Public transport (PT) agencies are increasingly keen on integrating ride-hailing (RH) services with PT to improve overall mobility. Understanding the traffic flow distribution in the integrated system is vital for the policy decision-making and services design of such a system. We propose a stochastic user equilibrium (SUE) model for multimodal transport systems consisting of private car, PT and RH. The travel costs in the SUE model are investigated using a multimodal graph representation to capture the relationship of different travel modes in the integrated system. We apply the proposed model to a toy case and a real-world case. A RH subsidy strategy is compared with the benchmark to demonstrate travellers’ route and mode shifts in the integrated system. Our findings offer insights on subsidising RH services through the proposed model, and provide valuable knowledge on the planning and design of the integrated system.

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Metro disruptions due to unexpected events reduce transit system reliability, resulting in significant productivity loss and long passenger delays. Bus bridging strategy is often used to connect stations affected by metro disruptions such that passengers could continue their journey. The literature usually designed bridging routes and then allocated buses to designed routes with specific frequencies. The restriction that each bus can only operate on a route greatly limits the service flexibility and decreases operation efficiency. We propose a flexible bus bridging strategy to deal with the disruptions of metro networks. The proposed strategy optimizes a tailored bridging path for each bus. The path dictates the stations that a bus should visit in sequence once it is dispatched from the depot. A two-stage model that balances the needs of transit agency and passengers is developed to optimize the tailored bridging paths based on affected metro stations, reserved buses, bus capacity, passenger demands and bus travel times. The Stage I model produces schematic bridging paths by minimizing the maximum bus bridging time. The Stage II model further details the paths by minimizing average passenger delay. The superiority of the proposed strategy to a traditional strategy is demonstrated in a case study in Rotterdam, The Netherlands.@en