Deep Recurrent Neural Networks and Optimization Meta-Heuristics for Green Urban Route Planning with Dynamic Traffic Estimates

Abstract

Within the current technological landscape sketched out by Intelligent Transport Systems (ITS), traffic flow prediction and route planning are two of the cornerstones on which the scientific community has been focused for years. Applications leveraging advances in these fields range from individual mobility planning to the establishment of optimal delivery routes, with doubtless benefits yielded to an immense strata of society. Intuitively, combining both prediction and route planning in a single, robust system could boost even further their paramount importance within the ITS field. However, most approaches reported so far in literature develop route planning techniques relying on actual traffic data (current or past observations) rather than on future traffic estimations, which could reliably represent the traffic flow status while the route is being performed. Unfortunately, research efforts around the monolithic hybridization of traffic prediction and route planning are still scarce. This manuscript embraces this noted issue as its main motivation by proposing an advanced routing platform endowed with a Long Short-Term Memory (LSTM) model for traffic forecasting purposes. The predictive output of this model serves as the input to a route planner, which constructs optimal green routes minimizing not only the total travel time, but also the CO 2 emissions of the vehicle. The system has been tested using Open Trip Planner and real data collected over the city of Arhus (Denmark), from which three different types of routes have been built and analyzed along a selection of predictive time horizons. The obtained results are promising and underscore the need for considering traffic predictions along the route for an improved usability of current route planning frameworks.