Estimating Nonrecurring Post-Incident Traffic Recovery Time: Comparing Shockwave Theory And Simulation Modeling
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Date
2011
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Department
Civil Engineering
Program
Doctor of Engineering
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This item is made available by Morgan State University for personal, educational, and research purposes in accordance with Title 17 of the U.S. Copyright Law. Other uses may require permission from the copyright owner.
Abstract
The occurrence of an incident and its consequent recovery are critical to the smooth and efficient operations of freeways. Although many studies have been performed on incident detection, clearance, and management, travelers and traffic managers are unable to accurately predict the length of time required for full traffic recovery after occurrence of an incident. To date, there are limited practical studies available to estimate post-incident recovery time, particularly for varying traffic and incident scenarios. Post-incident recovery time for the purpose of this study is defined as the time for pre-incident normal traffic state to re-occur after clearance of an incident. This dissertation estimates traffic recovery time along a freeway using Monte Carlo simulation techniques and compares the simulation results with shockwave theory calculations to evaluate if the simulation model offers any advantages over the traditional queuing and shockwave methodology. The model explores varying traffic intensity (Rho) and incident duration for different lane closures, and their effects on recovery time. Finally, the impact of congestion and incident duration on the highway network is quantified by applying regression formulae for determining traffic recovery time. Based on the simulation data, the numerical results of the experiments indicate that a higher incident recovery time is required with each corresponding increase in Rho and incident time. Analysis of the simulated data show that for a given incident duration and lane blockage scenario, the recovery time of the traffic increased nonlinearly with the traffic intensity. In other words, congestion increases as incident duration increases at all Rho values, but increases at faster rates for higher Rho values. Analysis of the regression models confirms a nonlinear relationship between recovery time and the independent variables of traffic intensity, incident duration and lane blockage. In addition, the results from simulation estimate a longer recovery time on all scenarios for traffic to attain pre-incident travel conditions using the simulation method than the shockwave theory. Comparison of the simulated results with the shockwave methodology concurs with other studies which suggest that this methodology offers no advantages over simulation but instead underestimates traffic recovery time. Full incident traffic recovery time can be estimated on a freeway when the nonlinear regression formula derived in this study is applied within the defined constraints.