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Integration of Locational Decisions with the Household Activity Pattern Problem and Its Applications in Transportation Sustainability.

机译：位置决策与家庭活动模式问题的整合及其在交通可持续性中的应用。

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This dissertation focuses on the integration of the Household Activity Pattern Problem (HAPP) with various locational decisions considering both supply and demand sides. We present several methods to merge these two distinct areas--transportation infrastructure and travel demand procedures--into an integrated framework that has been previously exogenously linked by feedback or equilibrium processes. From the demand side, travel demand for non-primary activities is derived from the destination choices that a traveler makes that minimizes travel disutility within the context of considerations of daily scheduling and routing. From the supply side, the network decisions are determined as an integral function of travel demand rather than a given fixed OD matrix.;First, the Location Selection Problem for the Household Activity Pattern Problem (LSP-HAPP) is developed. LSP-HAPP extends the HAPP by adding the capability to make destination choices simultaneously with other travel decisions of household activity allocation, activity sequence, and departure time. Instead of giving a set of pre-fixed activity locations to visit, LSP-HAPP chooses the location for certain activity types given a set of candidate locations. A dynamic programming algorithm is adopted and further developed for LSP-HAPP in order to deal with the choices among a sizable number of candidate locations within the HAPP modeling structure. Potential applications of synthetic pattern generation based on LSP-HAPP formulation are also presented.;Second, the Location - Household Activity Pattern Problem (Location-HAPP), a facility location problem with full-day scheduling and routing considerations is developed. This is in the category of Location-Routing Problems (LRPs), where the decisions of facility location models are influenced by possible vehicle routings. Location-HAPP takes the set covering model as a location strategy, and HAPP as the scheduling and routing tool. The proposed formulation isolates each vehicle's routing problem from those of other vehicles and from the master set covering problem. A modified column generation that uses a search method to find a column with a negative reduced price is proposed.;Third, the Network Design Problem is integrated with the Household Activity Pattern Problem (NDP-HAPP) as a bilevel optimization problem. The bilevel structure includes an upper level network design while the lower level includes a set of disaggregate household itinerary optimization problems, posed as HAPP or LSP-HAPP. The output of upper level NDP (level-of-service of the transportation network) becomes input data for the lower level HAPP that generates travel demand which becomes the input for the NDP. This is advantageous over the conventional NDP that outputs the best set of links to invest in, given an assumed OD matrix. Because the proposed NDP-HAPP can output the same best set of links, a new OD matrix and a detailed temporal distribution of activity participation and travel are created. A decomposed heuristic solution algorithm that represents each decision makers' rationale shows optimality gaps of as much as 5% compared to exact solutions when tested with small examples.;Utilizing the aforementioned models, two transportation sustainability studies are then conducted for