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Unplanned disruptions, such as vehicle breakdowns, in a public transportation system can lead to severe delays and even service interruptions, preventing the successful implementation of subsequent plans and the overall stability of transit services. A common solution to address such issues is implementing a bus bridging service using an experience-based response strategy, involving the deployment of spare buses to continue affected services. However, with this approach, it becomes impractical and challenging to generate a feasible and rational rescheduling scheme for the remaining transit services when spare buses are insufficient or widespread disruptions occur. In response to this challenge, we propose an innovative model that integrates service capability and regularity, aiming to minimize rescheduling costs through timetable adjustments and scheduling reassignments. We apply dynamic programming to comprehensively consider the hysteresis effects of disruptions and achieve a long-term optimal rescheduling scheme. To efficiently solve the proposed model, the large neighborhood search algorithm is improved by incorporating operational rules. Finally, several experiments are conducted under an actual transit operation scenario in Shenzhen. The results demonstrate that our method significantly reduces trip cancellations and, simultaneously, diminishes the increase in the departure interval resulting from the adjusted schedule by 23.27%. [ABSTRACT FROM AUTHOR]

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Unplanned disruptions, such as vehicle breakdowns, in a public transportation system can lead to severe delays and even service interruptions, preventing the successful implementation of subsequent plans and the overall stability of transit services. A common solution to address such issues is implementing a bus bridging service using an experience-based response strategy, involving the deployment of spare buses to continue affected services. However, with this approach, it becomes impractical and challenging to generate a feasible and rational rescheduling scheme for the remaining transit services when spare buses are insufficient or widespread disruptions occur. In response to this challenge, we propose an innovative model that integrates service capability and regularity, aiming to minimize rescheduling costs through timetable adjustments and scheduling reassignments. We apply dynamic programming to comprehensively consider the hysteresis effects of disruptions and achieve a long-term optimal rescheduling scheme. To efficiently solve the proposed model, the large neighborhood search algorithm is improved by incorporating operational rules. Finally, several experiments are conducted under an actual transit operation scenario in Shenzhen. The results demonstrate that our method significantly reduces trip cancellations and, simultaneously, diminishes the increase in the departure interval resulting from the adjusted schedule by 23.27%.

This study proposes a novel Bayesian hierarchical approach for online bus speed prediction by explicitly accounting for the spatiotemporal interaction (STI) of speed observations. The use of Laplace approximation can expedite the estimation of Bayesian models and enable the implementation of online prediction. Large numbers of trials are carried out to identify significant predictors and the optimal length of the look-back time window to achieve the highest prediction accuracy. The spatiotemporal interacting patterns are also explored, and results show that the Type IV model assuming the structured spatial effect interacts with the structured temporal effect can best accommodate the bus speed data. Besides, prediction errors of the Type IV model randomly distribute over time and space. The proposed model can achieve high prediction accuracy and computational efficiency without compromising the interpretability of the contributing factors and the unobserved spatiotemporal heterogeneity. The proposed model can be used to assist public transit operation and management, such as bus scheduling, congestion warning, and the development of proactive measures to mitigate bus delays.

The spatial–temporal accessibility of a transport system assesses the spatial–temporal constraints faced by individuals based on their fixed activities and the ability of the transport system to facilitate trading time for space in movement. Previous studies either measured the individual spatial–temporal accessibility of a general transport network or measured the cumulative-opportunity accessibility by public transit through an exclusive computation of the bottom of the full network time prism. By contrast, the current study measures the individual spatial–temporal accessibility by public transit by integrating timetable and passenger departure time and computing the full network time prism. A public transit network is modelled as a time-dependent weighted directed graph, wherein every single directed arc is associated with a time-dependent travel time to represent the linkage between two adjacent stops on a transit route. The time-dependent travel time assigned to arcs is determined according to timetables, which is particularly assigned to infinity when transit service is unavailable between two stops. A modified network potential path area (N-PPA) algorithm based on the time-dependent weighted directed graph is employed to produce a potential path area for the activity participation of an individual by public transit. The proposed methodology is applied as a case study to measure individual spatial–temporal accessibility using the Salt Lake City TRAX system. Results indicate that the outcome of the proposed methodology is sensitive to departure time of passengers. The results of this study provide suggestions on potential improvement of bus/rail line layout and timetables and may aid in trip planning of passengers. [ABSTRACT FROM AUTHOR]