Journal articles on the topic 'Heavy vehicle crashes'
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1
Rangam, Harikrishna, Sathish Kumar Sivasankaran, and Venkatesh Balasubramanian. "Investigation of Injury patterns in Heavy-duty Single Vehicle crashes based on real-world accident data in Tamilnadu, India." Journal of Road Safety 32, no. 2 (May 1, 2021): 30–40. http://dx.doi.org/10.33492/jrs-d-20-00127.
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According to the reports of NITI (National Institute of Transforming India) Aayog Freight 2018, Road freight is the prime mode (59%) of transport in India with the highest per ton-mile cost than rail or water freight (NITI Aayog, 2018). This road freight usually uses heavy-duty vehicles to transmit voluminous goods and services to the destination in time. Due to this, the heavy-duty vehicle population increased on the Indian roads. Heavy-duty vehicle crashes cause a substantial economic burden to the nation and result in more severity to the involved because of differences in weight, speed, and size. Among heavy-duty vehicle crashes, a significant proportion of crashes are heavy-duty single-vehicle crashes. Single-vehicle crashes are those crashes where the vehicle drivers either involve in self-skidding or hit a stationary object (like a tree). The purpose of this study is to investigate the injury pattern in heavy-duty single vehicle crashes. For this study, the data is extracted from the RADMS (Road Accident Database Management System) database and linked with hospital data. This data includes demographic information, road, environmental and injury characteristics. Later, descriptive statistics performed on the dataset to analyse all heavy-duty single-vehicle crashes between January 2013 and December 2018. Overall, 4704 single heavy-duty vehicle crashes occurred during this period, among which 1244 were fatal crashes. Results show that male drivers aged 26 to 64 years old suffered more fatalities (88%), followed by the 18-25 age group (8%). Examination of injury information found that heavy-duty vehicle drivers mostly sustained multiple injuries (9.05%), head injuries (5.05%), followed by leg injuries (4.29%). The results showed that specific road and environmental factors increase the chance of fatal crashes among heavy-duty vehicle drivers. Furthermore, the proposed study gives insight into the injury characteristics and key contributing factors causing heavy-duty single-vehicle crashes. Finally, this study provides appropriate countermeasures and techniques that can mitigate heavy-duty single-vehicle collisions.
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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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Tulu, Getu, Robert Lisinge, and Bikila Wedajo. "Heavy vehicle crashes in Addis Ababa: Relationship between contributing factors and severity of outcomes." Journal of Sustainable Development of Transport and Logistics 7, no. 2 (November 19, 2022): 25–40. http://dx.doi.org/10.14254/jsdtl.2022.7-2.2.
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Random parameter logit regression is used to analyze police-reported data on 8,253 heavy vehicle-related crashes in Addis Ababa between July 2014 and June 2017. The analysis shows that fatal crashes are more likely to occur during the day and on weekdays, particularly when the circulation of trucks is high. It also shows the disproportionately high involvement of young drivers in heavy vehicle crashes in the city. However, the likelihood of crashes resulting in fatalities and serious injuries increases slightly compared to those resulting only in property damage as the age of drivers increases. Low levels of drivers' education, the fact that drivers are often not the owners of vehicles, ownership of vehicles by companies and government organizations, and inappropriate road medians' inappropriate design are also significant contributors to fatal crashes. Curbing deaths and injuries from heavy vehicle crashes in Addis Ababa requires strict enforcement of traffic rules and regulations, particularly speed limits; reforms in driver's training and certification; improved safety culture of vehicle owners and design of road infrastructure. Ethiopia's national road safety strategy launched in July 2022 addresses these issues. Hence the government is taking steps in the right direction.
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Wen, Huiying, Jiaren Sun, Qiang Zeng, Xuan Zhang, and Quan Yuan. "The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach." Journal of Advanced Transportation 2018 (August 7, 2018): 1–7. http://dx.doi.org/10.1155/2018/6964828.
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This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement.
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Wiratama, Bayu Satria, Li-Min Hsu, Yung-Sung Yeh, Chia-Che Chen, Wafaa Saleh, Yen-Hsiu Liu, and Chih-Wei Pai. "Joint Effect of Heavy Vehicles and Diminished Light Conditions on Paediatric Pedestrian Injuries in Backover Crashes: A UK Population-Based Study." International Journal of Environmental Research and Public Health 19, no. 18 (September 16, 2022): 11689. http://dx.doi.org/10.3390/ijerph191811689.
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Backover crashes cause considerable injuries especially among young children. Prior research on backover crashes has not assessed the joint effect of heavy vehicles and diminished light conditions on injuries. By analysing the United Kingdom STATS19 crash dataset from 1991 to 2020, this study focused on backover crashes involving paediatric cyclists or pedestrians aged ≤17 years and other motorised vehicles. By estimating the adjusted odds ratio (AOR) of multiple logistic regression models, pedestrians appeared to have 82.3% (95% CI: 1.78–1.85) higher risks of sustaining killed or serious injuries (KSIs) than cyclists. In addition, casualties involved in backover crashes with heavy vehicles were 39.3% (95% CI: 1.35–1.42) more likely to sustain KSIs than those involved in crashes with personal cars. The joint effect of heavy vehicles and diminished light conditions was associated with a 71% increased probability of sustaining KSIs (AOR = 1.71; 95% CI: 1.60–1.83). Other significant joint effects included young children (aged 0 to 5 years) as pedestrian (AOR = 1.92; 95% CI: 1.87–1.97), in diminished light conditions (AOR = 1.23; 95% CI: 1.15–1.31), and with heavy vehicle (AOR = 1.37; 95% CI: 1.28–1.47).
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Hanowski, Richard J., Robert J. Carroll, Walter W. Wierwille, and Rebecca L. Olson. "Light Vehicle-Heavy Vehicle Interactions: A Preliminary Assessment Using Critical Incident Analysis." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 22 (September 2002): 1844–47. http://dx.doi.org/10.1177/154193120204602214.
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Two recently completed on-road in situ data collection efforts, one involving local/short haul trucking and the other long-haul trucking, provided a large data set in which to conduct an examination of critical incidents (crashes and near-crashes) that occurred between light vehicles and heavy vehicles. Video and non-video data collected during the two studies were used to characterize critical incidents that were recorded between light vehicle and heavy vehicle drivers. Across both studies, 210 light vehicle-heavy vehicle (LV-HV) critical incidents were recorded. Of these, 78 percent were initiated by the light vehicle driver. Aggressive driving on the part of the light vehicle driver was found to be the primary contributing factor for light vehicle driver initiated incidents. For heavy vehicle driver initiated incidents, the primary contributing factor was poor driving technique. The results suggest that efforts at addressing LV-HV interaction incidents should focus on light vehicle drivers who drive aggressively. Additionally, it is recommended that heavy vehicle drivers might benefit from improved driver training that includes instruction on defensive driving.
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Schindler, Ron, Michael Jänsch, András Bálint, and Heiko Johannsen. "Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data." International Journal of Environmental Research and Public Health 19, no. 2 (January 7, 2022): 663. http://dx.doi.org/10.3390/ijerph19020663.
