Estimating performance of traffic signals based on link travel times

被引:3
|
作者
So, Jaehyun [1 ]
Stevanovic, Aleksandar [2 ]
Koonce, Peter [3 ]
机构
[1] Tech Univ Munich, Chair Traff Engn & Control, Arcis Str 21, D-80333 Munich, Bayern, Germany
[2] Florida Atlantic Univ, Dept Civil Environm & Geomat Engn, Glades Rd 777, Boca Raton, FL 33431 USA
[3] Portland Bur Transportat, 1120 SW Fifth Ave, Portland, OR 97205 USA
关键词
intersection performance measure; volume/capacity ratio; travel time; volume-delay function; traffic signals; DELAY; INTERSECTIONS;
D O I
10.1002/atr.1375
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Recent advances in communication and computing technology have made travel time measurements more available than ever before. In urban signalized arterials, travel times are strongly influenced by traffic signals. This study presents a novel method based on well-known principles to estimate traffic signal performance (or more precisely their major "through" movements) based on travel time measurements. The travel times were collected between signals in the field by using point-to-point travel time measurement technologies. Closed-circuit television cameras and signal databases were used to collect traffic demand and signal timings, respectively. Then, the volume/capacity ratio of major downstream signal movements was computed based on demand and signal timings. This volume/capacity ratio was then correlated with travel times on the relevant intersection approach. The best volume-delay function was found, along with many other functions, to fit the field data. This volume-delay function was then used to estimate volume/capacity ratios and, indirectly, a few other signal performance metrics. The method, called travel time-based signal performance measurements, was automated and displayed on a Google Map. The findings show that the proposed method is accurate and robust enough to provide necessary information about signal performance. A newly developed volume-delay function was found to work just slightly better than the Bureau of Public Roads curve. Several issues, which may reduce the accuracy of the proposed method, are identified, and their solutions are proposed for future research. Copyright (C) 2016 John Wiley & Sons, Ltd.
引用
收藏
页码:786 / 801
页数:16
相关论文
共 50 条
  • [41] Estimation of Travel Times and Identification of Traffic Excesses on Roads
    Silar, Jan
    Tichy, Tomas
    Prikryl, Jan
    TELEMATICS - SUPPORT FOR TRANSPORT, 2014, 471 : 166 - 173
  • [42] A Link-Based Stochastic Traffic Assignment Model for Travel Time Reliability Estimation
    Wei, Chong
    Asakura, Yasuo
    Iryo, Takamasa
    NETWORK RELIABILITY IN PRACTICE, 2012, : 209 - 221
  • [43] Link average travel time of traffic flow estimation based on floating car data
    Jiang, Gui-Yan
    Chang, An-De
    Cong, Yu-Liang
    Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2010, 40 (SUPPL.1): : 158 - 162
  • [44] Estimating Toll Road Travel Times Using Segment-Based Data Imputation
    Jedwanna, Krit
    Athan, Chuthathip
    Boonsiripant, Saroch
    SUSTAINABILITY, 2023, 15 (17)
  • [45] Floating Car Data Based Analysis of Urban Travel Times for the Provision of Traffic Quality
    Ehmke, Jan Fabian
    Meisel, Stephan
    Mattfeld, Dirk Christian
    TRAFFIC DATA COLLECTION AND ITS STANDARDIZATION, 2010, 144 : 129 - 149
  • [46] The performance of a link with multipriority traffic
    Shi, VTS
    Chu, W
    Perrizo, W
    IEEE TRANSACTIONS ON COMMUNICATIONS, 1998, 46 (06) : 743 - 746
  • [47] Estimating Maximum Queue Length for Traffic Lane Groups Using Travel Times from Video-Imaging Data
    Ma, Dongfang
    Lou, Xiaoqin
    Jin, Sheng
    Wang, Dianhai
    Guo, Weiwei
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2018, 10 (03) : 123 - 134
  • [48] Probabilistic estimation of link travel times in dynamic road networks
    Asghari, Mohammad
    Emrich, Tobias
    Demiryurek, Ugur
    Shahabi, Cyrus
    23RD ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2015), 2015,
  • [49] Predicting link travel times from floating car data
    Jones, Michael
    Geng, Yanfeng
    Nikovski, Daniel
    Hirata, Takahisa
    2013 16TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS - (ITSC), 2013, : 1756 - 1763
  • [50] Comparison of prediction methods for urban network link travel times
    Hartley, JK
    SIMULATION IN INDUSTRY, 2003, : 569 - 576