Using Microsimulation to Estimate the Impact of Transportation Improvements and Operational Policy Changes on Travel Time Reliability
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Traditionally, traffic engineers have designed roadway networks and operational strategies to manage congestion and minimize delays during the peak demand period for some “average” or “typical” day. However, increasingly, there is concern about not only the average traffic conditions along a route (during some period of the day), but also about the variability of the required time to traverse the route. Travel times vary as a function of the departure time according to relatively predictable changes in the traffic demands (i.e. travel times are longer during the peak commuting periods than during off peak periods). However, the time to complete the same trip at the same departure time also varies from day to day. The variability of travel time, and the associated additional costs, has introduced another performance measure in transportation engineering called travel time reliability (TTR). Travel time reliability has gained significant attention among the transportation researchers and practitioners recently. In this research, we aimed to implement traffic microsimulation models in order to model travel time reliability and finally to incorporate it into the alternative comparison. The contribution areas of this research are explained briefly in the following paragraphs. Previous work that has examined the impact of weather on the characteristics of the speed-flow-density relationship has defined the weather conditions a priori and then attempted to determine the macroscopic traffic stream characteristics for these categories. However, for the purposes of modeling travel time reliability, it is necessary to only capture those weather conditions for which the associated macroscopic characteristics are statistically different. In this research we develop a technique to distinguish distinct weather categories through an innovative method. Also, the process of determining macroscopic traffic stream characteristics requires the calibration of a macroscopic speed-flow-density model to field data. In employing this approach, we observed that the errors associated with the estimated parameters are impacted by the number and distribution of the observation points that used to calibrate the model. Therefore, we developed models to estimate the corresponding errors of the estimated traffic parameters and found that for most practical applications, the estimation of the jam density is most sensitive to the distribution of the calibration data. As a result, we suggested some specific conditions for which the jam density value should be assumed a priori rather than calibrated on the basis of the available field data. We additionally wanted to be able to model specific weather categories. We knew the traffic flow parameters of those weather conditions from the field data and we wanted the same traffic characteristics to be simulated in the traffic microsimulation model. Therefore, we proposed and evaluated a method to map the traffic flow characteristics to the TMM input parameters. The model developed in this research is not only applicable to simulate different weather categories, but also can be used to simulate any traffic condition -within the acceptable range of the model- when the traffic flow parameters are known. Furthermore, we aimed to monetize travel time (un)reliability. To do this we have adopted the unreliability cost in terms of the costs of arriving early or arriving late. This approach has been widely used to quantify the costs of unreliability of public transport system; however, for road transport, this construct requires that we know the scheduled travel time which, from the user’s perspective is the anticipated travel. We carried out a stated preference survey to estimate the anticipated travel time based on the travel time distribution. On the basis of the survey responses, we proposed two models in which travelers ignore unusually long travel times when determining their anticipated travel time. Finally, we incorporated all of these findings to create an approach to quantify the cost of travel time (un)reliability using traffic microsimulation tools. We demonstrate this approach to evaluate two road improvement alternatives. We used the traffic simulation model VISSIM to compare these two alternatives based on the travel time cost and travel time reliability cost together.
Cite this version of the work
Reza Golshan Khavas (2017). Using Microsimulation to Estimate the Impact of Transportation Improvements and Operational Policy Changes on Travel Time Reliability. UWSpace. http://hdl.handle.net/10012/11178