Open AccessComprehensive Analysis Based on Massive Taxi Trajectory Data
Author(s) -
Longyan Tan
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1994/1/012007
Subject(s) - taxis , computer science , cluster analysis , trajectory , duration (music) , scheduling (production processes) , operations research , poisson distribution , transport engineering , public transport , focus (optics) , resource (disambiguation) , statistics , mathematical optimization , mathematics , engineering , computer network , art , physics , literature , optics , astronomy , machine learning
With the increasing demand for taxis, a large number of taxi trajectory data have been used for in-depth analysis. However, most early studies only used limited data or simulations to analyze the route choice behavior or traffic congestion problems. To extract more useful information from taxi trajectory data, this paper analyzes massive raw dates collected in Chengdu from 4 different perspectives. To begin with, passenger demand is analyzed by counting the number of orders in each period. K -means clustering is used for regional planning and provides appropriate boarding points for passengers. Furthermore, this paper focus on quantity duration of order duration, which presents an approximate Poisson distribution. Finally, the ratio of driving distance to the linear distance between the start and end points is defined as the overtravel ratio. Through the comprehensive analysis of the taxi trajectory data, it can provide assistance to the travel demand of passengers and the scheduling of taxi drivers, and maximize resource efficiency. Moreover, the data collected in Chengdu are very representative and large, so the corresponding suggestions can also provide hints for public transport problems.