High Crash Locations Research Articles

Developing Safety Performance Functions for Severe Distraction-Related Crashes along Kentucky’s Rural and Urban Two-Lane Roadways

This study develops safety performance functions (SPFs) for severe (“KA” or “fatal and suspected serious injury”) distraction-related crashes along Kentucky’s rural and urban two-lane undivided roadway segments using recent four-year (2018–2021) crash records. Additional efforts were made to meticulously scrutinize crash narratives categorized as non-distracted and correct those cases. To account for crash under-dispersion, the Conway–Maxwell–Poisson, heterogeneous Conway–Maxwell–Poisson, zero-inflated Conway–Maxwell–Poisson, and zero-inflated heterogeneous Conway–Maxwell–Poisson (ZI-HTCMP) models were fitted and compared. The ZI-HTCMP model outperformed the other models with respect to several goodness-of-fit measures (e.g., mean absolute deviance and mean square prediction error). From the developed SPFs for rural and urban two-lane roads, wider lanes and higher speed limits (55 mph) were associated with increased severe distraction-related crash frequencies. Furthermore, some variables were found to be significant in rural areas, but insignificant in urban areas, and vice versa. For example, major collector roads, minor collector/local roads, the presence of roadside guardrails, wider right-hand shoulders, the presence of horizontal curves, and the presence of vertical grades were associated with increased crash frequencies along rural two-lane roads. In addition, the proportion of heavy vehicles (>5%) and the existence of paved shoulders were associated with increased crashes along urban two-lane roads. The empirical Bayes method was then used to rank the top 10 distraction-related high crash locations (HCLs) for both rural and urban two-lane segments. In-depth investigation of HCLs highlighted single-vehicle distraction-related crashes as the most common collision type. Countermeasures were proposed to help reduce severe distraction-related crashes, for example, installing chevron signs along rural two-lane roads.

Modeling the risk of single-vehicle run-off-road crashes on horizontal curves using connected vehicle data

Crash risk measures (CRMs) are widely used in safety analysis to complement crash reports. However, none of the existing CRMs are specifically developed for modeling the risk of single-vehicle run-off-road (SVROR) crashes, especially those on horizontal curves. This paper proposes a novel crash risk measure for modeling SVROR crash risk using connected vehicle data. The proposed SVROR crash risk measure (SVROR-CRM) is based on the concept of tetraquark in particle physics. It utilizes the adjusted position deviation risk force (Fposirisk) and adjusted attitude deviation risk moment (Γattirisk) to quantify SVROR crash risk. The SVROR crash risk is then estimated by the joint probability of Fposirisk and Γattirisk using a peak-over threshold approach. The risk threshold is automatically determined via a mean absolute error function. The SVROR-CRM is validated using connected vehicle and crash data from sixteen curves on Interstate 80 in Wyoming. The results suggest that the estimated SVROR crash risks well match historical crash records. Also, it is found that attitude deviation poses a higher risk of SVROR crash than position deviation on horizontal curves. Therefore, it is critical for drivers to steer properly on curves to minimize SVROR crash risks. The proposed approach bridges an important gap in crash risk measure research and can be used to estimate SVROR crash risk and identify unsafe trajectories and high-crash locations and/or periods on highway horizontal curves.

Crash proximity and equivalent property damage calculation techniques: An investigation using a novel horizontal curve dataset

Despite the numerous breakthroughs in crash analytics, there remains a lack of consensus among safety practitioners as to the optimal method for locating high crash locations. Two critical components in the traffic safety analysis process not agreed upon are 1) how the crash distance to a target location is included in the analysis and 2) how crashes are weighted based on crash-related characteristics. For example, the commonly used buffering technique to determine which crashes are associated with a specific target road segment does not associate crashes that are closer to a target road segment with any additional weight, even though it is likely to be more greatly associated with the characteristics of the target location. Additionally, the commonly used equivalent property damage only (EPDO) crash weight method has been found to weigh fatal crashes significantly more than serious injury crashes, even if the difference between the two outcomes was a single factor. This study proposes more robust crash weighting techniques for use in high-risk location identification using an application of a novel horizontal curve dataset. Specifically, a heteroscedastic censored regression approach was used to investigate the impact of different crash proximity weighting techniques and crash severity weighting methods on model outcomes. The results demonstrate that the use of a linear distance weighting factor used in conjunction with the buffering technique as well as a less precise EPDO weighting factor method results in more robust safety analysis outcomes. The improved results have the potential to improve hot spot identification and resource allocation at both the federal and regional levels by employing models that more accurately link specific crash segments with contributing crash characteristics.

