Collisions in Freeway Traffic: Influence of Downstream Queues and Interim Means to Address Them
Zhibin Li, Southeast University, China, presenter Koohong Chung, California Department of Transportation, presenter Michael J. Cassidy, University of California, Berkeley, presenter
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) upon encountering the queue. The implication is supported, and actually bolstered, with new and more detailed data that were painstakingly extracted from two freeway stretches in California. These data show how the likelihood of collision grows larger as both the spatial and the temporal proximities to the tail of a queue become smaller. It follows that collision risk may be reduced by instructing drivers to begin gently decelerating while still upstream of queues. Retarding vehicle progress toward a queue’s tail could retard the rate by which the likelihood of collision grows with time. And having vehicles approach a queue at diminished speeds may diminish the expected severity of a collision, should one still occur.
An outline for simple control logic is offered with the above in mind. The idea is to issue suitable speed advisories to drivers as they approach a queue. The logic could be made a resident tool in freeway traffic management centers. It could thereafter assist freeway managers to select specific speed 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 causal influences are ignored for now, though planned experiments to test and improve the present logic are discussed.
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13-0746
| E09 |
Identifying Secondary Crashes on Freeways Using Sensor Data
Hong Yang, Rutgers University, presenter Bekir Bartin, Rutgers University, presenter Kaan Ozbay, Rutgers University, presenter
Non-recurring traffic incidents such as motor vehicle crashes increase not only travel delays but also the risk of secondary crashes. Secondary crashes can cause additional traffic delays, and reduce safety. In order to implement effective countermeasures to prevent and/or reduce secondary crashes, first their characteristics should be investigated. However, the related research has been limited largely due to the lack of detailed incident and traffic data necessary to first identify the secondary crashes. Existing approaches such as static methods employed to identify secondary crashes cannot fully capture potential secondary crashes due to fixed spatio-temporal identification criteria. Improved approaches are needed to accurately categorize secondary crashes for further analysis. Therefore, this paper attempts to develop an enhanced approach for identifying secondary crashes using the existing crash database and archived traffic data from highway sensors. The proposed method is threefold: (a) defining secondary crashes; (b) examining the impact range of primary crashes that possibly relate to secondary crashes; and (c) identifying secondary crashes. The proposed methodology establishes a practical framework for mining secondary crashes from existing sensor data and crash records. A case study is performed on a 27-mile segment of a major highway in New Jersey to illustrate the performance of the proposed approach. The results show that the proposed method provides a more reliable and efficient categorization of secondary crashes than commonly used approaches.
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13-4808**
| E10 |
Defining Spatiotemporal Influence Areas in Freeways for Secondary Accident Detection
Maria-Ioanna Marianna Imprialou, Loughborough University, United Kingdom, presenter Foteini P. Orfanou, National Technical University of Athens, Greece, presenter Eleni I. Vlahogianni, National Technical University of Athens, Greece, presenter Matthew G. Karlaftis, National Technical University of Athens, Greece, presenter
Current research on secondary crash identification links the likelihood of secondary crashes occurrence to primary incidents using pre-defined spatial and temporal criteria. This paper extends past research on secondary crash detection by defining dynamic thresholds of the influence area of a primary incident using detailed real-time collected freeway traffic data from upstream loop detectors. Results offer influence curves for different characteristics with respect to the prevailing traffic conditions before the occurrence of the primary incident, to crash characteristics such as the number of vehicles involved or the number and type of blocked lanes. For accurately detecting secondary incidents, an empirical speed-based method is applied that imprints influence areas with respect to their dissipation pattern. The results of the proposed approach are compared to those of five prevailing static and dynamic methods for detecting secondary accidents and the differences are identified and discussed.
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13-0955
| E11 |
Corridor-Level Approach for Estimating the Contribution of Incidents to Delay
Tiffany Elizabeth Barkley, Iteris, Inc., presenter Arlen Spiro, Berkeley Transportation Systems, presenter Sarah Burnworth, Metropolitan Transportation Commission, presenter Radiah Victor, Metropolitan Transportation Commission, presenter
Understanding the relationship between incidents and delay is an important step in developing targeted measures to reduce non-recurrent congestion. Research over the last decade has focused on two key incident characteristics that dictate how much delay an incident will cause: (1) the incident clearance time; and (2) the capacity reduction of the incident. Research relating congestion with incidents has primarily leveraged incident data sets that report on these key variables; however, the reality is that data sets that report this information are uncommon and often take a year or more to produce. Until standards have been developed for incident reporting, work is needed to demonstrate how to best use more commonly available data sets to relate incidents with congestion.
