Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances

Abstract

The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial and temporal autocorrelation and heterogeneity issues among research zones have not been sufficiently addressed. A framework integrating geographically and temporally weighted regression (GTWR) and the Shannon entropy index (SEI) was thus proposed to investigate the spatio-temporal relationship between travel behaviors and built environment. An empirical study was conducted in Nanjing, China, by incorporating smart card data with metro route data and built environment data. Comparative results show GTWR had a better performance of goodness-of-fit and achieved more accurate predictions, compared to traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR). The spatio-temporal relationship between travel distances and built environment was further analyzed by visualizing the average variation of local coefficients distributions. Effects of built environment variables on metro travel distances were heterogeneous over space and time. Non-commuting activity and exurban area generally had more influences on the heterogeneity of travel distances. The proposed framework can address the issue of spatio-temporal autocorrelation and enhance our understanding of impacts of built environment on travel behaviors, which provides useful guidance for transit agencies and planning departments to implement targeted investment policies and enhance public transit services.

Lyme rashes disease classification using deep feature fusion technique

“…Deep feature fusion 25 is a method proposed for this purpose, which merges the features extracted by different deep learning models to boost classification accuracy 26 . Theoretically, this method could produce a more accurate representation of rash photos for classification purposes.…”

“…23 In the context of erythema migrans rash classification, deep learning methods could be used to extract features from images of rashes and use those features to classify the rashes as either indicative of Lyme disease or not. 24 Deep feature fusion 25 is a method proposed for this purpose, which merges the features extracted by different deep learning models to boost classification accuracy. 26 Theoretically, this method could produce a more accurate representation of rash photos for classification purposes.…”

A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules

“…Similarly, a method for estimating the number of passengers with different behaviors in a disruption event based on large scale smart card data was proposed by Sun et al [16]. Chen et al [17] proposed a method to analyze metro passenger travel distance by using smart card data. Further, Yu et al [18] analyzed the space-time variation of passenger flow and commuting characteristics of residents using smart card data from the Nanjing metro.…”

Investigating spatio-temporal mobility patterns and changes in metro usage under the impact of COVID-19 using Taipei Metro smart card data

“…As a comprehensive representation of public transit usage, data collected by AFC smart card systems has attracted much attention in research especially in the last decade (see Sun et al 2016 , for a useful overview). Two distinct, related topics are the investigation of factors influencing transit usage by means of regression analysis (Chen et al 2019a ; Choi et al 2012 ; Jun et al 2015 ; Kim et al 2018 ; Lin and Shin 2008 ; Singhal et al 2014 ; Zhao et al 2014a ) and passenger forecasting (Li et al 2018 ; Liu et al 2018 ; Liu et al 2019 ; Liu and Chen 2017 ; Sun et al 2015b ; Wei and Chen 2012 ). Furthermore, metro smart card data has been used to estimate origin-destination matrices for metro AFC systems where passengers tap their smart cards only when boarding but not when alighting the metro train (Cheng et al 2020 ).…”