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Heavy goods vehicles (HGVs) are involved in 4.5% of police-reported road crashes in Europe and 14.2% of fatal road crashes. Active and passive safety systems can help to prevent crashes or mitigate the consequences but need detailed scenarios based on analysis of region-specific data to be designed effectively; however, a sufficiently detailed overview focusing on long-haul trucks is not available for Europe. The aim of this paper is to give a comprehensive and up-to-date analysis of crashes in the European Union that involve HGVs weighing 16 tons or more (16 t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis, as follows. Crash statistics based on data from the Community Database on Accidents on the Roads in Europe (CARE) provide a general overview of crashes involving HGVs. These results are complemented by a more detailed characterization of crashes involving 16 t+ trucks based on national road crash data from Italy, Spain, and Sweden. This analysis is further refined by a detailed study of crashes involving 16 t+ trucks in the German In-Depth Accident Study (GIDAS), including a crash causation analysis. The results show that most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on nonhighway roads. Three main scenarios for 16 t+ trucks are characterized in-depth: rear-end crashes in which the truck is the striking partner, conflicts during right turn maneuvers of the truck with a cyclist riding alongside, and pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g., distraction) were the main crash causation factor in 72% of cases in the rear-end striking scenario while information access problems (e.g., blind spots) were present for 72% of cases in the cyclist scenario and 75% of cases in the pedestrian scenario. The three levels of data analysis used in this paper give a deeper understanding of European HGV crashes, in terms of the most common crash characteristics on EU level and very detailed descriptions of both kinematic parameters and crash causation factors for the above scenarios. The results thereby provide both a global overview and sufficient depth of analysis of the most relevant cases and aid safety system development.
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Rassafi, Amir Abbas, Mirbahador Yazdani, and Bahram Shirini. "Cross Sectional Crash Severity Analysis among Various Vehicle Driver Characteristics." Civil Engineering Journal 4, no. 9 (September 30, 2018): 2143. http://dx.doi.org/10.28991/cej-03091146.
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The current study evaluated road crashes in three categories of drivers: passenger car drivers, heavy vehicle drivers and pickup truck drivers. The crash data of road crashes that occurred from 2009 to 2012 in Iranian suburban roads were analyzed. The crashes involved 194,041 damage, 9,677 injury and 1,303 fatality crashes. Because of the ordinal nature of crash severity, ordered logit model was selected for each vehicle driver category. In the passenger car driver category, the independent variables of driver’s gender, driver’s age and driver’s educational level (uneducated, less than high school diploma, high school diploma) were obtained for modeling; in the heavy vehicle driver category, the independent variables of driver’s age and driver’s educational level (uneducated, less than high school diploma) were obtained for modeling, and in the pickup truck driver category, the independent variables of driver’s age and driver’s educational level (uneducated, high school diploma) were obtained for modeling. The variable of driver’s gender with respect to passenger cars shows that crashes by female drivers are more severe than those involving male drivers in suburban roads. Regarding the variable of driver’s age, if the driver’s age increases in each vehicle driver category, the probability of occurrence of a severe crash will decrease. The variable of driver’s educational level shows that the severity of crashes involving a driver that has a low level of education is more compared with a driver that has a high level of education.
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Hassan, Hany M., Nuha M. Albusaeedi, Atef M. Garib, and Hussain A. Al-Harthei. "Exploring the Nature and Severity of Heavy Truck Crashes in Abu Dhabi, United Arab Emirates." Transportation Research Record: Journal of the Transportation Research Board 2517, no. 1 (January 2015): 1–9. http://dx.doi.org/10.3141/2517-01.
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Traffic crashes involving heavy trucks long have been a major concern in the field of traffic safety because of their great effect on accident severity. The emirate of Abu Dhabi, capital of the United Arab Emirates, features a unique situation: several roads designed mainly for truck movement. Even though those roads were constructed more than 10 years ago to decrease the severity of truck-related crashes, no prior studies have examined their effects on traffic safety improvements. The goals of this study were to understand better the nature, characteristics, and causes of heavy truck crashes occurring in Abu Dhabi; to identify the factors associated with crash severities; and to examine the probability of truck crashes involving fatalities on truck roads versus on mixed-vehicle roads. Data were analyzed from a sample of 1,426 heavy truck–related crashes with reported fatalities or injuries that occurred in Abu Dhabi between 2007 and 2013. First, conditional distributions, two-way analysis, and odds ratios were performed. Second, ordered probit and structural equation models were developed. Results indicated that the likelihood of truck crashes involving fatalities was 35% higher on truck roads than on mixed-vehicle roads. In addition, findings showed that human error, driver education, location, road type, and road speed variables were significant in affecting the severity of heavy truck– related crashes. Finally, practical suggestions on how to reduce the number of heavy truck–related crashes in Abu Dhabi are presented and discussed.
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Anderson, Jason, and Salvador Hernandez. "Heavy-Vehicle Crash Rate Analysis: Comparison of Heterogeneity Methods Using Idaho Crash Data." Transportation Research Record: Journal of the Transportation Research Board 2637, no. 1 (January 2017): 56–66. http://dx.doi.org/10.3141/2637-07.
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Studies investigating crash rates by roadway classification are few and far between and even more rare if extended to focus on heavy vehicles. This study explored and compared two advanced econometric methods—random-parameter Tobit regression and latent class Tobit regression—to determine contributing factors for heavy-vehicle crashes per million vehicle miles traveled while accounting for the unobserved heterogeneity present in crash data. The increasing crash rates in Idaho, crash proportion by roadway classification, and available data made an ideal case study. Empirical results show that although the random-parameter Tobit regression model provides better insight into heavy-vehicle crash rates than the fixed-parameter approach, the latent class Tobit regression model is the preferred methodology for the given data set. Traffic volumes, roadway characteristics, and traffic control devices were among the variables found to be statistically significant. Results from this study provide an alternate framework to account for heterogeneity while identifying key factors by roadway classification that influence heavy-vehicle crash rates. The illustrated framework and analysis by roadway classification can provide guidance to transportation agencies and policy makers and prompt future studies to include a latent class analysis, analysis by road classification, or both.
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Zhao, Peibo, and Chris Lee. "Analysis and Validation of Surrogate Safety Measures by Types of Lead and Following Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2659, no. 1 (January 2017): 137–47. http://dx.doi.org/10.3141/2659-15.
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This study analyzed rear-end collision risk in a mixed traffic flow of cars and heavy vehicles on a freeway using two surrogate safety measures: time to collision (TTC) and postencroachment time (PET). The study estimated surrogate safety measures for types of lead and following vehicles (car or heavy vehicle) by using the individual vehicle trajectory data. The vehicle trajectory data were collected from a segment of the US-101 freeway in Los Angeles, California. It was found that the distributions of TTC and PET were significantly different between types of lead and following vehicles. Also, the mean values of TTC and PET were higher for heavy vehicles following cars than for cars following cars and for cars following heavy vehicles. The study also validated TTC by using the simulated traffic data for a few minutes before the time of crashes that occurred on a section of the Gardiner Expressway in Toronto, Ontario, Canada. It was found that TTC reflects higher collision risk in the time intervals closer to the crash time and it reflects higher collision risk for the crash case than for the noncrash case. The findings suggest that the difference in rear-end collision risk between types of vehicle pairs should be considered in safety assessment of mixed traffic flow of cars and heavy vehicles.
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Jiang, C. M., J. J. Lu, and L. J. Lu. "Analysis of Single-Vehicle Crash Injury Severities in Urban River-Crossing Road Tunnels." Applied Mechanics and Materials 743 (March 2015): 526–32. http://dx.doi.org/10.4028/www.scientific.net/amm.743.526.
