More research on safety management from a comprehensive, systems approach is desirable. This session will allow you to interact one-on-one with the authors to discuss a broad range of safety management topics from the overall roadway safety management process and safe system approach to specific data sources and analysis methods to investigate transportation safety. Topics also cover all surface transportation modes from pedestrians and bicyclists to motorized vehicles and even golf carts. These papers cover the 4Es of safety and touch on various safety issues and opportunities related to infrastructure, road user behavior, socioeconomics, and advanced technology (e.g., connected/automated vehicles).
A Spatiotemporal Analysis of Motorcyclist Injury Severity: Implications from 20 Years’ Traffic Crashes in Pennsylvania
Xiaobing Li (email@example.com), University of AlabamaShow Abstract
Jun Liu, University of Alabama
Zihe Zhang, University of Alabama
Allen Parrish, Alabama Transportation Institute
Motorcyclists face higher risks of severe injuries in crashes compared to motor vehicle drivers who are often protected by seatbelts and airbags during collisions. A report by the National Highway Traffic Safety Administration reveals that motorcyclists have 27 times the risk of fatality in traffic crashes relative to motor vehicle drivers. Previous studies have identified a list of risk factors associated with motorcyclist injury severity and generated valuable insights for countermeasures protecting motorcyclists in crashes. These studies have shown that wearing helmets, motorcycle-specific reflective clothing and boots, alcohol/drug-free driving, obeying traffic regulations are good practices for safe motorcycling. However, these practices and other risk factors are likely to interact with local geographic, socio-economic, and cultural contexts, leading to diversified correlations with motorcyclist injury severity, which remains under-explored. Such correlations may exhibit variations across space and time. The objective of this study is to revisit the correlates of motorcyclist injury severity with a focus on the spatial and temporal variations of correlations between risk factors and injury severity. This study employed an integrated spatiotemporal analytical approach to mine comprehensive statewide 20 years’ motorcycle-involved traffic crashes (N=50,823) in Pennsylvania. Non-stationarity tests were performed to examine the significance of variations in spatially and temporally local correlations. The results show that most factors such as helmet, engine size, vehicle life, pillion passenger, at-fault striking, and speeding hold significant non-stationary relationships with motorcyclist injury severity. Furthermore, cluster analysis of estimations reveals the regional similarities of correlates, which may help practitioners develop regional motorcyclist safety countermeasures.
The Impact of COVID-19 on Traffic Crash trends in Tennessee
Morgan Chatmon, Tennessee State UniversityShow Abstract
Beatriue Cherop, Tennessee State University
Deo Chimba, Tennessee State University
The first Coronavirus case detected in Wuhan city, Hubei province in China towards the end of 2019. In order to decrease the rate of transmission of COVID-19, the United States passed an order requiring people to work from home, closure of schools and non-essential businesses and barring mass gatherings. The move reduced number of people travelling, and altered travel patterns. The trend of traffic crashes before and during the COVID-19 pandemic is presented in this paper. The analysis of 4-year crash trends covering the months of March, April and May for 2017, 2018, 2019 (averaged as pre- COVID-19) and 2020 (during COVID-19) is presented. The decline in crashes was due to the limited movement and travel which decreased road traffic by more than 38%. Non parametric test was used to compare the mean of crashes before and during COVID-19, the results showed that the mean of crashes during COVID-19 was significantly lower than pre- COVID-19 for the same range of months. The geometric and traffic factors used to analyze the traffic crashes included the number of lanes, AADT, speed limit, land use, population density, median income and weather. Negative Binomial regression was used to model the impact of these factors on crashes. It was found that for each unit increase in the factors, traffic crashes increased with the increase being less for the COVID-19 period. The restrictions put in place to minimize the spread of COVID-19 decreased number of traffic crashes and generally increased road safety.
A Low-Cost Approach to Identify Hazard Curvature for Local Road Networks Using Open-Source Data
Qinglin Hu (firstname.lastname@example.org), University of AlabamaShow Abstract
Xiaobing Li, University of Alabama
Jun Liu, University of Alabama
ABSTRACT Vehicle crashes are a leading cause of death in the United States. Among those crashes, curvature in local roadway was identified as one of the most significant factors correlated with fatal crashes. Given the large number of local roads and their relatively lower traffic compared with interstates or freeways, most local roads may not receive priorities in the first phase of highway upgrades. However, critical locations, e.g., sharp curves (vertical and/or horizontal), in the network that may be a deadly threat for both new advanced autonomous vehicles and conventional vehicles. In addition, Identifying local roadway curvatures exists various uncertainty by most authorities, such as high budget and lack of data. To fill this gap, this study offers a low-cost approach to constructing three-Dimensional geometric profiles for local roads in a relatively large study area using open-source data. With the profiles, critical road segments, including extreme horizontal and vertical curves and their combinations, can be identified. Our study redefined the local road segments into 20 sub-categories based on the calculated vertical grades and curve radius that were incorporated into a zero-inflated native binomial model. Model results showed that grades or curves were associated with decreased crash frequency compared with straight and flat roads. However, segments with larger horizontal curve radius and low grades were found to associate with increased crash frequency. More implications are discussed in the paper.
Benchmarking Road Safety Development Across OECD Countries: An Empirical Analysis for a Decade
Jingyang Lyu, University of ChicagoShow Abstract
Tianye Wang, University of Virginia
Faan Chen (email@example.com), Harvard University
Benchmarking performance, monitoring progress, and then recalibrating interventions is widely recognized as a valuable process for achieving continuous improvement in road safety. In this study, a systematic and effective methodology, IV-VIKOR with FNBC, is developed to perform the benchmarking of road safety development in an integrative manner for OECD (Organisation for Economic Co-operation and Development) countries. Linking to other method and measure as the references, 36 OECD Member countries are ranked and grouped into several classes based on their overall achievement regarding road safety from the past decade (2009-2018). This provides government officials and policymakers, across the OECD Member countries, with a flexible tool to comprehensively benchmark road safety development. Providing the ability to identify delays in action plan implementations and proactively redistribute resources toward more effective measures where required. Such a tool can also serve to increase political will and stakeholder accountabilities, at the highest level of government and the private sector for all OECD members: Thereby keeping the implementation of action plans on schedule. It helps OECD Member countries to establish the capacity for sustainable safety management; supporting them in developing future strategies and reforms to create better policies for better lives.
