A Spatial Hazard-Based analysis for modelling vehicle selection in station-based carsharing systems

Jian, S; Hossein Rashidi, T; Wijayaratna, KP; Dixit, VV

A Spatial Hazard-Based analysis for modelling vehicle selection in station-based carsharing systems

Jian, S Hossein Rashidi, T Wijayaratna, KP Dixit, VV

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Transportation Research Part C: Emerging Technologies, 2016, 72, pp. 130-142
Issue Date:: 2016-11-01

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Field	Value	Language
dc.contributor.author	Jian, S
dc.contributor.author	Hossein Rashidi, T
dc.contributor.author	Wijayaratna, KP
dc.contributor.author	Dixit, VV
dc.date.accessioned	2022-08-08T09:59:11Z
dc.date.available	2022-08-08T09:59:11Z
dc.date.issued	2016-11-01
dc.identifier.citation	Transportation Research Part C: Emerging Technologies, 2016, 72, pp. 130-142
dc.identifier.issn	0968-090X
dc.identifier.uri	http://hdl.handle.net/10453/159769
dc.description.abstract	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 program, it is critical to understand what factors affect the users’ behavior when selecting vehicles. This study attempts to investigate the importance of users’ attributes and fleet characteristics on choice set formation behavior in selecting vehicles using a Spatial Hazard Based Model (SHBM). In the SHBM model, “distance to a vehicle” is considered as the prospective decision criteria that carsharing users follow when evaluating the set of alternative vehicles. This variable is analogous to the duration in a conventional hazard-based model. In addition, user socio-demographic attributes, vehicle characteristics, land use type of the trip origin, etc., collected from the Australian carsharing company GoGet are utilized to parameterize the shape/scale/location parameter of the hazard function. 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 can provide a starting point for carsharing organizations to optimize their pod locations and types of cars available at different pods to maximize usage.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Transportation Research Part C: Emerging Technologies
dc.relation.isbasedon	10.1016/j.trc.2016.09.008
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Logistics & Transportation
dc.title	A Spatial Hazard-Based analysis for modelling vehicle selection in station-based carsharing systems
dc.type	Journal Article
utslib.citation.volume	72
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-08T09:59:09Z
pubs.publication-status	Published
pubs.volume	72

Abstract:

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 program, it is critical to understand what factors affect the users’ behavior when selecting vehicles. This study attempts to investigate the importance of users’ attributes and fleet characteristics on choice set formation behavior in selecting vehicles using a Spatial Hazard Based Model (SHBM). In the SHBM model, “distance to a vehicle” is considered as the prospective decision criteria that carsharing users follow when evaluating the set of alternative vehicles. This variable is analogous to the duration in a conventional hazard-based model. In addition, user socio-demographic attributes, vehicle characteristics, land use type of the trip origin, etc., collected from the Australian carsharing company GoGet are utilized to parameterize the shape/scale/location parameter of the hazard function. 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 can provide a starting point for carsharing organizations to optimize their pod locations and types of cars available at different pods to maximize usage.

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http://hdl.handle.net/10453/159769