Abstract The emergence and development of taxi ride-sharing can improve the efficiency of urban transportation systems and reduce carbon dioxide emissions in cities. Using GPS tracking data from taxis in Chengdu, China, we firstly provide a method for judging the conditions under which taxi rides are shareable, and then analyse the potential demand for taxi ride-sharing from both time and space perspectives. We develop a variable selection model to evaluate the key influencing factors for taxi ride-sharing. The results show there is a large potential taxi ride-sharing market in Chengdu, and the development of shared transportation is dependent on certain practical conditions. Both orders for taxis and those for taxi ride-sharing are concentrated in central city zones. It is verified that the number of taxi orders is positively correlated to the potential demand for taxi ride-sharing. It is also learnt that factors including population density, average traffic speed and regional GDP are negatively correlated to the demand for taxi ride-sharing. Factors such as traffic facilities, bus stops and life services have positive effects. In terms of the first-grade indices, weather and climatic conditions, traffic infrastructure construction and some land-use functions have a positive effect on the demand, while population and regional economic conditions and other environmental features have negative effects. In fact, only the indicators of population density, GDP, average traffic speed and the number of traffic facilities and bus stops have great effects on the demand for taxi ride-sharing. The other 29 factors in our indicator system play a much less important role. This quantitative model finds the key influencing factors and can help us to understand the dynamics driving residents’ demand for taxi ride-sharing. It may assist the relevant government departments to optimize taxi resources and promote the sustainable development of taxi ride-sharing.
Understanding the influencing factors of taxi ride-sharing: A case study of Chengdu, China
2023-08-18
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Real-Time Distributed Taxi Ride Sharing
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