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Based on the originally unprocessed data from the Official Platform of“110”Alarming Receiving Center (OP110ARC) of Shanghai Public Security Bureau (SPSB), 529 single-vehicle crashes reported during one year and a half which happened at the thirteen urban road tunnels going across the Huangpu River are used in this study. To investigate the factors affecting the crash influence severity levels, ordered probit regression is established. Several categories of factors are considered as explanatory variables in the models. The study finds that the entrance of the tunnels is the site where severe injury crashes trend to occur. Rainy and snowy days impose vehicles and motorists driving via the tunnel sections in danger. Tunnels with a low speed limit (40 km/h in this study) may be not as safe as we thought before. Two-wheel vehicles without sufficient physical protection for its drivers and heavy vehicles also show a negative effect on the operation safety of single-vehicle at these studied tunnels. Alcohol involved drivers are more likely to suffer from a severe crashes and gets badly hurt.
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Chen, Zhen, and Wei (David) Fan. "Modeling Pedestrian Injury Severity in Pedestrian-Vehicle Crashes in Rural and Urban Areas: Mixed Logit Model Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 1023–34. http://dx.doi.org/10.1177/0361198119842825.
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Pedestrian-vehicle crashes are more likely to result in severe pedestrian incapacitating injuries and fatalities than other types of crashes. In this study, mixed logit models are developed to investigate and identify significant contributing factors to the pedestrian injury severity in pedestrian-vehicle crashes in both rural and urban areas in North Carolina, United States. Pedestrian-vehicle crash data from the Highway Safety Information System database from 2005 to 2012 are collected and used in this study. Crash injury severities are classified into five categories: fatality; injury class 1 (disabling injury); injury class 2 (evident injury); injury class 3 (possible injury); and no injury (property damage only). The estimation results show that factors such as a bad driver’s physical condition, heavy trucks, dark light condition, speed limit between 35 and 50 mph and speed limit above 50 mph will significantly increase pedestrian injury severities in both rural and urban areas. The developed model and analysis results provide insights on developing effective countermeasures to reduce pedestrian injury severities in pedestrian-vehicle crashes and improve traffic system safety performance.
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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.
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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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Maruyama, Takuya, and Kazutake Taguchi. "Increased motor vehicle crashes following the 2016 Kumamoto earthquake, Japan: an interrupted time series analysis of property damage crashes." Natural Hazards 108, no. 2 (April 28, 2021): 1877–99. http://dx.doi.org/10.1007/s11069-021-04760-3.
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AbstractDriving after natural disasters entails a substantial amount of stress; therefore, the number of motor vehicle crashes may increase. However, few studies have examined this issue. This study investigated motor vehicle crashes after the 2016 Kumamoto earthquake in Japan. Monthly data about crashes resulting in property damage from 49 municipalities in Kumamoto from 2015 to 2018 were used. An interrupted time series analysis using Poisson or negative binomial regression models was conducted for 49 municipalities; the models were estimated for four classified areas to obtain the robust results. We found that property damage crashes increased significantly in the heavily affected area (Relative Risk (RR) = 1.48, 95% Confidence interval (CI): 1.29, 1.71) and the affected area (RR = 1.25, 95% CI: 1.15, 1.36) after the earthquake. A mountainous area showed a reduction in property damage crashes despite its heavy damage (RR = 0.74, 95% CI: 0.67, 0.82), which can be attributed to the closure of its main gate routes. The unaffected area showed no difference before and after the earthquake. Geographical presentation of the result demonstrates a clear positive association of earthquake damage and increased crashes. The findings of this study highlight the importance of motor-vehicle-crash alerts after an earthquake.
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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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Gunawardane, Damitha Asanga, Samath D. Dharmaratne, and Samath D. Dharmaratne. "Level of fatigue and daytime sleepiness among heavy vehicle drivers in Sri Lanka." South East Asia Journal of Public Health 4, no. 1 (February 2, 2015): 9–16. http://dx.doi.org/10.3329/seajph.v4i1.21832.
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Heavy vehicle crash rates are high in Sri Lanka. International studies showed that fatigue and day time sleepiness are linked to heavy vehicle crashes. The aim of this study is to examine the level of fatigue, day time sleepiness and their associated factors among heavy vehicle drivers in Sri Lanka. A descriptive cross-sectional study was carried out in 403 heavy vehicle drivers through an interviewer administered structured questionnaire consisting of the Fatigue Severity Scale (FFS) and the Epworth Sleepiness Scale (ESS). The mean age of the study sample was 36.5±8.6 years and all were males. They drive an average of 129.03±87.24 km/day. It was found that the mean FFS score in the study participants was 2.43±0.99 and the mean ESS score was 6.57±4.07. Prevalence of fatigue (FSS score ?4) was 8.7% (n=35) and the prevalence of excessive day time sleepiness (EDS) (ESS score ?10) was 14.4% (n=58). Working for non-fixed schedule, married marital status, and working more than 11 hours/day are found to be significant risk factors for being fatigue. Drivers who were having night sleep of less than 6 hours were found to have a higher risk of EDS. A high prevalence of fatigue (8.7%) and EDS (14.4%) among heavy vehicle drivers was found which may link to heavy vehicle crashes. The occupational health service providers should be aware of the possibility of fatigue and daytime sleepiness in heavy vehicle drivers and its overall risk on the roads to take neces-sary actions.DOI: http://dx.doi.org/10.3329/seajph.v4i1.21832 South East Asia Journal of Public Health Vol.4(1) 2014: 9-16
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Zhao, Jinbao, and Wei Deng. "THE USE OF BAYESIAN NETWORK IN ANALYSIS OF URBAN INTERSECTION CRASHES IN CHINA." TRANSPORT 30, no. 4 (October 16, 2013): 411–20. http://dx.doi.org/10.3846/16484142.2013.816365.
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Traffic fatalities and injuries on urban roads especially at urban intersections constitute a growing problem in China. This study aims at researching urban intersection crashes in China and drawing conclusions by using hierarchical structured data with reference to Bayesian network (BN). On the basis of 3584 recorded crashes collected from the urban intersections of Changshu, China, a BN topological structure is developed to reflect the hierarchical characteristic of crash variables. The parameter learning process is completed with Dirichlet prior distribution. Junction tree engine is used to make inference on crash types at urban intersections with two respective given evidences, i.e. human factor and vehicle type. Parameter learning results suggest the efficacy of BN approach in the prediction accuracy. The average learned probability of illegal driving is 40.83%, which is much higher than other learned probabilities of human factors. The inferred probabilities of frontal collision at urban intersection crashes involving bicycles and electric bikes are 43.16% and 40.44% respectively, which is higher than the probabilities involving small cars and heavy vehicles. However, heavy vehicles have a higher inferred probability in side collision than light vehicles, whose inferred side collision probability is 41.02%. This study has a good potential in traffic safety discipline to reveal the correlation exists in traffic risk factors. By means of BN, researchers can make an intensive study on the hierarchical traffic crash data, determine the key risk factors and then propose corresponding and appropriate improvement measures.
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Zhang, Junxiang, Bo Yu, Yuren Chen, You Kong, and Jianqiang Gao. "Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity." International Journal of Environmental Research and Public Health 19, no. 19 (October 6, 2022): 12779. http://dx.doi.org/10.3390/ijerph191912779.
