Trip quality in an ad-hoc shared-ride system Lin-Jie Guan and Stephan Winter Department of Geomatics, University of Melbourne, Australia l.guan4@pgrad.unimelb.edu.au winter@unimelb.edu.au INTRODUCTION AND MOTIVATION In an ad-hoc shared-ride system, transportation clients and hosts negotiate for shared rides in a continuously changing environment, using wireless geosensor networks (Winter and Nittel 2006). Due to the peculiarity of this system—a local peer-to-peer communication strategy, and a complex and non-deterministic transportation network—clients will always have limited transportation knowledge, both from a spatial and a temporal perspective. Clients hear only from near hosts, and they do not know the future availability of current or new hosts. Hence, a client can plan an optimal trip prior to departure according to his current knowledge, but it is unlikely that this trip will be finally the optimal trip due to the continuously changing traffic condition. Therefore, it is necessary to evaluate the quality of trips generated in this dynamic environment in order to assess different communication and wayfinding strategies. The aim of this paper is to develop a quality model comparing the realized sub-optimal trips with the optimal trip. Technically, the main objective of the paper is to calculate an optimal trip with entire knowledge of the network over time for a given cost function. The comparison between traveled and optimal trip is an objective measure of quality for the various methods of ad-hoc shared-ride trip planning. The hypothesis of this paper is that this quality can be determined in shared-ride system simulations. In terms of trip quality in an ad-hoc shared-ride system, multiple criteria such as travel time, distance, costs and transfer numbers can be considered as cost functions. Shortest path algorithms find an optimal path according to any of these cost functions. In this paper, we choose travel time as a criterion for identifying the globally optimal trip. The globally optimal trip is compared to the trip realized by a client applying some communicaton and ad-hoc trip planning strategies in a simulation environment.