Crash Occurrence Research Articles

Traffic crash prediction in Kano, Nigeria: multivariate long short-term memory method

Accurate traffic crash prediction is crucial for implementing effective road safety measures. In this study, the performance of long short-term memory (LSTM) and multivariate LSTM (MLSTM) models in forecasting total crash count data in Kano state, Nigeria were compared. Human and vehicle factors, including speed violations, tyre bursts, brake failures, sign/light violations and phone use while driving, were incorporated as covariates in the MLSTM model. An autoregressive integrated moving average with exogenous variables (Arimax) model was used to investigate the effects of the covariates. The MLSTM model outperformed both the basic LSTM model and individual covariate models, emphasising the synergistic effect of considering a broad range of factors. The Arimax model results revealed that speed violation is significantly positively correlated with total crashes; the other covariates showed positive correlations but did not reach the statistical significance. The findings underscore the importance of a multivariate approach in enhancing traffic crash prediction. The MLSTM model's superior performance highlights the value of considering a comprehensive range of factors that influence crash occurrence to achieve more accurate predictions. Practical applications of these models could involve leveraging them for proactive traffic safety measures, including increased enforcement of traffic rules, improvements to road infrastructure and targeted driver education and campaigns.

The hidden cost of your ‘too fast food’: stress-related factors and fatigue predict food delivery riders’ occupational crashes

ABSTRACT Objectives. For several years, the so-called ‘gig economy’ has kept transforming urban transportation dynamics. However, the literature has often overlooked the demanding, stressful and safety-compromising conditions under which delivery riders carry out their occupational tasks. This research aimed to examine whether fatigue acts as a mediating mechanism in the complex relationships among job settings, stress-related psychosocial factors at work and the occurrence of occupational traffic crashes among two-wheeled food delivery riders. Methods. This cross-sectional study field-surveyed 248 food delivery riders operating across various platforms. Participants responded to a questionnaire on work features, psychosocial factors and occupational safety issues. The data underwent both descriptive analyses and structural equation modeling (SEM). Results. As hypothesized, the occupational (riding) crashes of food delivery riders can be largely explained through work-related fatigue, which exerts a full mediation between job settings, stress-related factors and riding safety outcomes. Conclusions. These results highlight fatigue as a significant yet overlooked threat in this occupation, emphasizing the need to connect stress-related conditions with safety incidents, a relationship not previously explored among delivery riders. Moreover, our findings stress the necessity for policies and interventions targeting stress and fatigue management to improve occupational health and road safety in the gig economy era.

Leveraging Machine Learning Algorithms to Predict and Analyze Single-Vehicle and Multi-Vehicle Crash Occurrences on Motorways

Leveraging Machine Learning Algorithms to Predict and Analyze Single-Vehicle and Multi-Vehicle Crash Occurrences on Motorways

Road Safety Evaluation during the COVID-19 Pandemic: An Exploratory Analysis on Brazilian Federal Highways

Because of the increase in the number of cases of COIVD-19, government authorities needed to contain the spread and implemented several measures, including restricting hours of operation and social distancing measures, such as isolation. This leads to a considerable decrease in urban mobility and, consequently, to an effect on road safety. Therefore, the problem question arises: What is the impact of the COVID-19 pandemic on road safety on Brazilian federal highways? Thus, the objective of this paper is to evaluate the safety trends during the COVID-19 pandemic in 2020. This paper used the crash database from the Brazilian Federal Highway Police (PRF), was analyzed, which enabled identifying changes in crash occurrence in 2020 when compared to the previous years (2011 - 2019). A decrease in the number of crashes on federal highways was observed, which might not have a direct relationship with the COVID-19 pandemic.

A crash occurrence risk prediction model based on variational autoencoder and generative adversarial network

Traffic crash risk prediction is pivotal for proactive traffic safety management but faces challenges due to the extreme imbalance between crash and non-crash data. This paper proposes integrating variational autoencoder into a generative adversarial network (VAE-GAN) for crash data augmentation without information loss. VAE-GAN generates higher-quality data due to its superior deep generative capabilities. For the crash occurrence risk prediction task, we utilize a convolutional neural network (CNN) trained on the balanced datasets generated by VAE-GAN. Two kinds of category determinations are tested for better prediction results by using prediction probability maximum and selecting threshold. The prediction model integrating variational autoencoder into a deep convolutional generative adversarial network (VAE-DCGAN) exhibits the best performance. Furthermore, we leverage Shapley additive explanations (SHAP) to interpret the key features and patterns impacting prediction results. This analysis gains insights into the underlying mechanisms of crash occurrences, and helps improve proactive traffic management and control strategies.

