Graph-based deep learning for representing events at the Large Hadron Collider

联网和自动驾驶汽车有可能更好地利用现有的道路基础设施。该项目将通过向用户建议个性化路线并根据个人偏好进行优化，以及通过使用小额支付创建激励机制来遵循建议的路线来实现这一点。我们设想一个系统，其中不同的用户有不同的偏好，如旅行时间、拥堵和价格，以及环境因素，如污染。此外，还将利用机制设计来激励用户遵循建议的路线。特别是，该项目将整合以下几个方面的研究：1.拥堵预测。为了建议一条特定的路线，需要对拥堵进行预测。为了准确地做到这一点，系统将使用系统其他用户共享的路线和目的地来预测未来的拥堵情况。同时，系统需要考虑隐私，并通过设计确保隐私。这部分研究将建立在[1].2中发表的先前工作的基础上。建议优化。基于预测和个人用户的喜好，系统将提出最大化“社会福利”(即每个人的总效用)的路线建议。与现有工作相比，这里的新奇之处在于，不仅将考虑时间，还将考虑其他方面，如污染水平和电动汽车充电站的可用性。因此，这是一个多维优化问题。用于奖励的小额支付。该项目的这一部分将使用博弈论/机制设计技术来创建付款，以激励用户遵守建议。这项工作建立在机构设计在相关工作中的应用上，特别是在电动汽车充电方面(见[2])。这里的新奇之处在于，付款是“事后”的，即它们可以根据观察到的实际情况进行更改。例如，如果用户被重新路由到一条“更好”的路线，但结果出乎意料地拥堵，这将改变支付方式。

Connected and autonomous vehicles have the potential to make better use of the existing road infrastructure. This project will achieve this by suggesting personalised routes to users and optimising them based on personal preferences, and creating incentive mechanisms through the use of micropayments to follow the suggested routes. We envision a system where different users have different preferences, such as travel time, congestion and price, as well as environmental factors such as pollution. In addition, mechanism design will be used to incentivise users to follow the suggested route. In particular, the project will integrate the following strands of research:1. Congestion prediction. In order to suggest a certain route, prediction of congestion is needed. In order to do so accurately, the system will use routes and destinations shared by other users of the system to predict future congestion. At the same time, the system needs to consider privacy and ensuring a privacy-by-design approach. This part of the research will build on previous work published in [1].2. Suggestion optimisation. Based on the prediction and the personal preferences of individual users, the system will come up with route suggestions that maximise "social welfare" (i.e. the total utility of everyone). The novelty here compared to existing work is that, not only time, but other dimensions such as pollution levels and availability of EV charging stations, will be considered. Hence this is a multidimensional optimisation problem.3. Micro payments for incentives. This part of the project will use game theory/mechanism design techniques to create payments that incentivise users to adhere to the recommendation. This work builds on the mechanism design applications in related work, particularly on electric vehicle charging (see [2]). The novelty here is that the payments are "ex-post", i.e. they can change on the actual conditions observed. For example, if the user was rerouted to a "better" route but it turns out to be unexpectedly congested, this would change the payment.