Does Displaying Safety Messages on Dynamic Message Signs have Measurable Impacts on Crash Risk?

2021 ◽  
pp. 036119812110444
Author(s):  
Megat-Usamah Megat-Johari ◽  
Nusayba Megat-Johari ◽  
Peter T. Savolainen ◽  
Timothy J. Gates ◽  
Eva Kassens-Noor
Keyword(s):  
Negative Binomial ◽  
Driver Behavior ◽  
Field Research ◽  
Crash Risk ◽  
Traffic Crashes ◽  
Crash Data ◽  
Safety Issues ◽  
Dynamic Message Signs ◽  
Safety Message ◽  
Message Signs

Transportation agencies have increasingly been using dynamic message signs (DMS) to communicate safety messages in an effort to both increase awareness of important safety issues and to influence driver behavior. Despite their widespread use, evaluations as to potential impacts on driver behavior, and the resultant impacts on traffic crashes, have been very limited. This study addresses this gap in the extant literature and assesses the relationship between traffic crashes and the frequency with which various types of safety messages are displayed. Safety message data were collected from a total of 202 DMS on freeways across the state of Michigan between 2014 and 2018. These data were integrated with traffic volume, roadway geometry, and crash data for segments that were located downstream of each DMS. A series of random parameters negative binomial models were estimated to examine total, speeding-related, and nighttime crashes based on historical messaging data while controlling for other site-specific factors. The results did not show any significant differences with respect to total crashes. Marginal declines in nighttime crashes were observed at locations with more frequent messages related to impaired driving, though these differences were also not statistically significant. Finally, speeding-related crashes were significantly less frequent near DMS that showed higher numbers of messages related to speeding or tailgating. Important issues are highlighted with respect to methodological concerns that arise in the analysis of such data. Field research is warranted to investigate potential impacts on driving behavior at the level of individual drivers.

Examining Safety on Two-Lane and Multilane Highways in Consideration of Access Spacing

2019 ◽  
Vol 2673 (11) ◽  
pp. 388-397
Author(s):  
Raha Hamzeie ◽  
Megat-Usamah Megat-Johari ◽  
Iftin Thompson ◽  
Timothy P. Barrette ◽  
Trevor Kirsch ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Random Effect ◽  
Management Strategies ◽  
Negative Binomial Regression ◽  
Access Point ◽  
Management Policy ◽  
Crash Risk ◽  
Access Management ◽  
Crash Data ◽  
Crash Rates

Access management strategies, such as the introduction of minimum access point spacing criteria and turning movement restrictions, have been shown to be important elements in optimizing the operational and safety performance of roadway segments. The relationship between safety and these types of access policies is a complex issue, and the impacts of such features on traffic crashes is critical to the development of appropriate access management strategies. The purpose of this study was to provide a quantitative evaluation of how crash risk on multilane and two-lane highways varies with respect to access spacing in support of the development of a revised access management policy. Data were obtained for approximately 1,247 and 5,795 mi of segments across multilane and two-lane highways, respectively. Crash data were obtained for a five-year period from 2012 to 2016 and a series of random effect negative binomial regression models were estimated for each facility to examine the association between crash frequency, access point spacing, and traffic volume. For both facility types, crashes were found to increase consistently as the average spacing of access points along road segments decreased. Crash rates were highest when consecutive accesses were within 150 ft of one another and the frequency of crashes decreased substantively as spacing was increased to 300 ft and, particularly, 600 ft. With spacing beyond 600 ft, crash rates continued to decrease, although these improvements were less pronounced than at the lower range of values. These findings were generally consistent on multilane and two-lane highways.

Estimating Pedestrian Volumes for Signalized and Stop-Controlled Intersections

2020 ◽  
Vol 2674 (9) ◽  
pp. 799-808
Author(s):  
Minh Le ◽  
Srinivas R. Geedipally ◽  
Kay Fitzpatrick ◽  
Raul E. Avelar
Keyword(s):  
Pedestrian Safety ◽  
Crash Risk ◽  
Volume Data ◽  
Demand Model ◽  
Public Agencies ◽  
Traffic Crashes ◽  
Safety Issues ◽  
Fatal Crashes ◽  
Federal Highway

