Dynamic Traffic Signal Timings for Urban Bus Services in Congested Road Networks with GPS Data
DescriptionHong Kong has one of the busiest road networks in the world which carries 70% of local daily trips despite its highly developed metro system. With the limited road space, sustainable development relies on enhancing and maintaining the attractiveness of high-capacity buses over private and low-capacity vehicles. This calls for transforming the traditional road management philosophy from minimising delay for general traffic to maximising service level of high-capacity buses. For service monitoring and management, many buses nowadays are tracked with GPS (Global Positioning System) in real-time. With this real-time tracking, conventional bus-oriented traffic control systems primarily aim to reduce buses’ running times through providing them signal priority. It is however noted that the effectiveness of bus signal priority is highly dependent on prevailing traffic conditions. Without reliable estimates of its traffic impact, bus signal priority could also induce significant and unnecessary general traffic delays which would deteriorate the bus services in return due to onset of congestion. An effective bus service management system should consider and adopt different control objective settings according to prevailing traffic conditions. It should also be able to capture dynamics of both general traffic and buses with all relevant data sources.This project aims to develop a multi-objective signal optimisation framework for urban bus services. The objectives considered herein include buses’ running times, headway deviations, and delays of general traffic. The framework operates on a hybrid model which consists of interacting macroscopic and microscopic components. The macroscopic component models dynamic network traffic flow with the fundamental diagram. This allows the model to capture traffic characteristics ranging from free-flow to oversaturated conditions. The microscopic component models trajectories of individual buses and their interaction with surrounding traffic flow. Its hybrid nature allows the model to incorporate both flow-based data of general traffic and trajectory-based GPS data of buses. Recognising that different buses would request for timing plan changes at different intersections at different times, the optimisation framework will be solved by a decentralised algorithm which allows it to accommodate effectively the asynchronous timing plan change requests from different buses. The optimisation framework will be tested through a selected real-world scenario. The effect of different decomposition schemes and algorithmic designs in the decentralised solution procedure on global performance will be investigated over different traffic conditions. The project contributes to network-wide public transport management by introducing hybrid traffic modelling and decentralised optimisation techniques with use of GPS data.
|Effective start/end date||1/01/20 → …|