Traffic speed prediction under weekday, time, and neighboring links' speed: Back propagation neural network approach

Abstract

The ATIS (Advanced Traveler Information System) provides travelers with real-time and precise information about the shortest path to the destination, the traffic condition, travel time estimation, and so on. To offer these services, we have to collect the speed data which are necessary to ATIS. However many data are lost due to communication or sensor errors during collecting the data. In order to provide accurate service, the lost data have to be compensated. Thus, a lot of prediction methods have been proposed to compensate the lost speed data. In this paper, we propose new prediction method adopting the back propagation neural network under neighboring links' speed as well as weekday and time. Experimental results show that our method reduces prediction error up to 41.8 % compared to the previous method. © Springer-Verlag Berlin Heidelberg 2007.