Pedestrian fatal crashes in the U.S. have increased over the years. From 2007 to 2016, pedestrian fatalities increased 27% nationally, while all other traffic fatalities decreased 14%. On average, a pedestrian was killed every 1.5 h in traffic crashes in 2016. The Federal Highway Administration (FHWA) has been working with public agencies toward developing more data-driven approaches to identify and mitigate pedestrian safety issues. However, pedestrian exposure to risk is not readily available. The absence of pedestrian exposure data makes it challenging to identify and prioritize high-crash risk locations. Using Dallas, Texas, as a case study, researchers wanted to use exposure in relation to volumes—both vehicular and pedestrian volume—to determine pedestrian risk. Although the vehicular volume is extensively available, the pedestrian volume is seldom available. The objective of this study is to explore options for collecting or estimating pedestrian volume data, particularly at intersections with high pedestrian activity. Researchers successfully developed a direct-demand model that estimates pedestrian volumes at signalized and stop-controlled intersections. The final model showed that pedestrian volume: increases 4 times within downtown; increases 12% per school within 1 mi of intersection; increases 4.8 times per 1% increase in commercial/multi-family residential land uses within 300 ft of intersection; increases 4.7 times with presence of higher education, hospitals, or malls; and decreases 36% per 5 mph increase in the intersections’ maximum posted speed limit. This research can help advance pedestrian safety analyses by providing a method of estimating pedestrian volumes for intersections by control type, particularly when volumes are infeasible to measure.

scholarly journals Comprehensive Analysis of Dynamic Message Sign Impact on Driver Behavior: A Random Forest Approach

Urban Science ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 49
Author(s):  
Snehanshu Banerjee ◽  
Mansoureh Jeihani ◽  
Danny D. Brown ◽  
Samira Ahangari
Keyword(s):  
Random Forest ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Route Choice ◽  
Potential Effect ◽  
Dynamic Message Signs ◽  
Road Users ◽  
Choice Analysis ◽  
Simulation Session ◽  
Message Signs

This study investigates the potential effect(s) of different dynamic message signs (DMSs) on driver behavior using a full-scale high-fidelity driving simulator. Different DMSs are categorized by their content, structure, and type of messages. A random forest algorithm is used for three separate behavioral analyses—a route diversion analysis, a route choice analysis, and a compliance analysis—to identify the potential and relative influences of different DMSs on these aspects of driver behavior. A total of 390 simulation runs are conducted using a sample of 65 participants from diverse socioeconomic backgrounds. Results obtained suggest that DMSs displaying lane closure and delay information with advisory messages are most influential with regards to diversion, while color-coded DMSs and DMSs with avoid route advice are the top contributors potentially impacting route choice decisions and DMS compliance. In this first-of-a-kind study, based on the responses to the pre- and post-simulation surveys as well as results obtained from the analysis of driving-simulation-session data, the authors found that color-coded DMSs are more effective than alphanumeric DMSs, especially in scenarios that demand high compliance from drivers. The increased effectiveness may be attributed to reduced comprehension time and ease with which such DMSs are understood by a greater percentage of road users.

A Comparative Analysis of Factors Affecting the Frequency and Severity of Freight-Involved and Non-Freight Crashes on a Major Freight Corridor Freeway

2018 ◽  
Vol 2672 (34) ◽  
pp. 49-62 ◽  
Author(s):  
Samuel G. Taylor ◽  
Brendan J. Russo ◽  
Emmanuel James
Keyword(s):  
Los Angeles ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Impact ◽  
The United States ◽  
Environmental Data ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Crash Data ◽  
Severity Prediction

Traffic crashes cost society billions of dollars each year as a result of property damage, injuries, and fatalities. Additionally, traffic crashes have a negative impact on mobility, as they are a primary cause of non-recurring delay. With the Interstate 10 corridor between the ports of Los Angeles and Houston being one of the most vital links for goods movement across the United States, safety and mobility along this freeway, particularly for freight traffic, are of significant concern. This study, which utilized six years of crash data from the state of Arizona, explores factors affecting the frequency and severity of crashes along the Arizona portion of the I-10 corridor, with a particular focus on freight-related crashes. The safety performance along the I-10 is analyzed through the development of crash frequency and severity prediction models using integrated crash, roadway, traffic, and environmental data. Negative binomial and ordered logit models, with the incorporation of random parameters, were estimated to provide a detailed understanding of factors associated with freight-involved crashes and how they compare to non-freight crashes in terms of frequency and severity. The results showed that several roadway- crash-, vehicle-, and person-related variables were associated with the frequency and/or severity of crashes along the study corridor. These findings provide important insights which can be used to develop or plan countermeasures aimed at improving the safety and efficiency of freight travel, which may include new ITS technologies, and targeted educational and enforcement campaigns.

scholarly journals Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ying Chen ◽  
Zhongxiang Huang
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Safety Data ◽  
Method Comparison ◽  
Weather Conditions ◽  
Finite Mixture ◽  
Crash Risk ◽  
Crash Data ◽  
Road Crashes ◽  
Inclement Weather

Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available.

