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1

Cordiez, Maxence. "Le trafic aérien nous amène-t-il au crash ?" DARD/DARD N° 1, no. 1 (2019): 75–83. http://dx.doi.org/10.3917/dard.001.0075.

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2

Coufal, Tomáš, and Marek Semela. "Determination of Selected Crash Parameters in Head-on Vehicle Collision with Rollover." PROMET - Traffic&Transportation 28, no. 1 (2016): 71–79. http://dx.doi.org/10.7307/ptt.v28i1.1650.

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The paper presents complete results of the head-on small overlap crash test of vehicle with driver moving at a speed of approximately 12 m/s against stationary vehicle with post-crash rollover. When a crash does not involve the main crush-zone structures, the occupant compartment is not well protected. The emphasis in the paper was put on determination and presentation of crash parameters for the application in traffic accident analyses and for simulation with the help of software for accident reconstruction. The experimentally measured data from the crash test were analysed and important crash parameters which are necessary for accident reconstruction were obtained. The crash test was specific because of rollover of the impacting vehicle resulting from small overlap. The results have shown that small overlap accident is extremely dangerous for the crew with the possibility of vehicle rollover and occupant head and neck injury. Also in this case, at relative low speed, the driver suffered light neck and head injury in the following days and the longitudinal damage was relatively large. The input parameters for accident reconstruction software as the result of performed crash test were gained.
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3

Pešić, Dalibor, Boris Antić, Emir Smailovic, and Bojana Todosijević. "The impact of the average traffic flow speed on occurrence risk of road crash." Put i saobraćaj 65, no. 2 (2019): 29–36. http://dx.doi.org/10.31075/pis.65.02.05.

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Traffic flow characteristics have a significant impact on occurrence risk of road crash. The most important characteristics of the traffic flow, the impact of which is the subject of numerous studies, are the traffc flow, density, average traffic flow speed, dispersion of traffic flow speeds, as well as the contents of vehicle in traffic flow. These characteristics are in strong correlation between each other, so changes in one parameter conditional make change of other parameters. Research shows that speed-related traffic flow parameters have a significant impact on occurrence risk of road crash. Therefore, in this study an analysis of the impact of the change in the average speed of the traffic flow on the risk of an accident occurred. The research includes a section of the single carriageway from Preljina to Ljig. After the construction of the highway in the stated section of the signle carriageway, a change in the characteristics of the traffic flow occurred, with this study examining the impact of changing the average speed of the traffic flow to the occurrence risk of road crash. The connection between the speed of traffic flow and the risk of accidents has been confirmed in this study, so with the increase in average speed the risk of accidents increases.
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4

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 (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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5

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 (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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6

Lee, Chris, Bruce Hellinga, and Frank Saccomanno. "Proactive freeway crash prevention using real-time traffic control." Canadian Journal of Civil Engineering 30, no. 6 (2003): 1034–41. http://dx.doi.org/10.1139/l03-040.

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This paper makes use of a probabilistic model that predicts the likelihood of crashes (crash potential) on freeways on the basis of traffic flow conditions, in real-time crash prevention. The model was developed using incident logs and loop detector data collected over a 13-month period on the Gardiner Expressway in Toronto. Previous work suggested that an increase in levels of traffic turbulence generally yields high crash potential. Traffic turbulence was defined in terms of a series of crash precursors that represent traffic conditions that were present prior to crash occurrence. To apply the model in crash prevention, the link needs to be established between crash potential and real-time safety intervention. The objective of this paper is to explore this link for different thresholds of crash potential. The paper discusses the guidelines for evaluating the safety benefit of one crash prevention strategy (variable speed limits) and suggests the risk-based evaluation framework for real-time traffic control.Key words: crash, accident, freeway, safety, traffic flow, real-time control.
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7

Zhang, Hui, Siyao Li, Chaozhong Wu, Qi Zhang, and Yafen Wang. "Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data." Journal of Advanced Transportation 2020 (March 20, 2020): 1–8. http://dx.doi.org/10.1155/2020/8523818.

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Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway.
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8

Bhattarai, Sujan. "Crash Prediction for Prioritization of Intersections for Safety Improvement: Case Study of Kathmandu Valley." Journal of Advanced College of Engineering and Management 5 (December 18, 2019): 165–79. http://dx.doi.org/10.3126/jacem.v5i0.26765.

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Every year globally 1.3 million people lose their lives from road traffic crashes (RTAs). Similarly, increasing rate of RTAs has been observed in Nepal including Kathmandu valley. This study is focused on the analysis of crash trends and respective site specific geometric features of urban road intersections in Kathmandu valley. Seventeen major intersections based on the data availability and traffic volume, are considered for the analysis of crash type. Previous crash data and traffic volume records of one year have been analysed. Common types of three and four legged intersections were taken for the study. Classified traffic volume at those intersections were converted into the Annual Average Daily Traffic. Evaluation factors for the crash analysis were determined by using predictive method. Crash frequency, crash rate, critical crash rate and crash prediction methods were used for ranking of the intersection. Priority for the safety improvement is prepared based on the results of this study.
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9

Cao, Yi, Shiwen Li, and Chuanyun Fu. "An Assessment Method of Urban Traffic Crash Severity Considering Traveling Delay and Non-Essential Fuel Consumption of Third Parties." Sustainability 12, no. 17 (2020): 6806. http://dx.doi.org/10.3390/su12176806.

