Koohong Chung

TEST OF THEORY OF DRIVER BEHAVIOR ON HOMOGENEOUS FREEWAYS

A theory that can explain even some of the more puzzling aspects of freeway traffic flow is tested here. Some of the observations furnished to this end pertain to dynamic features of traffic that have not been previously verified with data. These include findings that (a) freeway traffic can enter a semicongested state marked by a fast-moving queue in the passing lane with unqueued flow in the shoulder lane; (b) the possible devolution of traffic to a fully congested state when vehicle speeds in the passing lane drop below those in the shoulder lane supports the contention that freeway capacity reductions can be caused by loss of driver motivation; and (c) the transition between semicongested and fully congested traffic can propagate upstream at velocities much higher than those of ordinary kinematic waves. These and other findings are in agreement with the theory, which suggests that the theory is a reasonable description of certain driver behavior.

Surrogate safety measure for evaluating rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks

This study presents a surrogate safety measure for evaluating the rear-end collision risk related to kinematic waves near freeway recurrent bottlenecks using aggregated traffic data from ordinary loop detectors. The attributes of kinematic waves that accompany rear-end collisions and the traffic conditions at detector stations spanning the collision locations were examined to develop the rear-end collision risk index (RCRI). Together with RCRI, standard deviations in occupancy were used to develop a logistic regression model for estimating rear-end collision likelihood near freeway recurrent bottlenecks in real-time. The parameters in the logistic regression models were calibrated using collision data gathered from the 6-mile study site between 2006 and 2007. Findings indicated that an additional unit increase in RCRI results in increasing the odds of rear-end collision by 21.1%, a unit increase in standard deviation of upstream occupancy increases the odds by 19.5%, and a unit increase in standard deviation of downstream occupancy increases the odds by 18.7%. The likelihood of rear-end collisions is highest when the traffic approaching from upstream is near capacity state while downstream traffic is highly congested. The paper also reports on the findings from comparing the predicted number of rear-end collisions at the study site using the proposed model with the observed traffic collision data from 2008. The proposed models true positive rates were higher than those of existing real-time crash prediction models.

The Continuous Risk Profile Approach for the Identification of High Collision Concentration Locations on Congested Highways

This paper documents a new method for monitoring traffic collision data from con- tinuous roadway facilities to detect high collision concentration locations. Many exist- ing methods for detecting collision concentration locations require segmentation of roadways and assume traffic collision data are spatially uncorrelated, resulting in both false positives (i.e., identifying sites for safety improvements that should not have been selected) and false negatives (i.e., not identifying sites that should have been se- lected). The proposed method does not require segmentation of roadways; spatial cor- relation in the collision data does not affect the results of analysis. This new method has a lower false positive rate than the conventional sliding moving window approach. This paper shows how the proposed method can proactively identify high col- lision concentration locations and capture the benefit of safety improvements observed in the project location and in neighboring sites.

Evaluating the performance of network screening methods for detecting high collision concentration locations on highways

This paper documents findings from evaluating performances of three different methods for segmenting freeway sites for the purpose of identifying high collision concentration locations: Sliding Moving Window (SMW), Peak Searching (PS) and Continuous Risk Profile (CRP). The traffic collision data from sites segmented in each method under two different roadway definitions were used to estimate excess expected average crash frequency with Empirical Bayes adjustment with respect to two different sets of Safety Performance Functions (SPFs). The estimates from each of the methods were then used to prioritize the detected sites for safety investigation and these lists were compared with previously confirmed high collision concentration locations (or hot spots). The input requirements for each of three methods were identical, yet their performance markedly varied. The findings revealed that CRP method has the lowest false positive (i.e., requiring a site for safety investigation while it is not needed) rate. The performances of SMW and PS significantly varied when different sets of SPFs were used while that of CRP was less affected.

Safety Performance of High-Occupancy Vehicle (HOV) Facilities: Evaluation of HOV Lane Configurations in California

Collision data from High Occupancy Vehicle (HOV) facilities with two different types of access, continuous and limited, are examined in this paper. The findings show that HOV facilities with limited access offer no safety advantages over those with a continuous access. Compared with continuous access HOV lanes, a higher percentage of collisions was concentrated on limited access HOV lanes. Limited access HOV lanes also had higher collision rates. Findings from investigating the relationship between collision rates in HOV lanes with respect to shoulder width, length of access, and the proximity of access to its neighboring ramps are also documented. These findings provide enhanced understanding about the effects of geometric factors on the collision rates in HOV lanes.

