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Predictive distance-based road pricing — Designing tolling zones through unsupervised learning
Transportation Research Part A: Policy and Practice ( IF 6.3 ) Pub Date : 2023-02-13 , DOI: 10.1016/j.tra.2023.103611
Antonis F. Lentzakis , Ravi Seshadri , Moshe Ben-Akiva

Congestion pricing is a standard approach to mitigate traffic congestion in a number of urban networks around the world. The advancement of satellite technology has spurred interest in distance-based congestion pricing schemes, which obviate the need for fixed infrastructure such as gantries that are used in area- and cordon-based pricing. Moreover, distance-based pricing has the potential to more effectively manage traffic congestion. In the context of distance-based congestion pricing, we propose the use of sparse subspace clustering methods employing Elastic Net optimization (SSCEL) and Orthogonal Matching Pursuit (SSCOMP), as well as two hierarchical density-based clustering methods, (OPTICS, HDBSCAN*) for the derivation of tolling zones. These tolling zones are then used within a simulation-based framework for real-time predictive distance-based toll optimization to examine network congestion and performance of the tolling schemes. Within this framework, for a given definition of tolling zones, tolling function parameters are optimized in real-time using a simulation-based Dynamic Traffic Assignment (DTA) model. Guidance information generation is integrated into the predictive optimization framework and behavioral responses to the information and tolls along dimensions of departure time, route, mode, and trip cancellation are explicitly modeled. For the evaluation of network performance we make use of Travel Speed Index (TSI) data from the real-world Boston Central Business District urban network and demonstrate that tolling zones derived from the sparse subspace clustering are an effective means of operationalizing real-time distance-based toll optimization schemes, showing improvements in average travel time and social welfare relative to the baseline.



中文翻译:

基于距离的预测性道路定价——通过无监督学习设计收费区

拥堵收费是缓解全球许多城市网络交通拥堵的标准方法。卫星技术的进步激发了人们对基于距离的拥堵定价方案的兴趣,该方案消除了对固定基础设施的需求,例如用于区域和警戒线定价的龙门架。此外,基于距离的定价有可能更有效地管理交通拥堵。在基于距离的拥塞定价的背景下,我们建议使用采用弹性网络优化 (SSCEL) 和正交匹配追踪 (SSCOMP) 的稀疏子空间聚类方法,以及两种基于层次密度的聚类方法(OPTICS、HDBSCAN* ) 用于收费区的推导。然后,这些收费区将在基于仿真的框架内用于实时预测基于距离的通行费优化,以检查网络拥塞和收费方案的性能。在此框架内,对于给定的收费区定义,使用基于仿真的动态交通分配 (DTA) 模型实时优化收费功能参数。指导信息的生成被集成到预测优化框架中,并且对信息的行为响应和沿出发时间、路线、模式和行程取消的维度的通行费进行了明确建模。

更新日期:2023-02-14
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