Influences of alternative friction aggregates on texture and friction characteristics of high friction surface treatment

High friction surface treatment (HFST) is commonly used to improve surface friction of asphalt pavements at high crash rate locations, and the objective of this study is to assess the feasibility of using alternative friction aggregates in HFST. Three-wheel polishing device (TWPD) was used to simulate the actual traffic polishing in the laboratory. Firstly, the texture and friction properties of HFSTs with 12 friction aggregates were characterized using circular track meter (CTM) and dynamic friction tester (DFT) after various TWPD polishing cycles (0 k, 70 k, and 140 k). The laboratory results showed that both DFT60 and mean profile depth (MPD) decreased with TWPD polishing cycles, and the DFT60 and MPD results after 70 k TWPD polishing cycles had no significant difference with those values after 140 k TWPD polishing cycles. In addition, a good linear correlation was observed between DFT60 and MPD. Based on the DFT60 results, two bauxite HFSTs showed the best friction performance, and taconite was found to be a suitable alternative aggregate source for HFST. This study also found that slag, silica, and quartz were not good candidates for HSFT application due to the poor texture and friction properties of the corresponding HFSTs. Based on the laboratory evaluation results, eight friction aggregates were selected to pave the HFST sections at the National Center for Asphalt Technology (NCAT) Test Track, which was subjected to 2.6 million equivalent single axle loads (ESALs) in six months. The CTM, DFT, and lock-wheel skid trailer (LWST) were used to characterize the texture and friction properties of field HFST sections, and both DFT and LWST results showed that the bauxite HFST exhibited better friction performance than the granite and flint HFSTs. In addition, there were good linear correlations existing between laboratory DFT60 and MPD results with the corresponding field measurements. Lastly, the influences of aggregate properties (i.e., particle size, durability, angularity, and shape index) on the texture and friction properties of HFST were investigated. The statistical analysis indicated that particle size had significant effects on the MPD and DFT60 of HFST. Meanwhile, only angularity showed a linear relationship with the MPD results of HFST.

Safety effect analysis of motorcycle V2I collision warning system

Connected vehicle technology can help the driver make decisions, improve awareness of the road environment, and thereby possibly enhance traffic safety. This study explored the impact of a motorcycle vehicle-to-infrastructure (V2I) warning system on road safety through a field trial of a Motorcycle Safety Warning System (MSWS). This study used principal component analysis, K-means, and ordinal logistic regression to explore the effects of the MSWS. After system activation, the road safety level (RSL) improved at 50% of the field trial sites. This study provides a reference for evaluating RSLs through the composite index of this study and verifies that the V2I warning signs of the MSWS provide a certain degree of improvement of motorcycle traffic safety. A V2I system like the MSWS can be used to collect data on driving behaviours in a short period for safety effect analysis, as compared to crash data, which require an extended period. In addition, the traffic behaviour data collected by a V2I system can also be used to identify potential high crash risk locations. The warning signs developed in this study can prevent accidents by alerting drivers who are speeding and at risk of crashes.

Risky pedestrian behaviour and its relationship with road infrastructure and age group: An observational analysis

Research related to road safety and pedestrian behaviour from observations has been concentrated in high-income countries, so advancing its implementation in low- and middle-income countries nurtures the literature on these issues. This research aim is to study pedestrian behaviour at seven high crash occurrence locations near schools in the city of Manizales, Colombia. Based on direct observations at the points, behaviours related to crossing location, distractions while crossing, interactions with motorized actors, and people's crossing speed were analysed. Inference statistical analyses were carried out between variables taking into account the location of the points studied, the typology of the road and the type of intersection, and their relationship with age groups. Among the main results, it was found that most of the behaviours are independent of age group, except at points near primary roads where there is a high-motorized flow. Finally, the analyses by grouping variables avoid falling into Simpson's paradox.

