Incident-Induced Delays on Freeways

Abstract

Freeway incidents cause nonrecurrent congestion, which contributes significantly to overall travel delays. This paper provides a method to estimate the magnitude of delays resulting from incidents. Incident-induced delays can be estimated by establishing an incident-free reference travel time profile as a basis from which the incident-induced delay is measured. The main objective of this paper is to show how to establish the reference profile from a subset of candidate profiles of similar traffic patterns. To identify similar traffic patterns, a hybrid of travel time and volume profiles was used. To establish the reference profile, three methods for candidate profile identification were investigated: day of the week based, cluster analysis based, and k–nearest neighbor based. Weighted summations of the candidate profiles were also investigated: arithmetic average, Shepard's inverse distance method, and rank exponent method. A ground truth experiment was conducted, and the results showed that the best way to formulate the reference traffic profile was by using the k–nearest neighbor in combination with the weighted summation of rank exponent method. The proposed approach provides practical guidance to estimate incident-induced delays more accurately.

Impact of Data Loss on Multi-Step Forecast of Traffic Flow in Urban Roads Using K-Nearest Neighbors

“…Averaging the k nearest neighbors is often the common way used to predict future states. Though, many other approaches were also applied in the literature to obtain forecasts, including weighting the k nearest neighbors according to their distance to current state vector [33] and Gaussian-weight distance [12,13].…”

A Data-Driven Approach to Estimate Incident-Induced Delays Using Incomplete Probe Vehicle Data: Application to Safety Service Patrol Program Evaluation

“…Terefore, rapid incident detection, response, and cleanup are crucial to mitigate the negative impact of incidents. Consequently, transportation agencies have implemented various trafc incident management programs to better manage road incidents [1,2].…”

“…Terefore, accurately quantifying IIDs is critical to evaluating trafc incident management programs. Various methods have been proposed to estimate IID in the literature, including the deterministic queueing theory [3][4][5][6], simulation [7,8], and statistical analysis [1,[9][10][11][12][13][14]. Te method of the deterministic queueing theory estimates the IID by assuming constant trafc demand and reduced capacity caused by the incident.…”

“…Developing and calibrating simulation models are usually costly and time consuming [9]. To address the shortcomings of the methods mentioned above, data-driven approaches have been proposed using traveltime data [1,[9][10][11][12][13][14]. Data-driven IID calculation methods use trafc data from various sources to estimate the impact of incidents on the trafc fow.…”

“…Data-driven IID calculation methods use trafc data from various sources to estimate the impact of incidents on the trafc fow. For example, Habtemichael et al calculated the IID based on travel time diferences between an incident profle and a reference profle representing the normal trafc condition [1]. Te core of this method is to identify the reference profles for each incident.…”