Abstract

Route planning represents a major challenge with a substantial impact on safety, economy, and even climate. An ever-growing urban population caused a significant increase in commuting times, therefore, stressing the prominence of efficient real-time route planning. In essence, the goal is to compute the fastest route to reach the target location in a realistic environment where traffic conditions are time-evolving. Consequently, a large volume of traffic data is potentially required and the route continuously updated. We thereby address the re-routing problem to answer questions such as when, how often, and where is re-routing worthwhile. We base our study on a real dataset, comprising the travel times of the road segments of New York, London, and Chicago, collected over three months. By exploiting this dataset, we implement an optimal algorithm, able to mimic ideal predictions of road segment speeds in the network. Thereby, allowing us to compute the lower bound of travel-time to serve as a reference against other routing techniques. Mainly, we quantify the achieved travel-time gain of a static, no re-routing, and continuous re-routing strategies. Surprisingly, we find that traffic conditions are sufficiently stable for short time windows, and re-routing a vehicle is very seldom useful when exploiting accurate statistics at departure time. Typically, real-time re-routing should only be triggered during rush hours, for long routes, passing through well-identified road segments.

摘要

路线规划是一项重大挑战，对安全、经济甚至气候产生重大影响。不断增长的城市人口导致通勤时间显着增加，因此强调了高效实时路线规划的重要性。本质上，目标是在交通状况随时间变化的现实环境中计算到达目标位置的最快路线。因此，可能需要大量的交通数据，并且路线会不断更新。因此，我们解决了重新路由问题，以回答诸如何时、多久和在哪里等问题重新路由是值得的。我们的研究基于一个真实的数据集，包括三个月内收集的纽约、伦敦和芝加哥路段的旅行时间。通过利用该数据集，我们实现了一种优化算法，能够模拟网络中路段速度的理想预测。因此，允许我们计算旅行时间的下限，以作为其他路由技术的参考。主要是，我们量化静态、无重路由和连续重路由策略实现的旅行时间增益。令人惊讶的是，我们发现交通状况对于短时间窗口足够稳定，并且在利用出发时间的准确统计数据时，重新规划车辆很少有用。通常，实时重新路由只应在高峰时段触发，对于长途路线，