Application of Geographical Information System Techniques to Determine High Crash-Prone Areas in the Fort Peck Indian Reservation

Background: Historically, Indian reservations have been struggling with higher crash rates than the rest of the United States. In an effort to improve roadway safety in these areas, different agencies are working to address this disparity. For any safety improvement program, identifying high risk crash locations is the first step to determine contributing factors of crashes and select corresponding countermeasures. Methods: This study proposes an approach to determine crash-prone areas using Geographic Information System (GIS) techniques through creating crash severity maps and Network Kernel Density Estimation (NetKDE). These two maps were assessed to determine the high-risk road segments having a high crash rate, and high injury severity. However, since the statistical significance of the hotspots cannot be evaluated in NetKDE, this study employed Getis-Ord Gi* (d) statistics to ascertain statistically significant crash hotspots. Finally, maps generated through these two methods were assessed to determine statistically significant high-risk road segments. Moreover, temporal analysis of the crash pattern was performed using spider graphs to explore the variance throughout the day. Results: Within the Fort Peck Indian Reservation, some parts of the US highway 13, BIA Route 1, and US highway 2 are among the many segments being identified as high-risk road segments in this analysis. Also, although some residential roads have PDO crashes, they have been detected as high priority areas due to high crash occurrence. The temporal analysis revealed that crash patterns were almost similar on the weekdays reaching the peak at traffic peak hours, but during the weekend, crashes mostly occurred at midnight. Conclusion: The study would provide tribes with the tool to identify locations demanding immediate safety concerns. This study can be used as a template for other tribes to perform spatial and temporal analysis of the crash patterns to identify high risk crash locations on their roadways.

Assessing the applicability of the highway safety manual to gravel roads: A case study of Wyoming

Gravel road safety is a crucial area of road safety since gravel roads represent a substantial proportion of the entirety of the nation’s roadway network. Also, gravel roads pose inherent hazards that are otherwise absent in paved roads. Nonetheless, research related to gravel road crash count prediction models, known as safety performance functions (SPFs), was rarely undertaken. SPFs are required for identifying high crash risk locations, or hot-spots, and assessing changes in crash counts before and after implementing safety countermeasures. This research offers a diagnosis of the applicability of the Highway Safety Manual (HSM) to gravel roads in Laramie County, Wyoming. Even though the HSM does not address gravel road safety, the most appropriate methodology, provided by the HSM, for predicting counts of crashes on gravel roads is that of rural two-lane highways. This methodology is applied to Laramie County’s conditions and, as per the results, it is interpreted that the methodology is not applicable to such conditions. Therefore, developing SPFs for Wyoming’s conditions are recommended even though this involves labor hours, expertise and costs. Also, including a chapter in the HSM, devoted to gravel road safety, is suggested.

Built Environment Analysis for Road Traffic Crash Hotspots in Kigali, Rwanda

Introduction: Road traffic injuries (RTIs) are a significant cause of morbidity and mortality in Rwanda. Investigations of the high risk areas for road traffic crashes (RTCs) are urgently needed to guide improvements in road safety. This study aims to identify RTC hotspots in Kigali, Rwanda, and to conduct a built environment analysis of these hotspots. Methods: RTC and RTC-prone locations were collected from the Kigali Traffic Police and high frequency road users, and hotspots were identified through kernel density estimation. Built environment characteristics (BEA), including road design, road safety, pedestrian safety, and traffic density, were collected for each hotspot. BEA characteristics were associated with risk of RTC using logistic regression and BEA scores were calculated using principal component analysis. Patterns of BEA were identified through exploratory cluster analysis and associated with risk for RTC using logistic regression. Results: 25 RTC hotspots were identified. High crash risk locations were less likely to have unpaved roads (21%, p=0.049) and road narrowing (21%, p=0.049). High crash risk locations were also more likely to have pedestrian walkways (100%, p=0.009), factors aiding pedestrian crossing (100%, p=0.026), and poor road surfaces (86%, p=0.005). Cluster analysis showed that hotspots with fewer urban characteristics, including road safety features, motor vehicle density, and pedestrian safety features, have significantly decreased odds of being a high mortality risk hotspot than a hotspot with more urban characteristics (OR=0.13, 95% CI 0.02-0.79). Conclusions: RTC hotspots were in the city center with high motor vehicle density but did have road and pedestrian safety features, suggesting that speeding is a major cause of RTCs. Effective traffic calming measures and enforcement of road safety laws may reduce the burden of road traffic injuries in Kigali but additional analyses are recommended.

