Journal articles on the topic 'Traffic crashes'
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Rezapour, Mahdi, Amirarsalan Mehrara Molan, and Khaled Ksaibati. "Application of Multinomial Regression Model to Identify Parameters Impacting Traffic Barrier Crash Severity." Open Transportation Journal 13, no. 1 (May 31, 2019): 57–64. http://dx.doi.org/10.2174/1874447801913010057.
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Background: Run Off The Road (ROTR) crashes are some of the most severe crashes that could occur on roadways. The main countermeasure that can be taken to address this type of crashe is traffic barrier installation. Although ROTR crashes can be mitigated significantly by traffic barriers, still traffic barrier crashes resulted in considerable amount of severe crashes. Besides, the types of traffic barriers, driver actions and performance play an important role in the severity of these crashes. Methods: This study was conducted by incorporating only traffic barrier crashes in Wyoming. Based on the literature review there are unique contributory factors in different crash types. Therefore, in addition to focusing on traffic barrier crashes, crashes were divided into two different highway classes: interstate and non-interstate highways. Results: The result of proportional odds assumption was an indication that multinomial logistic regression model is appropriate for both non-interstate and interstates crashes involved with traffic barriers. The results indicated that road surface conditions, age, driver restraint and negotiating a curve were some of the factors that impact the severity of traffic barrier crashes on non-interstate highways. On the other hand, the results of interstate barrier crashes indicated that besides types of barriers, driver condition, citation record, speed limit compliance were some of the factors that impacted the interstate traffic barrier crash severity. Conclusion: The results of this study would provide the policymakers with the directions to take appropriate countermeasures to alleviate the severity of traffic barrier crashes.
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Evans, Leonard. "Traffic Crashes." American Scientist 90, no. 3 (2002): 244. http://dx.doi.org/10.1511/2002.9.722.
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Hu, Li Wei, and Jian Xiong. "Practice Analysis of Road Traffic Crashes Accident of a City in China." Advanced Engineering Forum 5 (July 2012): 105–10. http://dx.doi.org/10.4028/www.scientific.net/aef.5.105.
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Many studies focused on the development of crash analysis approaches have resulted in aggregate practices and experiences to quantify the safety effects of human, geometric, traffic and environmental factors on the expected number of deaths, injuries, and/or property damage crashes at specific locations. Traffic crashes on roads are a major cause of road crashes in the metropolitan area of Xi’an. In an attempt to identify causes and consequences, reported traffic crashes for six years in Xi’an were analyzed using a sample of 2038 reports. The main types of information from such reports were extracted, coded, and statistically analyzed. Important results were obtained from frequency analyses as well as multiple contributory factors related to traffic crashes, including crash severity, time and location of occurrence, geometry of the road, AADT and v/c. This paper presents the results of such analyses and provides some recommendations to improve traffic safety and further studies to analyze potential crash locations.
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Zandi, Kamran, Ali Tavakoli Kashani, and Atsuyuki Okabe. "Influence of Traffic Parameters on the Spatial Distribution of Crashes on a Freeway to Increase Safety." Sustainability 15, no. 1 (December 28, 2022): 493. http://dx.doi.org/10.3390/su15010493.
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Significant research has been conducted in recent years to determine crash hotspots. This study focused on the effects of various traffic parameters, including average traffic speed and traffic volume, on the spatial distributions of freeway crashes. Specifically, this study analyzed the spatial distributions of crashes on the Qazvin–Abyek freeway in Iran using four-year crash records. Spatial crash clustering analysis was performed to identify hotspots and high cluster segments using global Moran’s I, local Moran’s I, and Getis-Ord Gi*. The global Moran’s I indicated that clusters were formed under the low range of hourly traffic volume (less than 1107 veh/h) and the high range of traffic speed (more than 97 km/h), which increased the number of heavy vehicle crashes in the early morning (time 03–06) around the 52 km segment. The results obtained from kernel density estimation (KDE), local Moran’s I, and Getis-Ord Gi* revealed similar crash hotspots. The results further showed different spatial distributions of crashes for different traffic hourly volumes, traffic speed, and crash times, and there was hotspot migration by applying different traffic conditions. These findings can be used to identify high-risk crash conditions for traffic managers and help them to make the best decisions to enhance road safety.
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Carlson, Kristin, Alireza Ermagun, Brendan Murphy, Andrew Owen, and David Levinson. "Safety in Numbers for Bicyclists at Urban Intersections." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 25, 2019): 677–84. http://dx.doi.org/10.1177/0361198119846480.
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This study assesses the estimated crashes per bicyclist and per vehicle as a function of bicyclist and vehicle traffic and tests whether greater traffic reduces the per-vehicle crash rate, a phenomenon referred to as “safety in numbers” (SIN). We present a framework for comprehensive bicyclist risk assessment modeling, using estimated bicyclist flow per intersection, observed vehicle flow, and crash records. Testing a two-part model of crashes, we reveal that both the average of annual average daily traffic (AADT) over a 14-year period and the estimated daily bicyclist traffic (DBT) have a diminishing return to scale in crashes. This accentuates the positive role of SIN. Higher volumes of vehicles and cyclists lowers not only the probability of crashes, but the number of crashes as well. Measuring the elasticity of the variables, it is found that a 1% increase in the average of AADT across the time window increases the probability of crashes by 0.14% and the number of crashes by 0.80%. However, a 1% increase in the estimated DBT increases the probability of crashes by 0.09% and the number of crashes by 0.50%.
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Yang, Bo, Yao Wu, and Weihua Zhang. "Analysis of Freeway Secondary Crashes in Different Traffic Flow States by Three-Phase Traffic Theory." Journal of Advanced Transportation 2020 (September 27, 2020): 1–10. http://dx.doi.org/10.1155/2020/8890351.
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The objective of this study is to analyse the relationship between secondary crash risk and traffic flow states and explore the contributing factors of secondary crashes in different traffic flow states. Crash data and traffic data were collected on the I-880 freeway in California from 2006 to 2011. The traffic flow states are categorised by three-phase traffic theory. The Bayesian conditional logit model has been established to analyse the statistical relationship between the secondary crash probability and various traffic flow states. The results showed that free flow (F) state has the best safety performance of secondary crash and synchronized flow (S) state has the worst safety performance of secondary crashes. The traditional logistic regression model has been used to analyse the contributing factors of secondary crashes in different traffic flow states. The results indicated that the contributing factors in different traffic flow states are significantly different.
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Wu, Shubo, Quan Yuan, Zhongwei Yan, and Qing Xu. "Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model." Journal of Advanced Transportation 2021 (September 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/3771640.
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Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.
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Adegbite, Qasim, Khondoker Billah, Hatim Sharif, and Samer Dessouky. "Urban Intersections and Traffic Safety in the City of San Antonio." MATEC Web of Conferences 271 (2019): 06003. http://dx.doi.org/10.1051/matecconf/201927106003.
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Intersections are high-risk locations on roadways and often experience high incidence of crashes. Better understanding of the factors contributing to crashes and deaths at intersections is crucial. This study analyzed the factors related to crash incidence and crash severity at intersections in San Antonio for crashes from 2013 to 2017 and identified hotspot locations based on crash frequency and crash rates. Binary logistic regression model was considered for the analysis using crash severity as the response variable. Factors found to be significantly associated with the severity of intersection crashes include age of driver, day of the week, month, road alignment, and traffic control system. The crashes occurred predominantly in the highdensity center of the city (downtown area). Overall, the identification of risk factors and their impact on crash severity would be helpful for road safety policymakers to develop proactive mitigation plans to reduce the frequency and severity of intersection crashes.
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Ning, Huajing, Yunyan Yu, and Lu Bai. "Analyzing the Impact of Traffic Violation Behaviors on Traffic Fatal Crashes Using Multilevel Models on Expressways." Mathematical Problems in Engineering 2022 (September 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/9917877.
