Intelligent decision support for traffic management

Urban traffic control systems such as the widely deployed SCOOT system, incrementally respond to changing traffic conditions. Such systems are often complemented by traffic control centres where road network managers intervene manually to mitigate rapidly developing congestion events. An Intelligent Decision Support (IDS) system developed by the authors within the UK FREEFLOW (FF) project to aid the network managers is presented in this paper. The primary objective of the FF IDS system is to identify traffic congestion in near-real-time and to recommend appropriate traffic control intervention measures. The FF-IDS consists of multiple internal components. A state estimation component monitors live traffic sensor data and determines if there is a congestion problem on the road network. If a problem is identified, a binary neural pattern-matching component is used to identify past time periods with similar congestion events. This is able to rapidly search large historic traffic datasets finding sets of traffic control interventions carried out during similar historical time periods. The effectiveness of each intervention is evaluated using a Performance Index (PI) and the intervention that resulted in the highest improvement in PI is recommended to network managers. The FF-IDS system can also present traffic incidents and equipment faults that occurred during these historical time periods to the network manager as potential causes of the problem. This paper describes the FF-IDS system in detail. The system is currently under development. An early version of the FF-IDS system was trialled using off-line data from London, yielding encouraging preliminary results.