A scaled and sustainable demand responsive transport service

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。私家车96%的时间处于闲置状态，占道路交通总二氧化碳排放量的60.7%。为了减少二氧化碳排放，同时减轻社会损失，连接服务差的地区，缓解老年人和残疾人负担得起交通的挑战，本研究建议开发提供可持续共享交通所需的数学工具，特别是需求响应交通服务（DRTS）。我们将设计新的算法来优化与DRTS动态定价相结合的路由和调度。实时解决这些大规模的硬组合优化问题，将使DRTS（规模化共享交通解决方案的新兴领域之一）的转型成为可能，从而鼓励更多的共享出行。DRTS允许乘客预订门到门的服务，要求接送时间，就像出租车一样，但与其他乘客共享一辆车，这些乘客可能会在沿途接送。类似的服务，如“叫车服务”，是为了满足特定的需求而存在的，但它们的范围被缩小了，并得到了地方议会和交通部的大量补贴。他们缺乏路线规划的灵活性，无法管理高需求。在规模上，有了优化的动态定价和路线、现实的需求预测、知情准确的行为模型，并受到提高接受度和诱导自愿行为改变的政策的激励，DRTS在财务上是可行的，比私家车使用更具可持续性。以高效、复杂的优化算法为形式的原始变革性科学，以及在本项目中开发的对共享出行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；从而促进共享出行，显著减少本地出行的二氧化碳排放。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、灵活、低碳的DRTS。1)针对不同类型车辆的高效调度和路线优化算法，可以对行程请求提供即时的接受/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，并在后台持续优化整个车队的调度。2)新的收益管理公式，允许旅程价格动态变化，价格取决于旅程长度和服务质量；因此，支持该服务的财政可持续性。3)对客户行为和偏好的丰富理解，这将通过开展调查和焦点小组，并使用收集到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并激励行为改变。将这三个工作组成部分结合起来，全面解决了实际挑战，并为共享绿色交通推进了一个令人兴奋的跨学科新研究领域。这种算法方法在未来也有可能适用于电动汽车和自动驾驶汽车。

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.