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This study is intended to explore the impact of traffic violation behaviors on traffic fatal crashes on expressways. Based on the generalized additive model method, for independent traffic violation data and traffic fatal crash data from the Traffic Administration Bureau of Anhui Provincial Public Security Department, the relationship among the violations and traffic fatal crashes was discussed. Applying the proposed multilevel analysis method, which can effectively show the evolution trend among traffic violations and traffic fatal crashes influenced by time, space, and inter-reaction. The results show that hierarchical data structures should not be ignored. Meanwhile, in order to avoid fatal traffic collisions, we should reduce traffic violations, especially the control of speed on expressways This is because speeding is the most common traffic violation behavior and has a significant impact on traffic fatal crashes. The results could help strengthen our focus on key traffic violations and provide a reference for traffic safety management and decision-making.
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Jiang, Ze-Hao, Xiao-Guang Yang, Tuo Sun, Tao Wang, and Zheng Yang. "Investigating the Relationship between Traffic Violations and Crashes at Signalized Intersections: An Empirical Study in China." Journal of Advanced Transportation 2021 (April 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/4317214.
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About 90% of traffic crashes are caused by human factors, within which traffic violations are one of the most typical and common causes. In order to investigate the relationship between traffic violations and traffic crashes, this research targets signalized intersections in two Chinese cities: Yinchuan and Suqian. Thirty-one intersections are selected as the research sites, and additionally, the traffic volume, traffic violation, and traffic crash data of each intersection are collected for one year. A White’s test is conducted to test the homoscedasticity of the data and a multiple linear regression model is employed to investigate the relationship between traffic crashes and violations. The results show the following: (1) although the research sites are located in two different cities, the data is homoscedastic, which suggests that the above result may be statistically stable between different cities. (2) There is a significant multiple linear regression relationship (R2 = 0.782, adjusted R2 = 0.716) between the total number of traffic crashes and traffic violations. Among the chosen 7 independent variables, four are significantly related to the dependent variable, namely, driving commercial vehicle during internship, wrong-way entry, speeding, and traffic-light violation. (3) With the increase of annual average daily traffic (AADT), the number of total crashes goes up; however, the injury-or-fatality rate decreases, which means that intersections with smaller traffic volumes tend to have higher traffic crash severity. Based on the above conclusions, it is possible to conduct more targeted enforcement to improve the safety of intersections.
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Rad, Mahdieh, Alexandra LC Martiniuk, Alireza Ansari-Moghaddam, Mahdi Mohammadi, Fariborz Rashedi, and Ardavan Ghasemi. "The Pattern of Road Traffic Crashes in South East Iran." Global Journal of Health Science 8, no. 9 (January 4, 2016): 149. http://dx.doi.org/10.5539/gjhs.v8n9p149.
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<p><strong>BACKGROUND:</strong> In the present study, the epidemiologic aspects of road traffic crashes in South East of Iran are described.</p><p><strong>METHODS:</strong> This cross-sectional study included the profile of 2398 motor vehicle crashes recorded in the police office in one Year in South East of Iran. Data collected included: demographics, the type of crash, type of involved vehicle, location of crash and factors contributing to the crash. Descriptive statistics were used for data analysis.</p><p><strong>RESULTS:</strong> Collisions with other vehicles or objects contributed the highest proportion (62.4%) of motor vehicle crashes. Human factors including careless driving, violating traffic laws, speeding, and sleep deprivation/fatigue were the most important causal factors accounting for 90% of road crashes. Data shows that 41% of drivers were not using a seat belt at the time of crash. One- third of the crashes resulted in injury (25%) or death (5%).</p><p><strong>CONCLUSIONS:</strong> Reckless driving such as speeding and violation of traffic laws are major risk factors for crashes in the South East of Iran. This highlights the need for education along with traffic law enforcement to reduce motor vehicle crashes in future.<strong></strong></p>
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Taylor, Samuel G., Brendan J. Russo, and Emmanuel James. "A Comparative Analysis of Factors Affecting the Frequency and Severity of Freight-Involved and Non-Freight Crashes on a Major Freight Corridor Freeway." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 34 (June 11, 2018): 49–62. http://dx.doi.org/10.1177/0361198118776815.
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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.
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Ewing, Reid, Li Chen, and Cynthia Chen. "Quasi-Experimental Study of Traffic Calming Measures in New York City." Transportation Research Record: Journal of the Transportation Research Board 2364, no. 1 (January 2013): 29–35. http://dx.doi.org/10.3141/2364-04.
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This paper provides a large-scale, rigorous evaluation of traffic calming projects in one U.S. city. The study area is New York City, which treated 391 streets with speed tables between 1996 and 2003. On the basis of crash frequencies for 5 years before treatment and 5 years after for treated streets and well-matched comparison streets, no evidence emerged that New York City's ambitious traffic calming program has led to a reduction in total crashes, pedestrian crashes, or injury crashes. This is in contrast to earlier, less carefully controlled evaluations that have reported significant reductions in crashes with traffic calming.
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Alhomaidat, Fadi, and Lusanni Acosta-Rodriguez. "How Does Pedestrian-Driver Behavior Influence in the Number of Crashes? A Michigan’s Case Study." Transport and Telecommunication Journal 22, no. 2 (April 1, 2021): 152–62. http://dx.doi.org/10.2478/ttj-2021-0012.
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Abstract This study provides with a safety assessment of the pedestrian’s crash data in one of the largest cities of the state of Michigan, Grand Rapids. Crash data reviewed included a 9-year period between years 2010 and 2018. Crash clusters with largest number of accidents were selected to perform analysis based on the normalization of crash with population (using Census Bureau information). Geographic Information System (GIS) software was used to gather this data using a 250-feet buffer around the clusters. Also, GIS was used to identify the infrastructure design and locations nearby the studied area (e.g. schools and hospitals) to understand the crash environments. Observation of the associated factors with pedestrian crashes were studied at the location of interest. An analysis of all safety efforts was completed and a list of recommendations and possible implementation strategies (e.g. pedestrian countermeasures). Finally, it was found that four types of pedestrian crashes were most representative that crashes involved left-turning vehicle, crashes involved right-turn vehicle, crashed involved pedestrian in crosswalk and through traffic, and pedestrian were not cross at designated cross location
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Yang, Jia, Keiichi Higuchi, Ryosuke Ando, and Yasuhide Nishihori. "Examining the Environmental, Vehicle, and Driver Factors Associated with Crossing Crashes of Elderly Drivers Using Association Rules Mining." Journal of Advanced Transportation 2020 (February 1, 2020): 1–8. http://dx.doi.org/10.1155/2020/2593410.
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In the aging society, reducing vehicle crashes caused by elderly drivers has become a crucial issue. To find effective methods to reduce these vehicle crashes, it is necessary to give some insights into the characteristics of vehicle crashes and those of traffic violations caused by elderly drivers. However, multiple significant factors associated with crossing crashes due to elderly drivers were not extensively observed in previous studies. To fill this research gap, this study identifies the crash pattern and examines the environmental, vehicle, and driver factors associated with crossing crashes due to elderly drivers. The 5-year crash data in Toyota City, Japan, are used for empirical analysis. The emerging data mining method called association rules mining is applied to discover various factors associated with crossing crashes of elderly and nonelderly drivers, respectively. The significant findings indicate that (1) elderly drivers are more likely to lead to crossing or right-turn crashes, compared with nonelderly drivers; (2) there are more factors including crash location (intersection without signal), lighting (daylight), road condition (dry and other), weather condition (clear and raining), vehicle type (light motor truck), and traffic violation (fail to confirm safety) associated with the large proportion of crossing crashes due to elderly drivers. The findings of this study can be used by traffic safety professionals to implement some countermeasures to reduce the crossing crashes due to elderly drivers.
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Shihab, Mason Alexander, and Brittany Shoots-Reinhard. "Ironic effects of political ideology and increased risk-taking in Ohio drivers during COVID-19 shutdown." PLOS ONE 17, no. 12 (December 19, 2022): e0279160. http://dx.doi.org/10.1371/journal.pone.0279160.
