Improving the Cost-Performance Tradeoff in Traffic Control Using Autonomous Intelligent Agents

Rapid and concentrated growth in many urban areas has led to a dramatic increase in the number of commuters and a subsequent strain on many cities’ traffic signal facilities. Although traffic delays have almost become an accepted part of our culture, significant efforts (and funding) are focussed on improving the overall performance of urban traffic-control systems. Unfortunately, responses to congestion have typically focussed on centralized control systems that are extremely expensive. Recent developments in overhead infrared traffic sensors enable inexpensive access to more detailed information on traffic conditions [1]. In conjunction with more intelligent controllers that can take advantage of the better information, this new sensor technology can lead to greater efficiency at much lower cost. The task that we pose is to improve the overall efficiency of traffic flow in urban settings. More specifically, we want to find an ideal tradeoff between the efficiency of traffic flow and the costs of development, acquisition, installation, and maintenance of the necessary physical upgrades. In order to select the appropriate tradeoff, we must identify the significant sources of improvement (over current traffic-control systems), quantify the resulting improvements, and determine the fiscal impact of implementing these improvements. We recognize that current safety requirements must be strictly maintained and assume that they are not subject to any tradeoff.