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Bayesian network-based formulation and analysis for toll road utilization supported by traffic information provision

机译:基于贝叶斯网络的交通信息提供支持的收费公路利用公式化和分析

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摘要

Congestion pricing has been proposed and investigated as an effective means of optimizing traffic assignment, alleviating congestion, and enhancing traffic operation efficiencies. Meanwhile, advanced traffic information dissemination systems, such as Advanced Traveler Information System (ATIS), have been developed and deployed to provide real-time, accurate, and complete network-wide traffic information to facilitate travelers' trip plans and routing selections. Recent advances in ATIS technologies, especially telecommunication technology, allow dynamic, personalized, and multimodal traffic information to be disseminated and impact travelers' choices of departure times, alternative routes, and travel modes in the context of congestion pricing. However, few studies were conducted to determine the impact of traffic information dissemination on toll road utilizations. In this study, the effects of the provisions of traffic information on toll road usage are investigated and analyzed based on a stated preference survey conducted in Texas. A Bayesian Network (BN)-based approach is developed to discover travelers' opinions and preferences for toll road utilization supported by network-wide traffic information provisions. The probabilistic interdependencies among various attributes, including routing choice, departure time, traffic information dissemination mode, content, coverage, commuter demographic information, and travel patterns, are identified and their impacts on toll road usage are quantified. The results indicate that the BN model performs reasonably well in travelers' preference classifications for toll road utilization and knowledge extraction. The BN Most Probable Explanation (MPE) measurement, probability inference and variable influence analysis results illustrate travelers using highway advisory radio and internet as their primary mode of receiving traffic information are more likely to comply with routing recommendations and use toll roads. Traffic information regarding congested roads, road hazard warnings, and accident locations is of great interest to travelers, who tend to acquire such information and use toll roads more frequently. Travel time formation for home-based trips can considerably enhance travelers' preferences for toll road usage. Female travelers tend to seek traffic information and utilize toll roads more frequently. As expected, the information provided at both pre-trip and en-route stages can positively influence travelers' preferences for toll road usage. The proposed methodology and research findings advance our previous study and provide insight into travelers' behavioral tendencies concerning toll road utilization in support of traffic information dissemination. (C) 2015 Elsevier Ltd. All rights reserved.
机译:已经提出并研究了拥堵定价,作为优化交通分配,减轻拥堵并提高交通运营效率的有效手段。同时,已经开发并部署了高级交通信息传播系统,例如高级旅行者信息系统(ATIS),以提供实时,准确和完整的全网络交通信息,以方便旅行者的旅行计划和路线选择。 ATIS技术(尤其是电信技术)的最新进展使动态,个性化和多模式交通信息得以传播,并在拥挤定价的情况下影响旅客对出发时间,替代路线和出行方式的选择。但是,很少进行研究来确定交通信息传播对收费公路利用的影响。在这项研究中,根据在德克萨斯州进行的既定偏好调查,调查和分析了交通信息规定对收费公路使用的影响。开发了一种基于贝叶斯网络(BN)的方法,以发现旅行者对全网交通信息提供支持的收费公路利用的意见和偏好。确定各种属性之间的概率相互依赖关系,包括路线选择,出发时间,交通信息传播模式,内容,覆盖范围,通勤人口统计信息和出行方式,并量化其对收费公路使用的影响。结果表明,BN模型在收费公路利用和知识提取的旅行者偏好分类中表现良好。 BN最有可能解释(MPE)的测量,概率推断和可变影响分析结果表明,使用高速公路咨询无线电和互联网的旅行者作为其接收交通信息的主要方式,更可能遵循路线建议和使用收费公路。与拥挤的道路,道路危险警告和事故发生地点有关的交通信息对旅行者非常感兴趣,他们往往会获取此类信息并更频繁地使用收费公路。家庭旅行的旅行时间安排可以大大提高旅行者对收费公路使用的偏好。女性旅行者倾向于寻求交通信息并更频繁地使用收费公路。正如预期的那样,在出行前和途中提供的信息可以对旅行者对收费公路的使用产生积极影响。拟议的方法和研究结果将推动我们之前的研究,并为旅行者提供有关收费公路利用的行为趋势的见解,以支持交通信息的传播。 (C)2015 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Transportation research》 |2015年第11期|339-359|共21页
  • 作者单位

    Univ New Mexico, Dept Civil Engn, Albuquerque, NM 87131 USA;

    Univ New Mexico, Dept Civil Engn, Albuquerque, NM 87131 USA;

    Harbin Inst Technol, Sch Transportat Sci & Engn, Harbin 150090, Peoples R China;

    Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing 100124, Peoples R China;

    Rutgers State Univ, Dept Civil & Environm Engn, New Brunswick, NJ 08901 USA;

    Univ Texas Austin, Dept Civil Environm & Architectural Engn, Austin, TX 78712 USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Toll road; Traffic information provision; Nested logit model; Bayesian network;

    机译:收费公路;交通信息提供;嵌套的logit模型;贝叶斯网络;

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