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In March 2020, Ohio, along with many other states, enacted a stay-at-home order (i.e., “shutdown”) to limit the spread of COVID-19. As a result of lower traffic, crashes should also have declined. We investigated whether crash rates declined in Ohio during the stay-at-home order and explore possible predictors for the decrease, such as reduced travel in compliance with the order, along with speeding, alcohol, and drug use. In addition, we examined whether support for President Trump would relate to greater travel and greater crashes (particularly during the stay-at-home order, when greater travel indicated lower compliance). The overall rate of crashes fell as people stayed home, mainly due to a decline in minor crashes. In contrast, the rate of serious crashes did not fall. Instead, percentage of alcohol-related crashes increased during the stay-at-home order, and the reduction in travel was associated with greater speeding-related crashes. Because alcohol and speeding tend to increase crash severity, these two factors may explain why severe crash rates were not reduced by lower traffic. Instead, it appears that those drivers remaining on the roads during the shutdown may have been more prone to risky behaviors, evidenced by a greater percentage of alcohol-related crashes across the state during the shutdown and greater speed-related crashes in counties with less traffic. In addition, county-level support for President Trump indirectly predicted greater rates of crashes (of all types) via increased travel (i.e., lower compliance with the shutdown), even while controlling for county-level income, rurality, and Appalachian region. Importantly, this mediated effect was stronger during the weeks of the shutdown, when greater travel indicated lower compliance. Thus, lower compliance with the stay-at-home order and increased risky driving behaviors by remaining drivers may explain why lower traffic did not lead to lower serious crashes.
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Rezapour, Mahdi, Shaun S. Wulff, and Khaled Ksaibati. "Predicting Truck At-Fault Crashes Using Crash and Traffic Offence Data." Open Transportation Journal 12, no. 1 (April 16, 2018): 128–38. http://dx.doi.org/10.2174/18744478018120100128.
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Introduction:The number of truck-related injuries and deaths can be reduced by understanding the factors that contribute to the higher risk of truck-related crashes and violations. Truck drivers are at fault of more than 80% of all the truck crashes on Wyoming interstates, and the literature review indicated that in order to identify appropriate countermeasure to crashes, each crash type should be analyzed individually. The literature review also revealed that relationships exist between driving records and driver culpability in crashes.Method:This study employed two approaches to identify contributory factors to truck-at-fault fatal and injury crashes, and truck-related violations. Interstate 80, a Wyoming corridor in a mountainous area with one of the highest truck crash rates in the US, was selected as a case study. Only truck-at-fault crashes and specific types of truck-related violations were considered in this study. The analyses include two approaches. First, the logistic regression model was employed to explore vehicle, driver, crash, and environmental characteristics that contribute to truck-at-fault fatal and injury crashes. Second, truck violations were used as a proxy for truck crashes to examine the tendency to violate truck-related traffic laws in relation to driver and temporal characteristics. Based on the literature, only violations associated with higher risk of severe crashes were included in the analyses. The included violations accounted for more than 70% of all the violations.Result:This study considered more than 30 variables and found that only 10 variables impact truck-at-fault crashes. These factors included: gender, history of past violation, crashes involving multiple vehicles, exceeding the speed limit, occupant distraction, driver ejection, fatigued driving, non-seat belt usage, overturn, and head-on collision. Results of the second analysis indicated that both residency and time of crash are factors that impact truck-related violations. Results of the analysis also indicated that both residency and time of the crash are factors that impact truck-related violations.
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Haq, Muhammad Tahmidul, Milan Zlatkovic, and Khaled Ksaibati. "Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (May 18, 2019): 333–42. http://dx.doi.org/10.1177/0361198119847980.
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The State of Wyoming experiences a high percentage of truck traffic along all its highways, especially Interstate 80 (I-80). The increased interactions between trucks and other vehicles have raised many operational and safety concerns. This paper presents a safety analysis and a development of safety performance functions (SPFs) along I-80, with a focus on truck crashes. Nine years of historical crash data in Wyoming (2008–2016) were used to observe the involvement of light, medium, and heavy trucks in crashes. Analysis of the major contributory factors showed that 54% of the total truck-related crashes occurred during icy road conditions and about 46% during snowy weather conditions, and approximately 45% involved driving too fast and driving in improper lane. The analysis also included segments with horizontal curves and vertical grades and their impacts on truck crashes. The crash rate analysis showed higher truck crash rate compared with total crash rate considering equal vehicle miles traveled as exposure. A zero-inflated negative binomial model was applied to develop Wyoming-specific SPFs for various truck crash types. The effects of traffic, road geometry characteristics, and weather parameters influencing different truck-related crashes were quantified from these models. Downgrades and steep upgrade sections were found to increase truck-related crashes. The number of rainy days per year was found to be a significant variable affecting truck-related crashes. On the other hand, the presence of climbing lanes has significant safety benefits.
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Chawla, Hitesh, Ilker Karaca, and Peter T. Savolainen. "Contrasting Crash- and Non-Crash-Involved Riders: Analysis of Data from the Motorcycle Crash Causation Study." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (June 10, 2019): 122–31. http://dx.doi.org/10.1177/0361198119851722.
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Motorcycle crashes and fatalities remain a significant public health problem as fatality rates have increased substantially as compared to other vehicle types in the United States. Analysis of causal factors for motorcycle crashes is often challenging given a lack of reliable traffic volume data and the fact that such crashes comprise a relatively small portion of all traffic crashes. Given these limitations, on-scene crash investigations represent an ideal setting through which to investigate the precipitating factors for motorcycle-involved crashes. This study examines motorcycle crash risk factors by employing data recently made available from the Federal Highway Administration Motorcycle Crash Causation Study (MCCS). The MCCS represents a comprehensive investigative effort to determine the causes of motorcycle crashes and involved the collection of in-depth data from 351 crashes, as well as the collection of comparison data from 702 paired control observations in Orange County, California. This dataset provides a unique opportunity to understand how the risk of crash involvement varies across different segments of the riding population. Logistic regression models are estimated to identify the rider and vehicle attributes associated with motorcycle crashes. The results of the study suggest that motorcycle crash risks are related to rider age, physical status, and educational attainment. In addition to such factors outside of the rider’s control, several modifiable risk factors, which arguably affect the riders’ proclivity to take risks, were also found to be significantly associated with motorcycle crash risk, including motorcycle type, helmet coverage, motorcycle ownership, speed, trip destination, and traffic violation history.
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Mohamadi Hezaveh, Amin, and Christopher R. Cherry. "Applying a Home-Based Approach to the Understanding Distribution of Economic Impacts of Traffic Crashes." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (October 22, 2020): 360–71. http://dx.doi.org/10.1177/0361198120953431.
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The current practice of road safety attributes traffic crash costs to the location of traffic crashes. Therefore it is challenging to estimate the economic cost of traffic crashes and individuals who are more prone to the burden of traffic crashes. To address this limitation, this study used the home address of individuals who were involved in traffic crashes in the Knoxville Regional Travel Model (KRTM) region between 2015 and 2016. After geocoding the home addresses, 110,312 individuals were assigned to the Traffic Analysis Zone (TAZ) corresponding to their home address and the economic cost of traffic crashes per capita (ECCPC) was calculated for each TAZ. The average ECCPC in the study area was $1,250. The KRTM output was used for extracting travel behavior data elements for modeling ECCPC at the zonal level. This study also established an index to measure average zonal activity in the transportation system for each TAZ. Analysis indicates that the burden of traffic crashes was more tangible in the TAZs with lower-income households and higher average zonal activities. To account for spatial autocorrelation, a Spatial Autoregressive model (SAR) and a spatial error model (SEM) were used. The SAR model was more suitable compared with SEM and ordinary least squares regression. Findings indicate that average zonal activity and traffic exposure have a significant positive association with ECCPC. The ECCPC could be used as an index for allocating proper countermeasures and interventions to groups and areas where the burden of traffic crashes is more tangible.
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Call, David A., and Guy A. Flynt. "The Impact of Snowfall on Crashes, Traffic Volume, and Revenue on the New York State Thruway." Weather, Climate, and Society 14, no. 1 (January 2022): 131–41. http://dx.doi.org/10.1175/wcas-d-21-0074.1.
