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

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.