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Urban traffic crashes may lead to only a few casualties, but may generate severe negative impacts on the surrounding traffic, such as evidently increasing traveling delay and non-essential fuel consumption of third parties (i.e., vehicles not involved in the crash). Such detrimental consequences of urban traffic crashes are usually ignored by the traditional crash severity evaluation approaches. Therefore, this study attempts to classify urban traffic crash severity by considering the traveling delay and non-essential fuel consumption of third parties in addition to casualties and property damages. Based on the losses of traveling delay and non-essential fuel consumption of third parties, the losses of crash casualties, and property damages, a comprehensive index of urban traffic crash severity was developed. Moreover, the thresholds of the proposed comprehensive index for urban crash severity classification were determined based on the crash data from 2013 to 2014 collected from Harbin, China. The developed comprehensive index was applied to a case study, which also compared the crash severity classification outcomes from the developed method and the current approach. The results indicate that the developed method of urban traffic crash severity classification is more reasonable than the existing approach. Such superiority of the proposed urban crash severity classification method is due to considering the traveling delay and non-essential fuel consumption of third parties caused by a crash.
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10

Lei, Tian, Jia Peng, Xingliang Liu, and Qin Luo. "Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach." Journal of Advanced Transportation 2021 (March 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/8820402.

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Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.
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11

Chen, Zhi, Xiao Qin, Renxin Zhong, Pan Liu, and Yang Cheng. "Predicting Imminent Crash Risk with Simulated Traffic from Distant Sensors." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 12–21. http://dx.doi.org/10.1177/0361198118791379.

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The aim of this research was to investigate the performance of simulated traffic data for real-time crash prediction when loop detector stations are distant from the actual crash location. Nearly all contemporary real-time crash prediction models use traffic data from physical detector stations; however, the distance between a crash location and its nearest detector station can vary considerably from site to site, creating inconsistency in detector data retrieval and subsequent crash prediction. Moreover, large distances between crash locations and detector stations imply that traffic data from these stations may not truly reflect crash-prone conditions. Crash and noncrash events were identified for a freeway section on I-94 EB in Wisconsin. The cell transmission model (CTM), a macroscopic simulation model, was applied in this study to instrument segments with virtual detector stations when physical stations were not available near the crash location. Traffic data produced from the virtual stations were used to develop crash prediction models. A comparison revealed that the predictive accuracy of models developed with virtual station data was comparable to those developed with physical station data. The finding demonstrates that simulated traffic data are a viable option for real-time crash prediction given distant detector stations. The proposed approach can be used in the real-time crash detection system or in a connected vehicle environment with different settings.
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12

Yang, Yanfang, Yong Qin, Limin Jia, and Honghui Dong. "Traffic Safety Region Estimation Based on SFS–PCA–LSSVM: An Application to Highway Crash Risk Evaluation." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (2016): 1555–70. http://dx.doi.org/10.1142/s0218194016400179.

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Accurate real-time crash risk evaluation is essential for making prevention strategy in order to proactively improve traffic safety. Quite a number of models have been developed to evaluate traffic crash risk by using real-time surveillance data. In this paper, the basic idea of traffic safety region is introduced into highway crash risk evaluation. Sequential forward selection (SFS), principal components analysis (PCA) and least squares support vector machine (LSSVM) are used to estimate the traffic safety region and classify the traffic states (safe condition and unsafe condition). The proposed method works by first extracting state variables from the observed traffic variables. Two statistics [Formula: see text] and squared prediction error (SPE) are calculated by SFS–PCA and used as the final state variables for traffic state space. Next, LSSVM is used to estimate the boundary of traffic safety region and identify the traffic states in the traffic state space. To demonstrate the advantage of the proposed method, this study develops two crash risk evaluation models, namely SFS–LSSVM model and PCA–LSSVM model, based on crash data and non-crash data collected on freeway I-880N in Alameda. Validation results show that the method is of reasonably high accuracy for identifying traffic states.
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13

Lee, Chris, Frank Saccomanno, and Bruce Hellinga. "Analysis of Crash Precursors on Instrumented Freeways." Transportation Research Record: Journal of the Transportation Research Board 1784, no. 1 (2002): 1–8. http://dx.doi.org/10.3141/1784-01.

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Traffic flow characteristics that lead to crashes on urban freeways are examined. Since these characteristics are observed prior to crash occurrence, they are referred to as “crash precursors.” The objectives are ( a) to explore factors contributing to changes in crash rate for individual vehicles traveling over an urban freeway and ( b) to develop a probabilistic model relating significant crash precursors to changes in crash potential. The data used to examine crash precursors were extracted from 38 loop detector stations on a 10-km stretch of the Gardiner Expressway in Toronto for a 13-month period. An aggregate log-linear model was developed relating crash rates to the selected crash precursors observed upstream of the crash site. The results of this analysis suggest that the variation of speed and traffic density are statistically significant predictors of crash frequency after controlling for road geometry, weather, and time of day. With the model, crash potential can be established based on the precursors obtained from real-time traffic data.
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14

Lee, Chris, Bruce Hellinga, and Frank Saccomanno. "Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic." Transportation Research Record: Journal of the Transportation Research Board 1840, no. 1 (2003): 67–77. http://dx.doi.org/10.3141/1840-08.