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Abstract Snow has numerous effects on traffic, including reduced traffic volumes, greater crash risk, and increased travel times. This research examines how snow affects crash risk, traffic volume, and toll revenue on the New York State Thruway. Daily data from January for a 10-yr period (2010–19) were analyzed for the Thruway from the Pennsylvania state line in western New York to Syracuse. Anywhere from 35% to 50% of crashes are associated with inclement weather, with smaller impacts, proportionally, in areas with greater traffic volumes. As expected, snow was almost always involved when weather was a factor. “Unsafe speed” was the most common cause of crashes in inclement weather with all other factors (e.g., animals, drowsiness) much less likely to play a role. The percentage of crashes resulting in an injury did not change significantly with inclement conditions when compared with crashes occurring in fair conditions, and there were too few fatal crashes to make any inferences about them. Daily snowfall rates predicted about 30% of the variation in crash numbers, with every 5.1 cm of snowfall resulting in an additional crash, except in Buffalo where 5.1 cm of snow resulted in an additional 2.6 crashes. Confirming earlier results, daily snowfall had a large impact on passenger vehicle counts whereas commercial vehicle counts were less affected. Revenue data showed a similar pattern, with passenger revenue typically decreasing by 3%–5% per 2.5 cm of snow, whereas commercial revenue decreases were 1%–4% per 2.5 cm of snow. Significance Statement While it seems obvious that snowfall increases the number of crashes, decreases traffic volume, and reduces toll revenues, research is limited to support these assumptions, especially the latter two. This study involved an analysis of such items for the New York State Thruway. We found that increasing amounts of snow did cause more crashes. While traffic counts decreased, most of the decrease was in the number of passenger vehicles; commercial vehicle traffic was much less affected. Every 2.5 cm of snow costs the New York State Thruway approximately $1300 at each toll barrier and about $331 at each exit. These findings are helpful to law enforcement, emergency responders, and highway managers.
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Xian, Huacai, Yujia Hou, Yu Wang, Shunzhong Dong, Junying Kou, and Zewen Li. "Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety." Sustainability 15, no. 1 (December 26, 2022): 398. http://dx.doi.org/10.3390/su15010398.
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The causes of traffic crashes are complex and uncertain, among which the risky driving behaviors of drivers and the types of road sections in high-crash areas are all critical influencing factors. We used ArcGIS software to draw traffic heat maps under different thresholds to prevent the occurrence of traffic crashes accurately and effectively according to the vehicle GPS data of urban expressways in Jinan City, Shandong Province. This paper studied the relationship between risky driving behaviors (rapid acceleration, rapid deceleration, and overspeed) and road types with traffic crashes. The traffic safety evaluation model of urban expressways based on ordered logistic was established to predict the safety level of the urban expressway. The model’s accuracy was 85.71%, and the applicability was good. The research results showed that rapid deceleration was the most significant influencing factor of crashes on urban expressways. When the vehicle deceleration was less than or equal to −4 m/s2, the probability of a crash was 22.737 times greater than when the vehicle deceleration was at −2 to −2.5 m/s2; when the vehicle acceleration was greater than or equal to 3 m/s2, the probability of a crash was 19.453 times greater than when the vehicle acceleration was at 1 to 1.5 m/s2. The likelihood of a crash at a road section with a ramp opening was 8.723 times greater than that of a crash at a non-ramp opening; the crash probability of a speeding vehicle was 7.925 times greater than that of a non-speeding vehicle; the likelihood of a crash on a curve was 6.147 times greater than that on a straight. The research results can provide adequate technical support for identifying high-risk sections of expressways and active early warning of traffic crashes.
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Kitali, Angela E., Emmanuel Kidando, Paige Martz, Priyanka Alluri, Thobias Sando, Ren Moses, and Richard Lentz. "Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (July 21, 2018): 128–37. http://dx.doi.org/10.1177/0361198118788207.
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Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.
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Gill, G., T. Sakrani, W. Cheng, and J. Zhou. "INVESTIGATION OF ROADWAY GEOMETRIC AND TRAFFIC FLOW FACTORS FOR VEHICLE CRASHES USING SPATIOTEMPORAL INTERACTION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1163–66. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1163-2017.
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Traffic safety is a major concern in the transportation industry due to immense monetary and emotional burden caused by crashes of various severity levels, especially the injury and fatality ones. To reduce such crashes on all public roads, the safety management processes are commonly implemented which include network screening, problem diagnosis, countermeasure identification, and project prioritization. The selection of countermeasures for potential mitigation of crashes is governed by the influential factors which impact roadway crashes. Crash prediction model is the tool widely adopted by safety practitioners or researchers to link various influential factors to crash occurrences. Many different approaches have been used in the past studies to develop better fitting models which also exhibit prediction accuracy. In this study, a crash prediction model is developed to investigate the vehicular crashes occurring at roadway segments. The spatial and temporal nature of crash data is exploited to form a spatiotemporal model which accounts for the different types of heterogeneities among crash data and geometric or traffic flow variables. This study utilizes the Poisson lognormal model with random effects, which can accommodate the yearly variations in explanatory variables and the spatial correlations among segments. The dependency of different factors linked with roadway geometric, traffic flow, and road surface type on vehicular crashes occurring at segments was established as the width of lanes, posted speed limit, nature of pavement, and AADT were found to be correlated with vehicle crashes.
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Kirk, Adam, and Nikiforos Stamatiadis. "Crash Rates and Traffic Maneuvers of Younger Drivers." Transportation Research Record: Journal of the Transportation Research Board 1779, no. 1 (January 2001): 68–74. http://dx.doi.org/10.3141/1779-10.
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Although the population of younger drivers has decreased over recent decades, their crash rates have increased. Research has associated their higher crash rates with societal influences and youthful behavior. Research was conducted to identify the specific driving maneuvers of which unsuccessful undertaking results in specific types of crashes involving younger drivers. Four types of crashes were identified as the most prominent for young drivers: intersection, rear end, passing, and single vehicle. The analysis was performed by examining the Kentucky crash database for the 1994-1996 period by using the quasi-induced exposure method. The results showed that for all crashes, there is a general trend of decreasing involvement with increasing age, which indicates that these drivers’ inexperience is the largest single contributor to their increased crash rates. Of significance is that for all crashes, a dramatic decrease of involvement after the first year of driving between the ages of 16 and 17 is observed. This may be indicative of a steep learning curve in the first years of driving regarding the ability to control a vehicle. Therefore, little can be done to improve this phenomenon. Increasing awareness among young drivers about these issues and their likely crash involvement appears to be the only viable approach. However, preliminary efforts from the graduated license program show that some trends have been reduced, indicating a possible influence on the crash rates of young drivers.
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Wang, Chen, Ming Zhong, Hui Zhang, and Siyao Li. "Impacts of Real-Time Traffic State on Urban Expressway Crashes by Collision and Vehicle Type." Sustainability 14, no. 4 (February 16, 2022): 2238. http://dx.doi.org/10.3390/su14042238.
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With the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. Variation of traffic flow is considered to have significant impact on the safety performance of expressways. Therefore, the motivation of this study is to explore the mechanism of how the variation of traffic flow measurements such as average speed, speed variation and traffic volume impact the crash risk. Firstly, the crashes were classified according to crash type and vehicles involved: and they are labeled with rear-end collisions or side-impact collisions, they are labeled with heavy-vehicle related collisions or light-vehicle related collisions as well. Then, the corresponding crash data were aggregated based on the similarity of traffic flow conditions and types of crashes. Finally, a random effect negative binomial model was introduced to consider the heterogeneity of the crash risk due to the variance within the traffic flow and crash types. The results show that the significant influencing factors of each type of crashes are not consistent. Specifically, the percentage of heavy vehicles within traffic flow is found to have a negative impact on rear-end collisions and light-vehicle-related collisions, but it has no obvious correlation with side-impact collisions and heavy-vehicle-related collisions. Average speed, speed variation and traffic volume have an interactive effect on the crash rate. In conclusion, if the traffic flow is with higher speed variation within lanes and is with lower average speed, the risk of all types of crashes tends to be higher. If the speed variation within lanes decreases and the average speed increases, the crash risk will also increase. In addition, if the traffic flow is under the conditions of higher speed variation between lanes and lower traffic volume, the risk of rear-end collisions, side-impact collisions and heavy-vehicles related collisions tend to be higher. Meanwhile, if the speed variation between lanes decreases and the traffic volume increases, the crash risk is found to increase as well.
