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Integrated model for highway-based travel time forecasting with application to truck transportation.

机译：基于公路的行驶时间预测的集成模型在卡车运输中的应用。

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摘要

Highways are increasingly congested in metropolitan areas, especially in major cities that rely on highway transportation. As a consequence, travel time forecasting on highways has become an active research field, playing an important role in Intelligent Transportation Systems (ITS) and Advanced Traveler Information Systems (ATIS). Based on current traffic information and forecasting techniques, fuzzy methods were created in this thesis to predict highway travel time to support decisions for travelers and transportation industries in real-time. The method starts from analyzing real-time data from single loop detectors and ultimately provides real-time travel time estimation for whole trips.; Two applications, truck-to-air connectivity and forecasts during a transit strike, were used to demonstrate the real-time travel time forecasting model. Express Package Transport carriers operate many local terminals within major cities as well as hub terminals. Trucks depart from local terminals carrying express shipments to airports with sorting facilities at the end of each day. It is important for trucks to arrive on time and for shipments to be processed on time, because truck delay can postpone sorting procedures and transferring processes. Another application that we investigated is travel time forecasting during a transit strike. Highways are especially susceptible to congestion during strikes because travelers have little opportunity to adjust and equilibrate their travel patterns. Hence, the prediction of travel time becomes more important for transportation industries during strikes.; A practical website was implemented along with the application of truck-to-air connectivity. We acquire real-time traffic information from loop detectors on highways and calculate real-time travel times for each truck schedule. By implementing a web-based decision support tool with real-time travel time forecasting, outbound aircraft from express companies can depart on schedule. The experiments conducted show that the fuzzy model can predict travel time during peak periods and abnormal traffic conditions. Morning rush hours, evening rush hours, and incident conditions have been evaluated to test the models. The proposed fuzzy model performed better then a Weighted Moving Average Model for the traffic conditions investigated.
机译：大城市地区的高速公路日益拥堵，尤其是在依赖公路运输的主要城市。因此，高速公路上的旅行时间预测已成为一个活跃的研究领域，在智能交通系统（ITS）和高级旅行者信息系统（ATIS）中发挥着重要作用。基于当前的交通信息和预测技术，本文创建了模糊方法来预测公路行驶时间，以支持旅行者和运输行业的实时决策。该方法从分析来自单回路检测器的实时数据开始，并最终提供整个行程的实时行程时间估计。卡车到空中的连通性和公交罢工期间的预测这两个应用程序被用来演示实时旅行时间预测模型。快递包裹运输公司在主要城市以及枢纽站运营着许多本地码头。每天结束时，卡车都会从当地的候机楼出发，运送快递货物到有分拣设施的机场。对于卡车而言，准时到达和按时处理装运非常重要，因为卡车延误会推迟分类程序和转移过程。我们研究的另一项应用是公交罢工期间的旅行时间预测。高速公路在罢工期间特别容易拥堵，因为旅行者几乎没有机会调整和平衡他们的出行方式。因此，对于罢工期间的运输行业而言，行程时间的预测对于运输行业变得更加重要。实施了一个实用的网站，并应用了卡车到空中的连接。我们从高速公路上的环路检测器获取实时交通信息，并为每个卡车时间表计算实时行驶时间。通过实施具有实时旅行时间预测的基于Web的决策支持工具，快递公司的出境飞机可以按计划出发。进行的实验表明，模糊模型可以预测高峰时段和异常交通状况下的旅行时间。评估了早上高峰时间，晚上高峰时间和事件条件以测试模型。对于所研究的交通状况，所提出的模糊模型的性能优于加权移动平均模型。

著录项

  • 作者

    Lo, Shih-Che.;

  • 作者单位

    University of Southern California.;

  • 授予单位 University of Southern California.;
  • 学科 Engineering Industrial.; Engineering System Science.; Transportation.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 157 p.
  • 总页数 157
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;系统科学;综合运输;
  • 关键词

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