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The likelihood of a crash or crash potential is significantly affected by the short-term turbulence of traffic flow. For this reason, crash potential must be estimated on a real-time basis by monitoring the current traffic condition. In this regard, a probabilistic real-time crash prediction model relating crash potential to various traffic flow characteristics that lead to crash occurrence, or “crash precursors,” was developed. In the development of the previous model, however, several assumptions were made that had not been clearly verified from either theoretical or empirical perspectives. Therefore, the objectives of the present study were to ( a) suggest the rational methods by which the crash precursors included in the model can be determined on the basis of experimental results and ( b) test the performance of the modified crash prediction model. The study found that crash precursors can be determined in an objective manner, eliminating a characteristic of the previous model, in which the model results were dependent on analysts’ subjective categorization of crash precursors.
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15

Yan, Xuedong, Bin Wang, Meiwu An, and Cuiping Zhang. "Distinguishing between Rural and Urban Road Segment Traffic Safety Based on Zero-Inflated Negative Binomial Regression Models." Discrete Dynamics in Nature and Society 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/789140.

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In this study, the traffic crash rate, total crash frequency, and injury and fatal crash frequency were taken into consideration for distinguishing between rural and urban road segment safety. The GIS-based crash data during four and half years in Pikes Peak Area, US were applied for the analyses. The comparative statistical results show that the crash rates in rural segments are consistently lower than urban segments. Further, the regression results based on Zero-Inflated Negative Binomial (ZINB) regression models indicate that the urban areas have a higher crash risk in terms of both total crash frequency and injury and fatal crash frequency, compared to rural areas. Additionally, it is found that crash frequencies increase as traffic volume and segment length increase, though the higher traffic volume lower the likelihood of severe crash occurrence; compared to 2-lane roads, the 4-lane roads have lower crash frequencies but have a higher probability of severe crash occurrence; and better road facilities with higher free flow speed can benefit from high standard design feature thus resulting in a lower total crash frequency, but they cannot mitigate the severe crash risk.
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16

SONY, BODANAPU. "Analysis of Roadway Crash Prediction Studies on Urban Roads under Heterogeneous Traffic Conditions." Journal of Research on the Lepidoptera 51, no. 1 (2020): 292–301. http://dx.doi.org/10.36872/lepi/v51i1/301025.

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17

Cheng, Zeyang, Zhenshan Zu, and Jian Lu. "Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections." Sustainability 11, no. 1 (2018): 160. http://dx.doi.org/10.3390/su11010160.

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Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang.
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18

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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19

Lyon, Craig, Bhagwant Persaud, and Scott Himes. "Investigating Total Annual Average Daily Traffic as a Surrogate for Motorcycle Volumes in Estimating Safety Performance Functions for Motorcycle Crashes." Transportation Research Record: Journal of the Transportation Research Board 2637, no. 1 (2017): 9–16. http://dx.doi.org/10.3141/2637-02.

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Data on traffic volumes are required to estimate the safety performance functions (SPFs) used to develop crash modification factors and for various safety management applications. Estimation of SPFs for motorcycle crashes can be especially challenging because few jurisdictions collect motorcycle traffic volume data systematically. To address this challenge, analyses with data from Florida, Pennsylvania, and Virginia were conducted to explore how much predictive power for an SPF was lost when motorcycle traffic volumes were not known. The results of the analyses showed that when motorcycle volumes were unknown, the use of total annual average daily traffic on its own was sufficient to develop motorcycle crash SPFs. The potential bias from missing motorcycle-specific annual average daily traffic was sufficiently negligible where it existed, not to preclude SPF development. A more significant issue in the development of motorcycle crash SPFs is to work with a crash type that is relatively rare, so that SPFs cannot be developed for all motorcycle crash types or site types.
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Wang, Xu, and Kai Liu. "A Crash Surrogate Metric considering Traffic Flow Dynamics in a Motorway Corridor." Journal of Advanced Transportation 2018 (June 27, 2018): 1–7. http://dx.doi.org/10.1155/2018/9349418.

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We proposed a new crash surrogate metric, i.e., the maximum disturbance that a car following scenario can accommodate, to represent potential crash risks with a simple closed form. The metric is developed in consideration of traffic flow dynamics. Then, we compared its performance in predicting the rear-end crash risks for motorway on-ramps with other two surrogate measures (time to collision and aggregated crash index). To this end, a one-lane on-ramp of Pacific Motorway, Australia, was selected for this case study. Due to the lack of crash data on the study site, historical crash counts were merged according to levels of service (LOS) and then converted into crash rates. In this study, we used the societal risk index to represent the crash surrogate indicators and built relationships with crash rates. The final results show that (1) the proposed metric and aggregated crash index are superior to the time to collision in predicting the rear-end crash risks for on-ramps; (2) they have a relatively similar performance, but due to the simple calculation, the proposed metric is more applicable to some real-world cases compared with the aggregated crash index.
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21

Al-Bayati, Amjad H., Ahmad S. Shakoree, and Zahraa A. Ramadan. "Factors Affecting Traffic Accidents Density on Selected Multilane Rural Highways." Civil Engineering Journal 7, no. 7 (2021): 1183–202. http://dx.doi.org/10.28991/cej-2021-03091719.

