Collaborative activity- based ridesharing: linking human movement, social network, and platial semantics for future transportation

Abstract

© 2018 Dr. Yaoli WangThis thesis presents a collaborative activity space model and the implemented prototype for ridesharing under the umbrella of this model. The ridesharing prototype accommodates socio-psychological barriers and travel flexibilities in multi-person travel behaviours. Despite the positiveness of ridesharing, such as reducing private car usage for social and environmental sakes, people find it uncomfortable or inconvenient to share rides. The two issues underneath are social preference and travel flexibility. If people are prioritised to match with those they prefer, and if an efficient strategy exists to reduce detour cost or the probability of no-ride, commitment to ridesharing might be increased. Current software or algorithms on the market, however, mostly overlook the socio-psychological aspects of ridesharing, and thus cannot better exploit the full potential of this new travel mode. This work provides the solutions incorporating social network preference and travel flexibility from three perspectives: 1) A method called Social Network based Ridesharing allows travellers to prioritise their closer acquaintances as ride partners while still considering local strangers of low detour cost. 2) The Activity-based Ridesharing Algorithm involves travel aims and functions of places to provide alternative destination choices. 3) A combination of the social network based and the activity-based methods, called Collaborative Activity-based Ridesharing, is suggested where social network preference is not only satisfied but also used as a space search heuristic for fast retrieval of alternative destinations and potential ride partners. The implementation of the model is experimented with agent-based simulation in a real study area with travel survey datasets. The outcomes justify that the proposed collaborative activity model and algorithms are capable of significantly increasing match rates and reducing socio-psychological obstacles for ridesharing