Adequacy of negative binomial models for managing safety on rural local roads

Count models, such as negative binomial regression, are well-established statistical methods for analyzing road safety. Although count models are widely used for arterial roads, their application to rural local roads is sparse, partly due to the concern of possible estimation bias caused by low crash counts. This paper revisits the matter to further evaluate the suitability of negative binomial models for rural local roads with low crash frequencies, comparing the performance of the model to probabilistic regression (ordered probit) proposed in the past.The negative binomial model was estimated to predict crashes for rural local intersections and compared to predictions obtained from the ordered probit model. Bivariate versions of both models were applied to improve model efficiency by incorporating correlation between two severity outcomes, fatal/injury (FI) and property damage only (PDO) crashes. The estimated models included several significant variables with intuitive signs. These results are discussed in the paper to support the claim that both models are adequate. Furthermore, the cumulative sums of the model-predicted and observed crashes conditioned on the estimated effects were compared to detect any systematic bias in the results. Although both models showed similar performance and no obvious biases could be detected, the negative binomial model seemed to behave slightly better than the ordered probit model, demonstrating the model’s suitability in the analyzed case. The results point to the possibility of applying the Highway Safety Manual methodology to lower-volume county roads with focus shifted from individual high-crash locations to safety-deficient road features present at multiple locations.

Comparative analysis of multiple techniques for developing and transferring safety performance functions

Safety performance functions (SPFs) are crash count prediction models that are used for identifying high crash risk locations, evaluating road safety before and after countermeasure deployment and comparing the safety of alternative site designs. The traditional method of modeling crash counts is negative binomial (NB) regression. Furthermore, the Highway Safety Manual (HSM) provides analytical tools, including NB SPFs, to assess and improve road safety. Even though the HSM’s SPFs are restricted to NB models, the road safety literature is rich with a variety of different modeling techniques. Researchers have calibrated the HSM’s SPFs to local conditions using a calibration method prescribed by the HSM. However, studies in which SPFs are developed and transferred to other localities are uncommon. In this paper, we develop and transfer rural divided multilane highway segment SPFs of Florida, Ohio, Illinois, Minnesota, California, Washington and North Carolina to each state. For every state, NB, zero-inflated NB, Poisson lognormal (PLN), regression tree, random forest (RF), boosting and Tobit models are developed. A hybrid model that coalesces the predictions of both the Tobit and the NB model is proposed and developed as well. All SPFs are transferred to each state and their predictive performances are evaluated to discern which model type is the most transferable. According to the transferability results, there is no single superior model type. However, the Tobit, RF, tree, NB and hybrid models demonstrate better predictive performances than those of the other methods in a considerably large proportion of transferred SPFs.

Development of a Crash Risk-Scoring Tool for Pedestrian and Bicycle Projects in Oregon

Methods for identifying and prioritizing high-crash locations for safety improvements are generally crash-based. There are fewer reported crashes involving non-motorized users and, in most states, reported crashes must involve a motor vehicle. This means that minor, non-injury events are not reported and those crashes that are reported tend to be more severe. Selecting projects based only on crash performance is sometimes limiting for these crash types and predicting where these crashes will occur next is also a challenging task. An alternative to crash-based selection is to develop risk-based criteria and methods. This paper presents the results of a research effort to develop a risk-scoring method with weights derived from data for use in project screening and selection in Oregon. To develop the risk model, data were collected from 188 segments and 184 intersections randomly selected on both state and non-state roadways. Geometric, land use, volume, and crash data were collected from Google Earth, EPA’s Smart Location Database, and the Oregon Department of Transportation crash database from 2009 to 2013. The sample included 213 bicycle and pedestrian crashes on the segments and 238 at intersections. Logistic regression models were developed and the outputs used to create pedestrian and bicycle risk-scoring tools for segments and intersections. The risk-scoring tool was applied to safety projects identified in the 2015 All Roads Transportation Safety (ARTS) project lists from Oregon. The risk scores for the case study applications aligned reasonably well with the project’s benefits-costs estimates.