Collisions in Freeway Traffic: Influence of Downstream Queues and Interim Means to Address Them

Findings from previous studies indicate that a freeway traffic collision is more likely to occur in close physical proximity to the tail of a queue. The implication is that collision likelihood increases when drivers abruptly alter their trajectories (e.g., by decelerating or changing lanes) on encountering the queue. The implication is supported and bolstered with new and detailed data that were painstakingly extracted from two freeway stretches in California. These data show how the likelihood of collision increases as both the spatial and the temporal proximities to the tail of an expanding or receding queue become smaller. It follows that collision risk may be reduced by instructing drivers to begin decelerating while still upstream of queues. Retarding vehicle progress toward a queues tail could retard the rate by which the likelihood of collision grows with time. Having vehicles approach a queue at diminished speeds may diminish the expected severity of a collision, should one still occur. This information is considered in this development of the ideas behind simple control logic for issuing speed advisories to drivers as they approach a queue. This logic could assist freeway managers in selecting suitable advisories so as to diminish the empirical estimate of collision likelihood at a specified time in the immediate future and by some target amount. In the interim, the likelihood estimates would be based solely on vehicles’ spatiotemporal proximities to queues. Other influences would be ignored for now, although planned experiments to test and improve the present logic are discussed.

Denoising traffic collision data using Ensemble Empirical Mode Decomposition (EEMD) and its application for constructing Continuous Risk Profile (CRP)

Filtering out the noise in traffic collision data is essential in reducing false positive rates (i.e., requiring safety investigation of sites where it is not needed) and can assist government agencies in better allocating limited resources. Previous studies have demonstrated that denoising traffic collision data is possible when there exists a true known high collision concentration location (HCCL) list to calibrate the parameters of a denoising method. However, such a list is often not readily available in practice. To this end, the present study introduces an innovative approach for denoising traffic collision data using the Ensemble Empirical Mode Decomposition (EEMD) method which is widely used for analyzing nonlinear and nonstationary data. The present study describes how to transform the traffic collision data before the data can be decomposed using the EEMD method to obtain set of Intrinsic Mode Functions (IMFs) and residue. The attributes of the IMFs were then carefully examined to denoise the data and to construct Continuous Risk Profiles (CRPs). The findings from comparing the resulting CRP profiles with CRPs in which the noise was filtered out with two different empirically calibrated weighted moving window lengths are also documented, and the results and recommendations for future research are discussed.

Evaluating and addressing the effects of regression to the mean phenomenon in estimating collision frequencies on urban high collision concentration locations

Two different methods for addressing the regression to the mean phenomenon (RTM) were evaluated using empirical data: Data from 110 miles of freeway located in California were used to evaluate the performance of the EB and CRP methods in addressing RTM. CRP outperformed the EB method in estimating collision frequencies in selected high collision concentration locations (HCCLs). Findings indicate that the performance of the EB method can be markedly affected when SPF is biased, while the performance of CRP remains much less affected. The CRP method was more effective in addressing RTM.

Discussion of theoretical and practical challenges in developing high collision concentration location detection procedures

ABSTRACT The resource allocation for conducting site investigation and implementation of safety countermeasures is determined based the results of high collision concentration location (HCCL) detection procedures. HCCL detection plays a vital role in prioritizing how government agencies utilize limited resources to improve the safety of the roadway system. However, there exists no universally accepted, specific method of detecting HCCL, and the resulting HCCL list can differ markedly depending on which HCCL procedures were employed to evaluate the traffic collision data. The objective of this literature review paper is to discuss both theoretical and practical challenges in developing HCCL procedures and their potential adverse effects. This paper also discusses how some of the challenges can be addressed with findings from recent studies.

Developing a Proxy Measure for Monitoring Freeway Segment Traffic Conditions along Detours during Roadway Closures

Monitoring traffic conditions solely based on probe vehicle travel times or traffic volumes sporadically observed along roadways typically allows a transportation agency to divert the traffic reactively after a significant deterioration in traffic conditions is observed. Lack of data at key locations makes the already challenging task of monitoring traffic conditions along a lengthy detour more intractable. To address this problem, the current study developed a proxy measure that can assist transportation agencies in proactively diverting traffic to various detours during extended major freeway closures. The proxy measure was estimated using travel times observed along a ten-mile freeway segment and traffic flow departing the site. The proxy measure revealed important insights about the relationship between segment travel times and vehicle accumulation within the segment that can aid transportation agencies in proactively diverting traffic in a timely manner.