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Rezapour Mashhadi, Mohammad Mahdi, Promothes Saha, and Khaled Ksaibati. "Impact of traffic Enforcement on Traffic Safety." International Journal of Police Science & Management 19, no. 4 (September 20, 2017): 238–46. http://dx.doi.org/10.1177/1461355717730836.
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Motor vehicle crashes (MVCs) have a huge cost to society in terms of death, injury and property damage. The cost of fatal MVCs alone is estimated at US $44 billion per year. Among many confounding factors, traffic citations as an element that may reduce MVC frequency are not well understood, and most research carried out to date has evaluated the effects of the total number of citations on the number of MVCs. However, certain types of citations may be more likely to reduce the number of MVCs, whereas other types are not very effective. This research was set out to examine the impact of different types of traffic citations on MVCs on two hazardous main US highways in Wyoming US-30 and US-26. A negative binomial modeling technique was implemented by exploiting 4 years of crash and citations data to identify the causal impacts of traffic citations on crash frequency by incorporating traffic and geometric features. The modeling results showed that higher numbers of speeding and seat belt citations reduce the number of crashes significantly. These findings are the results of law enforcement efforts along the highways. Traffic count and the number of horizontal curves were found to significantly increase the number of MVCs.
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Fan, Wen Sheng, and Jian Ping Xu. "Study on Risk Factors for Transport Crashes Involving Fatigued Professional Drivers." Advanced Materials Research 790 (September 2013): 458–62. http://dx.doi.org/10.4028/www.scientific.net/amr.790.458.
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Fatigue-related traffic crashes refers to a crash caused by drivers falling asleep during driving. Unlike drunk driving or overspeed, driver fatigue is affected by many factors. To identify the contributory factors affecting the occurrence of fatigue-related crashes among professional drivers, a case-control study was conducted. The traffic data used in the study was obtained from Department of Public Security Traffic Administrative Bureau Data System in Jiangxi. Crashes (N=58131) occurring during 2006-2011 were researched, and the data of fatigue-related crashes among professional drivers was extracted. Potential risk factors such as human, environment and road were examined. Professional drivers age and gender, driving year, road pavement type and alignment, terrain, time of the accident and streetlight condition are significant factors impacting the occurrence of fatigue-related crashes. Besides, frontal impact, rear-end collision and bump fixation matter are identified as the most common accident types in all of fatigue-related crashes.
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Zeng, Junwei, Yongsheng Qian, Bingbing Wang, Tingjuan Wang, and Xuting Wei. "The Impact of Traffic Crashes on Urban Network Traffic Flow." Sustainability 11, no. 14 (July 21, 2019): 3956. http://dx.doi.org/10.3390/su11143956.
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This paper aims to investigate the impact of occasional traffic crashes on the urban traffic network flow. Toward this purpose, an extended model of coupled Nagel–Schreckenberg (NaSch) and Biham–Middleton–Levine (BML) models is presented. This extended model not only improves the initial conditions of the coupled models, but also gives the definition of traffic crashes and their spatial/time distribution. Further, we simulated the impact of the number of traffic crashes, their time distribution, and their spatial distribution on urban network traffic flow. This research contributes to the comprehensive understanding of the operational state of urban network traffic flow after traffic crashes, towards mastering the causes and propagation rules of traffic congestion. This work also a theoretical guidance value for the optimization of urban traffic network flow and the prevention and release of traffic crashes.
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Sheykhfard, Abbas, Farshidreza Haghighi, and Reza Abbasalipoor. "An analysis of influential factors associated with rural crashes in a developing country: A case study of Iran." Archives of Transport 63, no. 3 (September 30, 2022): 53–65. http://dx.doi.org/10.5604/01.3001.0015.9927.
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Road traffic deaths continue to rise, reaching 1.35 million in recent years. Road traffic injuries are the eighth leading cause of death for people of all ages. Note that there is a wide difference in the crash rate between developed and developing countries and that developed countries report much lower crash rates than developing and underdeveloped countries. World Health Organization reports that over 80% of fatal road crashes occur in developing countries, while developed countries account for about 7% of the total. The rate of road crashes in developing countries is higher than the global average, despite some measures reducing deaths over the last decade. Numerous studies have been carried out on the safety of urban roads. However, comprehensive research evaluating influential factors associated with rural crashes in developing countries is still neglected. Therefore, it is crucial to understand how factors influence the severi-ty of rural road crashes. In the present study, rural roads in Mazandaran province were considered a case study. The Crash data collected from the Iranian Legal Medicine Organization covers 2018 to 2021, including 2047 rural crash-es. Dependent variables were classified as damage crashes and injury-fatal crashes. Besides, independent variables such as driver specifications, crash specifications, environment specifications, traffic specifications, and geometrical road specifications were considered parameters. The logit model data indicate that factors associated with driver and crash specifications influence rural crashes. The type of crashes is the most critical factor influencing the severity of crashes, on which the fatal rate depends. The findings suggested that implementing solutions that minimize the effect of the factors associated with injury and death on rural roads can reduce the severity of crashes on rural roads that share the same safety issues as the case study. Further studies can also be conducted on the safety and mechanics of the vehicle by focusing the research on the types of vehicles and the sources of the damage.
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Jiao, Junfeng, Shunhua Bai, and Seung Jun Choi. "Understanding E-Scooter Incidents Patterns in Street Network Perspective: A Case Study of Travis County, Texas." Sustainability 13, no. 19 (September 24, 2021): 10583. http://dx.doi.org/10.3390/su131910583.
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Dockless electric scooter (E-scooters) services have emerged in the United States as an alternative form of micro transit in the past few years. With the increasing popularity of E-scooters, it is important for cities to manage their usage to create and maintain safe urban environments. However, E-scooter safety in U.S. urban environments remains unexplored due to the lack of traffic and crash data related to E-scooters. Our study objective is to better understand E-scooter crashes from a street network perspective. New parcel level street network data are obtained from Zillow and curated in Geographic Information System (GIS). We conducted local Moran’s I and independent Z-test to compare where and how the street network that involves E-scooter crash differs spatially with traffic incidents. The analysis results show that there is a spatial correlation between E-scooter crashes and traffic incidents. Nevertheless, E-scooter crashes do not fully replicate characteristics of traffic incidents. Compared to traffic incidents, E-scooter incidents tend to occur adjacent to traffic signals and on primary roads.
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Mohanty, Malaya. "Assessment of traffic safety at median openings using surrogate safety measures: a case study in India." European Transport/Trasporti Europei 80, ET.2020 (December 2020): 1–12. http://dx.doi.org/10.48295/et.2020.80.3.
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Traffic safety is an integral part of transportation engineering. In developing countries, its importance is even more. Additionally, at uncontrolled median openings, the severity of road crashes increase many fold. Conventionally, road crash data were used to analyse safety. However, in developing countries, the accuracy of this data is highly questionable. Therefore, in this study, a new technique in addition to post encroachment time (PET), which is a surrogate safety measure is used to predict the severity of probable road crashes at median openings. After the extraction of PET values from field data, they have been compared with the minimum braking times obtained from calculation of minimum stopping sight distance. The comparison shows that while the number of road crashes may be less at lower traffic volume levels, however the severity of those crashes is much higher as compared to the road crashes occurring at high traffic volumes.
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Abdel-Aty, Mohamed, Nizam Uddin, and Anurag Pande. "Split Models for Predicting Multivehicle Crashes during High-Speed and Low-Speed Operating Conditions on Freeways." Transportation Research Record: Journal of the Transportation Research Board 1908, no. 1 (January 2005): 51–58. http://dx.doi.org/10.1177/0361198105190800107.
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The future of traffic management and highway safety lies in proactive traffic management systems. Crash prediction models that use real-time traffic flow variables measured through a series of loop detectors are the most important component of such systems. A previous crash prediction model was developed with the matched case–control logistic regression technique. Although the model achieved reasonable classification accuracy, it remained open to improvement because of the limited study area, sample size, and transferability issues. Therefore, the previous work had been extended. Multivehicle freeway crashes under high- and low-speed traffic conditions were found to differ in severity and in their mechanism. The distribution of 5-min average speeds obtained immediately before the crash from the loop detector station closest to the crash shows two approximate mound-shaped distributions. This distribution is used as the basis to separate the models for crashes occurring under the two speed conditions. The results show that, as expected, variables that entered in the final models (for crashes under high and low speeds) were not the same. However, they were found to be consistent with the probable mechanisms of crashes under the respective speed conditions. A possible implementation of the separate models with the use of the odds ratios and with the balancing of the threshold between achieving high classification of crash potential and the false alarm situation is presented.
