Efficient traffic management requires an understanding of mobility patterns in the road network, where one important component is route choice. This study aims to analyze how route choice models can be adapted to efficient urban traffic management and intelligent transport systems (ITS), by constructing route sets and attributes from GPS and network data. With a route choice model that is responsive to traveltime changes in the network, travel behavior during incidents can be predicted to evaluate traffic management policies, such as traveler information and traffic control. The dataset consists of about 400,000 vehicle trips and is divided into a training dataset and a test dataset. The two datasets are compared, and the experiments show that the routes used are similar. Discrete route choice models are estimated with one data-driven path identification approach (DDPI) and one where the data-driven path set is augmented with routes from a network-based shortest path generation with link penalty (NBPA). The result suggests that the traveltime has a larger impact on the route choice when the model is trained on the NBPA route set and that the route's simplicity, length, and traveltime are important attributes for the route choice, which are useful insights in a traffic management context.

Traffic incidents on roads, defined as non-recurring events causing a temporary reduction in capacity, causes congestion, injuries and public costs. Knowledge about how travelers change their behavior, in particular how they adjust their route, during incidents can give important insights into how to manage traffic when incidents occur. This paper aims to analyze the impact of incidents on route choice to enable decision support systems during incidents. Reported incidents and GPS trajectories from road vehicles in Stockholm are used in the analysis. We propose a method to map incidents to network links using speed data. We also analyze incident impact on speed reduction, route choice at the network level, and route choice at the local level. A route choice model based on GPS data is evaluated on incident scenarios. The result shows that 5-12% of the incidents have a significant impact on the route choice. The impact of an incident on route choice appears to be influenced by the type and location of the incident, its duration, and the extent of the resulting speed reduction. Up to 22% of the travelers change their route to alternatives not passing the incident. The route choice model tends to overestimate this number.