Dynamic Short-Term Projections of Travel Time Distributions in Urban Signalized Networks utilizing Composite Information of Traffic Characteristics

Abstract

Abstract: The estimation of experienced travel-time in road networks stands for an important feature in Advanced Traveler Information Systems (ATIS). In the era of data availability, the dissemination of accurate traffic information to travelers could have a huge impact on their trip choices and thus in systems’ performance. The scope of this paper is time travel modeling and prediction based on alternative configurations of mixed duration and time series models. The proposed framework enables the dynamic probabilistic estimation of travel time between a predefined set of Origin-Destinations (O-D) locations, by taking into account available spatial data. For achieving that, suitable formulated hazard models are combined with time series input, forming a hybrid methodological framework and compared to two different theoretical duration models. The resulted mechanism provides valuable probabilistic estimations for travel time for O-D pairs, able to cover extensive urban road networks. In particular, the proposed modeling approach uses past travel time observations as inputs and provides distributions of future travel times. The proposed application is tested on a realistic road network, namely that of Cyprus. The aim of the study is to provide reliable travel information to travelers in a methodologically sound, straightforward and comprehensible way.