Urban traffic modeling: representation of complex flow interactions among lane groups for signalized intersections
The activity of the urban traffic network remains a challenge within the Intelligent Transportation Systems (ITS) due to the intrinsic complexity of traffic systems. An updated, reliable and real-time urban traffic flow prediction is the groundwork of traffic management and control in the urban context. Research studies and literature reviews have demonstrated that the application of ITS has the potential to lead to a decrease in congestion; but as ITS technology solutions are also based on their ability to provide an accurate estimate of travel time prediction, it is necessary that this prediction is accurate so as to obtain the expected benefits on transport costs.
Despite the results obtained in literature, some critical issues remain to be addressed. First, most dynamic queue models do not integrate the multiple signal phases. In second place, the spillback phenomena has not been explicitly modeled during congested conditions. For this reason, we propose a traffic flow prediction model, named Cell Transmission Model for Urban Traffic (CTM-UT), to capture urban traffic dynamics taking into account complex flow interactions among lane groups at upstream of signalized intersections. In this work we define a traffic simulation model that:
- provides a good trade-off between accuracy and computational complexity with respect to the microscopic model;
- makes short-term prediction of traffic flow on the large traffic network;
- captures spillback effect and queues dissipation for lane;
- represents flow interactions of queues among neighboring lanes and intersections.
The experiments indicate that the model proposed can better predict the realism of vehicular conflict at upstream channelized zone. Other experiments compared with one of the most important microscopic traffic flow simulation software indicate that, for simple scenarios, the model can predict the short-term traffic flow promptly and with precision.
More details about CTM-UT can be found on the following paper:
