scholarly journals Risk Factors in Work Zone Safety Events: A Naturalistic Driving Study Analysis

2019 ◽  
Vol 2673 (1) ◽  
pp. 379-387 ◽  
Author(s):  
Nipjyoti Bharadwaj ◽  
Praveen Edara ◽  
Carlos Sun
Keyword(s):  
Risk Factors ◽  
Driver Behavior ◽  
Work Zone ◽  
Crash Risk ◽  
Work Zones ◽  
Binary Logistic Regression Model ◽  
Work Zone Safety ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving ◽  
The Impact

Identification of crash risk factors and enhancing safety at work zones is a major priority for transportation agencies. There is a critical need for collecting comprehensive data related to work zone safety. The naturalistic driving study (NDS) data offers a rare opportunity for a first-hand view of crashes and near-crashes (CNC) that occur in and around work zones. NDS includes information related to driver behavior and various non-driving related tasks performed while driving. Thus, the impact of driver behavior on crash risk along with infrastructure and traffic variables can be assessed. This study: (1) investigated risk factors associated with safety critical events occurring in a work zone; (2) developed a binary logistic regression model to estimate crash risk in work zones; and (3) quantified risk for different factors using matched case-control design and odds ratios (OR). The predictive ability of the model was evaluated by developing receiver operating characteristic curves for training and validation datasets. The results indicate that performing a non-driving related secondary task for more than 6 seconds increases the CNC risk by 5.46 times. Driver inattention was found to be the most critical behavioral factor contributing to CNC risk with an odds ratio of 29.06. In addition, traffic conditions corresponding to Level of Service (LOS) D exhibited the highest level of CNC risk in work zones. This study represents one of the first efforts to closely examine work zone events in the Transportation Research Board’s second Strategic Highway Research Program (SHRP 2) NDS data to better understand factors contributing to increased crash risk in work zones.

Investigating the Impact of Fog on Freeway Speed Selection using the SHRP2 Naturalistic Driving Study Data

2018 ◽  
Vol 2672 (16) ◽  
pp. 93-104 ◽  
Author(s):  
Md Nasim Khan ◽  
Ali Ghasemzadeh ◽  
Mohamed M. Ahmed
Keyword(s):  
Preliminary Analysis ◽  
Motor Vehicle ◽  
Driver Behavior ◽  
Age Groups ◽  
Weather Conditions ◽  
Naturalistic Driving Study ◽  
Speed Selection ◽  
Naturalistic Driving ◽  
The Impact ◽  
Clear Weather

The negative effect of reduced visibility on driver performance has been recognized as one of the main causes of motor vehicle crashes in fog. Although many studies have concentrated on driver behavior during foggy weather in a simulated environment, there is a lack of studies that have addressed the impact of fog on driver behavior and performance in naturalistic settings. This paper utilized the data from the SHRP2 Naturalistic Driving Study (NDS) database to understand driver behavior in general and speed selection in particular during clear and foggy weather conditions. In this study, a comparative preliminary analysis and an ordered logit model were developed to evaluate driver speed behavior in fog and clear weather conditions. Results from the preliminary analysis showed 10% and 3% reduction in speed because of near fog and distant fog, respectively. In addition, results from the speed selection model showed that the odds of reducing speed were 1.31 and 1.28 times higher for drivers traveling in near fog and distant fog, respectively, compared with drivers who were driving in clear weather conditions. However, there is an over-representation of young drivers in the SHRP2 NDS database, which was reflected in the dataset used in this study. Therefore, a more representative sample of age groups might provide different results. The results from this study could provide a better insight into driver speed selection during foggy weather conditions, which can be utilized to improve various safety strategies including variable speed limits.

Risk Analysis Techniques for Safety Evaluation of Highway Work Zones

2000 ◽  
Vol 1715 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Ana Maria Elias ◽  
Zohar J. Herbsman
Keyword(s):  
Risk Analysis ◽  
Safety Evaluation ◽  
Highway Safety ◽  
Point Of View ◽  
Work Zone ◽  
Work Zones ◽  
Construction Sites ◽  
Work Zone Safety ◽  
Accident Data ◽  
Temporary Closure

Construction sites or work zones create serious disruptions in the normal flow of traffic, resulting in major inconveniences for the traveling public. Furthermore, these work zones create safety hazards that require special consideration. Current legislation and programs, at both state and national levels, emphasize the need for a better understanding of work zone problems to address work zone safety. This reality—coupled with the temporary closure of more miles of highway every year for rehabilitation and maintenance—makes the analysis of safety at construction sites a serious matter. A summary of a comprehensive study associated with the development of a new practical approach to address highway safety in construction zones is presented. Because empirical models require sample sizes that are not attainable due to the intrinsic scarcity of construction zone accident data, the problem was studied from the point of view of risk analysis. Monte Carlo simulations were used to develop risk factors. These factors are meant to be included in the calculations of additional user costs for work zones, or simply applied as risk measurements, to optimize the length and duration of closures for highway reconstruction and rehabilitation projects. In this way, it will be possible to assess the danger of work zones to the traveling public and minimize adverse effect of work zones on highway safety.

Application of LiDAR and Connected Vehicle Data to Evaluate the Impact of Work Zone Geometry on Freeway Traffic Operations

2018 ◽  
Vol 2672 (16) ◽  
pp. 1-13 ◽  
Author(s):  
Michelle M. Mekker ◽  
Yun-Jou Lin ◽  
Magdy K. I. Elbahnasawy ◽  
Tamer S. A. Shamseldin ◽  
Howell Li ◽  
...  
Keyword(s):  
Case Studies ◽  
Work Zone ◽  
Extensive Literature ◽  
Vehicle Speed ◽  
Connected Vehicle ◽  
Traffic Operations ◽  
Work Zones ◽  
Geometric Data ◽  
Vehicle Data ◽  
The Impact

Extensive literature exists regarding recommendations for lane widths, merging tapers, and work zone geometry to provide safe and efficient traffic operations. However, it is often infeasible or unsafe for inspectors to check these geometric features in a freeway work zone. This paper discusses the integration of LiDAR (Light Detection And Ranging)-generated geometric data with connected vehicle speed data to evaluate the impact of work zone geometry on traffic operations. Connected vehicle speed data can be used at both a system-wide (statewide) or segment-level view to identify periods of congestion and queueing. Examples of regional trends, localized incidents, and recurring bottlenecks are shown in the data in this paper. A LiDAR-mounted vehicle was deployed to a variety of work zones where recurring bottlenecks were identified to collect geometric data. In total, 350 directional miles were covered, resulting in approximately 360 GB of data. Two case studies, where geometric anomalies were identified, are discussed in this paper: a short segment with a narrow lane width of 10–10.5 feet and a merging taper that was about 200 feet shorter than recommended by the Manual on Uniform Traffic Control Devices. In both case studies, these work zone features did not conform to project specifications but were difficult to assess safely by an inspector in the field because of the high volume of traffic. The paper concludes by recommending the use of connected vehicle data to systematically identify work zones with recurring congestion and the use of LiDAR to assess work zone geometrics.

Evaluation of Interstate Work Zone Mobility using Probe Vehicle Data and Machine Learning Techniques

2019 ◽  
Vol 2673 (2) ◽  
pp. 811-822 ◽  
Author(s):  
Mohsen Kamyab ◽  
Stephen Remias ◽  
Erfan Najmi ◽  
Kerrick Hood ◽  
Mustafa Al-Akshar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Work Zone ◽  
Third Party ◽  
Machine Learning Techniques ◽  
Work Zones ◽  
Vehicle Data ◽  
Gps Devices ◽  
Future Work ◽  
The Impact

According to the Federal Highway Administration (FHWA), US work zones on freeways account for nearly 24% of nonrecurring freeway delays and 10% of overall congestion. Historically, there have been limited scalable datasets to investigate the specific causes of congestion due to work zones or to improve work zone planning processes to characterize the impact of work zone congestion. In recent years, third-party data vendors have provided scalable speed data from Global Positioning System (GPS) devices and cell phones which can be used to characterize mobility on all roadways. Each work zone has unique characteristics and varying mobility impacts which are predicted during the planning and design phases, but can realistically be quite different from what is ultimately experienced by the traveling public. This paper uses these datasets to introduce a scalable Work Zone Mobility Audit (WZMA) template. Additionally, the paper uses metrics developed for individual work zones to characterize the impact of more than 250 work zones varying in length and duration from Southeast Michigan. The authors make recommendations to work zone engineers on useful data to collect for improving the WZMA. As more systematic work zone data are collected, improved analytical assessment techniques, such as machine learning processes, can be used to identify the factors that will predict future work zone impacts. The paper concludes by demonstrating two machine learning algorithms, Random Forest and XGBoost, which show historical speed variation is a critical component when predicting the mobility impact of work zones.

Driver Behavior in Response to Flashing Lights

2019 ◽  
Vol 2673 (5) ◽  
pp. 703-708 ◽  
Author(s):  
Kristin Kersavage ◽  
Nicholas P. Skinner ◽  
John D. Bullough ◽  
Philip M. Garvey ◽  
Eric T. Donnell ◽  
...  
Keyword(s):  
Driver Behavior ◽  
Sequential Pattern ◽  
Work Zone ◽  
Lane Change ◽  
Work Zones ◽  
The Road ◽  
The Individual ◽  
The Right ◽  
Warning Lights ◽  
Current Standards

Flashing yellow warning lights notify drivers about the presence of work along the road. Current standards for these lights address performance of the individual light but not how lights should function when multiple lights are used. In the present study, warning lights were used to delineate a lane change taper in a simulated work zone. Lights flashed with varying intensities and either randomly or in sequence, with lights flashing in turn along the length of the lane change taper, either to the right or to the left. In half of the trials, a flashing police light bar was used on a vehicle located within the simulated work zone. Participants were asked to drive a vehicle approaching the work zone and to identify, as quickly as possible, in which direction the taper’s lane change was (either to the right or left). Drivers were able to correctly identify the taper from farther away when the lights flashed in a sequential pattern than when the flash pattern was random; and the presence of a police light bar resulted in shorter identification distances. The results, along with previous research, can inform standards for the use of flashing lights and police lights in work zones for the safety of drivers and workers.

Driver speed selection and crash risk: Insights from the naturalistic driving study

2017 ◽  
Vol 63 ◽  
pp. 187-194 ◽  
Author(s):  
Raha Hamzeie ◽  
Peter T. Savolainen ◽  
Timothy J. Gates
Keyword(s):  
Crash Risk ◽  
Naturalistic Driving Study ◽  
Speed Selection ◽  
Naturalistic Driving

Estimating Crash Risk

2011 ◽  
Vol 19 (4) ◽  
pp. 8-12 ◽  
Author(s):  
Thomas A. Dingus ◽  
Richard J. Hanowski ◽  
Sheila G. Klauer
Keyword(s):  
Driver Behavior ◽  
Crash Risk ◽  
Video Cameras ◽  
Naturalistic Driving ◽  
And Performance ◽  
Empirical Approaches ◽  
Insight Into

Naturalistic driving research involves the instrumentation of vehicles, including video cameras, for the purpose of precisely recording participants as they normally drive as well as in the seconds leading up to crashes and near-crashes. The results provide new insight into driver behavior and performance that cannot be gained through traditional empirical approaches. Naturalistic driving studies provide context of the overall driving environment, information that is absent from other methods. This article highlights how results from naturalistic driving research have reshaped our understanding of driver behavior and crash risk, including the fact that some findings are contrary to results from other empirical approaches.

Sign in / Sign up

Export Citation Format

Share Document