Intelligent Bus Routing with Heterogeneous Human Mobility Patterns
Optimal planning for public transportation is one of the keys helping to bring a sustainable development and a better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. However, in many cities people prefer to take private transportation other than public transportation due to the inconvenience of public transportation services. In this paper, we focus on the identification and optimization of flawed region pairs with problematic bus routing to improve utilization efficiency of public transportation services, according to people's real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on the mobility patterns, we propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing by taking into account both bus and taxi travel demands. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real-world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.
Y. Liu et al., "Intelligent Bus Routing with Heterogeneous Human Mobility Patterns," Knowledge and Information Systems, vol. 50, no. 2, pp. 383-415, Springer Verlag, Feb 2017.
The definitive version is available at https://doi.org/10.1007/s10115-016-0948-6
Keywords and Phrases
Bus routing; Human mobility; Public transportation
International Standard Serial Number (ISSN)
Article - Journal
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