For many years traffic control has been the task of traffic centres. Road congestion is reduced via traffic control based on the sensor information of the current traffic state. Actuators are used to create a better spread and throughput over the network. A powerful means to further reduce congestion is to shift from the classical reactive paradigm to a proactive paradigm. In this concept the traveller is included in the traffic control process in the sense that travellers are given advice about their travel scheme. This travel scheme presents the predicted travel time depending on time of departure and selected route. Today people use their smartphones to navigate. Via GPS and smart phone applications they optimise their route. Most of these applications use static traffic state information. In our research we develop a method to reduce congestion delay by including user decisions. According to the travel time preferences of the user a departure time/travel time curve is presented to the user. This curve shows the expected travel time corresponding to a specific departure time. Actuators are adapted according to the expected departure times of the app users. By including travellers information and preferences we want to analyse the resulting throughput and corresponding travel time in the network. To this end we study these effects for a small network with large peak arrivals in a short time period. Actuators in this network are adapted to the expected traffic flow and optimised accordingly.
Additional Metadata
THEME Logistics (theme 3)
Publisher Springer
Persistent URL dx.doi.org/10.1007/978-3-319-20855-8_11
Series Lecture Notes in Mobility
Project Verbeteren en optimaliseren van software producten op het gebied van Dynamisch Verkeersmanagement
Conference International Forum on Advanced Microsystems for Automotive Applications
Citation
van Leeuwen, D, van der Mei, R.D, & Ottenhof, F. (2015). Optimal traffic control via smartphone app users: A model for actuator and departure optimisation. In Advanced Microsystems for Automotive Applications 2015 (AMAA 19) (pp. 131–139). Springer. doi:10.1007/978-3-319-20855-8_11