conducted to explore the correlations between parameters of instantaneous speed distributions and traffic related factors; (ii) a powerful clustering method using Kullback-Leibler divergence as the distance measure is proposed to grade the road sections of a bus route. These results can be utilized in fields such as bus operations management, bus priority signal control and infrastructure transformation aiming to improve the efficiency of bus operations systems. [ABSTRACT FROM AUTHOR]