Abstract
Dynamic signal control strategies are effective in relieving congestions during nontypical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. The developed capacity reduction models were used to estimate delay due to an urban street incident. The delay was calculated as a combination of the delay due to queuing on the incident link and the increase in upstream intersection control delays due the reduction in maximum throughputs resulting from queue spillback to the upstream intersection The HCS-based method estimated a reduction in delay resulting from the new signal timing plan to be around 3,404 vehicle-hours, whereas the VISSIM shows that the new signal timing saving in delay is 4,008 vehicle-hours. This confirms that the developed method and VISSIM estimation of the benefits are consistent.
Highlights
Dynamic signal control strategies are effective in relieving congestions during nontypical days, such as those with high demands, incidents with different attributes, and adverse weather conditions
The use of simulation models can be costly in terms of data collection, model input preparation, and calibration, especially when the incident management strategies need to be evaluated at the regional levels when the stochastic nature of incident features and locations need to be considered in the analysis, and when the incident impact has to be analysed for a long period of time, and for real-time operations
This chapter discusses the incorporation of the method in a data analytical tool and its use to inform the identification of a special signal timing plan to reduce incident impacts as part of a decision support system
Summary
Dynamic signal control strategies are effective in relieving congestions during nontypical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. The delay was calculated as a combination of the delay due to queuing on the incident link and the increase in upstream intersection control delays due the reduction in maximum throughputs resulting from queue spillback to the upstream intersection The HCS-based method estimated a reduction in delay resulting from the new signal timing plan to be around 3,404 vehicle-hours, whereas the VISSIM shows that the new signal timing saving in delay is 4,008 vehicle-hours. This chapter first discusses the extension of existing analytical procedures to allow better assessment of the impacts of incidents considering the interactions between the reductions in capacity below demands at midblock urban street segment locations and upstream and downstream signalized intersection operations, as explained in the previous chapter. This chapter discusses the incorporation of the method in a data analytical tool and its use to inform the identification of a special signal timing plan to reduce incident impacts as part of a decision support system
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