Effect of Socioeconomic and Demographic Factors on Crash Occurrence
Shraddha Sagar, Gresham Smith and PartnersShow Abstract
Nick Stamatiadis, Kentucky Transportation Center
Arnold Stromberg, University of Kentucky
Road traffic crashes are a leading cause of death in the United States. In Kentucky, per capita crash rates and crash-related fatalities have outpaced the national average for over a decade. Researchers have argued that the region’s unique socioeconomic conditions provide a compelling explanation for these trends. This study examined the relationship between highway safety and socioeconomic characteristics using crash data from Kentucky. This research sought to identify at-risk drivers based on the socioeconomic and demographic attributes of their residence zip codes. Using the quasi-induced exposure approach, binary logistic regression was used to predict the probability of be the at-fault driver in a single- and two-unit crashes based on socioeconomic characteristics of their residence zip code. Statistical analysis found that variables such as income, education level, poverty level, employment, age, gender, rurality, and number of traffic-related convictions of a driver’s zip code influence the likelihood of their being at fault in a crash, while educational attainment is observed to have an impact only on single-unit crash occurrence. Finally, it is concluded that younger and older drivers residing in zip codes with low socioeconomic conditions have a higher likelihood of causing a crash for both single- and two-unit crashes. This finding can be used to identify zip codes or groups of drivers with higher likelihood to be involved in crashes and develop targeted and efficient safety programs.
SAVE-T: Safety Analysis Visualization and Evaluation Tool
Yuan Zhu (firstname.lastname@example.org), Inner Mongolia UniversityShow Abstract
Sami Demiroluk, AgileAssets, Inc.
Kaan Ozbay, New York University
Kun Xie, Old Dominion University
Hong Yang, Old Dominion University
Di Sha, New York University
Traffic crashes are one of the biggest issues which constitute a threat to lives the motorists and disrupt operations of the transportation system. To reduce the number of crashes and alleviate their impacts, it is necessary to scrutinize the factors contributing to the risk of traffic crashes. Lately, visual analytics tools become very popular for data exploration and obtaining insights from the data. In this paper, a new web-based data visualization tool called Safety Analysis Visualization and Evaluation Tool (SAVE-T) was introduced. This tool enables users to interactively create queries and visually explore the results. By utilizing an on-line crash database, it offers various innovative functionalities for analysis and visualization of the crash data such as custom query development module, and a subway-like map for easily visualizing the accident on the roadway segments. This tool provides an effective and efficient way to transportation agencies and professionals for traffic safety analyses and visualizations.
Emphasis Areas and Risk Factors for Angle Collisions in Virginia
Alexei Tsyganov, Virginia Department of TransportationShow Abstract
Stephen Read, Virginia Department of Transportation
Recently the FHWA Office of Safety developed sets of low-cost countermeasures for systemic intersections safety improvements. The suggested basic treatments are low in unit cost and collectively effective in terms of reducing future crash potential. For the effective implementation of the FHWA recommendations, the Virginia Department of Transportation (VDOT) Highway Safety Improvement Program (HSIP) initiated a study targeting analysis of angle collisions, identification of emphasis areas for improvements and associated risk/human factors. The conducted descriptive crash analysis considered multiple factors, such as day, time, highway functional class, traffic control system, travel conditions, crash severity, driver age, driver pre-crash improper action and maneuver, as well as influences of intoxication, distraction, inattention, and vision obstruction. The analysis identifies the emphasis areas for angle collisions related safety improvements and quantify their significance in terms of contribution to the overall statewide highway crashes and severity. The detailed analysis of the 2,122 police crash reports together with the review of crash sites, allowed identification and classification of various pre-crash events, as well as driver risk/human factors, and quantification of their significance. Based on the study results, a procedure for crash tree analysis was developed with the identified risk factors. The study results provide more detailed information for the selection of the most applicable and effective safety improvement strategies and measures targeting angle collisions.
Navigating School Zones: 5 Challenges for Deploying Automated Vehicles Near Schools
Michael Clamann, UNC Highway Safety Research CenterShow Abstract
Nancy Pullen-Seufert, UNC Highway Safety Research Center
The variability of conditions among school zones combined with a high density of traffic during peak times operating near pedestrians and bicyclists whose safety is paramount represents a complex operational design domain for automated driving systems (ADS). These characteristics represent safety challenges that should be addressed through technology, design, and regulatory approaches before ADS are deployed. However, these issues have not been comprehensively addressed to date, and to reach the full safety potential of ADS, their design will need to account for the complexity and uncertainty in and around school zones. The goal of this work was to address this gap and characterize the safety challenges to pedestrians that will need to be addressed before ADS can be deployed near schools. Building on an existing research framework, and interviews with school transportation experts, attributes of school transportation infrastructure were cross referenced against safety issues faced by pedestrians and automated vehicles to identify current challenges related to transportation within school zones. The themes that emerged from the results of the analysis consolidated around five challenge areas for schools and automated driving systems including levels of automation, operational design domain of schools, young students, school transportation stakeholders, and test strategies. Addressing these challenges areas now would lay a foundation to prepare for future ADS deployments and addressing some current challenges to pedestrian safety.
