Retrieval of Origin/Destination-Matrices from Bluetooth-based Floating Car Observer Data Using Big Data Algorithms

Abstract

The complete knowledge of the travel demand is the cornerstone for many applications in the field of transport and traffic management. Knowing the actual demand is important in order to establish the effectiveness of the network in handling the need of the road users, and to measure the impact of network changes on the overall traffic flow. A good estimate of the present state of the network is the preliminary point to any mobility analysis and therefore a problem of great interest. The state of the network can be described by the Origin/Destination (OD) matrix. The OD matrix is double-entry table M in which each element Mij contains a census of the volume of journeys, from origin i to destination j which is often used to track traffic volumes over space and time. The Bluetooth technology is being increasingly used to track vehicles throughout their trips, within urban networks and across freeway stretches. One important opportunity offered by this type of data is the measurement of Origin-Destination patterns, emerging from the aggregation and clustering of individual trips. Compared to plate recognition and GPS track recording, the Bluetooth technology features some major advantages as it is easy to install and maintain and does not require accurate calibration. Moreover, the effectiveness of the detection does not depend on the orientation of the scanners or the vehicles, thus capturing the traffic regardless of the direction of travel. Another important aspect is that the detection is anonymous, in that the electronic identifier (MAC address) of the detected vehicles can be converted into an encrypted (hash) code at the sensor site. A novel approach in using Bluetooth technology associated with traffic monitoring is the Bluetooth-based Floating Car Observer (FCO) developed by the German Aerospace Center (DLR). Its detections are made indirectly by floating traffic observers using wireless radio-based technologies while passing other traffic objects (vehicles, cyclists, pedestrians), and thus, enabling spatiotemporal traffic data through the covered network. The aim of the paper is twofold. We first give an extensive overview on the issues and challenges of the usage of a Bluetooth-based FCO in order to retrieve OD matrices. Further, we investigate the potential of the data base by proposing a methodology to generate and to analyse OD matrices. Due to the high volume and velocity of detected data it is crucial to use efficient algorithm for data analytics. Therefore, we experiment with Hadoop-based Big Data technologies. The evaluation of our postulated methods will be done by a field experiment. For that experiment, a special equipped taxi-fleet of 30 vehicles will collect data in the region of Berlin (Germany) for at least one month.