摘要: |
Periodic resurfacing, rehabilitation, restoration, and reconstruction work is needed on the aging highway system to maintain a desired level of service for the traveling public. However, temporary work zones on highways disrupt the normal flow of traffic and reduce the level of service. Freeway work zones have become a major source of traffic congestion and travelers' delays which result in reduced freeway capacity, increased driver frustration, increased traffic accident, increased road user delay cost, and increased fuel consumption and vehicle emissions. In this research, scientific models have been created for estimation of the work zone capacity for the first time. A CBR model has been created for freeway work zone traffic management considering work zone layout, traffic demand, work characteristics, traffic control measures, and mobility impacts. A freeway work zone traffic delay and cost optimization model has been developed in terms of the length of the work zone segment and the starting time of the work zone using average hourly traffic data. A neuro-fuzzy logic model has been developed for estimation of the freeway work zone capacity taking into account seventeen different factors impacting the work zone capacity. An object-oriented model has been developed for freeway work zone capacity and queue delay and length estimation. The model has been implemented into an interactive software system, called IntelliZone. IntelliZone's capacity estimation engine is based on pattern recognition and neural network models incorporating a large number of factors impacting the work zone capacity. This research provides the foundation for a new generation of advanced decision support systems for effective management of traffic at work zones. The extensive parametric study of main factors impacting the work zone capacity provides quantitative and objective results of value to work zone engineers and highway agencies when creating traffic management plans for work zones. |