TY - JOUR
T1 - A time-varying parameters vector auto-regression model to disentangle the time varying effects between drivers’ responses and tolling on high occupancy toll facilities
AU - Ma, Xiaolei
AU - Sun, Shuo
AU - Liu, Xiaoyue Cathy
AU - Ding, Chuan
AU - Chen, Zhuo
AU - Wang, Yunpeng
N1 - Funding Information:
This paper is supported by the National Natural Science Foundation of China ( 61773036 and U1564212 ), Beijing Natural Science Foundation ( 9172011 ) and Young Elite Scientist Sponsorship Program by the China Association for Science and Technology ( 2016QNRC001 ).
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/3
Y1 - 2018/3
N2 - 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.
AB - 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.
KW - High Occupancy Toll (HOT)
KW - Impulse response function
KW - Time series
KW - Time-varying parameter vector autoregressive model
KW - Tolling
UR - http://www.scopus.com/inward/record.url?scp=85044655977&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2018.01.025
DO - 10.1016/j.trc.2018.01.025
M3 - Article
AN - SCOPUS:85044655977
SN - 0968-090X
VL - 88
SP - 208
EP - 226
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
ER -