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Estimations of average crash density as a function of traffic elements and characteristics can be used for making good decisions relating to planning, designing, operating, and maintaining roadway networks. This study describes the relationships between total, collision, turnover, and runover accident densities with factors such as hourly traffic flow and average spot speed on multilane rural highways in Iraq. The study is based on data collected from two sources: police stations and traffic surveys. Three highways are selected in Wassit governorate as a case study to cover the studied locations of the accidents. Three highways are selected in Wassit governorate as a case study to cover the studied locations of the accidents. The selection includes Kut–Suwera, Kut–ShekhSaad, and Kut–Hay multilane divided highways located in the south of Iraq. The preliminary presentation of the studied highways was performed using Geographic Information System (GIS) software. Data collection was done to obtain crash numbers and types over five years with their locations, hourly traffic flow, and average spot speed and define roadway segments lengths of crash locations. The cumulative speed distribution curves introduce that the spot speed spectrum for each highway's whole traffic extends over a relatively wide range, indicating a maximum speed of 180 kph and a minimum speed of 30 kph. Multiple linear regression analysis is applied to the data using SPSS software to attain the relationships between the dependent variables and the independent variables to identify elements strongly correlated with crash densities. Four regression models are developed which verify good and strong statistical relationships between crash densities with the studied factors. The results show that traffic volume and driving speed have a significant impact on the crash densities. It means that there is a positive correlation between the single factors and crash occurrence. The higher volumes and the faster the driving speed, the more likely it is to crash. As the hourly traffic flow of automobile grows, the need for safe traffic facilities also extended. Doi: 10.28991/cej-2021-03091719 Full Text: PDF
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Li, Guofa, Yuan Liao, Qiangqiang Guo, Caixiong Shen, and Weijian Lai. "Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016." International Journal of Environmental Research and Public Health 18, no. 3 (2021): 1176. http://dx.doi.org/10.3390/ijerph18031176.

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Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China.
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Mashros, Nordiana, SittiAsmah Hassan, Yaacob Haryati, et al. "Road traffic accidents on Senai-Desaru expressway." MATEC Web of Conferences 250 (2018): 02002. http://dx.doi.org/10.1051/matecconf/201825002002.

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Understanding and prioritising crash contributing factors is important for improving traffic safety on the expressway. This paper aims to identify the possible contributory factors that were based on findings obtained from crash data at Senai-Desaru Expressway (SDE), which is the main connector between the western and eastern parts of Johor, Malaysia. Using reported accident data, the mishaps that had occurred along the 77.2 km road were used to identify crash patterns and their possible related segment conditions. The Average Crash Frequency and Equivalent Property Damage Only Average Crash Frequency Methods had been used to identify and rank accident-prone road segments as well as to propose for appropriate simple and inexpensive countermeasures. The results show that the dominant crash type along the road stretches of SDE had consisted of run-off-road collision and property damage only crashes. All types of accidents were more likely to occur during daytime. Out of the 154 segments, the 4 most accident-prone road segments had been determined and analysed. The results obtained from the analyses suggest that accident types are necessary for identifying the possible causes of accidents and the appropriate strategies for countermeasures. Therefore, this accident analysis could be helpful to relevant authorities in reducing the number of road accidents and the level of accident severity along the SDE.
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Kim, Jong-Duck, and Jun-Kyu Yoon. "Reliable Study on the Collision Analysis of Traffic Accidents Using PC-Crash Program." Journal of the Institute of Webcasting, Internet and Telecommunication 12, no. 5 (2012): 115–22. http://dx.doi.org/10.7236/jiwit.2012.12.5.115.

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25

Yasmin, Shamsunnahar, Salah Uddin Momtaz, Tammam Nashad, and Naveen Eluru. "A Multivariate Copula-Based Macro-Level Crash Count Model." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 30 (2018): 64–75. http://dx.doi.org/10.1177/0361198118801348.

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The current study contributes to safety literature both methodologically and empirically by developing a macro-level multivariate copula-based crash frequency model for crash counts. The multivariate model accommodates for the impact of observed and unobserved effects on zonal level crash counts of different road user groups including car, light truck, van, other motorized vehicle (including truck, bus and other vehicles), and non-motorists (including pedestrians and cyclists). The proposed model is estimated using Statewide Traffic Analysis Zone (STAZ) level road traffic crash data for the state of Florida. A host of variable groups including land-use characteristics, roadway attributes, traffic characteristics, socio-economic characteristics and demographic characteristics are considered. The model estimation results illustrate the applicability of the proposed framework for multivariate crash counts. Model estimation results are further augmented by evaluation of predictive performance and policy analysis.
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Xu, Chengcheng, Chen Wang, and Pan Liu. "Evaluating the Combined Effects of Weather and Real-Time Traffic Conditions on Freeway Crash Risks." Weather, Climate, and Society 10, no. 4 (2018): 837–50. http://dx.doi.org/10.1175/wcas-d-17-0124.1.

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Abstract The study presented in this paper investigated the combined effects of environmental factors and real-time traffic conditions on freeway crash risks. Traffic and weather data were collected from a 35-km freeway segment in the state of California, United States. The weather conditions were classified into five categories: clear, light rain, moderate/heavy rain, haze, and mist/fog. Logistic regression models using unmatched case-control data were developed to link the likelihood of crash occurrences to various traffic and environmental variables. The sample size requirements for case-control studies and the interaction between traffic and environmental variables were considered. The model estimation results showed that the light rain, moderate/heavy rain, and mist/fog significantly increase freeway crash risks. The interaction between light rain and upstream occupancy was also found to be statistically significant. Bootstrap analyses were conducted to quantify the interaction effect between these two variables. The crash risk model was compared to a reduced model in which environmental information was not included. It was found that the inclusion of environmental information improved both goodness of fit and prediction performance of the crash risk prediction model. The inclusion of environmental information in crash risk models improved the prediction accuracy of crash occurrences by 6.8% and reduced the false alarm rate by 1.3%. It was also found that the inclusion of environmental information had minor impacts on the prediction performance of the crash risk model in clear weather conditions.
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Chimba, Deo, Emmanuel Masindoki, Xiaoming Li, and Casey Langford. "Safety Evaluation of Freight Intermodal Connectors in Tennessee State." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 3 (2019): 237–46. http://dx.doi.org/10.1177/0361198119834906.

