Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data

Qi Zhang,Hong Chen,Hongchao Liu,Wei Li,Yibin Zhang

doi:10.1155/2020/8816020

Abstract

Origin-destination- (O-D-) based travel time reliability (TTR) is fundamental to next-generation navigation tools aiming to provide both travel time and reliability information. While previous works are mostly focused on route-based TTR and use either ad hoc data or simulation in the analyses, this study uses open-source Uber Movement and Weather Underground data to systematically analyze the impact of rainfall intensity on O-D-based travel time reliability. The authors classified three years of travel time data in downtown Boston into one hundred origin-destination pairs and integrated them with the weather data (rain). A lognormal mixture model was applied to fit travel time distributions and calculate the buffer index. The median, trimmed mean, interquartile range, and one-way analysis of variance were used for quantification of the characteristics. The study found some results that tended to agree with the previous findings in the literature, such that, in general, rain reduces the O-D-based travel time reliability, and some seemed to be unique and worthy of discussion: firstly, although in general the reduction in travel time reliability gets larger as the intensity of rainfall increases, it appears that the change is more significant when rainfall intensity changes from light to moderate but becomes fairly marginal when it changes from normal to light or from moderate to extremely intensive; secondly, regardless of normal or rainy weather, the O-D-based travel time reliability and its consistency in different O-D pairs with similar average travel time always tend to improve along with the increase of average travel time. In addition to the technical findings, this study also contributes to the state of the art by promoting the application of real-world and publicly available data in TTR analyses.

Highlights

Travel time reliability (TTR) plays a vital role in various applications such as evaluation of network performance [1], measuring the improvement of traffic operations and management strategies [2], quantification of service quality [3], enhancing the experience of traveler’s route choice [4], and determining freeway bottlenecks [5].Among the route level, origin-destination (O-D) level, and network level studies, the route level TTR analyses have received much more attention in the past
In a review of the literature, we found that studies are limited with respect to the impact of rain on the O-D level TTR; most of the data used in previous studies were project-specific and only covered a short period of time that was not even sound for a full-scale statistical analysis
E rest of the paper is organized as follows: Section 2 introduces the data used in this research, which includes the O-D-based travel time data from Uber Movement and historical weather data collected from the Weather Underground website; Section 3 depicts the typical TTR measures and the analytical approach developed based on the Gaussian mixture model; Section 4 presents the results, and Section 5 summarizes the findings and conclusions and concludes the paper by discussions and future research

Summary

Research Article

Origin-Destination-Based Travel Time Reliability under Different Rainfall Intensities: An Investigation Using Open-Source Data. While previous works are mostly focused on route-based TTR and use either ad hoc data or simulation in the analyses, this study uses open-source Uber Movement and Weather Underground data to systematically analyze the impact of rainfall intensity on O-D-based travel time reliability. E authors classified three years of travel time data in downtown Boston into one hundred origin-destination pairs and integrated them with the weather data (rain). In addition to the technical findings, this study contributes to the state of the art by promoting the application of real-world and publicly available data in TTR analyses

Introduction

Mean SD

Methods

Histogram LMM curve

Moderate rain Heavy rain