TRB Pubsindex
Text Size:
x

« Prev Next »

Return to Results | New Search

Bookmark and Share

Title:

Modeling Vehicle Choices and Charging Behavior of Plug-In Electric Vehicle Owners Jointly Using Dynamic Discrete Choice Model

Accession Number:

01661620

Record Type:

Component

Abstract:

Plug-in Electric Vehicle (PEV) owners’ decisions on which vehicle to use for a travel day can be influenced by the expectation of the charging opportunities during the day, and the charging decision at one stop can be influenced by the characteristics of future charging opportunities. The models of previous studies usually treat vehicle choices (which car to drive for the travel day) and charging choices of PEV owners as isolated decisions, which could cause underestimation of the demand for public charging facilities. This paper applies the dynamic discrete choice model (DDCM) with a finite horizon to jointly analyze vehicle choices and charging choices of PEV owners in a travel day based on stated preference data. The combination of Nested Fixed Point Algorithm and finite mixture solution based on EM algorithm proposed by Arcidiacono and Jones is utilized to capture the heterogeneity of decision-making among PEV users. The final model groups the PEV users into two classes: 63% in class 1 and 37% in class 2. The respondents in class 1 tend to pay significantly more money to avoid being stranded and would try to avoid using Battery Electric Vehicles (BEVs) when the public charging supply is not sufficient. In this group, the Plug-in Hybrid Electric Vehicle (PHEV) users tend to value the charging cost and gasoline cost similarly. The other group (class 2) of PEV users appears to be less cautious about being stranded and tends to use BEV more frequently for long distance trips than class 1. The PHEV owners in class 2 appear to try to avoid using gasoline because they value gasoline cost significantly more heavily than charging cost.

Supplemental Notes:

This paper was sponsored by TRB committee ADC80 Standing Committee on Alternative Transportation Fuels and Technologies.

Report/Paper Numbers:

18-05951

Language:

English

Authors:

Ge, Yanbo
MacKenzie, Don

Pagination:

8p

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:

Figures; References; Tables

Uncontrolled Terms:

Dynamic discrete choices

Subject Areas:

Highways; Planning and Forecasting; Vehicles and Equipment

Source Data:

Transportation Research Board Annual Meeting 2018 Paper #18-05951

Files:

TRIS, TRB, ATRI

Created Date:

Jan 8 2018 11:32AM