Identifying relationships between socioeconomic indicators and crash frequency in Pennsylvania
Rebeka Yocum, Pennsylvania State University, University ParkShow Abstract
Vikash Gayah (email@example.com), Pennsylvania State University
Current crash prediction models utilize roadway and traffic data as independent variables to describe crash frequency on individual roadway segments. Recent work has moved toward predicting crashes within some region as a function of roadway and traffic data, as well as non-traditional variables, such as alcohol, gasoline prices, and socioeconomic measures. This paper aims to introduce measures of wealth into the crash modeling conversation by determining the effect of wealth on total, fatal and injury, and pedestrian crash frequencies in Pennsylvania counties. The analysis presented in this paper will serve as a case study with intentions to promote the development of more robust, wealth-inclusive crash prediction models in the future. The study reveals that population of unemployed individuals, percentage of the population on cash public assistance or receiving SNAP benefits, and the percentage of households without a vehicle are each positively related to the observed frequency of total, fatal + injury and pedestrian crashes in each county. This result not only supports previous work, but expands on that work by considering multiple crash types, and multiple wealth related variables. The existence of a relationship between crash frequency and wealth related variables opens the door to further exploration of including wealth in traditional crash prediction methods. This paper discusses this relationship and offers recommendations for future work.
Evaluating the Effectiveness of the Safety Improvement Program in Saskatchewan Using a Full-Bayes Before-After Study
Emanuele Sacchi (firstname.lastname@example.org), University of SaskatchewanShow Abstract
Saeid Tayebikhorami, University of Saskatchewan
Improving and maintaining acceptable levels of safety for rural roads is a major task for local highway agencies. For instance, the FHWA’s “Safety Improvements on High Risk Rural Roads” manual assists local agencies in selecting the most effective (“proven”) countermeasures and recommends an organized and systematic process for specific safety-related programs in a rural setting. A key step in this process is to determine whether the frequency and/or severity of collisions at the treatment sites have been reduced after the implementation of the program. This research focused on evaluating the safety performance of a sample of 50 locations that have been improved under the Saskatchewan Ministry of Highways and Infrastructure’s (MHI) Safety Improvement Program (SIP). SIP projects were designed to reduce the frequency and severity of collisions on provincial highways in rural areas through the implementation of proven safety countermeasures. The methodology adopted for estimating the safety benefits was a before-after study with the full Bayes method. Overall, SIP was found to reduce total collisions by 14.8% and to reduce severe (fatal-plus-injury) collisions by 25.4%. The reduction of non-severe (property-damage-only) collisions was not found to be statistically significant at the 90% and 95% confidence levels. Also, crash modification factors (CMFs) for the two most frequent SIP treatments, i.e., right-turn lanes and delineation lighting at intersections, were estimated and compared to the results of the literature.
Alternatives in Prioritizing Safety Improvement Projects
Ioannis Tsapakis (email@example.com), Texas A&M Transportation InstituteShow Abstract
sushant sharma, Texas A&M Transportation Institute
William Holik, Texas A&M Transportation Institute
The Highway Safety Improvement Program (HSIP) aims to reduce the number and severity of fatalities and serious injury crashes by implementing safety improvement projects. The Traffic Operations Division (TRF) of the Texas Department of Transportation (TxDOT) currently administers TxDOT’s HSIP. TRF requests HSIP projects from TxDOT districts every year. All proposed projects are subjected to a benefit-cost ratio (BCR), called Safety Improvement Index (SII). After projects are submitted to the program, the TRF prioritizes them based on the SII. Although the structure and main components of TxDOT’s HSIP comply with relevant requirements, a review of modern safety assessment methods and tools revealed that there are several areas for improvement, including economic analysis and prioritization of HSIP projects. The objectives of this study are to a) compare TxDOT’s BCR-based project prioritization approach against an improved incremental benefit-cost ratio (IBCR) method, recommended by the Highway Safety Manual (HSM), and b) minimize the amount of time and resources required to prioritize HSIP projects. To address the first objective, the researchers applied both methods using data from the 2016 TxDOT HSIP and compared the results. The comparison showed that the projects selected using the IBCR method were more cost-effective than the projects funded by the BCR method. Further, the IBCR method awarded high-cost projects where more crashes had been observed. To address the second objective, the authors developed a prioritization tool that automatically ranks candidate projects using the IBCR method. The average runtime to prioritize 1,000 projects is less than 0.5 seconds.
KABCO Severity Cost Estimation by Cluster Analysis for Injury-Only Crashes in Puerto Rico
Josie Bianchi, Recinto Universitario de Mayaguez Universidad de Puerto RicoShow Abstract
Didier Valdés, Recinto Universitario de Mayaguez Universidad de Puerto Rico
Raúl Macchiavelli, Recinto Universitario de Mayaguez Universidad de Puerto Rico
The costs associated with crash injuries in Puerto Rico are based on the Highway Safety Manual 2010 version and the Federal Highway Administration data. They follow the crash-injury severity scale named KABCO. The model is based on a 2005 study of the Federal Highway Administration that used a 2001-dollar value that did not include Puerto Rico. However, Puerto Rico’s transportation and police agencies only use three types of crashes in their crash type distinction: fatal, injury, and property-damage-only. To adequately address road safety efforts in Puerto Rico, a crash-cost injured severity estimation was developed. This process was based on medical expenses and associated costs for each type of crash by revising the KABCO injury scale for injury-only motor vehicle crashes on Puerto Rico. A K-means cluster analysis was performed with the medical service data from traffic-related injuries to ascertain if the three-level KABCO categorization for traffic-related injuries fits the Puerto Rico data. As a result, the best cluster or group configuration that maximized the distance among groups and minimized the distance within groups was obtained.
