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Unravelling System Optimums by trajectory data analysis and machine learning
Transportation Research Part C: Emerging Technologies ( IF 8.3 ) Pub Date : 2021-08-02 , DOI: 10.1016/j.trc.2021.103318
Ruiwei Chen , Ludovic Leclercq , Mostafa Ameli

This work investigates network-related trajectory features to unravel trips that contribute most to system under-performance. When such trips are identified, feature analysis also permits determining the best alternatives in terms of routes to bring the system to its optimum. First, we define a combination of network-related trajectory features that helps us unravel the critical trips which contribute the most to the network under-performance, based on the literature review and a factor selection process. Second, based on supervised learning methods, we propose a two-step data-driven methodological framework to reroute a part of the users and make the system close to its optimum. The learning models are trained with trajectory features to identify which users should be selected, and which alternative routes should be assigned, thanks to the data and features obtained from two reference dynamic traffic assignment (DTA) simulations, under User-Equilibrium (UE) and System-Optimum (SO). We only focus on trajectory features that are accessible in real time, such as network features and regular travel time information, so that the methods proposed can be implemented without requiring cumbersome network monitoring and prediction. Finally, we evaluate the efficiency of the methods proposed through microscopic DTA simulations. The results show that by targeting 20% of the users according to our selection model and moving them onto paths predicted as optimal alternative paths based on our rerouting model, the total travel time (TTT) of the system is reduced by 5.9% in comparison to a UE DTA simulation. This represents 62.5% of the potential TTT reduction from UE to SO, when all the users choose their path under the SO condition.



中文翻译:

通过轨迹数据分析和机器学习解开系统优化

这项工作调查了与网络相关的轨迹特征,以解开对系统性能欠佳贡献最大的行程。当识别出此类行程时,特征分析还允许确定路线方面的最佳替代方案,以使系统达到最佳状态。首先,我们定义了与网络相关的轨迹特征的组合,根据文献综述和因子选择过程,帮助我们解开对网络性能不佳贡献最大的关键行程。其次,基于监督学习方法,我们提出了一个两步数据驱动的方法框架来重新路由部分用户并使系统接近其最佳状态。学习模型使用轨迹特征进行训练,以确定应该选择哪些用户,以及应该分配哪些替代路线,得益于从用户均衡 (UE) 和系统优化 (SO) 下的两个参考动态流量分配 (DTA) 模拟中获得的数据和特征。我们只关注可实时访问的轨迹特征,例如网络特征和常规旅行时间信息,因此所提出的方法可以在不需要繁琐的网络监控和预测的情况下实施。最后,我们通过微观 DTA 模拟评估了所提出方法的效率。结果表明,通过根据我们的选择模型定位 20% 的用户并将他们移动到基于我们的重新路由模型预测为最佳替代路径的路径上,与相比,系统的总旅行时间 (TTT) 减少了 5.9% UE DTA 模拟。这代表了从 UE 到 SO 的潜在 TTT 减少的 62.5%,

更新日期:2021-08-03
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