Validating the Adaptability of Travel Time Reliability Measurements using Probe Data

Abstract

Travel time reliability (TTR) is considered a critical piece of information in highway performance evaluation. The L02 project from Strategic Highway Research Program 2 (SHRP2) has developed a holistic method using statistical probability functions of travel time as the TTR measure to build highway performance evaluation and monitoring systems. Compared with single-value reliability measures, the L02 measure is able to identify sources of unreliability and quantify their associated impacts. To validate the adaptability of L02 measure, TTR analysis on the I-15 freeway corridor in Salt Lake City, Utah using probe data has been conducted. The result is compared against output from the quadrant-based TTR measure that is currently used by the Utah Department of Transportation. Through cross-validation, it is determined that the two suites of measures demonstrate good consistency in relation to reliability assessment and unreliability source diagnoses. In addition, the study provides a method to calibrate the quadrant-based TTR measure, and new critical values were developed based on the cross-validation.

Statistical Distance-Based Travel-Time Reliability Measurement for Freeway Bottleneck Identification and Ranking

“…The Strategic Highway Research Program 2 (SHRP2) launched a series of projects that aimed to develop tools and systems for TTR monitoring and evaluation (37)(38)(39). The SHRP2 L02 project proposed a holistic method of describing TTR with the probability density function (PDF) and cumulative density function (CDF), which has been proved to be an effective TTR measurement (40). To evaluate the influence of non-recurrent factors on travel time, the method introduces a concept of regime, which is a combination of congestion level and non-recurrent factors, for example, incident, adverse weather, and work zone.…”

Exploring the Efficacy of Large-Scale Connected Vehicle Data in Real-Time Traffic Applications

“…To evaluate and understand CV probe data, researchers commonly use several key metrics, including market penetration rates, speed bias, coverage, travel time, latency, buffer index, planning index, and similarity index (2,12,(16)(17)(18). However, the selection of the performance metrics depends on the target applications.…”

Comparing travel time performance-based measures to assess the effect of a freeway road construction project on freeway and connecting arterial street links

“…Data for computing, analyzing, and modeling travel time performance-based measures are captured using different types of probe data collection systems such as test vehicles, Bluetooth detectors, toll tag readers, license plate readers, road-side sensors, cell phone tracking, automatic vehicle location, and global positioning systems (Brennan et al, 2018;Chen et al, 2019;Federal Highway Administration [FHWA], 2017;Lu & Dong, 2018;Martchouk et al, 2011;Singh et al, 2019). The data collected were used for characterizing congestion (Brennan et al, 2018), validating the adaptability of travel time performance-based measures (Chen et al, 2019), estimating travel time (Lu & Dong, 2018), exploring travel time reliability (FHWA (Federal Highway Administration), 2006;Lomax et al, 2004;Lyman & Bertini, 2008;Martchouk et al, 2011;McLeod et al, 2012;Sisiopiku & Islam, 2012;Van Lint & Van Zuylen, 2005), and examining distributions for modeling travel time or related reliability measures (Moylan & Rashidi, 2017;Yang & Wu, 2016;Zheng et al, 2017;Zhong et al, 2020;Zou et al, 2020). Heuristic and statistical methods were also explored to assess travel time reliability for various travel conditions (Abdel-Aty et al, 1995;Chen et al, 2002;Haitham & Emam, 2006;Du & Nicholson, 1997).…”