The goal of this paper is to present methods for maximizing the use of operational incident data sets for non-recurrent congestion analysis. It uses Computer-Aided-Dispatch data from the California Highway Patrol, which is continuously available in real-time, to estimate the amount of delay caused by different types of incidents. It builds upon previous research by accounting for the fact that different types of incidents have varying congestion impacts depending on the corridor and the time of day. This paper applies the developed methodology to a segment of northbound Interstate-880 in the San Francisco Bay Area, where an Integrated Corridor Management project is in progress. The outputs of the methodology can be used to identify priority time periods and corridors for incident management strategies.
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13-0999
| E12 |
Impact of Abandoned and Disabled Vehicles on Freeway Incident Durations
Deo Chimba, Tennessee State University, presenter Boniphace Kutela, Tennessee State University, presenter Gary Ogletree, Tennessee Department of Transportation, presenter Frank Horne, Tennessee Department of Transportation, presenter Raymond Hallavant, Tennessee Department of Transportation, presenter
In most cases, abandoned and disabled vehicles are left within the roadway right of ways. It is common to find a vehicle left on the shoulder, median, gore area or on the travel lane for certain period of time. Experience from the state of Tennessee has shown that 78% of the freeway traffic related incidents are due to disabled and abandoned vehicles. It is hypothesized that the longer the vehicle is left unattended within the right of way, the higher the probability of new incidents and secondary crashes. This paper utilized 2004 to 2010 freeway incident data in Tennessee to evaluate the impact of the length of incident durations caused by disabled and abandoned vehicles. Analysis evaluated the impact of these incidents with respect to roadway location, queue lengths, weather conditions, towing times, lane closure, and the source of incident notification. Temporal factors, including the spectra of the time of the day, the day of the week, and the seasons of the year were evaluated with respect to the number of incidents and incident durations. It was found that vehicles left on the left and right shoulders generated more incidents compared to other locations followed by gore areas and the ramps. Parametric hazard based log-logistic survival model was applied to determine the factors affecting the abandoned and disabled vehicles incident duration. Number of closed lanes, length of the queue formed, construction zones, trucks and towing involvement were found to be significantly associated with longer incident duration.
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13-1065
| E13 |
Impact of Heavy Vehicles on Freeway Operating Characteristics Under Congested Conditions
Umama Ahmed, Old Dominion University, Norfolk, presenter Alexander Drakopoulos, Marquette University, presenter ManWo Ng, Old Dominion University, Norfolk, presenter
The significant impact Heavy Vehicles (HV) have on freeway operations has been identified since the first edition of the Highway Capacity Manual (HCM). HCM 2010 used Passenger Car Equivalent (PCE) values and percent of trucks/buses and Recreational Vehicles (RV) to account for HV effect on roadway performance. Unfortunately, PCE values in the HCM 2010 relied on a limited field database, and simulation runs that were calibrated for steady-flow traffic conditions. On the other hand, the effect of HV on traffic flow can reasonably be expected to vary with traffic conditions. Very few studies have been conducted with extensive field data to examine traffic characteristics impacts due to the presence of HV under congested and forced-flow conditions. This paper presents such an effort by using urban freeway data containing 1.2 million individual vehicle observations. Results indicated a significant difference in Lagging-Leading behavior among vehicle pairs related to HV presence. Passenger Car and HV headways were found to increase with HV presence in the traffic stream. A similar pattern was found for the PCE factor. The PCE value, under congested conditions and more than 9% HV presence, was found to be 1.76, which is higher than the HCM 2010-recommended value of 1.5 for level freeway sections. The results of this paper can be used as input for future simulation runs of congested freeway flow conditions.
