Estimating freeway travel time and its reliability using radar sensor data

Abstract

Travel time and its reliability are intuitive system performance measures for freeway traffic operations. This paper proposes a method to estimate travel times based on data collected from roadside radar sensors, considering spatially correlated traffic conditions. Link-level and corridor-level travel time distributions are estimated using these travel time estimates and compared with the ones estimated based on probe vehicle data. The maximum likelihood estimation is used to estimate the parameters of Weibull, gamma, normal, and lognormal distributions. According to the log likelihood values, lognormal distribution is the best fit among all the tested distributions. Corridor-level travel time reliability measures are extracted from the travel time distributions. The proposed travel time estimation model can well capture the temporal pattern of travel time and its distribution.

Calculation of Urban Transportation Network Reliability Considering Correlation Among the Links Comprising a Route

“…Most existing studies on the travel time distribution (TTD) have put significant efforts into identifying the best fitting model. The fitted distributions can be classified as single-mode distributions [11][12][13][14][15][16][17][18] and other distributions (multi-mode distribution and truncated distribution) [19][20][21]. Herman and Lam [4] suggested gamma or log-normal distribution for TTD.…”

“…Moreover, knowing the reliability, one can determine total travel time over each route using Equation (16). The corresponding results to reliabilities of 80%.…”

Incorporating the standstill distance and time headway distributions into freeway car-following models and an application to estimating freeway travel time reliability

“…In this study, probe vehicle travel time data were queried from the Regional Integrated Transportation Information System (RITIS, 2016), which archives INRIX probe vehicle data at I-minute aggregation intervals (Lu & Dong, 2018). This dataset provides time-stamped segment-based speeds, travel times, historical average speed, free-flow speed, and confidence scores.…”

A Study on Travel Time Estimation of Diverging Traffic Stream on Highways Based on Timestamp Data

“…Travel time information can be obtained indirectly or directly. e indirect ways usually estimate travel time based on traffic flow and speed measured by point sensors on the roadside such as loop detectors, video cameras, and radars [8][9][10]. e direct ways measure the travel time using records at tollgates and roadside units (RSU), which are parts of automatic vehicle identification (AVI) technologies [11].…”