Examining the Impact of Adverse Weather on Travel Time Reliability of Urban Corridors in Shanghai

Abstract

Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

Research on Traffic Situation Analysis for Urban Road Network Through Spatiotemporal Data Mining: A Case Study of Xi’an, China

“…Some unusual weather situations (e.g., rain, snow, fog) or unusual road statuses may similarly influence the value of TTI. [30].…”

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

“…With the development of the expressway, traffic demand has grown rapidly. In daily operation, the expressway often suffers from congestion and unreliability due to the large volume of vehicles, bad weather and other stochastic factors [1][2][3]. On the other hand, there is high surplus capacity in off-peak hours.…”

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).…”