Methodological development for selection of significant predictors explaining fatal road accidents

A study of the impact of traffic investment on traffic fatalities in China, 2004-2020

PURPOSE

Road traffic injuries (RTIs) have been one of the most serious public health problems in China. The purpose of this study was to investigate the extent to which traffic investment affects traffic fatalities in China as well as regional differences.

METHODS

The study analyzed the correlation between traffic investment and traffic fatalities, incorporating additional factors such as economic conditions, road infrastructure, population density, and lighting. The selected variables included the number of traffic fatalities, traffic investment, urban per capita road area, urban road length, road mileage, urban road lighting, population size, and per capita gross domestic product. Relevant data between 2004 and 2020 were collected for an analysis using a fixed effect regression model. A p < 0.05 is considered statistically significant. To reduce the heterogeneity caused by regional differences, the provinces were divided into 6 groups according to administrative districts, and the clustering standard error analysis was carried out.

RESULTS

Overall, there has been a significant improvement in road safety in China from 2004 to 2020, but some regions show an increase in traffic fatalities. The model reveals that traffic investment is significantly and positively correlated with the number of traffic fatalities. Holding all other factors constant, each 10,000 yuan increase in transport investment was associated with an average increase of 0.22 road traffic fatalities. In the analysis of regional differences, there was a significant positive correlation between traffic investment and traffic fatalities in the Northwest region and an increase of 10,000 yuan leads to an increase of 0.47. There was a significant negative correlation between road mileage, urban road lighting system, and population and traffic fatalities. For example, holding other factors constant, a 10,000 km reduction in road length would increase the number of traffic deaths by 45.56. The model results of urban per capita road area, urban road length, per capita gross domestic product, and the explained variables showed that p > 0.100, which was not statistically significant.

CONCLUSIONS

Therefore, traffic investments are essential for governments to develop measures to enhance road safety and reduce the risk of road fatalities. Adjusting traffic road investment and other covariates is conducive to improving traffic safety and reducing the risk of road fatalities. The road safety situation in different regions of China varies greatly. Local governments should consider the actual conditions to provide better road safety configuration policies.

Planning traffic surveillance in Spain: How to optimize the management of police resources to reduce road fatalities

Although traffic police enforcement is widely recognized as a key action in the road safety field, it can be a costly policy to implement. In addition, governments often impose budget constraints that can limit the resources available for activities such as law enforcement and surveillance. To evaluate the impact of human traffic control resources planning on traffic fatalities on Spanish NUTS-3 regions interurban roads, this paper uses an econometric model to investigate the performance of police enforcement intensity by focusing on two crucial traffic law infractions (i.e., speeding and drunk driving). After controlling for a range of economic, demographic, climate, and risk exposure variables, results highlight the relevance of visible, human, and in-person traffic law enforcement through regular vehicle patrols for reducing traffic crashes, with a non-significant effect of automatic enforcement. Our findings have important implications for traffic police resource management regarding the effective maintenance of patrol cars and plans to digitalize and automatize police administrative tasks and procedures.

Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas

Understanding the relationship between social vulnerability and traffic crashes is a cornerstone for promoting social justice in transportation planning and policymaking. However, few studies have examined the disparities in traffic crashes by systemically considering the influence of social vulnerability via spatial analysis approaches. This study puts forward a new approach to assess the inequity in transportation safety by spatially examining the relationships between crash risks and the social vulnerability index (SVI) established by the Centers for Disease Control and Prevention (CDC). We performed spatial autocorrelation analyses to identify the clusters of high-risk and high-vulnerable census tracts in Texas. Meanwhile, we innovatively applied the Multiscale Geographically Weighted Regression model (MGWR) to assess the impacts of CDC SVI on crash risks spatially and statistically. The results demonstrate that the crash rate and the social vulnerability are significantly correlated in the highly urbanized regions as well as the southern border along the Rio Grande in Texas. The MGWR results indicate the minority status of census tracts is strongly correlated with overall crashes in north-central and northeastern Texas, and the socioeconomic status is tightly correlated with fatal crashes across Texas. The outcomes from this study have significant implications for transportation planning and policymaking.

SARIMA Modelling Approach for Forecasting of Traffic Accidents

To achieve greater sustainability of the traffic system, the trend of traffic accidents in road traffic was analysed. Injuries from traffic accidents are among the leading factors in the suffering of people around the world. Injuries from road traffic accidents are predicted to be the third leading factor contributing to human deaths. Road traffic accidents have decreased in most countries during the last decade because of the Decade of Action for Road Safety 2011–2020. The main reasons behind the reduction of traffic accidents are improvements in the construction of vehicles and roads, the training and education of drivers, and advances in medical technology and medical care. The primary objective of this paper is to investigate the pattern in the time series of traffic accidents in the city of Belgrade. Time series have been analysed using exploratory data analysis to describe and understand the data, the method of regression and the Box–Jenkins seasonal autoregressive integrated moving average model (SARIMA). The study found that the time series has a pronounced seasonal character. The model presented in the paper has a mean absolute percentage error (MAPE) of 5.22% and can be seen as an indicator that the prognosis is acceptably accurate. The forecasting, in the context of number of a traffic accidents, may be a strategy to achieve different goals such as traffic safety campaigns, traffic safety strategies and action plans to achieve the objectives defined in traffic safety strategies.

