Traffic in future cities: network optimisation under new mobility services

据估计，2016年英国的城市拥堵造成了308亿英镑的损失，英国在最拥堵的发达国家中排名第四。CBI 2015年基础设施报告的标题是“96%的公司担心道路网络的拥堵”，而拥堵成本预计将变得更糟：根据CEBR最近的一份报告，“从2013年到2030年，道路拥堵的年成本预计将上升63%”。自动驾驶汽车和智能移动服务的到来可能会进一步增加出行需求，但这些技术进步也为优化现有基础设施的使用开辟了新的途径。这是拟议项目的重点。开发模型和分析方法，以解决未来城市的拥堵和提高机动性，并了解交通网络的性能如何取决于其拓扑结构。这些发现将有助于制定更好的战略，以实现提高经济生产力、减少污染和改善城市人口福祉的更广泛目标。大量的学术研究涉及城市道路网的最佳设计和管理，以尽量减少延误、排放和燃料消耗。离散网络设计问题（DNDP）通常与道路建设（在哪里添加新的链接）有关。技术进步将扩大运输供应，并为优化网络带来新的机会。智能车辆路由和V2I（车对基础设施）通信将允许网络动态重新配置，以满足当前的出行需求。(i)车辆的路线可以尽量减少整个系统的旅行时间；目前，每个人都寻求最小化他们的个人旅行时间（即用户平衡），这不是系统最优的。（ii）道路连接/行车线的方向可以逆转（即整个网络的逆流），以适应网络流量和车辆实时改道。动态优化现有基础设施容量的使用是经济的，并避免增加城市空间用于交通供应。与此同时，旅行者将从各种出行服务中进行选择：不仅是私家车和公共交通，还需要响应性交通和拼车服务。（iii）拼车在减少交通挤塞方面有很大的潜力（取决于路线、空车量和对出行需求的反应）。开发一个优化框架来解决带有链路/车道反转的DNDP（适用于用户平衡和系统最优车辆路线）。在(1)下确定不同需求和供给配置下的效率增益。解决拼车的交通分配问题，并制定策略，通过激励乘客和拼车运营商遵循系统最优路径，从而改善网络层面的路线流量，从而最大限度地提高效率（从1）。在实施干线/车道反转时，需要为所有旅客保留可行的路线；这种约束的影响取决于网络拓扑（考虑网格和树形网络），并将影响（潜在的）效率增益。跨不同网络拓扑结构的基础设施优化是一个新兴的工作领域，其中目标1是原始的。这种方法为目标2奠定了基础，并将在目标3中进行扩展，以展示如何将拼车纳入运输系统，从而减少网络范围内的拥堵，从而使整个社会受益。1 http://inrix.com/scorecard/（访问日期：25/01/2018）2 http://www.cbi.org.uk/news/infrastructure-survey-2015/infrastructure-survey-2015/（访问日期：25/01/2018）

Urban congestion in the UK was estimated to cost £30.8bn in 2016, with the UK ranked as 4th most congested developed country1. The CBI 2015 infrastructure report2 headlined "96% of firms concerned about congestion on the road network", and congestion costs are forecast to get worse: "From 2013 to 2030, the annual cost of road congestion is forecast to rise by 63%" according to a recent CEBR report3. The arrival of autonomous vehicles and intelligent mobility services is likely to further increase travel demand, however these advances in technology also open up new ways to optimise the use of existing infrastructure. This is the focus of the proposed project. To develop models and analytical methods to tackle congestion and improve mobility in future cities, and gain understanding of how transport network performance depends on its topology. These findings will enable better strategies to be developed for broader aims of increasing economic productivity, reducing pollution and improving the wellbeing of the urban population.A substantial volume of academic research concerns the optimal design and management of urban road networks to minimize delays, emissions and fuel consumption. The discrete network design problem (DNDP) is usually associated with road construction (where to add a new link). Technological advances will broaden transport provision and lead to new opportunities for network optimization. Intelligent vehicle routing and V2I (Vehicle-to-Infrastructure) communication will allow the network to be dynamically reconfigured to meet current travel demands. (i) Vehicles could be routed in order to minimize total system travel time; currently each individual seeks to minimize their individual travel time (i.e. user equilibrium), which is not system-optimal. (ii) The direction of road link/traffic lanes can be reversed (i.e. network-wide contraflow) to accommodate network flows and vehicles rerouted in real time. Dynamically optimising the use of existing infrastructure capacity is economical and avoids increasing use of urban space for transport provision. Meanwhile, travellers will select from a variety of mobility services: not only private car and mass public transit, but demand responsive transport and ride sharing services. (iii) Ride sharing has significant potential to reduce congestion (depending on routing, volume of empty trips and the response of travel demand).Objectives1. Develop an optimisation framework to solve the DNDP with link/lane reversal (applicable to both user equilibrium and system optimal vehicle routing).2. Under (1) determine the efficiency gain under different configurations of demand and supply.3. Solve the traffic assignment problem with ride sharing and develop strategies to maximise efficiency gains (from 1) by incentivising travellers and ridesharing operators to follow system optimal paths and hence improve network-level route flows.When implementing link/lane reversal, feasible routes need to be maintained for all travellers; the impact of this constraint depends on the network topology (consider grid versus tree network), and will affect (potential) efficiency gains. Infrastructure optimisation across different network topologies is a newly emerging area of work in which Objective 1 is original. This approach establishes the foundation for Objective 2, which will be extended in Objective 3 to demonstrate how ridesharing can be incorporated into transport systems in a way that reduces network-wide congestion and consequently benefits society as a whole.1 http://inrix.com/scorecard/ (accessed 25/01/2018) 2 http://www.cbi.org.uk/news/infrastructure-survey-2015/infrastructure-survey-2015/ (accessed 25/01/2018)