Developing a Toolkit to Improve Transportation Safety on Indian Reservations

Indian reservations hold the highest rate of crashes that lead to fatal and incapacitating injuries across the United States. Limited resources, absence of coordination across jurisdictions, and limited crash data make it difficult for tribes to reduce the number of these fatal and serious crashes. A safety toolkit was developed in this work to identify the high-risk crash locations and determine low-cost safety improvement countermeasures. The toolkit serves as a guideline and provides information, field examples, and resources in key topic areas to lead the effort to improve safety via the use of a five-step methodology. These five steps are, namely, compile data and crash data analysis, Level I field evaluation, combined ranking, Level II field evaluation to identify countermeasures, and benefit–cost analysis. The objective of the toolkit is to assist tribes to reduce the number of fatal and serious crashes, and provides flexibility for the tribes to utilize the methodology compatible with the onsite data, preferences of the tribes, and other factors to meet demands. The methodology described was successfully implemented on the Wind River Indian Reservation, Standing Rock Sioux Tribe, Sisseton Wahpeton Oyate Tribe, and the Yankton Sioux Tribe and showed great success in identifying high-crash locations.

A new approach for calibrating safety performance functions

Safety performance functions (SPFs) are statistical regression models used for estimating crash counts by roadway facility classification. They are required for identifying high crash risk locations, assessing the effectiveness of safety countermeasures and comparing road designs in terms of safety. Roadway agencies may opt to develop local SPFs or adopt them from elsewhere such as the national Highway Safety Manual (HSM), provided by the American Association of State Highway and Transportation Officials. The HSM offers a simple technique to calibrate its SPFs to conditions of specific jurisdictions. A more recent calibration technique, also known as the calibration function, is similar to that of the HSM. In this research, we develop SPFs of total crashes for rural divided multilane highway segments in four states. The states are Florida, Ohio, California and Washington. We also calibrate each SPF to each state using the HSM calibration method and the calibration function. Furthermore, we propose the use of the K nearest neighbor data mining method for calibrating SPFs. According to the goodness of fit (GOF) results, our proposed calibration method performs better than the other two methods.

Transferring and calibrating safety performance functions among multiple States

Safety performance functions (SPFs), statistical regression models, by predicting traffic crash counts by crash type, severity and facility type, aid traffic engineers in the process of identifying high frequency crash locations. Developing SPFs requires the collection and processing of traffic, crash, road design and other characteristics’ data. Jurisdictional agencies may choose not to develop their own SPFs and cut back on their resources by adopting SPFs provided by the national Highway Safety Manual (HSM). The HSM also provides a technique to calibrate its SPFs to the specific jurisdictions’ conditions. Yet, the technique is subject to criticism. This research is aimed at exploring the transferability of SPFs of rural divided multilane highway segments of Florida, Ohio, Illinois, Minnesota, California, Washington and North Carolina. The SPFs are negative binomial (NB) models as are those provided by the HSM. We address the fault of instinctively applying the HSM’s SPFs to a particular locality without verifying whether the SPFs are transferable to the locality and compare different states’ crash patterns. Remarkably, it is found that specific SPFs of Ohio, Illinois, Minnesota and California are transferable to either of the four states. In addition, in this research, a calibration technique is proposed as an alternative to that of the HSM and two other calibration methods introduced in the traffic safety literature. They are the calibration function and the calibration of the transferred model’s constant term in conjunction with the over-dispersion parameter. Our proposed calibration technique, namely local regression, is demonstrated to be more reliable than the HSM’s and the ones previously proposed in the literature.

A composite rank measure based on principal component analysis for hotspot identification on highways

ABSTRACTIdentification of hotspots or high-crash locations on highways is important to ensure public safety and minimize loss of life, risk of injury and/or trauma; reduce crash cost to society; and any interruption to traffic flow. Many performance measures are available for use in identification of hotspots. Current performance measures, however, suffer from lack of accounting for traffic volume, crash injury severity, justification of weights used for ranking of sites, and issues with statistical distribution of crash data. This article proposes a composite rank measure based on principal component analysis to overcome limitations of existing measures for network screening. The results of a proposed measure called Composite Principal Rank Measure (CPRM) is demonstrated with interstate, U.S. and state highway data and compared against the commonly used sum-of-rank (SOR) measure. CPRM and SOR are evaluated based on several empirical and simulated data tests. CPRM was found to be robust and outperformed the SOR measure and is recommended for identification of hotspots. To ensure extensive evaluation of the measures, the entire highway routes are examined in this article.