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Zheng, Lai, and Tarek Sayed. "Comparison of Traffic Conflict Indicators for Crash Estimation using Peak Over Threshold Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 8, 2019): 493–502. http://dx.doi.org/10.1177/0361198119841556.
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Traffic conflict techniques have drawn considerable research interest and a number of conflict indicators have been developed. Previous studies have qualitatively analyzed indicator differences from their definitions and empirically investigated their similarities based on identified traffic conflicts. This study compares conflict indicators from a validity perspective by comparing crashes estimated from conflict indicators with observed crashes. The peak over threshold (POT) approach was employed for crash estimation. Four commonly used indicators are compared: time to collision (TTC), modified time to collision (MTTC), post encroachment time (PET), and deceleration to avoid a crash (DRAC). Based on the conflict and crash data collected from three signalized intersections, POT models are developed for different thresholds in the appropriate ranges, and crash estimation methods were proposed for individual conflict indicators. The identified conflicts and estimated crashes associated with different indicators are then compared. The results show that traffic conflicts identified by the four indicators vary, with MTTC generating the most accurate crash estimates. The crash estimates from TTC and PET are also reasonable but there is a tendency of overestimation for TTC and underestimation for PET. The crash estimates of DRAC are all outside the confidence intervals of observed crashes, which is likely related to the uncertainty of vehicle braking capacity.
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Hovenden, Elizabeth, Hendrik Zurlinden, and John Gaffney. "Safety on Heavily Trafficked Urban Motorways in Relation to Traffic State." Journal of Road Safety 31, no. 1 (February 1, 2020): 51–65. http://dx.doi.org/10.33492/jrs-d-19-00247.
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Motorways represent seven per cent of the urban arterial road network in Melbourne yet carry 40 per cent of the urban arterial road travel in terms of vehicle kilometres travelled and this percentage is growing. The number of casualty crashes on metropolitan Melbourne motorways has increased over the decade at a faster rate than on other urban roads in metropolitan Melbourne. Police crash reports more often attribute crash cause to traffic conditions and vehicle interactions rather than infrastructure. As urban motorways are generally built to the highest standards, a new way of looking at motorway safety is needed. This led to the formulation of a hypothesis that the dynamics of the traffic flow are a significant contributor to casualty crashes on urban motorways. To test this hypothesis, in-depth analysis was undertaken on metropolitan Melbourne motorways. Crash data was linked to traffic data including vehicle occupancy (a proxy measure for density), vehicle speed and flow. Occupancy was used to categorise the ‘traffic states’ ranging from free flow to flow breakdown (congestion). Applying a Chi Square Goodness of Fit Test to the linked showed a statistically significant association between traffic state and crashes, with a higher than expected crashes in the traffic states where flow breakdown is relatively certain or has occurred. The results of this analysis can be used to improve safety on urban motorways through the development of Intelligent Transport System strategies to keep the motorway operating at conditions that minimise flow breakdown risk.
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Hong, Jungyeol, Reuben Tamakloe, and Dongjoo Park. "A Comprehensive Analysis of Multi-Vehicle Crashes on Expressways: A Double Hurdle Approach." Sustainability 11, no. 10 (May 15, 2019): 2782. http://dx.doi.org/10.3390/su11102782.
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To maintain safe expressways, it is necessary to investigate the causes of severe traffic accidents and establish a strategy. This study aims to analyze crashes and identify the influence of crash-risk factors on multi-vehicle (MV) crashes. Crashes involving three types of vehicles namely passenger cars, buses, and freight trucks were analyzed using a seven-year data spanning 2011 to 2017 which consists of crashes that occurred on expressways in South Korea. We applied a double hurdle approach in which a model consists of two estimators: The first estimation, which is a binary logit model selects MV crashes from the dataset; and the second estimation which is a truncated regression model estimates the number of vehicles involved in the MV crash. We found that driver traffic violations such as the improper distance between vehicles, reversing and passing increases the probability of MV crashes occurring. MV crashes in tunnels and mainlines were found to be positively correlated with the number of vehicles involved in the crash, whereas fewer vehicles were involved in MV crashes at ramps and toll-booths. Further, we found that the hurdle model with an exponential form of conditional mean of the latent variable provides better estimation parameters.
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Xie, Shikun, Xiaofeng Ji, Wenchen Yang, Rui Fang, and Jingjing Hao. "Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model." Journal of Advanced Transportation 2020 (December 17, 2020): 1–14. http://dx.doi.org/10.1155/2020/8870497.
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Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads.
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Billah, Khondoker, Hatim O. Sharif, and Samer Dessouky. "How Gender Affects Motor Vehicle Crashes: A Case Study from San Antonio, Texas." Sustainability 14, no. 12 (June 8, 2022): 7023. http://dx.doi.org/10.3390/su14127023.
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Traffic crashes are among the leading causes of injuries and fatalities worldwide. The main assumption of this study is that traffic crash rates, injury severity, and driving behaviors differ by the driver’s gender. Utilizing ten years (2011–2020) of data from the Texas Crash Record and Information System database, this study investigates how some of the most prominent driving behaviors leading to crashes and severe injuries (distracted driving, speeding, lane departure, and driving under influence) vary by gender in San Antonio, Texas. The spatial distribution of crashes associated with these driving behaviors by gender is also investigated, as well as the influence of some environmental and temporal variables on crash frequency and injury severity. This study adopted bivariate analysis and logistic regression modeling to identify the effect of different variables on crash occurrence and severity by gender. Male drivers were more likely to be involved in a speeding/DUI/lane departure-related crash and subsequent severe injuries. However, female drivers were slightly more associated with distracted-driving crashes and subsequent injuries. Nighttime, interstate/highway roads, the weekend period, and divider/marked lanes as the primary traffic control significantly increased the crash and injury risk of male drivers. Driving behavior-related crashes were mostly concentrated on some interstate road segments, major intersections, and interchanges. The results from this study can be used by authorities and policy-makers to prioritize the use of limited resources, and to run more effective education campaigns to a targeted audience.
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Popoola, M. O., O. A. Apampa, and O. Adekitan. "Impact of Pavement Roughness on Traffic Safety under Heterogeneous Traffic Conditions." Nigerian Journal of Technological Development 17, no. 1 (April 22, 2020): 13–19. http://dx.doi.org/10.4314/njtd.v17i1.2.
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H ighway safety is a major priority for public use and for transportation agencies. Pavement roughness indirectly influence drivers' concentration, vehicle operation, and road traffic accidents, and it directly affect ride quality. This study focuses on analyzing the influence of pavement roughness on traffic safety using traffic, pavement and accident data on dual and single carriageway operated under heterogeneous traffic conditions in South-west, Nigeria. Traffic crash data between 2012 and 2015 was obtained from the Federal Road Safety Commission (FRSC) and International Roughness Index (IRI) data from the Pavement Evaluation Unit of the Federal Ministry of Works, Kaduna. Crash road segments represented 63 percent of the total length of roads. IRI values for crash and non-crash segments was a close difference of 0.3,This indicates that roughness is not the only factors affecting occurrence of traffic crashes but a combination with other factors such as human error, geometric characteristics and vehicle conditions. Crash severity was categorized into Fatal, serious and minor injury crashes. In all cases, the total crash rate increases with increase in IRI value up to a critical IRI value of 4.4 and 6.15 for Sagamu-Ore road and Ilesha-Akure-Owo road respectively, wherein the crash rate dropped. The conclusion is key in improving safety concerns, if transportation agencies keep their road network below these critical pavement conditions, the crash rate would largely decrease. The study concluded that ride quality does not directly affect traffic crash rate.
Keywords: Pavement conditions, traffic safety, International Roughness Index, crash rate, carriageway.
