E. Rigas, S. Ramchurn, N. Bassiliades, “Algorithms for Electric Vehicle Scheduling in Mobility-on-Demand Schemes”, Proc. 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), Las Palmas de Gran Canaria, Spain, 15-18 Sep 2015, pp. 1339-1344.

Author(s): P. Samaras, A. Fachantidis, G. Tsoumakas, I. Vlahavas,


Appeared In: Proceedings of the 19th Panhellenic Conference on Informatics (pp. 129-134). ACM

Keywords: Machine Learning, Prediction Models, Public Transportation, Passenger Demand Prediction


Abstract: In this paper we present the passenger demand prediction model of BusGrid. BusGrid is a novel information system for the improvement of productivity and customer service in public transport bus services. BusGrid receives and processes real time data from the automated vehicle location (AVL) and the automated passenger counting (APC) sensors installed on a bus fleet and assists their operator on the improvement of bus schedules and the design of new bus routes and stops based on the expected demand. For the prediction of passenger demand in any bus stop, the raw sensor data were pre-processed and several different feature sets were extracted and tested as predictors of passenger demand. The pre-processed data were used for the supervised learning of a regression model that predicts people demand for any given bus stop and route. Experimental results show that the proposed approach achieved significant improvements over the baseline approaches. Knowledge representation, through the proposed feature set, played a key role on the ability of the prediction model to generalize well beyond its training set, to new bus stops and routes.

See Also: BusGrid Project