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With the growth of traffic demand, the number of newly built and renovated super multi-lane freeways (i.e., equal to or more than a two-way ten-lane) is increasing. Compared with traditional multi-lane freeways (i.e., a two-way six-lane or eight-lane), super multi-lane freeways have higher design speeds and more vehicle interweaving movements, which may lead to higher traffic risks. However, current studies mostly focus on the factors that affect crash severity on traditional multi-lane freeways, while little attention is paid to those on super multi-lane freeways. Therefore, this study aims to explore the impacting factors of crash severity on two kinds of freeways and make a comparison with traditional multi-lane freeways. The crash data of the Guangzhou-Shenzhen freeway in China from 2016 to 2019 is used in the study. This freeway contains both super multi-lane and traditional multi-lane road sections, and data on 2455 crashes on two-way ten-lane sections and 13,367 crashes on two-way six-lane sections were obtained for further analysis. Considering the effects of unobserved spatial heterogeneity, a hierarchical Bayesian approach is applied. The results show significant differences that influence the factors of serious crashes between these two kinds of freeways. On both two types of freeways, heavy-vehicle, two-vehicle, and multi-vehicle involvements are more likely to lead to serious crashes. Still, their impact on super multi-lane freeways is much stronger. In addition, for super multi-lane freeways, vehicle-to-facility collisions and rainy weather can result in a high possibility of serious crashes, but their impact on traditional multi-lane freeways are not significant. This study will contribute to understanding the impacting factors of crash severity on super multi-lane freeways and help the future design and safety management of super multi-lane freeways.
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Islam, Mouyid, and Seckin Ozkul. "Identifying Fatality Risk Factors for the Commercial Vehicle Driver Population." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 9 (May 7, 2019): 297–310. http://dx.doi.org/10.1177/0361198119843479.
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Commercial/large-truck fatal crash involvement by drivers of different age groups is a critical issue for the trucking industry. Escalating safety concerns related to these heavy vehicles serving the freight economy in the U.S. have an impact national freight reliability and economic growth. This study identifies major contributing factors leading to large-truck fatal crashes for four age groups of driver: <30, 30–49, 50–65, and 65+. The analysis in this study is based on five years (2012–2016) of Fatality Analysis Reporting System data and provides an overall picture of risk factors in large-truck fatal crashes. In total, 30 variables were found to be significant in the logit models, indicating varying risks associated with large-truck drivers of these four age groups. Model results indicate different risk factors associated with driver characteristics, spatial and temporal characteristics, vehicle and vehicle maneuvering characteristics, and environmental conditions at the time of the crashes. Identifying the risk factors for different age groups of drivers is important so proper countermeasures can be implemented from the perspective of human factors (e.g., safe speed choice, fatigue), roadway engineering (e.g., design of roadside barriers, radius of ramps), enforcement (e.g., presence of law enforcement personnel at critical locations), and emergency medical attention in remote areas. Considering the aging of the truck driver population in the U.S. and around the world, the findings of this study are vital to understand better the importance of safety in relation to large-truck fatal crashes.
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Kongwat, Suphanut, Thonn Homsnit, Chaimongkol Padungtree, Naphon Tonitiwong, Pornkasem Jongpradist, and Pattaramon Jongpradist. "Safety Assessment and Crash Compatibility of Heavy Quadricycle under Frontal Impact Collisions." Sustainability 14, no. 20 (October 18, 2022): 13458. http://dx.doi.org/10.3390/su142013458.
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An electric heavy quadricycle, categorized as an L7e vehicle, is an alternative solution for sustainable mobility with a lower carbon footprint and high energy consumption efficiency. However, accidental crashes of quadricycles with larger vehicle opponents can cause extensive damage to their structures and fatal injury to the occupants due to their geometry drawback in limited space in the front crumple zone. This work investigates the crashworthiness performance and safety assessment of the L7e vehicle under rigid wall crash tests and crash compatibility in car-to-car collisions with a sedan and an SUV. Crash scenarios are simulated using a nonlinear finite element analysis via LS-DYNA to evaluate structural crashworthiness and occupant injuries of a hybrid III 50th percentile male dummy. The compatible vertical alignment of the primary energy-absorbing structure substantially affects the safety of the quadricycle under a frontal crash. A secondary energy-absorbing component should be adapted to the L7e vehicle to achieve vertical alignment with different vehicle sizes. In addition, the typical rigid-wall frontal crash test at 50 kph considerably underestimates the structural damage and occupant injury of the L7e vehicle compared to car-to-car collisions. Thus, additional crash tests representing car-to-car collisions that account for the car’s smaller size and lighter mass should be included in the safety regulation for the L7e vehicle.
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Wang, Hwachyi, S. K. Jason Chang, Hans De Backer, Dirk Lauwers, and Philippe De Maeyer. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)." Sustainability 11, no. 13 (July 9, 2019): 3746. http://dx.doi.org/10.3390/su11133746.
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The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle–motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle–motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads.
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Balakrishnan, Sivanandan, Sara Moridpour, and Richard Tay. "Differences in single heavy vehicle crashes at intersections and midblocks." Journal of Advanced Transportation 50, no. 8 (December 2016): 2150–59. http://dx.doi.org/10.1002/atr.1451.
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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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Ma, Hon-Ping, Ping-Ling Chen, Shang-Ku Chen, Liang-Hao Chen, Vaclav Linkov, and Chih-Wei Pai. "Population-based case–control study of the effect of sun glare on pedestrian fatalities in Taiwan." BMJ Open 9, no. 8 (August 2019): e028350. http://dx.doi.org/10.1136/bmjopen-2018-028350.
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ObjectivesSun glare is a serious driving hazard and increases crash risks. Relatively few studies have examined the effects of sun glare on pedestrian fatalities, given that a crash has occurred. The primary objective of this study was to investigate the effect of sun glare on pedestrian fatalities.DesignA population-based case–control study.SettingTaiwan.ParticipantsUsing the Taiwan National Traffic Crash Data and sunrise and sunset data from the National Oceanic and Atmospheric Administration for the period 2003 to 2016, 100 411 pedestrians involved in crashes were identified. Of these crashes, 13 355 and 87 056 were glare-related (case) and non-glare-related (control) crashes, respectively.MethodsTo account for unobserved heterogeneity, mixed logit models were estimated to identify the determinants of pedestrian fatalities.Main outcome measuresPedestrian fatalities.ResultsPedestrians involved in glare-related crashes were more likely to be fatally injured than those in non-glare-related crashes (β=0.527; t=3.21). Other contributory factors to fatal injuries among pedestrians were older pedestrians (β=0.553; t=2.33), male drivers (β=0.324; t=2.33), older drivers (β=0.218; t=2.14), intoxicated motorists (β=0.606; t=2.85), rural roadways (β=0.985; t=3.92), overtaking manoeuvres (β=0.472; t=3.58), heavy vehicle crash partners (β=0.248; t=2.78) and sunset hours (β=0.274; t=3.08). Walking against traffic appeared beneficial for decreasing injury severity (β=−0.304; t=−2.76).ConclusionsSun glare is associated with pedestrian fatalities. Older pedestrians, male drivers, older drivers and intoxicated motorists are prevalent determinants of pedestrian fatalities in glare-related crashes.
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Wei, Fulu, Danping Dong, Pan Liu, Yongqing Guo, Zhenyu Wang, and Qingyin Li. "Quarterly Instability Analysis of Injury Severities in Truck Crashes." Sustainability 14, no. 21 (October 28, 2022): 14055. http://dx.doi.org/10.3390/su142114055.