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Evans, Leonard. "Factors Controlling Traffic Crashes." Journal of Applied Behavioral Science 23, no. 2 (May 1987): 201–18. http://dx.doi.org/10.1177/0021886387232005.
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Freeman, James, Alexander Parkes, Kerry Armstrong, and Jeremy Davey. "Characteristics of Fatal Road Traffic Crashes Associated with Alcohol and Illicit Substances in Queensland (2011-2015)." Journal of Road Safety 32, no. 3 (August 1, 2021): 4–14. http://dx.doi.org/10.33492/jrs-d-20-00146.
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Psychoactive substances affect driver behaviour in different ways, some of which can increase the risk of traffic crashes. This study investigated coroners findings for fatal road traffic crashes in Queensland for crash factors and driver behaviours associated with and without the presence of alcohol or illicit drugs. A total of 701 coroners reports for the period of 2011 to 2015 were analysed revealing 306 fatal incidents involving the detection of either alcohol or target illegal drugs (e.g., methamphetamine, THC [cannabis], cocaine or MDMA). Alcohol was most often detected (223 cases; 72.9% of the drug and alcohol sample and 31.8% of the entire sample), and a majority of fatalities involving alcohol (n = 114, 51% of alcohol cases) were at high range BAC levels (>.150g/100ml). Of these, 37 (32.5% of high range and 16.6% of alcohol cases) were detected with illicit drugs. Single vehicle and multi-vehicle crashes were evenly represented, although males were overrepresented in all crash types. Alcohol and poly drug consumption were more likely to be associated with single vehicle crashes (81.7% and 64.6% respectively), while detections of methamphetamines and THC in isolation without other substances were slightly overrepresented by multi-vehicle crashes (58.6% and 59.4% respectively). Single vehicle crashes usually involved speeding, loss of control and failure to negotiate a curve while multi-vehicle crashes were disproportionately represented by reckless driving and misjudging traffic conditions. Overall, an important theme to emerge was the contribution of illicit drugs and alcohol to the majority of single vehicle crashes, highlighting the increased risk of this type of crash for drivers who are positive with these substances.
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Rezapour, Mahdi, Er Yue, and Khaled Ksaibati. "Integrating GIS and statistical approaches to enhance allocation of highway patrol resources." International Journal of Police Science & Management 22, no. 1 (December 1, 2019): 84–95. http://dx.doi.org/10.1177/1461355719888939.
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Large truck crashes undermine the contribution of trucks to the U.S. economy due to the economic costs of the crashes. Wyoming has the highest truck crash rate and the lowest budget contribution for traffic enforcement in the USA. Because of the state’s intensive truck corridors, the Wyoming Highway Patrol (WHP) might not be able to use their resources efficiently. Previous studies have indicated that WHP performed better when they allocate their resources efficiently at the right locations and towards the right enforcements. This study used 4-year historical crash and enforcement data along Interstate 80 (I-80), which has the highest truck-related crash rate in Wyoming. Crash data were filtered to include truck crashes only. However, both truck and no-truck enforcements were included in the data because both could be at fault in truck crashes. This study used two approaches to help state policy-makers improve traffic safety on I-80. First, a statistical method was used to identify geometric variables contributing to allocated enforcement and truck crashes. Second, truck crashes and related enforcements were visually assessed using Geographical Information Systems (GIS) mapping. Crash data were disaggregated into the main driver actions of no improper driving, following too closely, improper lane change and driving too fast for the conditions. These driver actions accounted for more than 70% of all truck crashes on I-80. Related enforcements were also identified and disaggregated by driver actions. Disaggregated enforcements and crashes were visualized along the I-80 corridor using GIS maps to see if WHP allocated their resources efficiently. Cluster index, enforcement spatial coverage and mean density are some of the parameters used for the analyses. This study aimed to contribute to research on police effectiveness in reducing truck crashes, police innovation and the use of GIS applications in enforcement. This methodology can be used by other agencies to better allocate resources to improve traffic safety in most efficient ways.
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Jima, Debela, and Tibor Sipos. "The Impact of Road Geometric Formation on Traffic Crash and Its Severity Level." Sustainability 14, no. 14 (July 11, 2022): 8475. http://dx.doi.org/10.3390/su14148475.
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Road infrastructure has an impact on the occurrence of road traffic crashes. The aim of this study was to analyze the impact of road geometric formation on road traffic crashes. Based on the nature, convenience, and availability of data, the study used Budapest city road traffic crash data from 2017 to 2021. For organizing, analysis, and modeling, the study used Microsoft-Excel, the Statistical Package for Social Science, and Quantum Geographic Information System. Relative frequency distribution, Multinomial Logistic Regression, Multilayer Perceptron Artificial Neural Network, and Severity Index were used for the analysis. Both inferential and descriptive statistics are used to describe and summarize the study outcome. Multicollinearity tests, p-value, overdispersion, percent of incorrect error, and other statistical model testes were undertaken to analyze the significance of the data and variable for modeling and analysis. A large number of crashes were observed in straight and one-lane road geometric formationsr890. However, the severity level was high at the horizontal curve and in all three lanes of the road. The regression model indicated that light conditions, collision type, road geometry, and speed had a significant effect on traffic accidents at a p-value of 0.05. A collision between the vehicle (rear end collision), and a vehicle with a pedestrian was the probable cause of the crash. The Multilayer Perceptron Artificial Neural Network indicated that horizontally curved geometry has a positive and strong relationship with road traffic fatalities. The primary reasons for the occurrences of a road traffic crash at an intersection, horizontal curve, and straight road geometric formation were the improper use of road traffic signs, road pavement condition, and stopping sight distance problems, respectively. The hourly distribution showed that from 16:01 to 17:00 time interval was a peak hour for the occurrences of road traffic crashes. Whereas, driver plays vital role and responsible body for the occurrences of crashes at all geometric formations.
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Billah, Khondoker, Qasim Adegbite, Hatim O. Sharif, Samer Dessouky, and Lauren Simcic. "Analysis of Intersection Traffic Safety in the City of San Antonio, 2013–2017." Sustainability 13, no. 9 (May 10, 2021): 5296. http://dx.doi.org/10.3390/su13095296.
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An understanding of the contributing factors to severe intersection crashes is crucial for developing countermeasures to reduce crash numbers and severity at high-risk crash locations. This study examined the variables affecting crash incidence and crash severity at intersections in San Antonio over a five-year period (2013–2017) and identified high-risk locations based on crash frequency and injury severity using data from the Texas Crash Record and Information System database. Bivariate analysis and binary logistic regression, along with respective odds ratios, were used to identify the most significant variables contributing to severe intersection crashes by quantifying their association with crash severity. Intersection crashes were predominantly clustered in the downtown area with relatively less severe crashes. Males and older drivers, weekend driving, nighttime driving, dark lighting conditions, grade and hillcrest road alignment, and crosswalk, divider and marked lanes used as traffic control significantly increased crash severity risk at intersections. Prioritizing resource allocation to high-risk intersections, separating bicycle lanes and sidewalks from the roadway, improving lighting facilities, increasing law enforcement activity during the late night hours of weekend, and introducing roundabouts at intersections with stops and signals as traffic controls are recommended countermeasures.
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Lu, Zhenbo, Wei Zhou, Shixiang Zhang, and Chen Wang. "A New Video-Based Crash Detection Method: Balancing Speed and Accuracy Using a Feature Fusion Deep Learning Framework." Journal of Advanced Transportation 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/8848874.
Full textAbstract:
Quick and accurate crash detection is important for saving lives and improved traffic incident management. In this paper, a feature fusion-based deep learning framework was developed for video-based urban traffic crash detection task, aiming at achieving a balance between detection speed and accuracy with limited computing resource. In this framework, a residual neural network (ResNet) combined with attention modules was proposed to extract crash-related appearance features from urban traffic videos (i.e., a crash appearance feature extractor), which were further fed to a spatiotemporal feature fusion model, Conv-LSTM (Convolutional Long Short-Term Memory), to simultaneously capture appearance (static) and motion (dynamic) crash features. The proposed model was trained by a set of video clips covering 330 crash and 342 noncrash events. In general, the proposed model achieved an accuracy of 87.78% on the testing dataset and an acceptable detection speed (FPS > 30 with GTX 1060). Thanks to the attention module, the proposed model can capture the localized appearance features (e.g., vehicle damage and pedestrian fallen-off) of crashes better than conventional convolutional neural networks. The Conv-LSTM module outperformed conventional LSTM in terms of capturing motion features of crashes, such as the roadway congestion and pedestrians gathering after crashes. Compared to traditional motion-based crash detection model, the proposed model achieved higher detection accuracy. Moreover, it could detect crashes much faster than other feature fusion-based models (e.g., C3D). The results show that the proposed model is a promising video-based urban traffic crash detection algorithm that could be used in practice in the future.