Hybrid artificial neural network and structural equation modelling techniques: a survey

Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM-ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM-ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM-ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore ^® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM-ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

Looking for traces of the Troika's intervention in European road safety

The recent economic crisis has required the bailout of some European States by the so-called Troika, with capital injections accompanied by financial austerity. This paper analyzes econometrically the impact of this support programme on road safety for an original panel data (1995-2015). The findings also corroborate the Kuznets curve hypothesis for traffic accidents in the long term. Regarding the impact of intervention in the short term, despite reductions in safety policy budgets due to austerity, financial support, and related austerity measures might have led to an improvement in road safety, reducing both the number of accidents and fatalities. Therefore, it seems that our result is more linked to the austerity measures than to the financial support given by the Troika.

Interregional inequality and road accident rates in Spain

The aim of this study was to determine whether interregional inequality in Spain had the same impact on the risks of fatality and injury across the different provinces of Spain, in the period from 1999 to 2015. This allows us to map fatality and injury rates in Spanish provinces depending on their level of economic development. Provinces were divided in two large groups according to the mean weight of their per capita GDP on the national GDP from 2000 to 2015. Using fixed effects data panel models, estimations were obtained for each group of the impact of the relationships between per capita GDP, unemployment rate and other control variables on their risks of fatality and injury. The models reveal that economic conditions and education are explanatory factors with greater significance and impact on the risks of fatality and injury in provinces with higher levels of economic development. In this group, the penalty-points driving licence was found have a greater impact, although its effectiveness is now being questioned. In contrast, to reduce the risks of fatality and injury in less developed provinces, it is imperative to invest in road infrastructure, increasing the proportion of high capacity roads and investing more in road replacement and maintenance. The geographical distribution generated in this study allows us to better identify the areas with a higher risk of fatality or injury. This, in turn, confirms the need to improve the configuration of road safety policy, taking into account the different fatality or injury rates across provinces, the origins of which lie in the specific provincial conditions.

Forecasting Road Traffic Deaths in Thailand: Applications of Time-Series, Curve Estimation, Multiple Linear Regression, and Path Analysis Models

In 2018, 19,931 people were killed in road accidents in Thailand. Thus, reduction in the number of accidents is urgently required. To provide a master plan for reducing the number of accidents, future forecast data are required. Thus, we aimed to identify the appropriate forecasting method. We considered four methods in this study: Time-series analysis, curve estimation, regression analysis, and path analysis. The data used in the analysis included death rate per 100,000 population, gross domestic product (GDP), the number of registered vehicles (motorcycles, cars, and trucks), and energy consumption of the transportation sector. The results show that the best three models, based on the mean absolute percentage error (MAPE), are the multiple linear regression model 3, time-series with exponential smoothing, and path analysis, with MAPE values of 6.4%, 8.1%, and 8.4%, respectively.

Road Infrastructure Analysis with Reference to Traffic Stream Characteristics and Accidents: An Application of Benchmarking Based Safety Analysis and Sustainable Decision-Making

Road infrastructure sustainability is directly associated with the safety of human beings. As a transportation engineer and policymaker, it is necessary to optimize the funding mechanism for road safety improvement by identifying problematic road segments. Infrastructure improvement is one of the key targets for efficient road safety management. In this study, data envelopment analysis (DEA) technique has been applied in combination with a geographical information system (GIS) to evaluate the risk level of problematic segments of a 100 km-long motorway (M-2) section. Secondly, the cross efficient method has been used to rank the risky segments for prioritization and distribution of funding to improve the road safety situation. This study will help in efficiently identifying the risky segments for safety improvement and budget allocation prioritization. GIS map will further improve the visualization and visibility of problematic segments to easily locate the riskiest segments of the motorway.

Relationship between road traffic features and accidents: An application of two-stage decision-making approach for transportation engineers

INTRODUCTION

An efficient decision-making process is one of the major necessities of road safety performance analysis for human safety and budget allocation procedure.

METHOD

During the road safety analysis procedure, data envelopment analysis (DEA) supports policymakers in differentiating between risky and safe segments of a homogeneous highway. Cross-risk, an extension of the DEA models, provides more information about risky segments for ranking purpose. After identification of risky segments, the next goal is to identify the factors that are major contributors in making that segment risky.

RESULTS

This research proposes a methodology to analyze road safety performance by using a combination of DEA with the decision tree (DT) technique. The proposed methodology not only provides a facility to identify problematic road segments with the help of DEA but also identifies contributing factors with the help of DT. Practical applications: The applicability of the proposed model will help policymakers to identify the major factors contributing to road accidents and analysis of safety performance of road infrastructure to allocate the budget during the decision-making process.

A Novel Multilevel-SVD Method to Improve Multistep Ahead Forecasting in Traffic Accidents Domain

Here is proposed a novel method for decomposing a nonstationary time series in components of low and high frequency. The method is based on Multilevel Singular Value Decomposition (MSVD) of a Hankel matrix. The decomposition is used to improve the forecasting accuracy of Multiple Input Multiple Output (MIMO) linear and nonlinear models. Three time series coming from traffic accidents domain are used. They represent the number of persons with injuries in traffic accidents of Santiago, Chile. The data were continuously collected by the Chilean Police and were weekly sampled from 2000:1 to 2014:12. The performance of MSVD is compared with the decomposition in components of low and high frequency of a commonly accepted method based on Stationary Wavelet Transform (SWT). SWT in conjunction with the Autoregressive model (SWT + MIMO-AR) and SWT in conjunction with an Autoregressive Neural Network (SWT + MIMO-ANN) were evaluated. The empirical results have shown that the best accuracy was achieved by the forecasting model based on the proposed decomposition method MSVD, in comparison with the forecasting models based on SWT.