the adoption of Alternative Fuel Vehicles (AFVs). The challenges in adopting AFVs are directly related to the transportation infrastructure problems since the initial AFV refueling locations will need to provide comparable convenient travel experience for the early adopters when compared to the already matured gasoline fuel based transportation infrastructure. This work demonstrates the significance of the integration between travel demand model and infrastructure problems, but also draws insightful policy measurements regarding AFV adoption.;The first application study attempts to measure the household inconvenience level of operating AFVs. Two different scenarios are examined from two behavioral assumptions - keeping currently reported pattern and minimizing the inconvenience cost through HAPPR or HAPPC. From these patterns, the personal or household inconvenience level is derived as compared to the original pattern, providing quantified data on how the public sector would compensate for the increases in travel disutility to ultimately encourage the attractiveness of AFVs.;From the supply side of the AFV infrastructure, Location-HAPP is applied to the incubation of the minimum refueling infrastructure required to support early adoption of Hydrogen Fuel Cell Vehicles (HFCVs). One of the early adoption communities targeted by auto manufacturers is chosen as the study area, and then three different values of accessibility are tested and measured in terms of tolerances to added travel time. Under optimal conditions, refueling trips are found to be toured with other activities. More importantly, there is evidence that excluding such vehicle-infrastructure interactions as well as routing and scheduling interactions can result in over-estimation of minimum facility requirement.
机译：本文着重研究家庭活动模式问题（HAPP）与考虑供需双方的各种位置决策的整合。我们提出了几种方法来将这两个截然不同的领域（运输基础设施和旅行需求程序）合并为一个集成框架，该框架以前通过反馈或平衡过程进行了外源性链接。从需求方面来说，非主要活动的旅行需求是由旅行者做出的目的地选择得出的，该选择在考虑日常调度和路线安排的情况下将旅行的无效性降至最低。从供应方来看，网络决策被确定为出行需求的积分函数，而不是给定的固定OD矩阵。首先，开发了针对家庭活动模式问题的位置选择问题（LSP-HAPP）。 LSP-HAPP扩展了HAPP，增加了选择目的地的能力，同时还可以做出其他旅行决定，如家庭活动分配，活动顺序和出发时间。 LSP-HAPP并未提供一组固定的活动位置供访问，而是选择了给定一组候选位置的特定活动类型的位置。为了处理HAPP建模结构中相当数量的候选位置之间的选择，对LSP-HAPP采用了动态编程算法并对其进行了进一步开发。还提出了基于LSP-HAPP公式的合成模式生成的潜在应用。其次，提出了位置-家庭活动模式问题（Location-HAPP），这是一个考虑全日调度和选路的设施位置问题。这是位置路由问题（LRP）的类别，其中设施位置模型的决策受可能的车辆路由影响。 Location-HAPP将集合覆盖模型作为定位策略，并将HAPP作为调度和路由工具。提出的公式将每辆车的路径问题与其他车的路径问题以及主覆盖范围问题隔离开来。提出了一种使用搜索方法找到价格降低了负的列的改进列生成方法。第三，将网络设计问题与家庭活动模式问题（NDP-HAPP）集成为双层优化问题。双层结构包括上层网络设计，而下层结构包括一组分散的家庭行程优化问题，这些问题被提出为HAPP或LSP-HAPP。上层NDP的输出（运输网络的服务水平）成为下层HAPP的输入数据，从而产生旅行需求，该需求成为NDP的输入。给定假设的OD矩阵，这比输出要投资的最佳链路集的常规NDP更具优势。因为建议的NDP-HAPP可以输出相同的最佳链接集，所以创建了一个新的OD矩阵以及活动参与和旅行的详细时间分布。代表每个决策者基本原理的分解启发式求解算法显示，与经过小样本测试的精确解决方案相比，最优缺口高达5％。使用上述模型，然后进行了两项运输可持续性研究，以采用替代燃料车辆（AFV）。采用AFV的挑战与运输基础设施问题直接相关，因为与已经成熟的基于汽油燃料的运输基础设施相比，最初的AFV加油地点将需要为早期采用者提供可比的便捷旅行体验。这项工作证明了出行需求模型和基础设施问题之间整合的重要性，同时也对采用AFV进行了有见地的政策评估。首次应用研究试图衡量运营AFV的家庭不便程度。从两个行为假设中检查了两种不同的方案-保持当前报告的模式，并通过HAPPR或HAPPC最小化不便成本。从这些模式中，可以得出与原始模式相比的个人或家庭不便程度，提供了有关公共部门将如何补偿旅行无用度的增加以最终鼓励AFV的吸引力的量化数据。 AFV基础设施Location-HAPP用于孵化支持早期采用氢燃料电池汽车（HFCV）所需的最小加油基础设施。选择汽车制造商针对的早期采用社区之一作为研究区域，然后根据对增加的旅行时间的容忍度来测试和测量三种不同的辅助功能值。在最佳条件下，发现与其他活动一起进行加油旅行。更重要的是，有证据表明，排除此类车辆基础设施交互以及路由和调度交互可能会导致对最低设施要求的高估。

著录项

  • 作者

    Kang, Jee Eun.;

  • 作者单位

    University of California, Irvine.;

  • 授予单位 University of California, Irvine.;
  • 学科 Engineering Civil.;Transportation.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 241 p.
  • 总页数 241
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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