Location Optimization for Urban Taxi StandsBased on Taxi GPS Trajectory Big Data

Location Optimization for Urban Taxi StandsBased on Taxi GPS Trajectory Big Data
  A taxi stand can effectively regulate the behavior of taxi picking up passengers, reduceempty-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 rateand a waste of public space resources. This paper presents a novel three-stage strategy to address the taxistands location problem (TSLP) incrementally. First, taxi demands hotspots are mined from a massive taxiGlobal Positioning System (GPS) data with GIS platform, and the optimal area for taxi stands siting inthe following stages is determined. Then, the spatial interaction between taxi demands and taxi stands isexplored 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 costof passengers and the construction cost of taxi stands. The genetic algorithm-based procedure is adoptedfor TSLM optimization. A case study conducted in China verifies the effectiveness of the location strategyand investigate the impact of the maximum acceptable distance for passengers on TSLP. The experimentalresults describe the number and layout of taxi stand under a different demand coverage, which indicates thatthe proposed approach is beneficial to provide scientific reference for the municipal department in taxi standsite decisions and make a tradeoff between the interests of planners and users. https://srisivasakthitravels.com/
Taxi is an important part of urban transport, which has alwaysbeen favored by people for its unique convenience and com-fort since the 19th century [1]. In recent years, taxi servicessuch as telephone booking and online booking have becomemore and more popular, but in most countries and regions,such as China, the main operating mode of taxis is still hailingalong the roadside. In this traditional mode, taxis head toplaces with heavy traffic in searching for customers, whichcould aggravate traffic congestion and air pollution problems[2], [3]. In addition, the empirical cruise mode keeps pas-sengers and drivers in a state of information isolation. Thereis always a mismatch between demand and supply for taxisin time and space, leading to high empty-run rate and lowoperating efficiency
Location Optimization for Urban Taxi StandsBased on Taxi GPS Trajectory Big Data
This research intends to fill in the blank of the TSLPthrough a hierarchical methodology, which optimizes taxistand not only in quantity but also in layout. In this paper,we study the location selection of taxi stands from the per-spective of exploring the spatial-temporal dynamic attributespossessed in taxi demands and its spatial interaction with taxistands. A three-stage location strategy is proposed using taxiGPS big data to solve the TSLP incrementally. The researchflow chart is demonstrated in Fig. 1. The contributions ofour work lie in the following aspects. First, in order to seekthe appropriate setting environment of TSs, we extract theactual travel demands from taxi GPS big data and explorethe distribution regularity in time and space dimension toidentify the hotspots with high demands accurately. Second,we describe the spatial interaction between taxi demandsand taxi stands. On this basis, travel demand subsectionsand candidate taxi stands are generated on both sides ofthe road in a staggered arrangement. Third, we design ataxi stand location model (TSLM) to solve the TSLP thatachieve the objective of minimizing the access cost forpassengers and the construction cost. Considering the het-erogeneity of passenger behaviors, the concepts of demandcoverage and maximum acceptable walking distance areintroduced in the model to optimize the selection of thelocations. To verify the validity of the proposed strategy andTSLM, a case study is conducted. The results show that theapproach presented could be effectively applied to TSLP inmetropolitan
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