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The impact of trucks on road traffic safety has been extensively studied, but the factors influencing truck crash injury severity have not yet been examined from the quarterly perspective. Crash data for Shandong Province in China for 10 years (2012–2021) were reviewed to investigate the transferability of the determinants of the severity of truck crash injuries in four quarters. Three injury severity levels were considered and a random parameters logit model (RPL) considering the heterogeneity of means and variances was constructed to assess the factors affecting the severity of crash injury. The significant variables were explored from the influencing factors of driver, vehicle, crash type, road, environment, and temporal characteristics. A likelihood ratio test was employed to assess the transferability of the crash model over four quarters, and we used marginal effects to analyze the stability of the influencing factors. The results indicated that there was instability among the four quarterly variables that had to be modeled separately. There were also some variables, such as heavy vehicle and multiple-vehicle crashes, that simultaneously affected the severity of truck crash injuries across the four quarters, but the degree of impact was different. The results could enable engineers and policy makers to better formulate management rules and propose appropriate measures according to quarterly changes.
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Et.al, Sanjai R. J. "An Assessment on Driving Behaviourism of Goods Vehicle Drivers in Kerala." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 144–49. http://dx.doi.org/10.17762/turcomat.v12i6.1279.
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Road Safety has become a serious issue in this era as the population in terms of both people as well as vehicles has increased to a large extend. The behaviour of drivers is influenced by several factors, which include their socio-economic – demographic factors and vehicle characteristics. Accidents comprising of goods vehicles have been on the surge in Kerala over the years. Even though the numbers of heavy vehicles plying on Kerala roads are less compared to two wheelers and cars, the proportion of crashes involving heavy vehicles is a cause of concern. Fallacious driving behaviourism is generally considered as the leading root of such accidents. The present study intends to investigate the elements involved in the Driver Behaviour Questionnaire (DBQ), then to explore the relationships between the elements of the DBQ and accident involvement. The data were interpreted using percentage analysis. The analysis showed the level of violations, errors, lapses committed by drivers on the basis of driving behaviourism and non –driving behaviourism of the goods vehicle driver.
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Thanh Chuong, Nguyen, and Nguyen Minh Hieu. "A review of bus crash severity analyses." Transport and Communications Science Journal 73, no. 4 (May 15, 2022): 439–48. http://dx.doi.org/10.47869/tcsj.73.4.8.
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The bus plays an important role in serving the urban travel demand in both developed and developing countries. Unfortunately, the heavy operation of this mode may come together with crashes, which may cause adverse effects on its image and service quality from the view of the public, thereby leading to a reduction in ridership. The knowledge on characteristics of crashes is, therefore, desired. While most of the research focus is on the probability of crash occurrence, the severity of crashes has recently attracted increasing attention from researchers. This paper aims to synthesize previous studies on crash severity. By means of looking at 11 studies carefully selected from the SCOPUS database, this paper has provided a detailed synthesis of data collection, research areas, used samples, seven factor groups associated with the crash severity (Temporal characteristics, Location and infrastructure characteristics, Service and vehicle characteristics, Traffic characteristics, Crash characteristics, Weather characteristics, Driver characteristics and behaviours), and analytical methods. Afterwards, research gaps and shortcomings of the existing research are highlighted for proposing future research directions. This review could help to save efforts to obtain a clear understanding of bus crash severity. It may be an informative reference for those newly accessing the field of bus crash severity and/or intending to develop solutions to this problem.
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Sanjai, R. J. "Study on Driving Behavior of Public Transport Drivers in Kerala." International Journal of Engineering and Technologies 19 (December 2020): 1–9. http://dx.doi.org/10.18052/www.scipress.com/ijet.19.1.
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A lot of crashes involving heavy vehicles have been reported over the years in Kerala. Faulty driving behavior is widely regarded as the major cause of such crashes. This study considers the drivers’ perceptions and their socio-demographic background to understand the behavioral variations. The study is done based on a questionnaire survey on public transport operators from which their behavioral characteristics are noted. The study area is divided into three parts namely, North, Central and South Kerala with a fixed sample at each of these regions. The sample size is fixed based on Bill Godden’s method for infinite population. Accordingly, a sample of 384 vehicles was surveyed from each region. Thus, data for a total of 1,152 public transport vehicles were taken from all three regions namely North, South, and Central Kerala. From this sample, the variations in violations, errors and lapses among drivers are noted based on various causative factors. Analyses of these behavioral factors are done to study the influence on aberrant behavior and accident occurrence in public transport vehicle plying in Kerala. A linear regression model is developed to state the importance of causative factors in the occurrence of crashes.
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Sanjai, R. J. "Study on Driving Behavior of Public Transport Drivers in Kerala." International Journal of Engineering and Technologies 19 (December 7, 2020): 1–9. http://dx.doi.org/10.56431/p-v91j52.
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A lot of crashes involving heavy vehicles have been reported over the years in Kerala. Faulty driving behavior is widely regarded as the major cause of such crashes. This study considers the drivers’ perceptions and their socio-demographic background to understand the behavioral variations. The study is done based on a questionnaire survey on public transport operators from which their behavioral characteristics are noted. The study area is divided into three parts namely, North, Central and South Kerala with a fixed sample at each of these regions. The sample size is fixed based on Bill Godden’s method for infinite population. Accordingly, a sample of 384 vehicles was surveyed from each region. Thus, data for a total of 1,152 public transport vehicles were taken from all three regions namely North, South, and Central Kerala. From this sample, the variations in violations, errors and lapses among drivers are noted based on various causative factors. Analyses of these behavioral factors are done to study the influence on aberrant behavior and accident occurrence in public transport vehicle plying in Kerala. A linear regression model is developed to state the importance of causative factors in the occurrence of crashes.
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Hosseinpour, Mehdi, Ahmad Shukri Yahaya, Ahmad Farhan Sadullah, Noriszura Ismail, and Seyed Mohammad Reza Ghadiri. "EVALUATING THE EFFECTS OF ROAD GEOMETRY, ENVIRONMENT, AND TRAFFIC VOLUME ON ROLLOVER CRASHES." TRANSPORT 31, no. 2 (June 28, 2016): 221–32. http://dx.doi.org/10.3846/16484142.2016.1193046.
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There are a number of factors that cause motor vehicles to rollover. However, the impacts of roadway characteristics on rollover crashes have rarely been addressed in the literature. This study aims to apply a set of crash prediction models in order to estimate the number of rollovers as a function of road geometry, the environment, and traffic conditions. To this end, seven count-data models, including Poisson (PM), negative binomial (NB), heterogeneous negative binomial (HTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), hurdle Poisson (HP), and hurdle negative binomial (HNB) models, were developed and compared using crash data collected on 448 segments of Malaysian federal roads. The results showed that the HTNB was the best-fit model among the others to model the frequency of rollovers. The variables Light-Vehicle Traffic (LVT), horizontal curvature, access points, speed limit, and centreline median were positively associated with the crash frequency, while UnPaved Shoulder Width (UPSW) and Heavy-Vehicle Traffic (HVT) were found to have the opposite effect. The findings of this study suggest that rollovers could potentially be reduced by developing road safety countermeasures, such as access management of driveways, straightening sharp horizontal curves, widening shoulder width, better design of centreline medians, and posting lower speed limits and warning signs in areas with higher rollover tendency.
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Gudes, Ori, Richard Varhol, Qian (Chayn) Sun, and Lynn Meuleners. "Investigating articulated heavy-vehicle crashes in Western Australia using a spatial approach." Accident Analysis & Prevention 106 (September 2017): 243–53. http://dx.doi.org/10.1016/j.aap.2017.05.026.
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Chand, Sai, Emily Moylan, S. Travis Waller, and Vinayak Dixit. "Analysis of Vehicle Breakdown Frequency: A Case Study of New South Wales, Australia." Sustainability 12, no. 19 (October 7, 2020): 8244. http://dx.doi.org/10.3390/su12198244.