Examining Driver Compliance with a Move-Over/Slow Down Law in Consideration of Vehicle Type and Messages Displayed on Upstream Dynamic Message Signs

2021 ◽  
pp. 036119812110278
Author(s):  
Nusayba Megat-Johari ◽  
Megat-Usamah Megat-Johari ◽  
Peter Savolainen ◽  
Timothy Gates ◽  
Eva Kassens-Noor
Keyword(s):  
Driver Behavior ◽  
Field Studies ◽  
Minimal Impact ◽  
Logistic Regression Models ◽  
Dynamic Message Signs ◽  
Position Data ◽  
Lane Position ◽  
Compliance With The Law ◽  
The One ◽  
Message Signs

Move-over laws are intended to enhance the safety of road agency and law enforcement personnel who are working on or near the roadway. This study examined driver behavior through a series of field studies where these types of vehicles were located on the outside shoulder of a freeway with their lights activated. The study also evaluated the use of upstream dynamic message signs (DMS) to discern whether targeted safety messages had any impact on behavior under this scenario. Upstream and downstream speed and lane position data were collected from vehicles originally traveling in the rightmost lane upstream of the DMS and emergency/service vehicle at two locations in Michigan. Logistic regression models were estimated to assess driver compliance with the law while considering important contextual factors, such as the type of vehicle on the shoulder and the message displayed on the DMS. The results indicated that drivers were more likely to move over or reduce their speeds when a police car was located on the shoulder as compared to a transportation agency pickup truck. In general, the type of message displayed had minimal impact on driver behavior. The one exception showed that drivers were likely to drive at or below the speed limit when targeted move-over messages were shown as compared to standard travel time messages. For all message types, both speed and lane compliance improved if the roadside vehicle was a police car.

scholarly journals Exploratory Analysis of Pedestrian Road Trauma in Finland

2021 ◽  
Vol 13 (12) ◽  
pp. 6715
Author(s):  
Steve O’Hern ◽  
Roni Utriainen ◽  
Hanne Tiikkaja ◽  
Markus Pöllänen ◽  
Niina Sihvola
Keyword(s):  
Cluster Analysis ◽  
Motor Vehicle ◽  
Seasonal Effects ◽  
Unique Element ◽  
Driver Behaviour ◽  
Crash Data ◽  
Safety Issues ◽  
Key Issues ◽  
Development Goals ◽  
Pedestrian Crashes

In Finland, all fatal on-road and off-road motor vehicle crashes are subject to an in-depth investigation coordinated by the Finnish Crash Data Institute (OTI). This study presents an exploratory and two-step cluster analysis of fatal pedestrian crashes between 2010 and 2019 that were subject to in-depth investigations. In total, 281 investigations occurred across Finland between 2010 and 2019. The highest number of cases were recorded in the Uusimaa region, including Helsinki, representing 26.4% of cases. Females (48.0%) were involved in fewer cases than males; however, older females represented the most commonly injured demographic. A unique element to the patterns of injury in this study is the seasonal effects, with the highest proportion of crashes investigated in winter and autumn. Cluster analysis identified four unique clusters. Clusters were characterised by crashes involving older pedestrians crossing in low-speed environments, crashes in higher speed environments away from pedestrian crossings, crashes on private roads or in parking facilities, and crashes involving intoxicated pedestrians. The most common recommendations from the investigation teams to improve safety were signalisation and infrastructure upgrades of pedestrian crossings, improvements to street lighting, advanced driver assistance (ADAS) technologies, and increased emphasis on driver behaviour and training. The findings highlight road safety issues that need to be addressed to reduce pedestrian trauma in Finland, including provision of safer crossing facilities for elderly pedestrians, improvements to parking and shared facilities, and addressing issues of intoxicated pedestrians. Efforts to remedy these key issues will further Finland’s progression towards meeting Vision Zero targets while creating a safer and sustainable urban environment in line with the United Nations sustainable development goals.

scholarly journals GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 362
Author(s):  
Arshad Jamal ◽  
Tahir Mahmood ◽  
Muhamad Riaz ◽  
Hassan M. Al-Ahmadi
Keyword(s):  
Real Time ◽  
Control Charts ◽  
Negative Binomial ◽  
Highway Safety ◽  
Monitoring Methods ◽  
Count Data Models ◽  
Crash Data ◽  
Safety Surveillance ◽  
Study Results ◽  
Best Fit

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

scholarly journals Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aschalew Kassu ◽  
Michael Anderson
Keyword(s):  
Negative Binomial ◽  
Surface Condition ◽  
Negative Binomial Regression ◽  
Segment Length ◽  
Mean Values ◽  
Rural Highways ◽  
Explanatory Variables ◽  
Crash Data ◽  
Pavement Surface ◽  
Binomial Regression

This study examines the effects of wet pavement surface conditions on the likelihood of occurrences of nonsevere crashes in two- and four-lane urban and rural highways in Alabama. Initially, sixteen major highways traversing across the geographic locations of the state were identified. Among these highways, the homogenous routes with equal mean values, variances, and similar distributions of the crash data were identified and combined to form crash datasets occurring on dry and wet pavements separately. The analysis began with thirteen explanatory variables covering engineering, environmental, and traffic conditions. The principal terms were statistically identified and used in a mathematical crash frequency models developed using Poisson and negative binomial regression models. The results show that the key factors influencing nonsevere crashes on wet pavement surfaces are mainly segment length, traffic volume, and posted speed limits.

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