Study of Automated Shuttle Interactions in City Traffic Using Surrogate Measures of Safety
Etienne Beauchamp (firstname.lastname@example.org), Ecole Polytechnique de MontrealShow Abstract
Nicolas Saunier, Ecole Polytechnique de Montreal
Marie-Soleil Cloutier, Institut National de la recherche scientifique
Driving automation is happening at a rapid pace, with different driver assistance systems already available in mass-market cars. However, this rapid development in driving automation leads to concerns and questions about their impact on safety, in particular for vulnerable road users. While previous studies have been restricted to incident reports and simulation tools, the safety of automated vehicles (AVs) is not clearly demonstrated. Instead of crashes, which are extremely rare events, this study uses surrogate measures of safety (SMoS) to analyze the interactions between road users and low-speed automated shuttles that circulated in Montréal and Candiac, in Canada, during two pilot projects in mid and late 2019. Cameras were placed at seven intersections along the routes of the shuttles. More than 70 hours of footage were processed to extract the road user trajectories using computer vision techniques and compute various safety indicators: speed, acceleration, time headway, time-to-collision (TTC) and post-encroachment time (PET). The Kolmogorov–Smirnov test was used to compare the distributions of interactions involving AVs with the distributions of interactions involving motorized vehicles following paths similar to those of the AVs. The results indicate that these automated shuttles behave generally more safely: their speeds and accelerations are lower and their interactions are characterized by higher TTCs and PETs, notably with vulnerable road users. However, small headway times at one site with high speed differentials between the shuttles and other following vehicles raise concerns that warrant further research into the suitable context for these vehicles.
Impacts of COVID-19 Pandemic on Traffic Crashes in Florida
Amy Romeo-Garcia, Florida International UniversityShow Abstract
John Kodi, Florida International University
Angela Kitali, Florida International University
Priyanka Alluri, Florida International University
Albert Gan, Florida International University
The novel COVID-19 pandemic outbreak has brought significant impacts on all aspects of peoples’ lives in the entire world. While this pandemic is still unfolding, it has already had unprecedented health, social, and economic consequences. The virus being easily transmittable from person-to-person, social distancing through stay-at-home orders is considered as effective in containing the rapid spread of the disease. For example, in Florida the stay-at-home orders came into effect on April 01, 2020. These strategies have resulted in drastic changes in the traffic pattern and travel behavior which in turn has led to significant changes in traffic safety. This study investigated the impacts of the COVID-19 pandemic on traffic crashes. Traffic crashes for March and April (2018-2020) in Florida were analyzed to identify if there was a significant change in traffic crashes following the outbreak of the pandemic and statewide directives to prohibit person-to-person interaction. Compared to similar days in 2018 and 2019, the overall statewide traffic crashes dropped significantly by 10% and 45% in the first and last two weeks of March 2020 respectively, and by 58% in April 2020. A similar significant decrease was observed in the fatal and injury crashes although as a percentage of all crashes they increased in 2020 compared to 2018 and 2019. Also, a decrease in the rear-end crashes and an increase in the run-off-road and non-motorist crashes were observed. This study helps to understand the early impacts of the pandemic and may be useful in operational and strategic planning for future pandemics.
Relating Household Consumption Expenditures to Road Traffic Fatalities: A Rural-Urban Study
Ruchika Agarwala, Indian Institute of Technology, BombayShow Abstract
Vinod Vasudevan, University of Alaska, Anchorage
Traffic fatality risk is higher in rural areas than in urban areas. In developing countries, vehicle ownership and investments in public transportation typically increase with economic growth. These two factors together increase the vehicular population, which in turn impacts traffic safety. However, the impacts of personal and non-personal modes of travel on traffic safety in rural versus urban areas in developing countries is still unexplored. This paper fills this gap in the literature by presenting a study focused on the relationship of various factors—including household consumption expenditure data—with traffic fatality in rural and urban areas. An exhaustive panel data modelling approach is adopted. One important finding of this study is that evidence exists of a contrasting relationship between economy and traffic fatality in rural and urban areas. Increases in most expenditure variables, such as fuel, non-personal modes of travel, and two-wheeler expenditures, are found to be associated with an increase in traffic fatality in rural areas.
Macro Safety Analysis for Non-motorized Vehicles Based on Roadway and Safety Education Improvement Countermeasures
Zhicheng Dai, Tongji UniversityShow Abstract
Xuesong Wang (email@example.com), Tongji University
Wenbin Luo, Traffic Police Headquarters of Shanghai Public Security Bureau
Non-motorized vehicles such as bicycles and e-bikes have gained great popularity in recent decades because of their high mobility and economy. Because these road users have a higher risk of injury in a crash, macro safety analyses have been conducted according to crash location. However, this strategy could be inefficient when comprehensive improvements such as traffic safety education programs are considered, due to differences between crash locations and the locations of the crash-involved road users’ residences. To improve implementation of such countermeasures, this study proposes a new analysis strategy of separately aggregating crashes for roadway engineering improvement and road users for education improvement. Roadway, socioeconomic and land use characteristics from 213 Shanghai sub-districts were collected as independent variables. The dependent variables of crashes and road users were divided into four subjects by crash severity level: fatal and injury (FI) and property damage only (PDO). A multivariate Poisson lognormal conditional autoregressive (CAR) model was developed to examine the relationships between regional characteristics and traffic safety, and potential safety improvement (PSI) was calculated for each sub-district based on model results. Hot-zone identification showed significant differences in distribution of sub-districts with urgent need for roadway versus education improvement. False positive and false negative indexes were developed to identify the differences quantitatively. Results indicated that nearly half the identified hot zones were inconsistent in unnecessarily prioritizing either engineering or education improvement. The findings of this paper are of great practical significance to better utilize resources for non-motorized vehicle traffic safety improvement.