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13-1542
| E14 |
Data-driven Evaluation of Freeway Service Patrol in Low-traffic Areas Using Discrete-Event Simulation
Pengfei Li, University of Alberta, Canada, presenter Jennifer Walton, Kentucky Transportation Center, presenter
The freeway safe service patrol (FSP) is a low-cost but effective approach to identifying and clearing incidents quickly. This service has been deployed in most states of US. There is also rich literature on evaluation of the freeway service patrols, most of which focused on evaluating the travel time and the derived capital savings due to the reduction of incidents clearance time. However, although the non-recurrent travel delay is a major concern in metro areas, the primary goal of FSP in many other low-traffic areas is to assist stalled vehicles effectively and reliability with certain resource constraints. Few evaluations in the past were conducted from the operational perspective, such as the FSP crew¡¯s responding reliability, fuel consumption, vehicle mileage or fleet maintenance cost in various scenarios. Using a discrete-event simulation method, this paper presents an operational evaluation framework for of the FSP in low-traffic areas. Lastly, as an illustration, the reliability of SAFE Patrol program in Kentucky, which only serves rural areas in Kentucky, was evaluated and estimated under various scenarios.
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13-2263
| E15 |
Adaptive Freeway Incident Detection Algorithm Using Hilbert-Huang Transform
Sampson Kwasi Asare, University of Virginia, presenter Yaw Adu-Gyamfi, University of Delaware, presenter Nii Attoh-Okine, University of Delaware, presenter Hyungjun Park, University of Virginia, presenter
Automated detection of incidents is still an integral component of advanced traffic management systems (ATMS) and its role for the effective management of freeway operations should not be ignored. This paper presents a novel incident detection algorithm with adaptive thresholding capabilities based on the Hilbert-Huang transform (HHT). The HHT is a powerful tool for processing nonstationary data, and it employs the concept of instantaneous frequency; hence it is suitable for local analysis of traffic measurements. In particular, this paper demonstrates how the empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA) components of HHT could be used to detect traffic incidents on freeways.
Evaluation of the proposed algorithm was conducted using real-world traffic data with the California and low-pass filter (Minnesota) as baseline. The test results indicate that the HHT-based incident detection algorithm remarkably outperforms the benchmark algorithms with the highest detection rate (95.8%) and the lowest false alarm rate (0.001). This demonstrates the potential for practical application of the proposed algorithm in reality.
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13-3424
| E16 |
Estimation of Diversion Rate During Incidents Based on Mainline Detector Data
Mohammed Hadi, Florida International University, presenter Yan Xiao, Florida International University, presenter Maria Lucia Rojas, University of Florida, presenter
A number of intelligent transportation system (ITS) technologies are deployed for disseminating information to travelers. One of the most important parameters for assessing the impacts and benefits of these deployments is the diversion rates of travelers. In addition, the estimation allows the assessment of the guidelines and procedures of information dissemination and evaluating the impacts on alternative routes.
In this research, a method was developed to estimate traffic diversions based on mainline detector data without the need for off-ramp detectors. The validity of the developed methodology was verified by comparing the estimated values with real-world data. Case studies of the developed method indicate that the average diversion rate is about 10%-35% for 3-lane and 4-lane freeways depending on number of lanes blocked. A linear relationship between the average diversion rate and lane blockage ratio was also developed, indicating that there is a general trend of increase in diversion with the increase in the lane blockage ratio.
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13-3786**
| E17 |
Design and Evaluation of Operational Strategies for Deploying Emergency Response Teams in a Commuting Corridor: Case Study of the Capital Beltway
Shanjiang Zhu, George Mason University, presenter Woon Kim, University of Maryland, College Park, presenter Gang-Len Chang, University of Maryland, College Park, presenter Steven M. Rochon, Maryland State Highway Administration, presenter
Both patrolling and pre-positioned strategies for allocating emergency traffic response units have been implemented in practice. To compare the performance of both response strategies, this study has conducted the efficiency comparison based on the field data from the I-495/I-95 Capital Beltway. Our extensive experimental results have revealed that the effectiveness of those response strategies varies with some critical factors including the spatial distribution of incident frequency over different times of a day, the fleet size of the response team, the congestion level, and the available detection sources. In view of the resource constraints, the study has further presented a methodology to determine the most cost-benefit fleet size operated with our proposed strategies, considering the marginal cost and benefit of an additional response unit on the resulting total social benefits. Our analysis results with the Capital Beltway, despite exploratory in nature, could serve as the basis for highway agencies to review and optimize their incident response and management program.
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13-4760
| E18 |