Flow Breakdown, Travel Reliability and Real-time Information in Route Choice Behavior

被引:10
|
作者
Dong, Jing [1 ]
Mahmassani, Hani S. [1 ]
机构
[1] Northwestern Univ, Evanston, IL 60208 USA
关键词
D O I
10.1007/978-1-4419-0820-9_33
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
The objective of this study is to provide travelers in a congested network with information on the relative reliability of alternative travel routes, in addition to the usual travel time information. Towards this end, we develop a travel reliability measure that captures the probability of flow breakdown along a given facility, along with the conditional expected delay associated with occurrence of breakdown. The paper develops a methodology for estimating the key elements of this measure, and provides an empirical realization using commonly available freeway sensor data. Both elements of the reliability measure, namely the probability of flow breakdown and the extra delay caused by breakdown are represented as functions of flow rate, and calibrated for each road section based on field data. The proposed travel reliability measure could therefore be obtained off-line by analyzing historical data and computed on-line when real-time measurements are available. The reliability measure is incorporated in the generalized cost function underlying drivers' route choice behavior, as a basis for dynamic traffic assignment under reliability information provision to users. An analytical illustration using an idealized two route network is provided, confirming that reliability information could improve system performance and increase overall social welfare. Application of the approach to the Irvine, CA test network provides a real-network assessment of the value of travel reliability information in the context of real-time traveler information provision. The experimental results show that reliability information helps to relieve congestion on the freeway, increase system utilization and reduce travelers' trip time.
引用
收藏
页码:675 / 695
页数:21
相关论文
共 50 条
  • [31] Route choice behaviour of freeway travellers under real-time traffic information provision - Application of the best route and the habitual route choice mechanisms
    Jou, Rong-Chang
    Hensher, David A.
    Chen, Ke-Hong
    TRANSPORTATION PLANNING AND TECHNOLOGY, 2007, 30 (06) : 545 - 570
  • [32] Travel Route Prediction Using Travel Habits and Real-Time Traffic Condition
    Mu, Wanguo
    3RD ANNUAL INTERNATIONAL CONFERENCE ON CLOUD TECHNOLOGY AND COMMUNICATION ENGINEERING, 2020, 719
  • [33] Evaluating the Impact of Real-Time Mobility and Travel Time Reliability Information on Truck Drivers' Routing Decisions
    Kong, Xiaoqiang
    Eisele, William L.
    Zhang, Yunlong
    Cline, Daren B. H.
    TRANSPORTATION RESEARCH RECORD, 2018, 2672 (09) : 164 - 172
  • [34] Route choice behaviour with pre-trip travel time information
    Shiftan, Y.
    Bekhor, S.
    Albert, G.
    IET INTELLIGENT TRANSPORT SYSTEMS, 2011, 5 (03) : 183 - 189
  • [35] Real-Time Travel Time Prediction Framework for Departure Time and Route Advice
    Calvert, Simeon C.
    Snelder, Maaike
    Bakri, Taoufik
    Heijligers, Bjorn
    Knoop, Victor L.
    TRANSPORTATION RESEARCH RECORD, 2015, (2490) : 56 - 64
  • [36] Modeling Strategic Route Choice and Real-Time Information Impacts in Stochastic and Time-Dependent Networks
    Gao, Song
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2012, 13 (03) : 1298 - 1311
  • [37] A Hybrid Model for Driver Route Choice Incorporating En-Route Attributes and Real-Time Information Effects
    Srinivas Peeta
    Jeong Whon Yu
    Networks and Spatial Economics, 2005, 5 : 21 - 40
  • [38] A hybrid model for driver route choice incorporating en-route attributes and real-time information effects
    Peeta, S
    Yu, JW
    NETWORKS & SPATIAL ECONOMICS, 2005, 5 (01): : 21 - 40
  • [39] Capturing the interaction between travel time reliability and route choice behavior based on the generalized Bayesian traffic model
    Zhu, Zheng
    Mardan, Atabak
    Zhu, Shanjiang
    Yang, Hai
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2021, 143 : 48 - 64
  • [40] Bus travel time prediction with real-time traffic information
    Ma, Jiaman
    Chan, Jeffrey
    Ristanoski, Goce
    Rajasegarar, Sutharshan
    Leckie, Christopher
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 105 : 536 - 549