Using Drone Technology to Collect School Transportation Data
Cody Hodgson, University of IdahoShow Abstract
Kevin Chang (firstname.lastname@example.org), University of Idaho
Travel tally surveys are administered by elementary, middle, and high (K-12) schools to collect data that measure how students arrive and leave school each day. This data can be used to determine both transportation safety and mobility needs. Collecting this data is usually accomplished by asking teachers to collect a tally in their classrooms; the data are then compiled to determine a representative result for each school. This process requires advanced planning from school administrators and teachers to ensure that information gathering is coordinated and relies on the personal input of each student. Since the age of elementary school students may be as little as six or seven years old, this approach may not always be reliable.In this study, a new method using a quadcopter drone was examined. For comparison purposes, participatory student tally surveys and drone videos were collected on the same day at three different elementary school sites, and the results and effectiveness of each counting method were compared and analyzed. The study concluded that the survey and drone results did not always yield similar results for all modes, so an explanation as to why these deviations occurred and what it means for researchers and practitioners is discussed. Given that drone technology continues to evolve, the lessons learned from this study can be applied toward future school transportation and other mobility studies.
Traveler-Involved Traffic Crashes As A Negative Externality Of Tourism Industry
Amin Mohamadi Hezaveh (email@example.com), North Carolina Department of TransportationShow Abstract
Christopher Cherry, University of Tennessee, Knoxville
Candace Brakewood, University of Tennessee, Knoxville
Although it is well established that travelers have a higher risk of injury in traffic crashes compared to non-travelers, less is known about the magnitude of traffic crashes involving travelers and the negative externality of travelers’ crashes (NETC) imposed on non-travelers. In this note, we rely on the U.S. Travel Association’s definition of a traveler to conduct an empirical analysis focusing on the state of Tennessee; we define travelers as those who travel more than 50 miles from home or have a home-address outside of Tennessee state. We find that 19.2% (127,031 out of 694,276 from 2014-2016) of traffic crashes in Tennessee involve a traveler. The injury cost of non-traveler crashes due to a crash with a traveler (i.e., monetized value of NETC) exceeds $7.6 billion, or 12.3% of tourist expenditures between 2014-2016. Analyzing the net impact of travel (tourist expenditures minus NETC) at county level reveals that the NETC exceeds tourist expenditures in 19 of 97 counties (or 20%) in Tennessee. The results of this analysis reveal that an overlooked negative externality of tourism is traffic crashes involving travelers, which warrants further study and potentially policy remediation.
Exploring Pathways from Driving Errors and Violations to Crashes: The Role of Speed Volatility
Numan Ahmad (firstname.lastname@example.org), University of TennesseeShow Abstract
Ramin Arvin, University of Tennessee, Knoxville
Asad J. Khattak, University of Tennessee
Transportation safety can be enhanced by applying safe systems approach to harness new forms of large-scale data. To enhance safety, this study explores how pre-crash data can be used to categorize various driving errors and violations and explore their contribution to speed volatility and crash outcomes. A rigorous path-analytic framework is applied analyzing subsample of Naturalistic Driving Study (NDS) data (N = 9,239). NDS data not only includes realworld information on pre-crash driving behavior and vehicle kinematics but also data on baselines, near-crashes, and crashes which can help quantify crash risk. We first classify human factors into six driving errors and violations using our previously developed systematic taxonomy of errors. Results indicate that human factors still prevail, contributing to 92.43% crashes. Next, tobit regression and ordered probit regression are used to model speed volatility and event outcomes. Results indicate that compared to no error, all six types of driving errors and violations are positively associated with both speed volatility and crash risk. While speed volatility shows significant association with crash risk indicating that all six types of driving errors and violations not only increase crash risk directly but also through speed volatility. For instance, recognition errors associate with 16% higher crash risk while indirect effect of recognition error through speed volatility was found to be about 3%, with total effects of 19%. From practical implication standpoint, implementing technology-based strategies such as cruise control, collision warning system, and dilemma-zone mitigation system can correct or lessen potentially dangerous driving errors and violations.
Exploring Effects of Speed Management Strategies on Drivers’ Speeding Behavior on Urban and Suburban Arterials with Probe Speed Data
Qing Cai (email@example.com), University of Central FloridaShow Abstract
Mohamed Abdel-Aty, University of Central Florida
Nada Mahmoud, University of Central Florida
Ma'en Al-Omari, University of Central Florida
Cheng Yuan, University of Central Florida
Brenda Young, Florida Department of Transportation
Speeding is one of the major contributing factors to traffic fatalities. Various speed management strategies have been proposed to encourage drivers to select more appropriate speed on roads. This study attempts to collect road attributes related to speed management strategies such as road surface and lane narrowing on urban and suburban arterials and examine the effect of the collected road attributes on the speeding proportions. Probe speed data was used to calculate the speeding proportions. To overcome the variability of probe speed data caused by the signalized intersections, a method was suggested by developing a fractional split model to adjust the probe speed data. A Beta regression model was developed to analyze the speeding proportion. A grouped random modeling structure was adopted to realize the different effects of road attributes on speeding proportions of different road types. Besides, a fixed beta model was developed for the comparison. The results suggested the grouped random model could provide better performance over the counterpart and could realize the different effects of road features and other contributing factors on speeding of different roads. It is expected that the findings could help inform more appropriate road design in order to reduce speed limit violations on urban and suburban arterials.
EVALUATION OF THE IMPACT OF COMMUNICATION SYSTEM ON TRAFFIC SAFETY UNDER CONNECTED AND AUTOMATED VEHICLES ENVIRONMENT
Md Hasibur Rahman, University of Central FloridaShow Abstract
Mohamed Abdel-Aty, University of Central Florida
Mohamed H. Zaki, University of Central Florida
Zubayer Islam, University of Central Florida
Zubayer Islam, University of Central Florida
The operation of connected and automated vehicles (CAVs) depends on effective communication between vehicles and other roadway infrastructure. However, the impact of the communication network on road safety under the CAV environment is not thoroughly explored. Hence, the focus of this study is to evaluate the performance of various communication parameters and traffic conditions and their impacts on traffic safety in the CAV environment. This research considered Dedicated Short-Range Communications (DSRC) for vehicle ad-hoc network (VANET) and intelligent driver model (IDM) for driving behavior of CAV. For the safety evaluation, the crash risk was estimated based on the time-to-collision (TTC) value. A binary logistic regression model was developed for the safety assessment of different communication parameters and traffic conditions based on traffic conflicts. The simulation study was carried out on one of the major expressways (SR408) in Orlando, Florida. The results of the performance of different communication parameters indicated that queue size and transmission power have a significant effect on traffic safety. With the increase of queue size, crash risk was lower for a smaller number of packet drops. Higher transmission power creates more interference, which culminates in a higher number of traffic conflicts. From the transportation aspect, the study considered lane closure and different percentages of traffic flow scenarios. The results showed that lane closure increased the crash risk due to the higher number of communication collisions between packets. The crash risk was also higher with the increase in traffic flow.
