Adaptive short term prediction of freeways travel time distributions based on multivariate hazard models

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 disaggregate traffic data. For achieving that, a suitably formulated hazard model is 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 interurban road networks. In particular, the proposed modeling approach uses traffic flow variables and travel time observations as inputs and provides distributions of future travel times. The proposed application is tested on a realistic road system, namely that of Cyprus. The aim of the study is to provide reliable travel information to travelers in in a methodologically sound, straightforward and comprehensible way.