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This paper evaluates the traffic safety along freight intermodal connectors (FICs), which are also known as “first mile/last mile roadways,” connecting facilities that link freight-intensive land uses to main freight routes. Using Tennessee’s FICs as a case study, the paper digests the safety with reference to crash frequency, crash rates, and statistical significance of attributing traffic and geometric factors. It was found that connectors leading to pipeline terminals have high crash rates (almost double) compared with other type of terminals, whereas port terminal connectors have the lowest safety problem indices. The study established correlative contributing causes of crash frequencies and rates along FICs that included average annual daily traffic, lanes, shoulders, access, and median types. Traffic signal density was found to strongly and significantly affect the probability of crashes, together with the presence of a two-way left-turn lane (TWLTL), which surprisingly tends to decrease the probability of crashes along these connectors. The presence of shoulders along intermodal connectors was found to help reduce the probability of crashes, whereas the presence of curb and gutter tends to increase crash frequency. Analysis indicated that most of the FICs with high crash rates were also operating at a lower traffic operations level of service (LOS), especially for critical movements toward freight facilities because of high truck volumes.
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Shangguan, Qiangqiang, Ting Fu, Junhua Wang, Rui Jiang, and Shou’en Fang. "Quantification of Rear-End Crash Risk and Analysis of Its Influencing Factors Based on a New Surrogate Safety Measure." Journal of Advanced Transportation 2021 (April 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/5551273.

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Traditional surrogate measures of safety (SMoS) cannot fully consider the crash mechanism or fail to reflect the crash probability and crash severity at the same time. In addition, driving risks are constantly changing with driver’s personal driving characteristics and environmental factors. Considering the heterogeneity of drivers, to study the impact of behavioral characteristics and environmental characteristics on the rear-end crash risk is essential to ensure driving safety. In this study, 16,905 car-following events were identified and extracted from Shanghai Naturalistic Driving Study (SH-NDS). A new SMoS, named rear-end crash risk index (RCRI), was then proposed to quantify rear-end crash risk. Based on this measure, a risk comparative analysis was conducted to investigate the impact of factors from different facets in terms of weather, temporal variables, and traffic conditions. Then, a mixed-effects linear regression model was applied to clarify the relationship between rear-end crash risk and its influencing factors. Results show that RCRI can reflect the dynamic changes of rear-end crash risk and can be applied to any car-following scenarios. The comparative analysis indicates that high traffic density, workdays, and morning peaks lead to higher risks. Moreover, results from the mixed-effects linear regression model suggest that driving characteristics, traffic density, day-of-week (workday vs. holiday), and time-of-day (peak hours vs. off-peak hours) had significant effects on driving risks. This study provides a new surrogate safety measure that can better identify rear-end crash risks in a more reliable way and can be applied to real-time crash risk prediction in driver assistance systems. In addition, the results of this study can be used to provide a theoretical basis for the formulation of traffic management strategies to improve driving safety.
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Li, Zongzhi, Hoang Dao, Harshingar Patel, Yi Liu, and Bei Zhou. "Incorporating Traffic Control and Safety Hardware Performance Functions into Risk-based Highway Safety Analysis." PROMET - Traffic&Transportation 29, no. 2 (2017): 143–53. http://dx.doi.org/10.7307/ptt.v29i2.2041.

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Traffic control and safety hardware such as traffic signs, lighting, signals, pavement markings, guardrails, barriers, and crash cushions form an important and inseparable part of highway infrastructure affecting safety performance. Significant progress has been made in recent decades to develop safety performance functions and crash modification factors for site-specific crash predictions. However, the existing models and methods lack rigorous treatments of safety impacts of time-deteriorating conditions of traffic control and safety hardware. This study introduces a refined method for computing the Safety Index (SI) as a means of crash predictions for a highway segment that incorporates traffic control and safety hardware performance functions into the analysis. The proposed method is applied in a computation experiment using five-year data on nearly two hundred rural and urban highway segments. The root-mean square error (RMSE), Chi-square, Spearman’s rank correlation, and Mann-Whitney U tests are employed for validation.
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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.

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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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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 (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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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 (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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33

Zurlinden, Hendrik, Anita Baruah, and John Gaffney. "Towards linking driving complexity to crash risk." Journal of Road Safety 31, no. 1 (2020): 66–80. http://dx.doi.org/10.33492/jrs-d-19-00070.