Traffic Safety During the COVID-19 Pandemic: A Study of How Incident-Based Traffic Safety Metrics Changed Over Time on State Highways in the San Francisco Bay Area and Los Angeles Regions
Matthew Hui, University of California, BerkeleyShow Abstract
Jonathan Kupfer, University of California, Berkeley
Offer Grembek, University of California, Berkeley
This research examined the traffic safety impacts of the COVID-19 pandemic and shelter in place on California state highways from February to May 2020 in the San Francisco Bay Area (SFBA) and the Los Angeles (LA) regions. This paper used police dispatch data and highway loop data to observe the vehicle miles traveled (VMT), number of incidents, and incident risk daily and during the peak hours. VMT data were used to establish four unique time periods; 2019 data were used for comparison. Our analysis found that the relative reduction in incident risk (incidents per VMT) was less than the relative reduction in the number of incidents. We found that the reductions in VMT, number of incidents, and incident risk were not uniform across time of day between the morning peak period, evening peak period, and the daily average. We also found notable differences in trends between the two regions. These findings help us better understand how traffic safety metrics are changing in response to the COVID-19 pandemic and illuminate questions for further research.
Safety Benefits for Maintenance Activities on Rural Two-Lane Roads: An Empirical Bayesian Before-After Study
Jiguang Zhao (firstname.lastname@example.org), HNTB CorporationShow Abstract
Rakesh Sharma, HNTB Corporation
Yongfeng Ma, Southeast University
Resurfacing, restoration and rehabilitation (3R) work is to preserve the serviceability of pavement surfaces with asphalt overlays and other interventions. The Missouri Department of Transportation deployed a combined 3R program of shoulder rumble stripes, shoulder widening and pavement resurfacing (RSR) to over 400 centerline miles of rural two-lane highway segments in Missouri. Crash modification factors (CMFs) for the RSR treatment were calculated using the empirical Bayesian (EB) before/after study method based on the roadway geometric, traffic and crash data collected. The calculated CMFs for the RSR treatment for almost all crash categories are below 1.000, with most CMFs significantly less than 1.000. In general the RSR treatment is effective at improving safety performance, particularly for fatal and injury crashes. CMFs of this study and that of previous studies were compared. The results of this study appear to be consistent with the aggregate results of the most comparable studies.
Effect of Speed on Crash Prediction Model of Rural Two-Lane Highways
Fahmida Rahman (email@example.com), Kentucky Transportation CabinetShow Abstract
Xu Zhang, Kentucky Transportation Cabinet
Mei Chen, Kentucky Transportation Center
Speed plays an important role in traffic safety. Previous works investigated speed’s effect on the crashes of rural two-lane highways using estimated speed due to inadequate speed data. It implies a need to understand the effect using measured speed on these roads. This study filled this gap by utilizing ubiquitous probe speed data. Zero Inflated Negative Binomial model was adopted for accounting the excess zeros in crash dataset. Four speed measures, including average speed, the 85 th percentile speed, difference in average speed and speed limit, and difference in 85 th percentile speed and speed limit, were evaluated. The average speed-based model was found to outperform other speed-based models as well as the traditional model. Later, to evaluate whether speed as a categorizer improves the overall model performance, separate prediction models were developed by dividing the dataset based on three-speed ranges: low, medium, and high speeds. Noticeably, speed becomes more significant for the crashes from low to high speed and is an obvious factor for the high-speed category. Compared to the traditional model, inclusion of speed reduced prediction error by 5% for the high-speed roads. Furthermore, for the medium-speed roads, using AADT as another categorizer resulted in further improvement over the model with speed categorizer only. Finally, the models developed for all three-speed ranges showed the lowest error in comparison to the no categorizer model. Since speed and AADT categorizer models enhance prediction accuracy, such an approach is recommended for developing crash prediction models for rural two-lane highways whenever possible.
Motorcycle Crash Causation Study: Exploratory Topic Models from Crash Narrative Reports
Subasish Das, Texas A&M UniversityShow Abstract
Anandi Dutta, University of Texas, San Antonio
Ioannis Tsapakis, Texas A&M Transportation Institute
The Motorcycle Crash Causation Study (MCCS) is a matched case-control study that contains a very wide list of crash contributing factors associated with motorcycle crash occurrences. It contains information such as motorcycle information, rider information, motorcycle information, and associated trip information. This study also provides crash narrative information that presents in-depth narrative discussion of the crash causation. Due to the plethora of information, it is critical to investigate MCCS related data. Some studies examined the structured information in MCCS datasets. There is no in-depth study that has examined the unstructured textual contents in the MCCS data. This study aims to mitigate this research gap by applying different natural language processing (NLP) tools (e.g., text mining, topic modeling). Fatal and non-fatal crash narratives are clustered separately to gain injury level specific insights. The findings of this study will contribute to the on-going studies on MCCS to better understand the crash causation mechanism associated with motorcycle crashes.
