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Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

Wei Yu (), Xiaofei Ye (), Jun Chen (), Xingchen Yan () and Tao Wang ()
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Wei Yu: College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China
Xiaofei Ye: School of Maritime and Transportation, Ningbo University, Fenghua Road 818#, Ningbo 315211, China
Jun Chen: School of Transportation, Southeast University, Southeast University Road 2#, Nanjing 210096, China
Xingchen Yan: College of Automobile and Traffic Engineering, Nanjing Forestry University, Longpan Road 159#, Nanjing 210037, China
Tao Wang: School of Architecture and Transportation, Guilin University of Electronic Technology, Jinji Road 1#, Guilin 541004, China

Sustainability, 2020, vol. 12, issue 3, 1-1

Abstract: The information level of the urban public transport system is constantly improving, which promotes the use of smart cards by passengers. The OD (origination–destination) travel time of passengers reflects the temporal and spatial distribution of passenger flow. It is helpful to improve the flow efficiency of passengers and the sustainable development of the city. It is an urgent problem to select appropriate indexes to evaluate OD travel time and analyze the correlation of these indexes. More than one million OD records are generated by the AFC (Auto Fare Collection) system of Nanjing metro every day. A complex network method is proposed to evaluate and analyze OD travel time. Five working days swiping data of Nanjing metro are selected. Firstly, inappropriate data are filtered through data preprocessing. Then, the OD travel time indexes can be divided into three categories: time index, complex network index, and composite index. Time index includes use time probability, passenger flow between stations, average time between stations, and time variance between stations. The complex network index is based on two models: Space P and ride time, including the minimum number of rides, and the shortest ride time. Composite indicators include inter site flow efficiency and network flow efficiency. Based on the complex network model, this research quantitatively analyzes the Pearson correlation of the indexes of OD travel time. This research can be applied to other public transport modes in combination with big data of public smart cards. This will improve the flow efficiency of passengers and optimize the layout of the subway network and urban space.

Keywords: complex network; origination–destination; travel time; correlation; big data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2020
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