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Title:

A Dynamic Approach Toward Stochastic Departure Time Choices Using a Linked Data Set

Accession Number:

01659505

Record Type:

Component

Abstract:

Commuter’s departure behavior is of interest to transportation modelers and numerous approaches have been proposed to capture the inherent choice determinants. Although the dynamic aspects of the problems have been identified, most of the existing studies are based on static discrete choice models. In this paper, a dynamic modeling framework is proposed to explore the relationship between commuters’ departure time choices and the evolution of en-route traffic. A data linkage method is developed to create an integrated dataset that enables the observation of commuters’ reaction to changes in travel time and traffic conditions over time. A regional household travel survey (2007-2008 Transportation Planning Board-Baltimore Metropolitan Council (TPB-BMC) household travel survey) is linked to travel information obtained from the Google Map application program interface (API), creating a synthetic longitudinal data set. Two decision rules, named perfect knowledge and stochastic changes, are applied to model the evolution of traffic. The results indicate that travel time, trip distance of commute trips, and commuters’ sociodemographic influence departure time choices. It is also found that accounting for dynamics improves model fit and out-of-sample predictions. The dynamic model and the data linkage method proposed, contribute to the last line of departure time decision studies and can be used to enhance demand analysis and urban planning policy studies.

Supplemental Notes:

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

Report/Paper Numbers:

18-00621

Language:

English

Authors:

Dong, Han
Cirillo, Cinzia

Pagination:

6p

Publication Date:

2018

Conference:

Transportation Research Board 97th Annual Meeting

Location: Washington DC, United States
Date: 2018-1-7 to 2018-1-11
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

References; Tables

Identifier Terms:

Google Maps

Geographic Terms:

Baltimore (Maryland)

Subject Areas:

Highways; Planning and Forecasting; Public Transportation

Source Data:

Transportation Research Board Annual Meeting 2018 Paper #18-00621

Files:

TRIS, TRB, ATRI

Created Date:

Jan 8 2018 10:10AM