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The purpose of this article is to present insights into the relationship between complex traffic flow phenomena on urban motorways and crash risk. Unstable or congested flow can trigger low speed/high density clusters (e.g. nucleations or shockwaves) creating ‘surprise elements’, therefore sharply increasing the cognitive workload for motorists. When combined with reduced road space and freedom to perform needed manoeuvres (e.g. lane changes), conditions can exceed the physical or mental capability and hence increase the likelihood of human error. There is overwhelming evidence that high traffic density drastically increases the crash risk. Some density concentrations can be avoided through appropriate planning and real-time traffic control, resulting in a reduction in crashes. Modern measurement devices allow for the analysis of individual vehicle behaviours such as ‘Brake’, ‘Speed alert’ or ‘Lane change’ events and show promise in providing robust data to further exploring what makes dense traffic complex. This allows establishing relationships between “events as elementary units of exposure” and crash occurrence resulting in a new way of understanding crash rates. These relationships are important to predict crashes, identify high-risk locations, and establish suitable measures for crash reduction.
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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 (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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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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36

Miller, John S. "Geographic Information Systems: Unique Analytic Capabilities for the Traffic Safety Community." Transportation Research Record: Journal of the Transportation Research Board 1734, no. 1 (2000): 21–28. http://dx.doi.org/10.3141/1734-04.

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Geographic information systems (GIS) have been used for more than a decade to display crash sites. Now that their novelty has worn off, the traffic records community should ask what additional analytical benefits GIS can uniquely provide. A literature review illustrates that safety-related GIS analytical capabilities surpass the common practice of producing crash location pin maps. Additional useful GIS techniques include using grid-based modeling; producing increasingly accurate collision diagrams; verifying disparate sources of crash data; applying spatially based statistical applications; examining crash location patterns for causal factors; aligning public opinion with real data; and improving routing capabilities for pedestrians, bicyclists, and hazardous material carriers. This research explores GIS analytic capabilities that can be practically applied to crash data evaluation. A case study demonstrates that despite limited budgets and imperfect data, GIS can still help to identify potential crash countermeasures. Using a typical non-GIS source of crash data (a software package that records crashes at either an intersection or a midblock location), it was possible to place approximately 82 percent of crash locations within a GIS. When private property crashes were excluded from this process, the placement rate climbed to an estimated 94 percent for intersections and 87 percent for midblock locations. By focusing the case study on understanding the practical spatial analytical capabilities rather than merely the mechanics of specific GIS software, one can eventually take advantage of the more extensive crash and roadway data sets that will become available. Despite its many capabilities, however, GIS has not eliminated the need for a comprehensive safety analysis framework integrating the spatial and statistical queries necessary for engineering, enforcement, or educational improvements.
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Liu, Xian, Jian Lu, Zeyang Cheng, and Xiaochi Ma. "A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway." Journal of Advanced Transportation 2021 (May 13, 2021): 1–12. http://dx.doi.org/10.1155/2021/5569143.

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Traffic crash is a complex phenomenon that involves coupling interdependency among multiple influencing factors. Considering that interdependency is critical for predicting crash risk accurately and contributes to revealing the underlying mechanism of crash occurrence as well, the present study attempts to build a Real-Time Crash Prediction Model (RTCPM) for urban elevated expressway accounting for the dynamicity and coupling interdependency among traffic flow characteristics before crash occurrence and identify the most probable risk propagation path and the most significant contributors to crash risk. In this study, Dynamic Bayesian Network (DBN) was the framework of the RTCPM. Random Forest (RF) method was employed to identify the most important variables, which were used to build DBN-based RTCPMs. The PC algorithm combined with expert experience was further applied to investigate the coupling interdependency among traffic flow characteristics in the DBN model. A comparative analysis among the improved DBN-based RTCPM considering the interdependency, the original DBN-based RTCPM without considering the interdependency, and Multilayer Perceptron (MLP) was conducted. Besides, the sensitivity and strength of influences analyses were utilized to identify the most probable risk propagation path and the most significant contributors to crash risk. The results showed that the improved DBN-based RTCPM had better prediction performance than the original DBN-based RTCPM and the MLP based RTCPM. The most probable risk influencing path was identified as follows: speed on current segment (V) (time slice 2)⟶V (time slice 1)⟶speed on upstream segment (U_V) (time slice 1)⟶Traffic Performance Index (TPI) (time slice 1)⟶crash risk on current segment. The most sensitive contributor to crash risk in this path was V (time slice 2), followed by TPI (time slice 1), V (time slice 1), and U_V (time slice 1). These results indicate that the improved DBN-based RTCPM has the potential to predict crashes in real time for urban elevated expressway. Besides, it contributes to revealing the underlying mechanism of crash and formulating the real-time risk control measures.
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Qu, Wenrui, Qiao Sun, Qun Zhao, Tao Tao, and Yi Qi. "Statistical Analysis of Safety Performance of Displaced Left-Turn Intersections: Case Studies in San Marcos, Texas." International Journal of Environmental Research and Public Health 17, no. 18 (2020): 6446. http://dx.doi.org/10.3390/ijerph17186446.

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Displaced left-turn (DLT) intersections are designed to increase the mobility of vehicles by relocating the left-turn lane (lanes) to the far-left side of the road upstream of the main signalized intersection. Since DLT is a relatively new design and very limited crash data are available, previous studies have focused mainly on the analysis of its operational performance rather than its safety performance. To fill this gap, in this study, we investigated the safety performance of two DLT intersections located in San Marcos, Texas. Crash data from 2011 to April 2018 were extracted from the TxDOT Crash Record Information System (CRIS). These crash data were analyzed using two different approaches, i.e., statistical analysis and collision diagram-based analysis. The results of this study indicated that DLT did not increase the overall crash frequencies at the studied intersections. Traffic crashes related to left turns and right turns were reduced significantly by DLT. Meanwhile, it also caused safety issues related to traffic signage, traffic signal, geometric design, and access management at DLT intersections. Thus, in the implementation of DLT intersections, traffic engineers need to carefully consider different aspects of the DLT intersection design, including access management, traffic signal coordination, and driver acceptance. As a result of these analyses, recommendations were provided for the safe implementation of the DLT design in the future.
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Tola, Alamirew Mulugeta, Tamene Adugna Demissie, Fokke Saathoff, and Alemayehu Gebissa. "Severity, Spatial Pattern and Statistical Analysis of Road Traffic Crash Hot Spots in Ethiopia." Applied Sciences 11, no. 19 (2021): 8828. http://dx.doi.org/10.3390/app11198828.