Adjusting vehicle secondary safety ratings to account for crash-avoidance technology fitment using real-world crash and injury data

Objectives Publicizing safety ratings of vehicles can motivate manufacturers to prioritize safety and help consumers choose safer vehicles, leading to safer fleets. The benefits of primary safety technologies that prevent crash occurrence are not currently incorporated in current ratings in a way that values their safety benefits consistently. We aimed to propose a method for assigning weights for each safety technology to account for established safety benefits using published effectiveness and prevalence from real-life data. Methods To illustrate this method, we present a worked example calculated using crash and injury data from Australia and New Zealand. The method proposed attenuates the weights for given safety technologies where two or more safety technologies fitted to the same vehicle are effective for the same types of crashes. Results In the worked example using Australasian data, large SUVs were estimated to have the largest safety increment from the fitment of all the technologies considered compared to vehicles without these primary safety technologies, with an almost 17% reduction in crash occurrence. Cars with all the technologies fitted had estimated average crash reduction of between 11% and 12%. Conclusions Different market groups have different crash patterns, so the safety attributable to safety technology fitment differs at the market group level. This study presents an approach for providing a summary measure of crash avoidance according to the fitment of safety technologies. If this measure is combined with an estimate of secondary safety (whether derived from existing crash and injury data or from new car crash assessment programs), the combined estimate then represents the important elements of safety provided by the vehicle. The methods presented here form a rational basis for assigning safety ratings to represent the benefits of swiftly developing safety technologies.

MODELLING THE FACTORS AFFECTING CRASH OCCURRENCE AND FREQUENCY RESULTED FROM MOBILE PHONE USE WHILE DRIVING: EVIDENCE FROM AL-NAJAF, IRAQ

The recent reports of road traffic accident statistics in Iraq have disclosed a rise in the number of crash injuries resulted from the use of mobile phone while driving. This paper aims to explore the factors contributing to the occurrence and prevalence of such crashes and near crashes in Al-Najaf governorate, Iraq. A representative sample of 417 drivers were interviewed as part of a questionnaire driving survey. Several frequency and modelling analyses were conducted using the IBM SPSS software. The frequency analysis revealed a high use of mobile phones for calling and texting activities while driving. Almost 20% and 55% of the interviewed drivers reported their involvement in a crash or in a near crash because of such use, respectively. Regarding the developed logistic models, the crash involvement sequential regression analysis revealed that factors such gender, education, handheld phoning, calling-answering frequency, and inadequate driving can affect the likelihood of crash occurrence. In contrast, the ordinal logistic near miss models revealed that age, gender, high phone use rate, and improper driving due to such use are influential factors in rising the likelihood of being in multiple near crashes. The analysis results confirm the influence of using phones in distracting the attention of drivers and hence threating their lives; as a result, these findings would be enlightening for agencies and policy makers interested in highway safety.

Retrofitting pedestrian and cyclist collision warning for improved safety of heavy trucks employed in urban areas: a case study in Florence and Pistoia, Italy

The increasing use of heavy vehicles for services such as public transport, waste collection and logistics in densely populated urban areas causes numerous collisions with serious consequences every year, particularly for pedestrians, cyclists and motorcyclists. Advanced Driver Assistance Systems (ADAS) have proven to be a solution because they can significantly reduce the occurrence and consequences of road crashes. Although many ADAS technologies will be mandatory on new trucks in 2024, it will take more than a decade before the benefits are realised due to the average fleet renewal time. To accelerate this process, the most promising solution is therefore the introduction of aftermarket ADAS (also referred to as ‘retrofit’ ADAS) on the circulating fleet. This study aims to identify a tailored solution for retrofitting ADAS capable of providing pedestrian collision warnings on heavy vehicle fleets currently circulating in urban areas. The study involves a sample of vehicles and drivers operating in the metropolitan areas of Florence and Pistoia for waste collection services. The company owner of the vehicles integrated a commercial camera-based ADAS system with three additional cameras installed on the frontal bumper and edges of the vehicle, linked with three screens positioned in the vehicle cabin. In this study, a field test campaign was performed integrating trials in a proving ground closed to traffic with testing and monitoring in urban areas. This allowed to define a set of recommended parameters depending on vehicle speed that represent a good balance between alert frequency, minimizing false positives and ensuring that the alerts indicate real potential hazards to the driver. The recommendations provided in this study can also be extended to other vehicle fleets so that rapid and substantial improvements in the safety of heavy vehicles operating in cities can be achieved.

