Abstract
Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.
Highlights
With the development of urbanization, people place increasing demands on urban traffic and transportation
Both the people seeking to take a taxi and taxi drivers have insufficient location information on the best location for a pick-up, which causes the phenomenon that a taxi driver has difficulty finding passengers while people find it difficult to locate a vacant taxi
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Summary
With the development of urbanization, people place increasing demands on urban traffic and transportation. The locations of taxi drivers picking up their passengers are highly random [2]. Several studies have considered the historical GPS trajectories of taxis, such as understanding human mobility [9,10,11,12], estimating traffic emissions [13,14,15,16], planning routes [17,18,19,20], and formulating taxi/passenger search strategies [3,6,21,22,23]. Hwang et al proposed a grid-based clustering approach considering four factors, including waiting time, distance, average revenue and the probability of finding passengers when clustering, to recommend the pick-up locations to taxi drivers [30]. The parameters were different for weekdays and weekends (Table 2)
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