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The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.
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40

Liu, Miaomiao, and Yongsheng Chen. "Predicting Real-Time Crash Risk for Urban Expressways in China." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6263726.

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We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then we proposed a new real-time crash risk prediction model using decision tree method and adaptive neural network fuzzy inference system (ANFIS). By comparing several real-time crash risk prediction methods, it was found that our proposed method had higher precision than others. And the training error and testing error were minimum (0.280 and 0.291, resp.) when the data during 0 to 30 minutes prior to crash occurrence was collected and the decision tree-ANFIS method was applied to train and establish the real-time crash risk prediction model. The prediction accuracy of the crash occurrence could reach 65% when 0.60 was considered as the crash prediction threshold.
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41

Shen, Jiajun, and Guangchuan Yang. "Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data." Sustainability 12, no. 23 (2020): 9888. http://dx.doi.org/10.3390/su12239888.

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This paper investigates the impacts of heavy vehicles (HV) on speed variation and assesses the rear-end crash risk for four vehicle-following patterns in a heterogeneous traffic flow condition using three surrogate safety measures: speed variation, time-to-collision (TTC), and deceleration rate to avoid a crash (DRAC). A video-based data collection approach was employed to collect the speed of each individual vehicle and vehicle-following headway; a total of 3859 vehicle-following pairs were identified. Binary logistic regression modeling was employed to assess the impacts of HV percentage on crash risk. TTCs and DRACs were calculated based on the collected traffic flow data. Analytical models were developed to estimate the minimum safe vehicle-following headways for the four vehicle-following patterns. Field data revealed that the variation of speed first increased with HV percentage and reached the maximum when HV percentage was at around 0.35; then, it displayed a decreasing trend with HV percentage. Binary logistic regression modeling results suggest that a high risk of rear-end collision is expected when HV percentage is between 0.19 and 0.5; while, when HV percentage is either below 0.19 or exceed 0.5, a low risk of rear-end collision is anticipated. Analytical modeling results show that the passenger car (PC)-HV vehicle-following pattern requires the largest minimum safe space headway, followed by HV-HV, PC-PC, and HV-PC vehicle-following patterns. Findings from this research present insights to transportation engineers regarding the development of crash mitigation strategies and have the potential to advance the design of real-time in-vehicle forward collision warnings to minimize the risk of rear-end crash.
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42

Astarita, Vittorio, Ciro Caliendo, Vincenzo Pasquale Giofrè, and Isidoro Russo. "Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data." Sustainability 12, no. 17 (2020): 6974. http://dx.doi.org/10.3390/su12176974.

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The traditional analysis of road safety is based on statistical methods that are applied to crash databases to understand the significance of geometrical and traffic features on safety, or in order to localize black spots. These classic methodologies, which are based on real crash data and have a solid background, usually do not explicitly consider the trajectories of vehicles at any given location. Moreover, they are not easily applicable for making comparisons between different traffic network designs. Surrogate safety measures, instead, may enable researchers and practitioners to overcome these limitations. Unfortunately, the most commonly used surrogate safety measures also present certain limits: Many of them do not take into account the severity of a potential collision and the dangers posed by road-side objects and/or the possibility of drivers being involved in a single-vehicle crash. This paper proposes a new surrogate safety indicator founded on vehicle trajectories, capable also of considering road-side objects. The validity of the proposed indicator is assessed by means of comparison between the calculation of surrogate safety measures on micro-simulated trajectories and the real crash risk obtained with data on real crashes observed at several urban intersection scenarios. The proposed experimental framework is also applied (for comparison) to classical indicators such as TTC (time to collision) and PET (post-encroachment time).
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43

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 (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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44

You, Jinming, Shouen Fang, Lanfang Zhang, John Taplin, and Jingqiu Guo. "Enhancing Freeway Safety through Intervening in Traffic Flow Dynamics Based on Variable Speed Limit Control." Journal of Advanced Transportation 2018 (September 26, 2018): 1–10. http://dx.doi.org/10.1155/2018/3610541.

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New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. The High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information. This paper proposes an innovative Variable Speed Limit (VSL) based approach to manage crash risks by intervening in traffic flow dynamics on freeways using HDMS data. We first conducted an empirical analysis on real-time crash risk estimation using a binary logistic regression model. Then, intensive microscopic simulations based on AIMSUN were carried out to explore the effects of various intervention strategies with respect to a 3-lane freeway stretch in China. Different speed limits with distinct compliance rates under specified traffic conditions have been simulated. By taking into account the trade-off between safety benefits and delay in travel time, the speed limit strategies were optimized under various traffic conditions and the model with gradient feedback produces more satisfactory performance in controlling real-time crash risks. Last, the results were integrated into lane management strategies. This research can provide new ideas and methods to reveal the freeway crash risk evolution and active traffic management.
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Cummings, P., and B. McKnight. "Accounting for vehicle, crash, and occupant characteristics in traffic crash studies." Injury Prevention 16, no. 6 (2010): 363–66. http://dx.doi.org/10.1136/ip.2009.025163.

