| Summary: | Multimodal public transport networks (MMPTNs) in modern cities are becoming increasingly complex. This makes finding optimal journey routes challenging due to a large number of transfer options that need to be properly considered. Furthermore, the complexity of the problem is compounded when multiple conflicting travel criteria are considered (e.g., travel time, walking distance, travel fare, etc.). This paper proposes a transfer graph (TG) model to explore the transfer opportunities of the MMPTN to support efficient journey route planning. TG considers all possible transfer opportunities, while employing a representative mechanism to optimize the TG structure that supports efficient route planning algorithms. Based on the proposed TG, we develop two exact algorithms to search the Pareto-optimal solutions for multi-criteria journey planning (MCJP) over the MMPTN. The first algorithm runs faster by eliminating many partial solutions at an early stage, which is more suited for lowering computation time at the expense of marginal degradation in output quality. In contrast, the second algorithm provides a more dependable solution by incorporating accurate journey time prediction that caters to the evolving traffic conditions. We also develop techniques to accelerate the TEDE and TEAE algorithms. Experiments on real-world public transport networks and traffic data demonstrate the effectiveness of our approach for MCJP. Experiment results also reveal interesting insights on the impact of the TOs, number of transfers, and number of travel criteria on MCJP algorithms, which can contribute to better public transportation planning.
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