Location Optimization for Urban Taxi Stands Based on Taxi GPS Trajectory Big Data

Abstract

A taxi stand can effectively regulate the behavior of taxi picking up passengers, reduce empty-run rate, and provide a convenient and orderly waiting environment for the public. However, the unreasonable setting of the existing taxi stands in most cities leads to an extremely low utilization rate and a waste of public space resources. This paper presents a novel three-stage strategy to address the taxi stands location problem (TSLP) incrementally. First, taxi demands hotspots are mined from a massive taxi Global Positioning System (GPS) data with GIS platform, and the optimal area for taxi stands siting in the following stages is determined. Then, the spatial interaction between taxi demands and taxi stands is explored to generate demand subsections and stand candidates along both the sides of the road. At last, a taxi stand location model (TSLM) is developed to minimize the total cost, which contains the access cost of passengers and the construction cost of taxi stands. The genetic algorithm-based procedure is adopted for TSLM optimization. A case study conducted in China verifies the effectiveness of the location strategy and investigate the impact of the maximum acceptable distance for passengers on TSLP. The experimental results describe the number and layout of taxi stand under a different demand coverage, which indicates that the proposed approach is beneficial to provide scientific reference for the municipal department in taxi stand site decisions and make a tradeoff between the interests of planners and users.INDEX TERMS Taxi stand, location strategy, spatial-temporal demand, GPS big data, genetic algorithm.

Using Taxi GPS Trajectory Data to Optimize the Spatial Layout of Urban Taxi Stands

“…The relevant parameters refer to the conclusion values from an actual investigation conducted by Qu et al (5).…”

“…Although these approaches have been successfully employed in the field of transportation service infrastructure such as the siting of bus stops ( 30 , 31 ), bike-sharing stations ( 32 , 33 ), charging stations for electric vehicles ( 34 , 35 ), park-and-ride facilities ( 36 , 37 ), and roads ( 38 ), they still leave a huge blank in the location decision-making applications for TS. In the latest relevant papers, Qu et al ( 5 ) present a three-stage strategy to choose the location of TS. Their objective is to minimize the access cost of passengers and the construction cost of TS.…”

“…To alleviate the problems described above, more and more cities are locating TS on the side of main roads. However, the site selection of roadside TS usually depends on the personal experience of the traffic manager or a sampling survey of drivers and passengers, which lacks scientific decision-making criteria ( 5 ). The inappropriate organizational layout not only fails to allow TS to play a positive role but also wastes the space resources and aggravates traffic in the surrounding areas.…”

Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization

“…On-demand ride-hailing services, such as Uber, Lyft, Didi, Grab, and Kakao T, have handled millions of ride-hailing orders per day in 2019. The increasing demand for ride-hailing services introduces various new technologies and challenges to service providers [1][2][3]. The most challenging problem is to maintain a proper number of ride service suppliers (taxis or drivers) in each service area.…”

Airport Taxi Dispatching Based on VISSIM and Multi-objective Programming Model

“…For example: Wei Zhang [5] and others used genetic algorithms to obtain the maximum payment ratio and cruising distance to ensure the interests of taxi passengers and drivers, thereby making bypass carpooling a reality. Zhaowei Qu [6] and others based on the taxi GPS data and combined the TSLM model to study the impact of the passenger's maximum acceptable distance on the TSLP. Babaei et al [7] studied the case of Zanjan city in Iran and established a linear scale to obtain a function to reduce total travel time, total waiting time and taxi route optimization.…”