A scaled and sustainable demand responsive transport service

规模化且可持续的需求响应运输服务

• 批准号: EP/X020835/1
• 负责人: Rym M'Hallah
• 金额: $ 118.77万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX020835%2F1
• 关键词: scaled; sustainable; demand; responsive; transport

Private mobility has a high carbon footprint due to the manufacturing, use, storage and disposal of vehicles. Private cars spend 96% of their time idle and were responsible for 60.7% of total CO2 emissions from road transport. To reduce CO2 emissions while mitigating societal loss, linking poorly served geographies and alleviating the challenges of elderly and disabled to afford mobility, this research proposes the development of the mathematical tools needed to deliver sustainable, shared mobility, specifically a Demand Responsive Transport Service (DRTS). We will design novel algorithms that optimise the routing and scheduling integrated with dynamic pricing of DRTS. Solving these large-scale hard combinatorial optimisation problems, in real time, will enable a transformation of DRTS, part of the emerging sector of scaled shared transport solutions, encouraging increased take up of shared mobility. DRTS allows passengers to book a door-to-door service requesting pick up or drop off times, much like a taxi, but sharing a vehicle with other passengers that may be collected or dropped off along the route. Similar services, such as Dial-a-Ride, exist to meet specific needs but they are reduced in scope and heavily subsidized by local councils and the Department for Transport. They lack route planning flexibility and cannot manage high demand. At scale, with optimized dynamic pricing and routing, realistic demand forecasts, informed accurate behavioural models, and incentivised by policies that enhance their acceptance and induce voluntary behaviour changes, DRTS would be financially viable and more sustainable than private car use. The original transformative science in the form of efficient, complex optimization algorithms, and the rich understanding of preferences and attitudes towards shared mobility developed in this project will help enable DRTS to be both efficient and cost-effective; thus, promoting shared mobility and significantly reducing CO2 emission of local travel.This project will integrate three important scientific components to deliver an attractive, flexible, low-carbon DRTS. 1) An effective efficient scheduling and routing optimisation algorithm for a fleet of vehicles of different types that can provide instant accept/reject decisions on journey requests. In order to do this effectively, the algorithm needs to anticipate potential future demand and be continuously globally optimising schedules across the fleet in the background. 2) New revenue management formulations that allow the prices of journeys to be changed dynamically, with prices dependent on journey length and service quality; thus, supporting the financial sustainability of the service. 3) A rich understanding of customer behaviour and preferences, which will be obtained by running surveys and focus groups and using the data collected to build choice models, describing how potential passengers make decisions. These models will support service design and motivate behaviour changes.Combining these three components of work comprehensively addresses the practical challenge and advances an exciting new interdisciplinary research area for shared green transportation. The algorithmic approach also has the potential to be adapted to electric and autonomous vehicles in the future.

由于车辆的制造、使用、储存和处置，私人交通具有很高的碳足迹。私人汽车96%的时间处于闲置状态，占道路运输二氧化碳排放总量的60.7%。为了减少二氧化碳排放，同时减轻社会损失，连接服务差的地理区域，减轻老年人和残疾人负担出行的挑战，本研究提出开发提供可持续的共享出行所需的数学工具，特别是需求响应式运输服务（DRTS）。我们将设计新的算法，优化路由和调度集成动态定价的DRTS。解决这些大规模的硬组合优化问题，在真实的时间，将使DRTS的转型，规模共享交通解决方案的新兴部门的一部分，鼓励增加采取共享移动。DRTS允许乘客预订门到门服务，要求上车或下车时间，很像出租车，但与其他乘客共用一辆车，可能会收集或下车沿着。类似的服务，如拨号乘车，是为了满足特定需求而存在的，但它们的范围缩小了，并得到了地方议会和运输部的大量补贴。他们缺乏路线规划的灵活性，无法管理高需求。在规模上，通过优化的动态定价和路线，现实的需求预测，知情的准确行为模型，以及提高其接受程度和诱导自愿行为改变的政策激励，DRTS在财务上是可行的，并且比私家车使用更可持续。以高效、复杂的优化算法为形式的原始变革性科学，以及对该项目中开发的共享移动偏好和态度的丰富理解，将有助于使DRTS既高效又具有成本效益;因此，促进共享移动性和显著减少当地旅行的二氧化碳排放。该项目将整合三个重要的科学组成部分，以提供一个有吸引力的，灵活的，低碳DRTS 1)一种有效的调度和路由优化算法，用于不同类型的车队，可以对行程请求提供即时接受/拒绝决策。为了有效地做到这一点，该算法需要预测潜在的未来需求，并在后台不断全局优化整个车队的时间表。2)新的收入管理公式允许动态改变旅行价格，价格取决于旅行长度和服务质量，从而支持服务的财务可持续性。3)对客户行为和偏好的深入了解，将通过调查和焦点小组获得，并使用收集的数据建立选择模型，描述潜在乘客如何做出决定。这些模型将支持服务设计和激励行为改变。结合这三个组成部分的工作全面解决了实际挑战，并推进了一个令人兴奋的新的跨学科研究领域的共享绿色交通。这种算法方法也有可能在未来适用于电动和自动驾驶汽车。