TY - JOUR
T1 - A stochastic analysis of highway capacity
T2 - Empirical evidence and implications
AU - Dong, Shangjia
AU - Mostafizi, Alireza
AU - Wang, Haizhong
AU - Li, Jia
N1 - Publisher Copyright:
© 2017, © 2017 Taylor and Francis.
PY - 2018/7/4
Y1 - 2018/7/4
N2 - This paper presents a stochastic characterization of highway capacity and explores its implications on ramp metering control at the corridor level. The stochastic variation of highway capacity is captured through a Space–Time Autoregressive Integrated Moving Average (STARIMA) model. It is identified following a Seasonal STARIMA model (0, 0, 23) × (0, 1, 0)2, which indicates that the capacities of adjacent locations are spatially–temporally correlated. Hourly capacity patterns further verify the stochastic nature of highway capacity. The goal of this paper is to study (1) how to take advantage of the extra information, such as capacity variation, and (2) what benefits can be gained from stochastic capacity modeling. The implication of stochastic capacity is investigated through a ramp metering case study. A mean–standard deviation formulation of capacity is proposed to achieve the trade-off between traffic operation efficiency and robustness. Following that, a modified stochastic capacity-constraint ZONE ramp metering scheme embedded cell transmission model algorithm is introduced. The numerical experiment suggests that considering capacity variation information would alleviate the spillback effect and improve throughput. Monte Carlo simulation further supports this argument. This study helps verify and characterize the stochastic nature of capacity, validates the benefits of using capacity variation information, and thus enhances the necessity of implementing stochastic capacity in traffic operation.
AB - This paper presents a stochastic characterization of highway capacity and explores its implications on ramp metering control at the corridor level. The stochastic variation of highway capacity is captured through a Space–Time Autoregressive Integrated Moving Average (STARIMA) model. It is identified following a Seasonal STARIMA model (0, 0, 23) × (0, 1, 0)2, which indicates that the capacities of adjacent locations are spatially–temporally correlated. Hourly capacity patterns further verify the stochastic nature of highway capacity. The goal of this paper is to study (1) how to take advantage of the extra information, such as capacity variation, and (2) what benefits can be gained from stochastic capacity modeling. The implication of stochastic capacity is investigated through a ramp metering case study. A mean–standard deviation formulation of capacity is proposed to achieve the trade-off between traffic operation efficiency and robustness. Following that, a modified stochastic capacity-constraint ZONE ramp metering scheme embedded cell transmission model algorithm is introduced. The numerical experiment suggests that considering capacity variation information would alleviate the spillback effect and improve throughput. Monte Carlo simulation further supports this argument. This study helps verify and characterize the stochastic nature of capacity, validates the benefits of using capacity variation information, and thus enhances the necessity of implementing stochastic capacity in traffic operation.
KW - mean–standard deviation trade-off
KW - ramp metering
KW - space–time ARIMA
KW - stochastic capacity
UR - http://www.scopus.com/inward/record.url?scp=85051747079&partnerID=8YFLogxK
U2 - 10.1080/15472450.2017.1396898
DO - 10.1080/15472450.2017.1396898
M3 - Article
AN - SCOPUS:85051747079
SN - 1547-2450
VL - 22
SP - 338
EP - 352
JO - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
JF - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
IS - 4
ER -