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Traffic incidents such as crashes, vehicle breakdowns, and hazards impact traffic speeds and induce congestion. Recognizing the factors that influence the frequency of these traffic incidents is helpful in proposing countermeasures. There have been several studies on evaluating crash frequencies. However, research on other incident types is sparse. The main objective of this research is to identify critical variables that affect the number of reported vehicle breakdowns. A traffic incident dataset covering 4.5 years (January 2012 to June 2016) in the Australian state of New South Wales (NSW) was arranged in a panel data format, consisting of monthly reported vehicle breakdowns in 28 SA4s (Statistical Area Level 4) in NSW. The impact of different independent variables on the number of breakdowns reported in each month–SA4 observation is captured using a random-effect negative binomial regression model. The results indicate that increases in population density, the number of registered vehicles, the number of public holidays, average temperature, the percentage of heavy vehicles, and percentage of white-collared jobs in an area increase the number of breakdowns. On the other hand, an increase in the percentage of unrestricted driving licenses and families with children, number of school holidays, and average rainfall decrease the breakdown frequency. The insights offered in this study contribute to a complete picture of the relevant factors that can be used by transport authorities, vehicle manufacturers, sellers, roadside assistance companies, and mechanics to better manage the impact of vehicle breakdowns.
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Wang, Chen, Yulu Dai, Wei Zhou, and Yifei Geng. "A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition." Journal of Advanced Transportation 2020 (January 17, 2020): 1–11. http://dx.doi.org/10.1155/2020/9194028.
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In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper.
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Nuruddin Ariffin, Kamarul-Azhar Kamarudin, Ahmad Sufian Abdullah, and Mohd Idrus Abd Samad. "Crash Investigation on Frontal Vehicle Chassis Frame using Finite Element Simulation." Journal of Advanced Research in Applied Sciences and Engineering Technology 28, no. 2 (October 17, 2022): 124–34. http://dx.doi.org/10.37934/araset.28.2.124134.
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Car chassis can be considered as the primary protective shield for the safety of the passenger during rear-end crashes. This study focuses on the deformation and failure behavior of the frontal car A-pillar chassis frame when subjected to collision with a heavy vehicle. Two different angles of the A-pillar chassis frame use are 45-degree and 70-degree. The crash simulation is conducted by using Finite Element software under the explicit dynamic. The car chassis frame geometries are designed by using SolidWorks 2021 and imported to the finite element software while a rigid block is designed in the finite element software as a rigid body to replicate the heavy vehicle. The chassis body is simulated for two types of materials, Aluminum alloy, and steel. The car speed impacted at 60 km/h. Results show that the intrusion of a rear barrier for 45 degrees of aluminum alloy will stop at 0.03 s but for 70 degrees it will intrude the car frame until the end. For the steel car frame, 45 degrees design is capable to withstand the intrusion of a rear barrier from a serious deform but for 70 degrees the intrusion will continue until the end. Car frame crush behavior, energy dissipation, and vehicle decelerations from the crash simulation were observed.
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Khamis, Nor Kamaliana, Baba M. Deros, Mohd Zaki Nuawi, and Rabiatul Basariah Omar. "Driving Fatigue among Long Distance Heavy Vehicle Drivers in Klang Valley, Malaysia." Applied Mechanics and Materials 663 (October 2014): 567–73. http://dx.doi.org/10.4028/www.scientific.net/amm.663.567.
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Long distance driving could result in driver’s fatigue. Driving fatigue has been identified as one of the main reasons behind fatal crashes and injuries. This study aims to identify the body parts that experience fatigue and investigate factors that contribute towards driving fatigue among long distance heavy vehicle drivers. Two methods were used to collect information; first, an oral face-to-face interview and second, a self-administered survey questionnaire. A set of questionnaire was developed, pilot tested for its reliability and validity; and distributed to the respondent. Respondents were 60 heavy vehicle drivers from Klang Valley. Majority of the heavy vehicle drivers agreed that long driving hours is the main factor that causes fatigue. Both oral interviews and survey findings showed that all respondents experienced fatigue while driving. Majority of them start to experience fatigue after two hours of driving. In addition, the result of this study shows the drivers experienced severe discomfort on shoulder and low back. Majority of them highlighted that lack of sleep before starting the journey is the main fatigue cause. In conclusion, the heavy vehicle drivers should have sufficient sleep before long distance driving and take a break after two hours of driving.
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Hagos, Ehitayhu, Tom Brijs, Kris Brijs, Geert Wets, and Bikila Teklu. "Safety Culture among Transport Companies in Ethiopia: Are They Ready for Emerging Fleet Technologies?" Sustainability 15, no. 4 (February 10, 2023): 3232. http://dx.doi.org/10.3390/su15043232.
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The safety culture and safety climate of transport companies have a significant impact on fleet safety outcomes. Ample research shows that transport companies with a strong safety culture also show lower crash statistics. In spite of modern technologies that help with having a safer fleet, it is difficult to achieve a safer fleet without a proactive safety culture and climate. In Ethiopia, it is assumed that most transport companies have failed to create a distinguishable safety climate in their fleet safety administration and that their heavy vehicle drivers have a poor safety culture. These could be important factors contributing to a higher rate of road traffic crashes involving heavy vehicles. This study aims to assess the existing safety culture among a sample of transport companies in Ethiopia and identify suitable intervention methods to improve the safety culture. Moreover, the study sought to identify the readiness of the transport companies to apply modern technology in their fleets by examining their safety culture and safety climate. In total, 10 fleet managers and 174 heavy vehicle drivers participated in the fleet safety audit survey. A descriptive analysis and a detailed fleet safety audit score were calculated. Based on the scale scoring, ten companies score below best practices, one scores well below best practices, and only one company meets the criteria to be considered achieving best practices. The results from this study show that the safety culture and safety climate in most transport companies are quite limited. In addition, most transport companies implement similar safety measures, including inconsistent driver training and annual maintenance.
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Yahya, Siti Aishah, Nur Baitul Izati Rasli, Nor Azam Ramli, and Syabiha Shith. "REAR SIDE CONSPICUITY OF HEAVY GOOD VEHICLES BASED ON LIGHTING COMPONENTS." International Journal of Engineering Technologies and Management Research 7, no. 9 (October 4, 2020): 72–79. http://dx.doi.org/10.29121/ijetmr.v7.i9.2020.786.
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In Malaysia, accidents that involve collisions with rigid and articulated trucks are mainly related to the conspicuity issue i.e. the issue of the conspicuity of the lighting components on heavy goods vehicles (HGVs). Thus, visibility and conspicuity must be improved because such an improvement can help drivers encounter safety measures on the psychological phenomenon. Conspicuity is the ability of a vehicle to be noticed without observers intentionally searching for it. In Malaysia, HGVs must be equipped with lighting components for improved visibility. The study aims to analyse the percentages of lighting components that comply with the MS ISO 303:2004. This research is conducted at Jawi Toll Plaza, Nibong Tebal, Pulau Pinang from 9:00 a.m. to 5:00 p.m. Results show that 93% of two-axle box trucks have installed rear lighting components that adhere to the standard. Meanwhile, other categories are between 38% and 91%. In conclusion, even though Heavy Goods Vehicles recorded as having high compliance levels of the rear lighting components, the accident still occurred. The types of crashes are based on the inadequate visibility involved when the moving vehicles are approaching the rear or sides of slow-moving or stationary vehicles at night.
