Analyzing distributions for travel time data collected using radiofrequency identification technique in urban road networks  

作　　者：GUO JianHua LI ChangGuang QIN Xiao HUANG Wei WEI Yun CAO JinDe 

机构地区：[1]Intelligent Transportation System Research Center, Southeast University [2]Transportation Sensing and Cognition Research Center, Southeast University [3]Department of Civil & Environmental Engineering, University of Wisconsin at Milwaukee [4]National Engineering Laboratory for Green and Safe Construction Technology in Urban Rail Transit [5]School of Mathematics, Southeast University

出　　处：《Science China(Technological Sciences)》2019年第1期106-120,共15页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.61573106);the Jiangsu Provincial Key Laboratory of Networked Collective Intelligence(Grant No.BM2017002)

摘　　要：Travel time distribution studies are fundamental for supporting transportation system reliability studies, particularly for urban road networks. However, such studies are generally based on travel time data sets with limited sample sizes, which provide inconsistent findings. In this paper, a large amount of travel time data collected from the emerging radio frequency identification(RFID) technique are used to conduct empirical investigations and estimations of travel time distributions, and three major findings are determined. First, travel time data are shown to have a complex statistical structure: the travel time distribution is in general peaky, multi-modal, and skewed to the right, which cross validates findings shown in previous publications. Second, unimodal distribution models are shown to be unable to capture the complex statistical dynamics embedded in the travel time data; therefore,a multistate distribution model is more appropriate for modeling travel time distributions. In this respect, a three-component gaussian mixture model(GMM) is tested and results consistently outperform those of unimodal distribution models. Finally, the aggregation time interval is shown to have a trivial effect on the shape of travel time distributions: the travel time distribution is stable under different aggregation time intervals. Future work is recommended to investigate further travel time variabilities and travel time distribution estimations.Travel time distribution studies are fundamental for supporting transportation system reliability studies, particularly for urban road networks. However, such studies are generally based on travel time data sets with limited sample sizes, which provide inconsistent findings. In this paper, a large amount of travel time data collected from the emerging radio frequency identification(RFID) technique are used to conduct empirical investigations and estimations of travel time distributions, and three major findings are determined. First, travel time data are shown to have a complex statistical structure: the travel time distribution is in general peaky, multi-modal, and skewed to the right, which cross validates findings shown in previous publications. Second, unimodal distribution models are shown to be unable to capture the complex statistical dynamics embedded in the travel time data; therefore,a multistate distribution model is more appropriate for modeling travel time distributions. In this respect, a three-component gaussian mixture model(GMM) is tested and results consistently outperform those of unimodal distribution models. Finally, the aggregation time interval is shown to have a trivial effect on the shape of travel time distributions: the travel time distribution is stable under different aggregation time intervals. Future work is recommended to investigate further travel time variabilities and travel time distribution estimations.

关 键 词：reliability TRAVEL TIME TRAVEL TIME distribution TIME INTERVAL RFID 

分 类 号：TH[机械工程]