Changing the Future?: Development and Application of Pedestrian Safety Performance Functions to Prioritize Locations in Seattle, Washington

This study aimed to use robust analysis methods to identify and screen locations at risk for pedestrian crashes and injuries to help Seattle, Washington, a Vision Zero city, broaden treatment priorities beyond only high-crash locations. For this objective, data from the entire network were used to develop safety performance functions (SPFs) for two pedestrian crash types: total pedestrian crashes at intersections (a high frequency type) and a subset of intersection crashes involving through motorists striking crossing pedestrians (a high severity type). Many variables from roadway, built environment, census, and activity measures were tested. A similar but not identical set of variables, including measures of activity and intersection size and complexity, significantly contributed to crash prediction in both models. Pedestrian volume exhibited a curved relationship to crashes and demonstrated a tendency for expected crashes to begin to decline above a threshold value; however, the causes of this relationship were unknown. The SPFs were used in several ranking methods, including SPF-predicted crashes, empirical Bayes estimated crashes, and potential for safety improvement, to aid in prioritization of locations that might have been candidates for safety improvement but that had not necessarily experienced a high frequency of crashes. On the basis of this example, this approach is feasible for jurisdictions that wish to be more proactive in addressing potential crashes and injuries. Jurisdictions must, however, begin routinely collecting the data needed to implement the method efficiently.

A new integrated GIS-based analysis to detect hotspots: A case study of the city of Sherbrooke

This paper proposes a two-step integrated method for identifying traffic accident (TA) hotspots on a roadway network. The first step includes a spatial analysis method called network kernel density estimation (KDE). The second step is a network screening method using the critical crash rate, which it described in the Highway Safety Manual (HSM). The method was examined by using three years of TAs (2011–2013) in Sherbrooke, Canada. The network KDE uses TAs to graphically display sites with a high crash density. Two different crash patterns were used for identifying these locations: (1) a crash pattern that includes three-year aggregated crash data, and (2) a crash pattern that involves three-year merged crash data. The results of the two crash patterns were evaluated based on a prediction accuracy index (PAI). It was found that the results obtained from the merged crash data outperformed the other. On the other hand, crash clustering in a site does not imply a site is hotspot and it is better to tested by other factors. High crash density locations were then tested by the critical crash rate, which helps to create an accurate comparison of sites. The importance of the critical crash rate is that it takes several factors into account such as the amount of exposure, the type of intersection, variance in crash data, etc. We realized that the hotspots determined using the two methods reflect very problematic locations and filter out the locations that do not have a problem. This approach could help transportation authorities and safety specialists to identify and prioritize sites that require more safety attention.

An evaluation of performance measures for hotspot identification

ABSTRACTAn important step in highway safety analysis is the identification of high-crash locations or “hotspots.” Various performance measures are currently used for hotspot identification (HSID). Current measures are mostly based on crash frequency. This article proposes a performance measure based on crash severity, namely, the crash factor measure (CFM). In this article, the performance measures are classified broadly by crash frequency and crash severity. The crash severity measures, the CFM, the equivalent property damage only (EPDO), and the Empirical Bayes supplemented with crash severity (EBCS), and the crash frequency measures, the crash rate (CR) and the Empirical Bayes (EB) are evaluated using eight tests based on empirical and simulated data. To ensure detailed assessment, the evaluation was carried out for three entire highway routes (an Interstate, a US, and a State highway), that is, beyond the top 1% to 10% of hotspots commonly examined. The results from the evaluation demonstrate that the EB and CFM measures performed consistently and are recommended for use. Based on inconsistent results, the CR measure is not recommended for HSID. Based on the overall results, it is also recommended that, for any proposed performance measure, the evaluation should be carried beyond 1% to 10% of the hotspots.

Need for Innovative Methods in Transportation Accident Data Recording and Mining

Road traffic accidents are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, Traffic Accidents are among the leading cause of death and injury; India in particular experiences the highest rate of such accidents. According to Transport Research Wing, Ministry of Road Transport and Highways [3], road accidents in India, 2012, Between 1970 and 2011, the number of accidents increased 4.4 times accompanied with 9.8 times increase in fatalities and 7.3 times increase in the number of persons injured. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. For this to happen reliable data set is of high importance and there is a huge crisis of accident data in India, hampering safety promotion and remedial activities. In this particular research project, need for big data which is rapidly expanding in Transportation arena for better models, estimation, prediction and prevention to minimize traffic related crashes is discussed. It is high time for extensive data mining and recording techniques are which can boost the existing traffic accident data, for better understanding of high crash locations, reasons for crashes and how these locations can be made crash free for safer livelihood.