A time-varying parameters vector auto-regression model to disentangle the time varying effects between drivers' responses and tolling on high occupancy toll facilities. (March 2018) - Ma, Xiaolei; Sun, Shuo; Liu, Xiaoyue Cathy; Ding, Chuan; Chen, Zhuo; Wang, Yunpeng
Highlights: We model the dynamic feedback mechanism between drivers' responses and toll rates. A Time Varying VAR model is proposed to capture the time-varying interdependency. Empirical data from SR167, WA are used for model validation. The time-varying effects between drivers' responses and toll rates are qualified. Drivers' perceptions on HOT lanes within distinct geographic locations vary. Abstract: High Occupancy Toll (HOT) lane systems are considered one of most effective countermeasures to mitigate freeway congestion. Existing studies have largely focused on developing optimal tolling strategies to maximize the benefits of congestion pricing. Limited effort has been made to model the dynamic feedback mechanism of drivers' responses to tolling. A thorough understanding of how the interactive relationship between demands (in both HOT lane and general purpose lanes) and toll rates evolves over time is necessary. The underlying mechanism can be used directly for guiding future HOT facilities investment decisions. This study builds upon the traditional vector autoregressive model and enables its parameters to be time-varying. Such a relaxation, namely, time-varying parameter vector autoregressive model (TVP-VAR), is used to answer the following two questions: (1) Is there a time varying effect between general purpose lane volume, HOT lane volume and dynamic toll rate? (2) If there is, how to quantify such time-varying interdependencies? Based on the empirical data from loop detectors and toll logs on Washington State Route 167 (SR167), we identified the existence of time-varying effects between drivers' responses and toll rates, and quantified the evolving interactions amongst HOT demand, general purpose demand and tolling via time-varying impulse responses. In addition, we found that drivers' perceptions on HOT lanes across distinct geographical locations are significantly different.