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46

Eboli, Laura, and Carmen Forciniti. "The Severity of Traffic Crashes in Italy: An Explorative Analysis among Different Driving Circumstances." Sustainability 12, no. 3 (2020): 856. http://dx.doi.org/10.3390/su12030856.

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Analyzing traffic accidents is very important due to their direct impact on the social environment. In the literature, many studies focus on the different aspects that influence traffic accidents, such as human, vehicle, road and environment risk factors. In this paper, we propose a methodology for testing the relationship between road, external environment, driver and vehicle characteristics, and certain circumstances that lead to the traffic crashes. Particularly, we elaborate on logistic regression models for evaluating how these different characteristics impact on crash severity, considering the combination of traffic circumstances that caused the crash. In each combination, a vehicle proceeded regularly, whereas the other vehicle did an incorrect maneuver (the vehicle proceeded: with distracted driving; without maintaining the safety distance; with speeding; by maneuvering to join the circulation flow; against the flow). The present work analyzes data related to road crashes which occurred in Italy during 2016 involving two vehicles. The results show that the variables significantly influencing crash severity are different depending on the combinations of circumstances that cause the crash.
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Turner, Shane, Fergus Tate, and Graham Wood. "The crash performance of seagull intersections and intersections with left turn slip lanes." Journal of the Australasian College of Road Safety 30, no. 3 (2019): 37–47. http://dx.doi.org/10.33492/jacrs-d-18-00111.

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Alternative intersection layouts may reduce traffic delays and/or improve road safety. Two alternatives are reviewed in this research: ‘priority-controlled Seagull intersections’ and ‘priority-controlled intersections with a Left Turn Slip Lane’. Seagull intersections are used to reduce traffic delays. Some do experience high crash rates, however. Left Turn Slip Lanes allow turning traffic to move clear of the through traffic before decelerating, thereby reducing the risk of rear-end crashes. Although there is debate about the safety problems that occur at Seagull intersections and Left Turn Slip Lanes there has been very little research to quantify the safety impact of different layouts. In this study, crash prediction models have been developed to quantify the effect of various Seagull intersection and Left Turn Slip Lane designs on the key crash types that occur at priority intersections. The analysis showed that seagulls are not safe on 4-lane roads, that roadway features like kerb-side parking and nearby intersections can increase crash rates and that left turners in LTSLs can restrict visibility and create safety problems.
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48

Rista, Emira, Timothy Barrette, Raha Hamzeie, Peter Savolainen, and Timothy J. Gates. "Work Zone Safety Performance: Comparison of Alternative Traffic Control Strategies." Transportation Research Record: Journal of the Transportation Research Board 2617, no. 1 (2017): 87–93. http://dx.doi.org/10.3141/2617-11.

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Work zone temporary traffic control strategies generally affect both traffic safety and operations. However, there is a substantial gap in the knowledge base with respect to the safety impacts of various work zone characteristics. The Highway Safety Manual provides crash modification functions that account for the effects of project length and duration on crash frequency as compared with normal road operations. However, these methods do not allow for explicit comparisons of expected safety performance among different work or closure types. This research examined the safety impacts of various temporary traffic control strategies on freeways, including shoulder closures, lane closures, and lane shifts. Data were collected for the periods during which these treatments were in effect and during similar nonconstruction periods from the preceding year. Safety performance functions were estimated that account for segment length, duration, traffic volume, and closure type. Random parameter count data models were estimated to accommodate segment-specific temporal correlation and unobserved heterogeneity. Crash rates were shown to vary roughly in proportion to traffic volumes. In contrast, segment length and project duration showed inelastic effects; this finding implies that crash rates increase more rapidly in work zones that are shorter in length or duration. Single-lane closures, multilane closures, and lane shifts were associated with an increase in crashes, whereas shoulder closures did not show a significant difference compared with similar, non-work-zone conditions. Ultimately, the study results provide important information that can be used to assess the crash risk for various temporary traffic control strategies.
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49

Gill, G., T. Sakrani, W. Cheng, and J. Zhou. "COMPARISON OF ADJACENCY AND DISTANCE-BASED APPROACHES FOR SPATIAL ANALYSIS OF MULTIMODAL TRAFFIC CRASH DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1157–61. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1157-2017.

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Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among crash data at macro-level (County). Multivariate Full Bayesian crash prediction models were developed using Decay-50 (distance-based) and Queen-1 (adjacency-based) weight matrices for simultaneous estimation crash counts of four different modes: vehicle, motorcycle, bike, and pedestrian. The goodness-of-fit and different criteria for accuracy at prediction of crash count reveled the superiority of Decay-50 over Queen-1. Decay-50 was essentially different from Queen-1 with the selection of neighbors and more robust spatial weight structure which rendered the flexibility to accommodate the spatially correlated crash data. The consistently better performance of Decay-50 at prediction accuracy further bolstered its superiority. Although the data collection efforts to gather centroid distance among counties for Decay-50 may appear to be a downside, but the model has a significant edge to fit the crash data without losing the simplicity of computation of estimated crash count.
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50

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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