The control and route guidance system to be evaluated is represented in the traffic management simulator (TMS). This simulator receives as input from MITSIM real-time traffic measurements, as reported by the surveillance systems. Based on the received surveillance data, TMS generates control and route guidance according to the implemented logic and updates the state of traffic signals and signs in the network.
For the traffic simulation laboratory to be useful in evaluating advance traffic management systems, it is important that TMS has a generic structure to model different types of systems. In general, control and route guidance systems can be classified as pre-timed and adaptive systems. In a pre-timed system, control and route guidance are pre-determined based on historical traffic information using off-line analysis; in an adaptive system, control and route guidance are generated on-line based on real time traffic information obtained from surveillance sensors and environmental conditions such as weather, scheduled construction work, etc.
Adaptive systems can be further divided into reactive and proactive systems. Most of the existing adaptive systems are reactive, whereas control and route guidance are provided based on prevailing traffic conditions. Proactive systems are the most recent development. A proactive system requires two main components:
Figure 2-3: Structure of proactive traffic
control and routing systems
The role of the Network State Estimation module is to obtain the best estimate of the current network state, represented by link flows and travel times, origin and destination matrix, and incidents using the available information (e.g. surveillance, reports, etc.). The Control and Routing Generation module generates signal control and route guidance for the next time period. The Network State Prediction module is used to predict future traffic conditions; it can use various approaches. The default traffic prediction module is based on a mesoscopic traffic simulator (MesoTS). MesoTS is described in detail in Chapter 5. Through the interaction between the Control and Routing Generation and the Network State Prediction, this system optimizes traffic control and generates consistent route guidance.