Price Optimisation In Networks for Transport with Signals using Artificial Intelligence (PointsAI)

该项目将利用进化人工智能来解决优化道路运输网络中的价格和信号的问题。设置道路价格和信号控制设置的能力是交通管理工具，可用于优化网络中的交通流量。然而，对价格和信号的改变将影响人们的旅行决策，因为人们有动力最大限度地减少自己的旅行成本（包括旅行时间和金钱成本）。这意味着，如果有便宜的路线，旅行者将转向这些路线。这使得优化控制变量的问题网络中的交通流被优化的程度需要相对于某些目标进行评估，例如最小化队列或减少污染，并且目标函数的使用可以用于精确地量化目标被满足的程度。遗传算法是一种人工智能技术，自然选择的过程来寻找解决问题的方法。它基于适者生存的思想;最适者生存的个体（即最有希望解决问题的个体）是那些最有可能生存和繁殖的个体。这种繁殖涉及以随机的方式从父母那里选择“染色体”，因此后代是两个“父母”解决方案的混合。在道路交通网络模型中采用这些控制设置，并调整流量（朝向成本较低的路线），直到找到用户平衡流量（当不再有路线交换时发生），从而评估特定解决方案（即特定价格和信号设置）的性能。基于这些用户平衡流的目标函数的值（其例如可以是网络中的所有队列长度的总和）给出解决方案的性能的指示。用于此目的的道路交通网络模型将是计算机模拟模型，目前正在进行的开发由RBM交通解决方案有限公司。该模型可以快速找到用户的平衡流量，这使得它非常适合大量的模型运行时，将需要实施遗传算法优化方法。

The project will utilise evolutionary artificial intelligence to address the problem of optimising prices and signals in road transport networks. The ability to set road prices and signal control settings are traffic management tools which can be utilised to optimise transport flows in the network. However, making changes to prices and signals will impact people's travel decisions, since individuals are motivated to minimise their own travel cost (which includes travel time as well as monetary cost). This means that travellers will swap to less costly routes should they be available. This makes the problem of optimising the control variables (for example the prices and signals) nonlinear and difficult to solve.The degree to which the traffic flows in a network are optimised needs to be assessed with respect to certain objectives, such as for example minimising queues or reducing pollution, and the use of an objective function can be used to precisely quantify the degree to which the objectives are met.A genetic algorithm is an artificial intelligence technique which uses a process of natural selection to find solutions to a problem. It is based on the idea of survival of the fittest; the fittest individuals (that is, the most promising solutions to the problem) being those most likely to survive and reproduce. This reproduction involves selection of 'chromosomes' from the parents in a random way, so that the offspring is a mix of the two 'parent' solutions. When repeated, this process generally leads to improving solutions over time.The performance of a particular solution (i.e. the particular price and signal settings) will be evaluated by adopting these control settings within a road traffic network model and adjusting flows (towards less costly routes) until the user equilibrium flows are found (which occurs when there is no more route swapping). The value of the objective function (which for example could be the sum of all the lengths of the queues in the network) based on these user equilibrium flows gives an indication of the performance of the solution. The road traffic network model utilised for this purpose will be the computer simulation model which is currently under ongoing development by RBM Traffic Solutions Ltd. This model can quickly find the user equilibrium flows, which makes it ideal for the large succession of model runs which will be needed when implementing the genetic algorithm optimisation approach.