Factors Contributing to Operating Speed on Different Context Classifications of Arterial Segments in Central Florida
Nada Mahmoud, University of Central FloridaShow Abstract
Mohamed Abdel-Aty, University of Central Florida
Qing Cai (firstname.lastname@example.org), University of Central Florida
Brenda Young, Florida Department of Transportation
Operating speed is fundamental in many fields of transportation engineering including traffic safety, transportation planning, and geometric design. Hence, many studies explored the impact of different exogenous variables on the operating speed represented by the 85th percentile speed. This study contributes to the literature by evaluating and identifying the factors influencing operating speed considering context classification. The study focused on three contect classifications: C3R-Suburban Residential, C3C-Suburban Commercial, and C4-Urban General. Further, it identifies the potential speed calming measures that influence the operating speed for specific context classification categories. Hence, a Tobit model was proposed and developed using big data including traffic roadway characteristics, land use attributes, and socio-demographic information. Three years INRIX speed data were obtained for around 1800 roadway segments and to calculate the 85th percentile speed. The study propsed an approach to adjust the 85th percentile speed from INRIX data since traffic flow on arterials could be disrupted by signalized intersections. Afterwards, empirical analysis was conducted by developing three Tobit models: Generic, C3C/C3R, and C4 models using the adjusted 85th percentile speed. In conclusion, for the three developed models, several variables (e.g., inside shoulder type, inside shoulder width, speed limit, and number of signalized intersections per mile) were found to have significant influence on the 85th percentile speed. The analysis also indicated the potential speed management countermeasures that have significant impact on the 85th percentile speed.
Best Practices for Guidelines for Inspection, Repair, and Use of Portable Concrete Barriers in Roadside Safety
Chiara Silvestri Dobrovolny, Texas A&M Transportation InstituteShow Abstract
Husain Aldahlki, Texas A&M University, College Station
Roger Bligh, Texas A&M Transportation Institute
The Manual for Assessing Safety Hardware (MASH) implementation agreement allows state transportation agencies to continue the use of portable concrete barriers (PCBs) manufactured on or before December 31, 2019, and successfully tested to National Cooperative Highway Research Program Report 350 or the 2009 edition of MASH, throughout their normal service life. PCBs can incur damage while in transit, in storage, or due to vehicular impact. Often, PCBs can experience damage such as broken or bent connections, cracks, concrete spalling, and more. The American Traffic Safety Services Association developed a basic qualitative guidance for evaluation criteria of such barriers. A few departments of transportation (DOTs) have developed their own guidelines for inspection of PCB segments or evaluation criteria for the acceptability of PCBs. Since no federal guidance has yet been developed to determine life expectancy or acceptability for PCBs, agencies are left to determine whether to repair or replace segments without adequate information, which could lead to unsafe barriers on the National Highway System. There is a need to develop a comprehensive review of best practices of Agencies guidelines which address replacement or repair of PCB segments based on the type and extent of barrier damage. A survey was conducted to complement a throughout literature review of identified guidelines to provide those DOTs in needs with proposed guidelines for inspection, evaluation and repair of PCB segments. This paper summarizes relevant survey findings and proposes future research work to validate developed guidelines for inspection, evaluation, and repair of PCBs.
VERIFICATION OF USRAP RISK ASSESSMENTS FOR RUN-OFF AND HEAD-ON CRASHES USING FIELD DATA
FAHMID HOSSAIN, University of UtahShow Abstract
Juan Medina, University of Utah
The United States Road Assessment Program (usRAP) provides a systemic approach to estimate the risk of severe injury and fatal crashes along roadway segments based on expected safety performance of roadway and roadside characteristics, together with a general estimation of traffic volume. Detailed crash data is not needed for safety assessments, providing advantages over more traditional crash-driven approaches. However, experiences with usRAP are limited in the U.S., and to date, the program has a growing but limited number of participating states. Verification of the adequacy of usRAP assessments is therefore of significant value, not only to identify strengths and limitations of the methodology within the U.S. context, but also to potentially expand the set of tools available to agencies. This paper presents a verification of usRAP risk assessments for run-off road and head-on crashes using over 7,000 miles of coded segments and five years of crash data collected in Utah. Comparisons between risk estimations from usRAP and actual crash rates provided insights on expected and observed effects of roadside objects and their distances from the traveled lanes, type of median present, as well as horizontal curves. A spatial correlation test also confirmed the agreement between usRAP risk assessments and crash data, providing additional promising indications of the suitability of this systemic methodology for safety applications.
Macro-Level Safety Analysis of Crashes and Violations: Influencing Factors and Crash Hotspots
Yingying Pei, Tongji UniversityShow Abstract
Xuesong Wang (email@example.com), Tongji University
Mingjie Feng, Tongji University
Zhixing Zhu, Traffic Police Headquarters of Jiangsu Province
Fang Liu, Traffic Police Department of Suzhou City
Zhongyang Qie, Traffic Police Department of Suzhou City
Paul P. Jovanis, Paul P. Jovanis Ph.D.
Regional traffic safety has been a public concern for many metropolitan areas, and it is urgent to turn this situation around by using an appropriate traffic safety analysis and crash hotspot identification method. Existing studies mainly focus on the effects of engineering-related indicators on regional crashes and violations, neglecting the traffic police enforcement-related factors. Meanwhile, the relationship between crashes and violations is insufficiently recognized. To address these gaps, this study selected Suzhou, a rapidly developing Chinese city, and collected socio-economic indicators, road features, land use intensity, facility data, and police enforcement information as independent variables. A Bayesian bivariate negative binomial spatial conditional autoregressive (BNB-CAR) model was developed to capture the association between crashes and violations, as well as their contributing factors. Results showed that (1) there existed a significantly correlated effect between crashes and violations; (2) engineering-related indicators had similar effects on crashes and violations, while some police enforcement-related factors were dual-effective. Based on the model results, this study used the potential for safety improvement (PSI) method to identify the hazardous areas of the 115 towns in Suzhou. It was observed that (1) the spatial distribution of crashes indicated the spatial correlations among the towns; (2) the fringe areas suffered higher crash risks than the downtown areas. Several engineering and enforcement countermeasures were provided for urban planning departments and traffic police to enhance their work effectiveness. Additionally, decision makers and administrators will benefit from this study to improve daily traffic safety management.
