Predicting Impacts of Intelligent Transportation Systems on Freeway Queue Discharge Flow Variability

Abstract

This study focuses on the problem of measuring the queue discharge flow rates for a nonbottleneck freeway section and on developing an approach for estimating the impacts of intelligent transportation system (ITS) measures on the mean and variance of the queue discharge flow rate. The whole-year mean and variance of the queue discharge flow rates for the subject section of freeway are computed on the basis of measured 5-min congested flow rates over the course of a year. The flow data are categorized into congested and uncongested flows on the basis of a speed threshold that separates congested flow conditions from uncongested conditions. The speed threshold is determined by plotting the change in speed between observations and identifying the speed at which speed is most unstable. The mean and variance of queue discharge flow rates for incident conditions are then computed by identifying when incidents were present on the freeway over the course of a year, identifying the corresponding flow rates in the study section for those periods, and segregating the observed flow rates for incidents into congested and uncongested flows by using the same speed threshold as before. Once the means and variances of the queue discharge flow rates have been obtained for the whole year and for incident conditions during the year, the variance decomposition formula is used to compute the mean and variance of queue discharge flow rates for nonincident conditions during the year. A methodology is then proposed and demonstrated for computing the impact of ITS measures (such as faster incident detection or improved congested flow rates during incidents) on the observed portion of the total variance that is attributable to incidents susceptible to amelioration with ITS measures. This same approach can also be used to compute the effectiveness of measures to reduce other causes of nonrecurrent congestion.

Rationale for Incorporating Queue Discharge Flow into Highway Capacity Manual Procedure for Analysis of Freeway Facilities

“…For example, Brilon et al set the speed threshold at 70 km/h (42 mph) in their study of a range of German freeways that used single loop sensors (2). Dowling et al selected a threshold of 45 mph and determined the start of breakdown by plotting the change in speed between sequential 5-min periods and finding the period with the greatest variation in speed (3).…”

“…In the present implementation, a partial queue time interval (queue clearing somewhere within 15 min) cannot be modeled without a more fundamental change to the computational code. Finally, the base scenario without two-capacity was reanalyzed with the iteratively estimated volumes, which include the following time intervals (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) and demands (vph):…”

Investigating Distribution of Capacity Based on Degradation Model

“…The main characteristic of traffic breakdown is associated with an abrupt decrease in traffic flow speed [19,20]. Therefore, traffic breakdown also is called speed breakdown and can be described by drop of speed [19,20]. Let V 0 be denoted as the speed threshold; that is, if the traffic speed is observed lower than V 0 , it indicates that traffic breakdown has occurred.…”

Comparison of queue estimation accuracy by shockwave-based and input-output-based models

“…Because of preceding illustration hereon, this study applied the speed drop as the breakdown indicator. For the sake of simplicity, Brilon's approach [4] is utilized to identify breakdown, which was adopted widely (see for example [24][25]). In Brilon's approach, traffic data is classified into 3 categories:…”