01557140

Component

Carsharing, as an alternative to private vehicle ownership, has spread worldwide in recent years due to its potential of reducing congestion, improving auto utilization rate and limiting the environmental impact of emissions release. To determine the most efficient allocation of resources within a carsharing programme, it is critical to understand what factors affect the users’ behaviour when selecting vehicles which have been little attended to in the literature. This study attempts to investigate the importance of users’ attributes and fleet characteristics on choice set formation behaviour in selecting vehicles using a Spatial Hazard Based Model (SHBM). In the SHBM model, “distance to the carsharing facility (vehicle pod)” is considered as the prospective decision criteria that carsharing users follow when selecting a vehicle among a set of alternatives This variable is analogous to the duration in a conventional hazard-based model. Other factors, such as user sociodemographic attributes, vehicle characteristics, land use type of the trip origin, etc., collected from the Australian carsharing company GoGet are utilized for model estimation. A number of forms of parametric SHBMs are tested to determine the best fit to the data. The accelerated failure time model with a Log-logistic distribution was found to provide the best fit. The estimation results of the coefficients of the parameters suggest that users who do not own a private vehicle, frequent users, elderly users and users with a restriction in the ability to drive tend to consider closer vehicle pods. These findings can provide a starting point for carsharing organisations to optimize their pod locations and types of cars available at different pods to maximize usage.

This paper was sponsored by TRB committee AP020 Emerging and Innovative Public Transport and Technologies. Alternate title: Hazard Based Modelling of Vehicle Selection in Carsharing Systems.

01550057

15-5097

English

17p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; References; Tables

Automobiles by type; Location; Optimization; Vehicle sharing

Hazard-based modeling; Vehicle choice

Australia

Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2015 Paper #15-5097

TRIS, TRB, ATRI

Dec 30 2014 1:42PM