Review of Road Traffic Accident Burdens in Transportation Sector of Ethiopia

The transportation sector has a very immense role in the economic development of a given country. Because human beings' day-to-day activities re highly depends on the transport service. But this sector has its own drawbacks, like road traffic crashes and environmental pollution. In Ethiopia, the road transport sector is poor in terms of quality and service delivery systems. Due to this, the frequent occurrence of road traffic crashes has been recorded in the country continuously. To that end, this paper was mainly intended to review road traffic accident burdens in the transportation sector of Ethiopia. In detail, reviewing the costs of road traffic accidents, the causing factors of road traffic accidents, the types of vehicles highly involved in road traffic accidents, and the victim groups of communities affected by road traffic accidents at the national level were the specific objectives of this paper. Meanwhile, all road traffic accident data found in Ethiopia from the federal police commission report papers, journal articles, and dissertation papers were used for analysis purposes. Besides, the collected data is presented in tables and figures. Furthermore, the data was analyzed and interpreted based on a descriptive research design. Finally, the result of this review shows that trends of road traffic accidents in Ethiopia have been increasing over time by killing, injuring people and damaging resources. Drivers’ bad behaviors, low know-how of road users, poor road infrastructure, old vehicle conditions, and environmental factors are aggravating the occurrence of road traffic accidents in the country. Thus, to tackle this problem, educating people, enforcing traffic laws, enhancing engineering measures, and giving emergency responses were the recommendations given in this paper.

How would autonomous vehicles behave in real-world crash scenarios?

Autonomous Vehicles (AVs) have the potential to revolutionize transportation systems by enhancing traffic safety. Safety testing is undoubtedly a critical step for enabling large-scale deployment of AVs. High-risk scenarios are particularly important as they pose significant challenges and provide valuable insights into the driving capabilities of AVs. This study presents a novel approach to assess the safety of AVs using in-depth crash data, with a particular focus on real-world crash scenarios. First, based on the high-definition video recording of the whole process prior to the crash occurrences, 453 real-world crashes involving 596 passenger cars from China In-depth Mobility Safety Study-Traffic Accident (CIMSS-TA) database were reconstructed. Pertinent static and dynamic elements needed for the construction of the testing scenarios were extracted. Subsequently, 596 testing scenarios were created via each passenger car’s perspective within the simulation platform. Following this, each of the crash-involved passenger cars was replaced with Baidu Apollo, a famous automated driving system (ADS), for counterfactual simulation. Lastly, the safety performance of the AV was assessed using the simulation results. A logit model was utilized to identify the fifteen crucial scenario elements that have significant impacts on the test results. The findings demonstrated that the AV could avoid 363 real-world crashes, accounting for approximately 60.91% of the total, and effectively mitigated injuries in the remaining 233 unavoidable scenarios compared to a human driver. Moreover, the AV maintain a smoother speed in most of the scenarios. The common feature of these unavoidable scenarios is that the AV is in a passive state, and the crashes are not caused by the AV violating traffic rules, but rather caused by abnormal behavior exhibited by the human drivers. Additionally, seven specific scenarios have been identified wherein AVs are unable to avoid a crash. These findings demonstrate that, compared to human drivers, AVs can avoid crashes that are difficult for humans to avoid, thereby enhancing traffic safety.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

Investigating built environment and traffic flow impact on crash frequency in urban road networks

While numerous studies have examined the factors that influence crash occurrence, there remains a gap in understanding the intricate relationship between built environment, traffic flow, and crash occurrences across different spatial units. This study explores how built environment attributes, and dynamic traffic flow characteristics affect crash frequency by focusing on proposed traffic density-based zones (TDZs). Utilizing a comprehensive dataset from Greater Melbourne, Australia, this research emphasizes on the dynamic traffic flow variables and insights from the Macroscopic Fundamental Diagram model, considering parameters such as shockwave velocity and congestion index. The association between the potential influencing factors and crash frequency is examined using a random parameter negative binomial regression model. Results indicate that the data segmentation based on TDZs is instrumental in establishing a more refined crash model compared to traditional planning-based zones, as demonstrated by improved goodness-of-fit measures. Factors including density (e.g., employment density), network design (e.g., road density and highway density), land use diversity (e.g., job-housing balance and land use mixture), and public transit accessibility (e.g., bus route density) are significantly associated with crash occurrence. Furthermore, the unobserved heterogeneity effects of the shockwave velocity and congestion index on crashes are revealed. The study highlights the significance of incorporating dynamic traffic flow variables in understanding crash frequency variations across different spatial units. These findings can inform optimal real-time traffic monitoring, environmental design, and road safety management strategies to mitigate crash risks.

