Adding Multi-Day Attributes for Ridesharing Simulations via Data Fusion

Abstract

Evaluating ridesharing potential is a trend in current research efforts because ridesharing provides additional mobility alternatives without extra putting vehicles on the road. Nevertheless, in most studied scenarios, the demand revealed by surveys and demographic information does not include multi-day characteristics of a trip such as frequencies on weekdays. Yet this is important for estimating the supply of rides, as the recurrence or regularity of a trip may affect the likelihood of a driver making the effort of registering the trip as being available for sharing. Likewise, if automated apps are used to recognize patterns in one’s trips and pro-actively offer them for sharing, the successful anticipation of such apps may again depend on the regularity of the trip. However, since multi-day data are complex to produce, in this paper, a data fusion procedure is proposed to generate an enriched synthetic demand for more realistic assessments. This can be achieved by combining standard single-day data sets with travel behavior patterns, which can be extracted from lifelogging data collected by most existing mobile apps. The resulting data sets after transferring information from the travel patterns to a recipient data set via statistical matching, will constrain matching trips by multi-day characteristics allowing complex scenarios. This approach enhances the evaluation of ridesharing and other shared-mobility systems and thus their ability to plan better strategies.