Abstract :
Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time savings and-if nothing else-on the reliability of travel times. As such, there is an increasing need for fast and reliable online travel time prediction models. Previous research showed that data-driven approaches such as the state-space neural network (SSNN) are reliable and accurate travel time predictors for freeway routes, which can be used to provide predictive travel time information on, for example, variable message sign panels. In an operational context, the adaptivity of such models is a crucial property. Since travel times are available (and, hence, can be measured) for realized trips only, adapting the parameters (weights) of a data-driven travel time prediction model such as the SSNN is particularly challenging. This paper proposes a new extended Kalman filter (EKF) based online-learning approach, i.e., the online-censored EKF method, which can be applied online and offers improvements over a delayed approach in which learning takes place only as realized travel times are available.
Keywords :
Kalman filters; learning (artificial intelligence); neural nets; nonlinear filters; road traffic; traffic information systems; available route alternatives; choice behavior; data-driven travel time prediction model; extended Kalman filter; freeway travel time prediction; individual drive behavior; online learning; state-space neural network; traffic networks; Context modeling; Neural networks; Particle measurements; Predictive models; Recurrent neural networks; Telecommunication traffic; Time measurement; Traffic control; Vehicle dynamics; Vehicles; Advanced traffic information systems (ATIS); extended Kalman filter; online learning; recurrent neural networks; state space neural networks; traffic information; travel time prediction;
