Determining Optimal Fleet Distribution for Dynamic Indirect Traffic Detection based on Bluetooth

Kurzfassung

Highly accurate spatio-temporal traffic data (e.g. origin destination matrices, route flows and paths) can be obtained by the newly developed Dynamic Indirect Traffic Detection (DITD) approach, which was recently developed by the German Aerospace Center. DITD enables an efficient and powerful traffic monitoring and control system on the basis of wireless communication systems, which minimizes the number of costly stationary traffic detection infrastructure (e.g. traffic sensors, detection gantries, etc.) and thus will be superior to existing costly traffic detection systems. With DITD all detections are made indirectly by traffic observers using wireless radio-based technologies (e.g. Bluetooth/Wi-Fi) while passing other traffic objects (vehicles, cyclists, pedestrians). Since many traffic participants use devices with activated Bluetooth/Wi-Fi functionality (e.g. mobile phones), a car equipped with a specific receiver (Mobile Traffic Observer Unit - MTOU) detects all traffic objects featuring Bluetooth/Wi-Fi devices and being in the detection area by their identification number. Augmented by time stamps and positions of the observer, the measured data can be processed to trajectories, travel times, etc. Due to the novelty of this approach several fundamental research questions have not been answered yet. For instance, questions regarding to the required number of cars featuring the MTOU, the size of the underlying network and the amount of edges and intersections which have to be contained or the mileage of the vehicle fleet are required to be answered to put the method into practice and to prove the approach to determine high quality traffic data. In this paper, the research will be taken to the next level. Hence, the focus is now on the optimal distribution of the equipped vehicle fleet within the network. By the use of analytical and model respective simulation based methods fleet related parameters like vehicle fleet flow rates and detection rates per edge and time interval as well as network related parameters like the amount of travelled kilometers or the sum of covered edges are identified to determine effective spatio-temporal mileages of the fleet and optimal dispersion patterns for given networks. In addition, the coverage effectiveness of different fleet types will be analysed too. Referring to the routing, there are two different fleet types to be determined within this study: observer cars with randomly chosen routes on the one hand, and preassigned routes as in the case of taxi or mail car fleets on the other hand. According to the fleet types’ characters diverse operating ranges are expected to occur. The mentioned research study refers to an internal project of the German Aerospace Center dealing with the improvement of the efficiency, safety and environmental friendliness of mobility and traffic and transportation management at different DLR sites in everyday life. Therefore, a concept is implemented, which includes traffic detection, simulation, communication, control and benchmark issues. Within this project the presented approach will be put in practice to display applicability in real environment.