Segment-Level Crash Risk Analysis for New Jersey Highways Using Advanced Data Modeling
Branislav Dimitrijevic, New Jersey Institute of TechnologyShow Abstract
Sina Darban Khales, New Jersey Institute of Technology
Roksana Asadi (firstname.lastname@example.org), New Jersey Institute of Technology
Joyoung Lee, New Jersey Institute of Technology
Kitae Kim, New Jersey Institute of Technology
Highway crashes are the most significant challenge to the goal of providing a safe and efficient highway transportation system. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to developing models to study and predict crash occurrence. More recently advancements have been made in developing proactive crash risk models, aiming to assess crash risks in the short term, and inform traffic management strategies to prevent and mitigate the negative effects of crashes. This study developed and tested several models for segment-level crash risk considering the data available to most transportation agencies in real-time on a regional network scale. The data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The models included Bayesian Logistics Regression (BLR), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), K-Nearest Neighbor (KNN), and Gaussian Naïve Bayes (GNB). The models were trained and tested using a dataset containing records of 10,155 crashes that occurred on two interstate highways in New Jersey over two years. It was found that for the given dataset the models provided limited predictive value. Keywords: Crash analysis, crash risk forecasting, machine learning
Heterogeneity in Naturalistic Driving Errors, Violations, and Crash Risk in Diverse Environmental Context
Asad J. Khattak, University of TennesseeShow Abstract
Numan Ahmad (email@example.com), University of Tennessee
Behram Wali, University of Tennessee
Driving errors and violations are identified as contributing factors in most crash events. Different types of driving errors and violations may vary across diverse roadway environments. Due to unique nature of several types of driving errors and violations, crash risk associated with each type of these driving errors and violations can be different. To empirically explore errors and violations in diverse built environments, this study harnesses unique and highly detailed pre-crash sensor data collected in SHRP2 Naturalistic Driving Study (NDS), containing 673 crashes, 1,331 near-crashes and 7,589 baselines (no-event). First, we apply our previously proposed systematic taxonomy of driving errors and violations to bring all types of human crash-contributing factors into systematic framework, and then compute crash risk associated with different driving errors and roadway environment. Based on percentage of crashes per percentage of baselines in a specific locality, interstates and rural and semi-rural settings may pose lower risks. Contrarily, urban, business/industrial, and school locations seem to have higher percentage of crashes per percentage of baselines indicating higher crash risk. Human errors and violations contributed to 93% of crashes. Recognition and decision errors occurred more frequently (each contributing to ~39% of crashes) in business or industrial land use environments (but not in dense urban localities). Distribution of driving errors and violations across different roadway environments can aid in implementation of place-based countermeasures with implications for connected and automated vehicle development, e.g., by understanding complex and unusual (fringe case) situations for safety, testing of connected and automated vehicles can be enhanced.
Assessing the Effectiveness of Built Environment-based Safety Measures by Urban and Rural Area for Reducing the Non-motorist Crashes
Shefa Arabia Shioma, University of ToledoShow Abstract
Ahmad Ilderim Tokey, University of Toledo
Muhammad Salaha Uddin, University of Toledo
Built environment (BE)-based safety measures are usually implemented for reducing the non-motorist crashes in urban and rural area. However, their usefulness differing the urban and rural area were not widely explored in literature. Therefore, this study was explored the effectiveness of built environment-based safety measures in urban and rural settings. The study used four years’ (2015-2018) non-motorist (pedestrian and bi-cyclist) crash data of Florida and examined the effect of built-environment based safety measures such as sidewalk, distance from the road, bike lane, barrier, land use mix. In this study urban and rural area were classified by applying the multivariate clustering method. The study used the negative binomial and geographically weighted Poisson regression (GWPR) for understanding the effects of BE factors assuming their spatial heterogeneity. The study finds that building the sidewalk only, and existence of intersection expose the people to crash incidents in urban areas while traffic volume works for increasing non-motorist crashes in the rural areas. The analysis also reveals that combinedly sidewalk and barrier can reduce the risks of non-motorist crashes. Signalized intersection also reduces the effect of high traffic volume on the frequency of crashes. Higher percentage of commercial Land uses (LU) in high mixed LU are helpful for ensuring the safety of pedestrian and cyclists. This study findings will be supporting for implementing the BE based safety measures considering their combined effectiveness as well as the urban and rural characteristics of the area.
Network Screening on Low-Volume Roads: A New Proposed Method
Ahmed Al-Kaisy (firstname.lastname@example.org), Montana State UniversityShow Abstract
Kazi Huda, Montana State University, Bozeman
This paper presents a proposed new method for network screening on rural low-volume roads. These roads constitute an important and integral part of the rural roadway network by providing access to remote rural areas including farms and ranches. The majority of low-volume roads belong to the lowest functional class (local rural roads) and many were built decades ago, and therefore their geometric features are often considered “substandard” by today’s design practices. The conventional hot spot newtwork screening techniques may not be appropriate for low-volume roads due to the sporadic nature of crashes occurring on these roads. Other approaches for network screening (e.g. Highway Safety Manual preditive methodology, EB method, etc.) require extensive roadway and traffic data that are often unavailable at local agencies for lack of resources, and/or impractical to use for lack of technical expertise. This research attempts to address these obstacles in low-volume roads network screening with the purpose of identifying candidate sites for safety treatments. The research used an extensive low-volume road sample from the state of Oregon and the Empirical Bayes expected number of crashes in developing the proposed models for network screening. The proposed models do not require exact measurement of roadway geometric features as all geometric variables were classified into categories that are easy to compile by local agencies. Further, the method could be used with and without traffic data without much compromising the effectiveness of the network screening process. Keywords: Low-volume roads, Empirical Bayes, network screening, safety improvements, risk factors
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