ANALYTICAL MODELING AND OPTIMIZATION OF URBAN PUBLIC TRANSIT SYSTEMS

城市公共交通系统的分析建模和优化

• 批准号: RGPIN-2022-03040
• 负责人: Wirasinghe, Sumedha
• 金额: $ 3.13万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749444
• 关键词: ANALYTICAL; MODELING; OPTIMIZATION; URBAN; PUBLIC

ANALYTICAL MODELING OF URBAN PUBLIC TRANSIT SYSTEMS Urban public transit plays an essential role in developing a sustainable transport system that provides increased mobility with an economy of scale. The long-term goal of the research program is to gain results & insights to improve the design, planning & operation of public transit.  Over 5 years, the research program will formulate a novel transit service evaluation metric and incorporate new advancement in technology (e.g. transit automation) while taking into consideration the needed adaptation in transit service and route network (e.g. demand responsive transit). These short-term objectives to be achieved are as follows with the first three objectives & previous research enabling the final two: 1: Develop a Route Level of Service (LofS) measure: Currently used LofS measures are arbitrary. Our objective is to find the LofS of an optimized route for each aspect & a unified LofS to compare routes and identify improvements. . 2. Analyze a fully Autonomous Bus Route: Autonomous systems are expected to evolve rapidly. Our objective is to study a fully autonomous bus that will serve a long route on regular roads, for travel from many origins to many destinations.   3. Analyze Electric Bus (EB) Reliability: Buses need time-points where they are held if running early. Additionally, EB's require identified stops at which batteries must be recharged causing unreliability. Our objective is to determine the time points chosen to be battery charging points to reduce unreliability. 4. Model a Demand Responsive Transit (DRT) Local Network: DRT is key to providing sustainable service in low demand situations. Our objective is to investigate the conditions under which communities become suitable for DRT. 5. Model Major Transit Networks: Transit agencies revise networks every few years. Our objectives are to optimize irregular transit networks, & propose how & when to transition from an existing network to a significantly different network.  The focus of the Short Term Objectives: Objective functions will be developed to include passenger time costs plus operating, environmental & capital costs. An analytical approach is taken to develop normative models & undertake optimization & reliability analysis. We will first focus on three individual route aspects that are currently neglected & using those results, & other available research, study a local transit network and one major transit network. The methodology can be applied to any route/network & provide significant insight. A unique aspect is to investigate the use of an "optimal" amount of data based on normative modeling as opposed to "big" data. The novelty here is the use of normative, continuum, analytical models and mostly calculus-based optimization to obtain deeply penetrative understanding of the issues. The significance is the contribution to the mobility of Canadians given the increasingly important role of transit in a sustainable society.

城市公共交通系统的分析模型 城市公共交通在开发可持续交通系统方面发挥着至关重要的作用，该系统通过规模经济提供更多的流动性。该研究项目的长期目标是获得结果和见解，以改进设计、该研究计划将在五年内制定一种新颖的交通服务评估指标，并纳入新的技术进步（例如交通自动化），同时考虑交通服务和路线所需的调整。这些要实现的短期目标如下，其中前三个目标和之前的研究支持最后两个目标： 1：制定路线服务水平 (LofS) 衡量标准：目前使用的 LofS 衡量标准是我们的目标是找到每个方面的优化路线的 LofS 以及统一的 LofS 来比较路线并确定改进。 2. 分析完全自主的公交路线：我们的目标是快速发展。是研究一种完全自动驾驶的公交车，该公交车将在常规道路上提供长路线服务，用于从多个出发点到多个目的地的旅行。 3. 分析电动公交车 (EB) 可靠性：如果提前运行，公交车需要停顿的时间点。 EB 要求确定必须对电池进行充电的停靠站，从而导致不可靠性。我们的目标是确定选择作为电池充电点的时间点，以减少不可靠性。 4. 对本地需求响应交通 (DRT) 进行建模。网络：DRT 是在低需求情况下提供可持续服务的关键。我们的目标是调查社区适合 DRT 的条件。 5. 主要交通网络模型：交通机构每隔几年就会对网络进行优化。短期目标的重点：将制定目标函数，以包括乘客时间成本加上运营、环境和资本成本。制定规范模型并进行优化我们将首先关注目前被忽视的三个单独的路线方面，并利用这些结果和其他可用的研究，研究当地的交通网络和一个主要的交通网络，该方法可应用于任何路线/网络并提供重要的信息。一个独特的方面是研究基于规范建模的“最佳”数据量，而不是“大”数据，这里的新颖之处在于使用规范、连续、分析模型和主要基于微积分的优化。获得对问题的深刻理解。鉴于交通在可持续社会中日益重要的作用，这是对加拿大人流动性的贡献。