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Lu, Linjun, Chen Wang, and Tao Wang. "Improving E-Bike Safety on Urban Highways in China." Discrete Dynamics in Nature and Society 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/415237.
Full textAbstract:
This paper aims to examine characteristics of e-bike fatal crashes on urban highways in China. Crash data were retrieved from the three-year crash reports (2010–2012) of Taixing City. Descriptive analysis was conducted to examine characteristics of e-bike riders, drivers, and crashes. The important findings include the following: (1) most fatal crashes were related to e-bike riders’ aberrant driving behaviors, including driving in motorized lanes, red-light running, driving against the direction of traffic, inattentive driving, and drunk driving; (2) e-bike riders with lower educational background tended to perform illegal or inattentive driving behaviors in fatal crashes; (3) most drivers were not found to commit any faults and very few drivers were found to commit drunk driving offences; (4) most nighttime fatal crashes were related to absence of street lightings; (5) heavy good vehicles (HGVs) and small passenger cars were the two vehicle types that were mostly involved in the e-bike fatal crashes. This study provides useful information that can help traffic engineers better understand e-bike safety in China and develop safety countermeasures.
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Cheng, Zu, Lu, and Li. "Exploring the Effect of Driving Factors on Traffic Crash Risk among Intoxicated Drivers: A case Study in Wujiang." International Journal of Environmental Research and Public Health 16, no. 14 (July 16, 2019): 2540. http://dx.doi.org/10.3390/ijerph16142540.
Full textAbstract:
Intoxicated driving is a threat to both drivers and other road users. Exploring the association between intoxicated driving factors and traffic crashes is essential for taking effective countermeasures. Most previous works have studied the relation between intoxicated driving and traffic crash based on some large-sized cities. The current study aims to evaluate the effect of driving factors on traffic crashes among intoxicated drivers in a small-sized city in China. Descriptive statistics and binary logistic regression analysis are performed to guide the study, and the data (N=1010) for the period 2016–2017 in Wujiang (i.e., a small-sized city in China) are employed as the target samples. The results demonstrate age, years of driving experience, road position, week, hour and blood alcohol concentration (BAC) are associated with traffic crashes in Wujiang. Specifically, the age of “18–25”, the years of driving experience of “≤2”, the “road intersection”, the “weekend”, the period of “0:00–6:59” and the BAC of “above 150 mg/100 mL” are more likely to cause traffic crashes among intoxicated drivers. The findings can be referred to make some targeted policies or measures to relieve Wujiang’s intoxicated driving situation and reduce the number of crashes caused by intoxicated driving.
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Yoon, Sangwon, Seung-Young Kho, and Dong-Kyu Kim. "Effect of Regional Characteristics on Injury Severity in Local Bus Crashes." Transportation Research Record: Journal of the Transportation Research Board 2647, no. 1 (January 2017): 1–8. http://dx.doi.org/10.3141/2647-01.
Full textAbstract:
As the importance of public transportation increases, the management of bus-involved crashes has become a crucial issue for traffic safety. However, there are relatively few studies on crash severity for buses in South Korea. This study investigated factors that influence the severity of injuries that occur in local bus crashes. The study used commercial vehicle crash data from a 5-year period from 2010 through 2014 in South Korea. To determine unobserved regional effects on crash severity, a hierarchical ordered model was applied to the analysis. Individual crash characteristics were set to lower-level variables, and regional characteristics were adopted as upper-level variables. At the lower level, the factors affecting severity of injuries included vehicle speed, vehicle age, road alignment, surface status, road class, and traffic light installation, as found in previous studies. At the upper level, the factors included pavement, emergent medical environment, traffic rate of compliance, and ratio of elderly in the community. There was a 5.1% unobserved variation between regions from the intraclass correlation analysis. The validity of a hierarchical model for local bus crashes was verified by applying the model to other long-distance buses, and it appeared there were no regional effects. This study found a regional effect for local bus crash severity, and thus this factor is important when developing prevention plans to reduce local bus crashes. These results contribute to the study of traffic safety.
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Raub, Richard A. "Occurrence of Secondary Crashes on Urban Arterial Roadways." Transportation Research Record: Journal of the Transportation Research Board 1581, no. 1 (January 1997): 53–58. http://dx.doi.org/10.3141/1581-07.
Full textAbstract:
Analysis of incidents and crashes occurring along urban arterial roadways suggests that as many as 15 percent of the crashes occurring along these roadways may have been, in part, caused by an earlier incident. To conduct the analysis, data covering reported vehicular crashes, fires, disablements, traffic enforcement, and other incidents affecting roadways were collected from seven contiguous urban municipalities in the Northern Chicago, Illinois, metropolitan region. Crashes represented 35 percent of the incidents; traffic enforcement, 30 percent; and disabled vehicles, 27 percent. Other events represented the remaining 8 percent. To associate an initial event with a subsequent crash, any crash that occurred on roadways affected by the primary event, within the duration of the primary event plus 15 min, and within 1600 m of that event (based on use of geographic information systems software) was assumed to have been related. Analysis of 1,796 incidents in the data base identified 97 secondary crashes related to 81 initial incidents. A random sample of all events in the data base showed that only 3.6 percent of the crashes instead of 15 percent might be expected to fall within the required temporal and spatial limits in relation to an earlier event. This finding suggested that the selected events may not have been random occurrences. Finally, analysis of the secondary crashes showed that 1 of every 10 may have been related to an earlier crash; 1 of every 11 to a disabled vehicle, and up to 1 of every 40 to a police stop for a major traffic violation. What must be studied in greater detail is the degree of relationship between the initial event and the subsequent crash. This can only be done through better crash reporting, in which officers clearly indicate any associated events that may have been contributing factors.
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Jamal, Arshad, Muhammad Tauhidur Rahman, Hassan M. Al-Ahmadi, and Umer Mansoor. "The Dilemma of Road Safety in the Eastern Province of Saudi Arabia: Consequences and Prevention Strategies." International Journal of Environmental Research and Public Health 17, no. 1 (December 24, 2019): 157. http://dx.doi.org/10.3390/ijerph17010157.
Full textAbstract:
Road traffic crashes (RTCs) are one of the most critical public health problems worldwide. The WHO Global Status Report on Road Safety suggests that the annual fatality rate (per 100,000 people) due to RTCs in the Kingdom of Saudi Arabia (KSA) has increased from 17.4 to 27.4 over the last decade, which is an alarming situation. This paper presents an overview of RTCs in the Eastern Province, KSA, from 2009 to 2016. Key descriptive statistics for spatial and temporal distribution of crashes are presented. Statistics from the present study suggest that the year 2012 witnessed the highest number of crashes, and that the region Al-Ahsa had a significantly higher proportion of total crashes. It was concluded that the fatality rate for the province was 25.6, and the mean accident to injury ratio was 8:4. These numbers are substantially higher compared to developed countries and the neighboring Gulf states. Spatial distribution of crashes indicated that a large proportion of severe crashes occurred outside the city centers along urban highways. Logistic regression models were developed to predict crash severity. Model estimation analysis revealed that crash severity can be attributed to several significant factors including driver attributes (such as sleep, distraction, overspeeding), crash characteristics (such as sudden deviation from the lane, or collisions with other moving vehicles, road fences, pedestrians, or motorcyclists), and rainy weather conditions. After critical analysis of existing safety and infrastructure situations, various suitable crash prevention and mitigation strategies, for example, traffic enforcement, traffic calming measures, safety education programs, and coordination of key stakeholders, have been proposed.