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Santos, Bertha, Valdemiro Trindade, Cláudia Polónia, and Luís Picado-Santos. "Detecting Risk Factors of Road Work Zone Crashes from the Information Provided in Police Crash Reports: The Case Study of Portugal." Safety 7, no. 1 (February 4, 2021): 12. http://dx.doi.org/10.3390/safety7010012.
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Several studies have shown that European police crash reports provide different detail degrees of work zone crash-related data. In this sense, the present study aims to verify the possibility of identifying significant risk factors involved in the occurrence of road work zone crashes with casualties, based on the official data usually available, through a descriptive, binary logistic, and probit regression statistical analysis. To accomplish the analysis, a total of 2597 police-reports related to 1767 Portuguese work zone crashes that occurred during the 2013–2015 period were considered and binary logistic and probit regression models were estimated by the main type of crash, contributing factor, and driver age group. Fifteen explanatory variables, selected based on the literature review and crash data provided in police crash reports, were considered in the analysis. The results obtained for the estimated coefficients and goodness-of-fit test values were found very similar for both link functions (logit and probit) and it was possible to identify risk factors. The modeling results pointed to excessive speed, disregard for vertical signs, luminosity, intersections, and motorcycle and heavy vehicle involvement as the most significant risk factors. Given the results, it is possible to conclude that binary logistic regression can be used in the statistical analysis of the available police official work zone crash data to identify and get some insight into the risk factors involved in work zone crashes. Data analysis also revealed the need to promote adequate and complete crash report filling by police officers. While police crash reports are not revised and standardized to incorporate more detailed work zone crash information, this approach can be used to support a more efficient road operation decision making and the review of some aspects related to work zone layout design.
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Archer, Jeffery, and William Young. "Signal Treatments to Reduce the Likelihood of Heavy Vehicle Crashes at Intersections: Microsimulation Modeling Approach." Journal of Transportation Engineering 136, no. 7 (July 2010): 632–39. http://dx.doi.org/10.1061/(asce)te.1943-5436.0000125.
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Pervez, Amjad, Jaeyoung Lee, and Helai Huang. "Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan." Journal of Advanced Transportation 2021 (February 28, 2021): 1–10. http://dx.doi.org/10.1155/2021/6636130.
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Motorcycle is a popular mode of transportation in many developing countries, including Pakistan. Since the last decade, the registered number of motorcycles in Pakistan has increased by six times, constituting 74% of the total registered vehicles. However, limited research efforts have been made to investigate motorcycle-related safety issues in Pakistan. Thus, the relationship between potential risk factors and injury outcomes of motorcycle crashes is still unclear in the country. This study, therefore, established a random parameter logit model to examine the factors associated with the motorcycle injury severity. The analysis is based on two years (2014–2015) of data collected through the road traffic injuries surveillance system from Karachi city, Pakistan. The results indicate that the summer season, weekends, nighttime, elderly riders, heavy vehicle, and single-vehicle collisions are positively associated with fatalities, while the presence of pillion passengers and motorcycle-to-motorcycle crashes are negatively associated with fatalities. More importantly, in the specific context of Pakistan, morning hours, young riders, and female pillion passengers whose clothes stuck in the wheel significantly increase the fatal injury outcomes. Based on the findings, potential countermeasures to improve motorcycle safety are discussed, such as strict enforcement to control motorcyclists' risky behavior and speeding, provision of exclusive motorcycles lanes, and education of female pillion passengers. The findings from this study would increase awareness of motorcycle safety and can be used by the policymakers to enhance road safety in Pakistan, as well as in other developing countries with similar situations.
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Clements, Lewis M., and Kara M. Kockelman. "Economic Effects of Automated Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2606, no. 1 (January 2017): 106–14. http://dx.doi.org/10.3141/2606-14.
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Connected and fully automated or autonomous vehicles (CAVs) may soon dominate the automotive industry. Once CAVs are sufficiently reliable and affordable, they will penetrate markets and thereby generate economic ripple effects throughout industries. This paper synthesizes and expands on existing analyses of the economic effects of CAVs in the United States across 13 industries and the overall economy. CAVs will soon be central to the automotive industry, with software composing a greater share of vehicle value than previously. The number of vehicles purchased each year may fall because of vehicle sharing, but rising travel distances may increase vehicle sales. The opportunity for heavy-truck drivers to do other work or rest during long drives may lower freight costs and increase capacity. Personal transport may shift toward shared autonomous vehicle fleet use, reducing that of taxis, buses, and other forms of group travel. Fewer collisions and more law-abiding vehicles will lower demand for auto repair, traffic police, medical, insurance, and legal services. CAVs will also lead to new methods for managing travel demand and the repurposing of curbside and off-street parking and will generate major savings from productivity gains during hands-free travel and reduction of pain and suffering costs from crashes. If CAVs eventually capture a large share of the automotive market, they are estimated to have economic impacts of $1.2 trillion or $3,800 per American per year. This paper presents important considerations for CAVs’ overall effects and quantifies those impacts.
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Long, Gary. "Easy-to-Apply Solution to a Persistent Safety Problem: Clearance Time for Railroad-Preempted Traffic Signals." Transportation Research Record: Journal of the Transportation Research Board 1856, no. 1 (January 2003): 239–47. http://dx.doi.org/10.3141/1856-26.
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Traffic signals near railroad grade crossings having active warning systems are normally interconnected and receive a signal from the railroad track circuitry when trains are approaching. This train approach signal is used by the traffic signal to interrupt and preempt its normal phasing and enter into higher priority special phasings to clear the track of any vehicles that might be stopped in the pathway of a train. The amount of preemption time needed to clear a vehicle from the pathway of a train is necessary information for appropriate preemption signal settings but there are no definitive guidelines on how to determine this amount of time. It is usually left to the judgment of the signal engineer, and frequently unfounded assumptions are used. This study investigated the time required to clear a vehicle in a queue backed up onto a track at railroad-preempted traffic signals. The two key time components are start-up delay and repositioning time. Queues where all preceding vehicles are short passenger cars cause the longest start-up delays, and heavy trucks at the track involve the longest repositioning times. The model developed is convenient because once it is decided that preemption is needed, only easily determined values are required—the minimum track clearance distance, the clear storage distance, and the types of vehicles that are permitted to use the roadway. The model adopts a high level of confidence to minimize the risk of crashes but avoids the "worst-case" concept to avert an invitation for litigation when an improbable or unforeseen worst case results in a crash.
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Vieira, Adriana, Bertha Santos, and Luís Picado-Santos. "Modelling Road Work Zone Crashes’ Nature and Type of Person Involved Using Multinomial Logistic Regression." Sustainability 15, no. 3 (February 2, 2023): 2674. http://dx.doi.org/10.3390/su15032674.
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The sustainable development goals “Good health and well-being” and “Sustainable cities and communities” of the United Nations and World Health Organization, alert governments and researchers and raise awareness about road safety problems and the need to mitigate them. In Portugal, after the economic crisis of 2008–2013, a significant amount of road assets demand investment in maintenance and rehabilitation. The areas where these actions take place are called work zones. Considering the particularities of these areas, the proposed work aims to identify the main factors that impact the occurrence of work zones crashes. It uses the statistical technique of multinomial logistic regression, applied to official data on road crashes occurred in mainland Portugal, during the period of 2010–2015. Usually, multinomial logistic regression models are developed for crash and injury severity. In this work, the feasibility of developing predictive models for crash nature (collision, run off road and running over pedestrians) and for type of person involved in the crash (driver, passenger and pedestrian), considering only one covariate (the number of persons involved in the crash), was studied. For the two predictive models obtained, the variables road environment (urban/rural), horizontal geometric design (straight/curve), pavement grip conditions (good/bad), heavy vehicle involvement, and injury severity (fatalities, serious and slightly injuries), were identified as the preponderant factors in a universe of 230 investigated variables. Results point to an increase of work zone crash probability due to driver actions such as running straight and excessive speed for the prevailing conditions.