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type – possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

An Emotion-based Explanation for Bitcoin’s High-frequency Price Crash Risk

ABSTRACT We characterize the high-frequency price crash risk of Bitcoin. Using advanced text-mining techniques, we quantify emotional factors in the cryptocurrency market and investigate whether the proliferation of emotions in the cryptocurrency profession predicts the frequent occurrence of instant market crashes. We find that the cross-sectional dispersion of a catalogue of emotions is positively associated with high-frequency crash risk in the cryptocurrency market. These results are more salient for negative emotions. Differences in negative emotions may increase conflicts within a profession, resulting in idiosyncratic market outcomes.

Impact of Speed Limit Enforcement Cameras on Speed Behavior: Naturalistic Evidence from Brazil

Speeding is widely recognized as a key contributor to the occurrence and severity of road crashes, making studies on speed reduction devices particularly relevant given poor road safety outcomes worldwide. This study investigates the impact of fixed speed cameras on driver behavior and speed reduction in urban arterials using a naturalistic driving study methodology. Data from 13 drivers and 116 trips in Curitiba, Brazil, were analyzed, with a focus on speed cameras placed on arterial roads. Speed data were grouped and analyzed by various categories, including topographic profile, day and week periods, and rain conditions. Mean comparisons were used to compare data sets, revealing an overall speed reduction effect of 0.69 km/h (−1.33%) around the speed camera. The study identified a pattern of punctual speed reduction, known as a “kangaroo jump,” a speed reduction followed by an increase in speed, referred to as the “compensation effect,” and a new pattern characterized by a non-significant speed reduction at the speed camera site followed by an increase in speed, referred to as the “cobra strike effect” because of its curve pattern. The largest speed reductions were observed for flat topographic profiles (−2.98%), daytime travel (−1.58%), and travel on working days (−1.75%) with rain (−1.80%). Conversely, the speed camera had little impact on vehicle speed for uphill topographic profiles, no rain conditions, and travels during weekend.

Short-term Safety Performance Functions by Random Parameters Negative Binomial-Lindley model for Part-time Shoulder Use

Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that allows the use of the left or right shoulder as a travel lane, typically during the peak hours of the day. Though PTSU is an effective strategy for increasing roadway capacity in congested traffic conditions, there is very limited quantitative information about PTSU design elements and operational strategy in the existing literature, which could impact the occurrence of crashes on freeways. This study contributes to the safety literature by analyzing various potential crash contributing factors related to PTSU operation and design elements through the development of short-term Safety Performance Functions (SPFs). A comparison of the estimated models demonstrated that by utilizing the mixed distribution and allowing the posterior parameter estimates of explanatory variables to vary from one observation to another, the Random Parameters Negative Binomial-Lindley (RPNB-L) model outperformed the traditional NB and fixed coefficient NB-L models. The results of the proposed RPNB-L model indicated that the PTSU implemented sections experienced a lower number of traffic crashes compared to the non-PTSU freeway sections. Among the attributes related to PTSU operation and design elements, the usage of the leftmost shoulder lane as PTSU, the presence of emergency rest areas for damaged vehicles, and adequate shoulder width would significantly reduce crash frequency for the PTSU implemented freeways. Moreover, investigation of the identified hotspots revealed that the transition areas (start/end locations of PTSU) are the most critical sections. The findings from this research could assist transportation agencies to take appropriate countermeasures for preventing and reducing crash occurrences on PTSU implemented freeways.

Fatal Crash Occurrence Prediction and Pattern Evaluation by Applying Machine Learning Techniques

