Fleet data based traffic modeling

Abstract

Although the available traffic data from navigation systems have increased steadily in recent years, it only reflects average travel time and possibly Origin-Destination information as samples, exclusively. However, the number of vehicles participating in the traffic – in other words, the traffic flows being the basic traffic engineering information for strategic planning or even for real-time management – is still missing or only available sporadically due to the limited number of traditional traffic sensors on the network level. To tackle this gap, an efficient calibration process is introduced to exploit the Floating Car Data combined with the classical macroscopic traffic assignment procedure. By optimally scaling the Origin-Destination matrices of the sample fleet, an appropriate model can be approximated to provide traffic flow data beside average speeds. The iterative tuning method is developed using a genetic algorithm to realize a complete macroscopic traffic model. The method has been tested through two different real-world traffic networks, justifying the viability of the proposed method. Overall, the contribution of the study is a practical solution based on commonly available fleet traffic data, suggested for practitioners in traffic planning and management.

Keywords

• Floating car data
• Origin-destination matrix
• Macroscopic traffic modeling
• Genetic algorithm
• Calibration