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Terrill, Trenna T., M. Mahdi Rezapour Mashhadi, and Khaled Ksaibati. "Developing a tool to help highway patrol in allocating resources to crashes." International Journal of Police Science & Management 18, no. 4 (October 5, 2016): 231–41. http://dx.doi.org/10.1177/1461355716665855.
Full textAbstract:
In recent years, the United States has experienced significant growth in the amount of freight transported by truck, and this is expected to increase in future. Collisions involving trucks have some of the greatest ramifications and are associated with economic and societal costs. The state of Wyoming has one of the highest rates of crashes involving trucks in the country. The Wyoming Highway Patrol puts a substantial amount of its resources into enforcing commercial vehicle inspections and traffic laws in the state. Because of the heavy truck traffic on Interstate 80 (I-80), the much of the Wyoming Highway Patrol’s resources are utilized in patrolling and performing inspections there. Because of this intensive truck corridor, the Wyoming Highway Patrol may not be focusing resources on other locations throughout the state. The Wyoming Department of Transportation and the Wyoming Highway Patrol jointly selected three zones throughout the state of Wyoming based on high truck traffic areas. Historical statewide crash data and statewide citations issued by the Wyoming Highway Patrol in these zones are analyzed in this study. The relationship between the numbers of citations issued along the studied route and the number of crashes that occurred is investigated in this article. Statistical analysis using ordinal least squares on I25 indicates that, on a monthly scale, the number of citations is a preventive measure for the number of crashes. Spatial temporal analysis on I25 showed that although Wyoming Highway Patrol resources are allocated to locations with the greatest number of crashes, it needs to allocate more resources on I25 north between midnight and 6 a.m. This study provides the Wyoming Highway Patrol with information regarding where more enforcement may be needed within other areas in the state of Wyoming.
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Meuleners, Lynn, Michelle L. Fraser, Matthew H. Govorko, and Mark R. Stevenson. "Determinants of the occupational environment and heavy vehicle crashes in Western Australia: A case–control study." Accident Analysis & Prevention 99 (February 2017): 452–58. http://dx.doi.org/10.1016/j.aap.2015.11.023.
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Casado-Sanz, Natalia, Begoña Guirao, and Maria Attard. "Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective." Sustainability 12, no. 6 (March 13, 2020): 2237. http://dx.doi.org/10.3390/su12062237.
Full textAbstract:
Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.
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Wang, Yuqing, Yosuke Tamura, Hirotoshi Ishikawa, and Kazuhiko Ando. "Development of a Three-Dimensional Simulation Model of Vehicle-Bridge Railing Crash: A Computer-Aided Approach to Bridge Railing Stiffness Design." Transportation Research Record: Journal of the Transportation Research Board 1647, no. 1 (January 1998): 104–10. http://dx.doi.org/10.3141/1647-13.
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To improve the conventional lengthy and inefficient design process for bridge railings, which features repetitions of costly full-scale experiments, and to support the bridge railing design with analytical data, this research developed a three-dimensional (3-D) simulation model that can reconstruct a vehicle-bridge railing impact analytically and visually using MADYMO (MAthematical DYnamic MOdel), developed by TNO. Both the vehicle and the bridge railing are modeled as rigid multi-body systems. A nonlinear tire model and a 3-D suspension are introduced to increase the reality of the vehicle motion during impact. The bridge railing is divided into poles and longitudinal railings. The bending characteristics of the poles and the longitudinal railings are described by the properties of corresponding universal joints attached to them. The simulation model is verified by experimental data of three different conditions using heavy trucks (8 tons and 20 tons). The validation reveals that the 3-D model can reconstruct vehicle-bridge railing crashes with a high degree of accuracy with respect to the vehicle performance and the deformation of the bridge railing, which are the most important concerns in the designs of bridge railings. The validated crash model is then employed to determine the necessary stiffness of the bridge railing under different conditions. Curves describing the relationship between the stiffness of the poles and the longitudinal railings are calculated for various impact severity values. These curves provide analytical grounds for the stiffness designs of the bridge railings.
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Flynn, Dan F. B., Andrew Breck, Olivia Gillham, Randolph G. Atkins, and Donald L. Fisher. "Dynamic Speed Feedback Signs Are Effective in Reducing Driver Speeds: A Meta-Analysis." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 12 (October 20, 2020): 481–92. http://dx.doi.org/10.1177/0361198120957326.
Full textAbstract:
Excess speed contributes to over a quarter of all fatal automobile crashes in the United States, costing society billions of dollars each year. Lowering excess speeds to reduce these human, societal, and economic costs is therefore a major focus of safety officials and highway engineers. This study presents a quantitative review of the effectiveness of dynamic speed feedback signs (DSFS), which provide drivers with real-time feedback on their speed so that drivers traveling above the posted speed can slow appropriately. Using a meta-analysis of 43 publications, this study demonstrates that DSFS can effectively reduce speeds in different contexts (e.g., school and work zones) and for different vehicle types (e.g., heavy and light duty). Across all types of contexts and vehicle types that were analyzed, the meta-analysis identified statistically significant reductions in speeds when DSFS are installed. Overall, reductions of 4 mph were detected as a result of DSFS installation for passenger cars, and reductions between 2 and 4 mph were detected across vehicle types in the different contexts assessed. As reductions in speed of just 4 mph of vehicles traveling 30–35 mph can reduce fatal pedestrian-vehicle strikes by upwards of 40%, these findings demonstrate that the reductions in speed at DSFS were not only statistically significant, but also practically significant in areas such as school zones and work zones.
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Li, Guanlong, Yueqing Li, Yalong Li, Brian Craig, and Xing Wu. "Investigation of Contributing Factors to Traffic Crash Severity in Southeast Texas Using Multiple Correspondence Analysis." Journal of Road Safety 32, no. 4 (November 1, 2021): 15–28. http://dx.doi.org/10.33492/jrs-d-21-00051.
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Driving is the essential means of travel in Southeast Texas, a highly urbanized and populous area that serves as an economic powerhouse of the whole state. However, driving in Southeast Texas is subject to many risks as this region features a typical humid subtropical climate with long hot summers and short mild winters. Local drivers would encounter intense precipitation, heavy fog, strong sunlight, standing water, slick road surface, and even frequent extreme weather such as tropical storms, hurricanes and flood during their year-around travels. Meanwhile, research has revealed that the fatality rate per 100 million vehicle miles driven in urban Texas became considerably higher than national average since 2010, and no conclusive study has elucidated the association between Southeast Texas crash severity and potential contributing factors. This study used multiple correspondence analysis (MCA) to examine a group of contributing factors on how their combinatorial influences determine crash severity by creating combination clouds on a factor map. Results revealed numerous significant combinatorial effects. For example, driving in rain and extreme weather on a wet road surface has a higher chance in causing crashes that incur severe or deadly injuries. Besides, other contributing factors involving risky behavioral factors, road designs, and vehicle factors were well discussed. The research outcomes could inspire local traffic administration to take more effective countermeasures to systematically mitigate road crash severity.