Background Highway safety remains a significant issue, with road crashes being a leading cause of fatalities and injuries. While several studies have been conducted on crash severity, few have analyzed and predicted specific types of crashes, such as fatal crashes. Identifying the key factors associated with fatal crashes and predicting their occurrence can help develop effective preventative measures. Objective This study intended to develop cluster analysis and ML-based models using crash data to extract the prominent factors behind fatal crash occurrences and analyze the inherent pattern of variables contributing to fatal crashes. Methods Several branches and categories of supervised ML models have been implemented for fatality prediction and their results have been compared. SHAP analysis was conducted using the ML model to explore the contributing factors of fatal crashes. Additionally, the underlying hidden patterns of fatal crashes have been evaluated using K-means clustering, and specific fatal crash scenarios have been extracted. Results The deep neural networks model achieved 85% accuracy in predicting fatal crashes in Kansas. Factors, such as speed limits, nighttime, darker road conditions, two-lane highways, highway interchange areas, motorcycle and tractor-trailer involvement, and head-on collisions were found to be influential. Moreover, the clusters were able to discern certain scenarios of fatal crashes. Conclusion The study can provide a clear image of the important factors related to fatal crashes, which can be utilized to create new safety protocols and countermeasures to reduce fatal crashes. The results from cluster analysis can facilitate transportation professionals with representative scenarios, which will benefit in identifying potential fatal crash conditions.

The value of Association Rule Analysis in understanding serious and fatal road traffic crashes - a case study of the N4 toll road between 2015 and 2019

In spite of the fact that the road traffic crash fatality rate in South Africa is significantly higher than the global average, the characteristics and causes of road traffic crashes are still not well understood. Without a clear understanding of the characteristics and causes, intelligence-led countermeasures to reduce crashes cannot be developed or applied. The analysis of local South African road crash data is of particular importance in identifying the underlying problems responsible for the high crash rates across the country. While descriptive analyses can be used to present a snapshot of the crash problem, they are largely inadequate when it comes to throwing light on the underlying patterns and relationships between the contributing factors and causes of the crashes themselves. This paper presents a study of the use of Association Rule Analysis (ARA) on data related to crashes on the N4 between Pretoria and the Mozambican border, between 2015 and 2019, to see what added value ARA may offer to standard descriptive analyses of crash data. Apriori Association Rule algorithm was employed on the dataset to identify what contributory factors were not initially evident in the occurrence of serious and fatal crashes. ARA revealed that pedestrians, negligent driving, overtaking and single-vehicle crashes are common factors likely to be present in a serious crash. However, this study found that descriptive statistical analysis and ARA methods are not mutually exclusive, but rather complementary. Where descriptive statistical analysis focuses on summarising and describing data, ARA focuses on identifying relationships and patterns within the data.

Comparison of supervised ML algorithms for road traffic crash prediction models in Rwanda

The development of Rwanda was accompanied by rapid growth of various modes of transport and the occurrence of road traffic crashes (RTCs). RTC severity has also increased in Rwanda, with the number of fatalities increasing progressively every year. The aim of this study was to compare the classification performance of eight supervised machine learning (ML) algorithms in order to determine the best one to predict crash severity and identify potential RTC-influencing factors in Rwanda. Quantitative data sets of RTCs, numbers of registered vehicles and annual average daily traffic (AADT) from 2010 to 2022 were used. The ML algorithms examined were logistic regression (LR), support vector machine (SVM), naive Bayes (NB), K-nearest neighbour (KNN), random forest (RF), decision table (DT), lazy Bayesian rules (LBR) and J48. Five algorithms (RF, DT, J48, LBR and KNN classifiers) were found to have an accuracy of more than 80%. The RF classifier was found to have the best performance for predicting crash severity in Rwanda, with an accuracy of more than 97%. The most influential factors were identified as AADT, number of registered vehicles, causes of crashes and the type of vehicles involved. The model results can be used to provide useful information to road safety decision makers during the planning and design of road infrastructure.

Multivariate analysis of pedestrian-related crashes on Vienna’s roads

Pedestrians are the least protected traffic group when compared to other participants. When compared to moto riders (independent of engine power) they have head protection provided by helmet or vehicle metal shield along with airbag that is providing highest level of body protection for personal vehicles drivers. This vulnerable group is characterized by the high presence of very young participants whose psychological characteristics place them in a vulnerable risky subgroup. According to the police original data on crashes involving pedestrians in the town of Vienna, since 2010 there was highest number of crashes in 2012 and after slow decrease is recorded but still high in total number of crashes involving only pedestrians is an extremely worrying problem.This research is essential to address leading characterizations in crashes with the aim to answer what is current trend in crash occurrence during 2010–2020 inside Vienna municipality, and what is predicted trend. Is there significant and distinctive difference based on gender and age with specific conditions under which crashes are occurring influencing different injury degree. Multiple regression undoubtedly points fields for action in statistically based findings providing the most important answer to this research: why crashes do occur so frequently and what is leading cause of injured pedestrians. Stepwise procedure in discriminant analysis at statistically significant level shows what differentiates injured and not injured pedestrians.