论文标题
元关节宏观量表交通流量优化来自城市运动数据
Metaheuristic macro scale traffic flow optimisation from urban movement data
论文作者
论文摘要
如何利用城市运动数据以改善城市中的交通流量？移动数据提供了有关人们可能会行驶的路线和特定道路的宝贵信息。这使我们能够查明在许多路线中发生的道路，因此对拥堵敏感。重新分配一些流量以避免不必要的道路使用可能是改善交通流量的关键因素。许多拟议的打击拥塞方法是静态的，要么不包含任何运动数据。在这项工作中，我们提出了一种通过向每个道路领域引入外部施加的可变成本来重新分配流量的方法，假设所有驾驶员都试图驾驶最便宜的路线。我们使用一种元启发式优化方法来最大程度地减少总旅行时间，以优化一组特定道路的可变成本参数，这些成本参数被用作基于交通流理论的目标函数的输入。本文考虑的东京市中心的优化方案是使用公共空间道路网络数据定义的，并从Foursquare获取了动作数据。实验结果表明，与当前运营的道路网络配置相比，我们提出的方案有可能在东京实现62.6 \％的总旅行时间改善，而没有施加可变成本。
How can urban movement data be exploited in order to improve the flow of traffic within a city? Movement data provides valuable information about routes and specific roads that people are likely to drive on. This allows us to pinpoint roads that occur in many routes and are thus sensitive to congestion. Redistributing some of the traffic to avoid unnecessary use of these roads could be a key factor in improving traffic flow. Many proposed approaches to combat congestion are either static or do not incorporate any movement data. In this work, we present a method to redistribute traffic through the introduction of externally imposed variable costs to each road segment, assuming that all drivers seek to drive the cheapest route. We use a metaheuristic optimisation approach to minimise total travel times by optimising a set of road-specific variable cost parameters, which are used as input for an objective function based on traffic flow theory. The optimisation scenario for the city centre of Tokyo considered in this paper was defined using public spatial road network data, and movement data acquired from Foursquare. Experimental results show that our proposed scenario has the potential to achieve a 62.6\% improvement of total travel time in Tokyo compared to that of a currently operational road network configuration, with no imposed variable costs.
