Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Ruone Zhang,Jiayu Zhu,Ke Wang,Dongjin Li,Xin Ye

doi:10.3390/su11102730

Abstract

Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

Highlights

Massive travel data have been passively generated and continuously stored in the background of some electronic device systems, which are not intentionally collected but can be potentially used for transportation research [1]
Estimation results of the passively collected data (PCD) model were all of expected sign of coefficients at a high significance level thanks to the large sample size; attributes of rail walking access/egress distance and all socio-demographic attributes were lacking in the data
In the joint model, coefficient of rail initial waiting time was estimated by PCD, and coefficients of lacked attributes in PCD were estimated by actively collected data (ACD)

Summary

Introduction

Massive travel data have been passively generated and continuously stored in the background of some electronic device systems, which are not intentionally collected but can be potentially used for transportation research [1]. Transit smart card system records the arrival/departure information of transit users, in-vehicle GPS system records drivers’ behaviors and travel paths ever since the engine starts. The passively collected data (PCD) record travel process of different modes, offering a large sample size and accurate measures over a long-term period. With the development of big data technology, travel behavior of every traffic participant could be revived by using passively collected data (PCD), such as mobile phone data [2,3], in-vehicle GPS data [4,5], transit smart card [6,7], loop detector and remote sensor data [8,9,10,11,12], etc.

Objectives

Methods

Results

Conclusion