Tag Archives: ride sharing

Activity-based ridesharing: Increasing flexibility by time geography

Y. Wang, R. Kutadinata, and S. Winter, “Activity-based ridesharing: Increasing flexibility by time geography,” in Proceedings of the 24th ACM SIGSPATIAL, 2016.


Ridesharing is an emerging travel mode that reduces the total amount of traffic on the road by combining people’s travels together. While present ridesharing algorithms are tripbased, this paper aims to achieve signi cantly higher matching chances by a novel, activity-based algorithm. The algorithm expands the potential destination choice set by considering alternative destinations that are within given space-time budgets and would provide a similar activity function as the originals. In order to address the increased combinatorial complexity of trip chains, the paper introduces an efficient space-time filter on the foundations of time geography to search for accessible resources. Globally optimal matching is achieved by binary linear programming. The ridesharing algorithm is tested with a series of realistic scenarios of diff erent population sizes. The encouraging results demonstrate that the matching rate by activity-based ridesharing is signi cantly increased from the baseline scenario of traditional trip-based ridesharing.

A Continuous Representation of Ad Hoc Ridesharing Potential

M. Rigby; S. Winter; A. Krüger, “A Continuous Representation of Ad Hoc Ridesharing Potential,” in IEEE Transactions on Intelligent Transportation Systems , vol.PP, no.99, pp.1-11
doi: 10.1109/TITS.2016.2527052


Interacting with ridesharing systems is a complex spatiotemporal task. Traditional approaches rely on the full disclosure of a client’s trip information to perform ride matching. However during poor service conditions of low supply or high demand, this requirement may mean that a client cannot find any ride matching their intentions. To address this within real-world road networks, we extend our map-based opportunistic client user interface concept, i.e., launch pads, from a discrete to a continuous space–time representation of vehicle accessibility to provide a client with a more realistic choice set. To examine this extension under different conditions, we conduct two computational experiments. First, we extend our previous investigation into the effects of varying vehicle flexibility and population size on launch pads and a client’s probability of pick-up, describing the increased opportunity. Second, observing launch pads within a real-world road network, we analyze aspects of choice and propose necessary architecture improvements. The communication of ride share potential using launch pads provides a client with a simple yet flexible means of interfacing with on-demand transportation.