Macroscopic models for traffic and traffic safety

Analysis of the impact of COVID-19 on collisions, fatalities and injuries using time series forecasting: The case of Greece

The current study aims to investigate the impact of the COVID-19 pandemic on road traffic collisions, fatalities, and injuries using time series analyses. To that aim, a database containing road collisions, fatalities, and slight injuries data from Greece were derived from the Hellenic Statistical Authority (HSA) and covered a ten-year timeframe (from January 2010 to August 2020. The chosen time period contained normal operations, as well as the period of the first COVID-19-induced lockdown period in Greece. Three different Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models were implemented in order to compare the observed measurements to forecasted values that were intended to depict assumed conditions; namely, without the appearance of the COVID-19 pandemic. Modelling results revealed that the total number of road collisions, fatalities, and slightly injured were decreased, mainly due to the sharp traffic volume decrease. However, the percentage reduction of the collision variables and traffic volume were found to be disproportionate, which probably indicates that more collisions occurred with regard to the prevailing traffic volume. An additional finding is that fatalities and slightly injured rates were significantly increased during the lockdown period and the subsequent month. Overall, it can be concluded that a worse performance was identified in terms of road safety. Since subsequent waves of COVID-19 cases and other pandemics may reappear in the future, the outcomes of the current study may be exploited for the improvement of road safety from local authorities and policymakers.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.

Automobile-dependency as a barrier to vision zero, evidence from the states in the USA

With a traffic fatality rate of 10.6 per 100,000 as of 2013-more than triple that in the UK, the Netherlands, and Sweden-the United States has the worst traffic safety performance of all developed countries. Statewide variations are even more pronounced. North Dakota registers more than twice the national average and five times the rate of Massachusetts. We used panel models and annual data from 1997 to 2013 to capture the effect of seven separate sets of factors that influence traffic safety: exposure, travel behavior, socioeconomics, macroeconomics, safety policies, and mitigating factors such as health care. The results of our panel models and supplementary analysis of state effects show that two variables - Vehicle Miles Traveled and Vehicles per Capita-have the strongest impact on traffic fatality rates. This is closely followed by Infant Mortality Rates, the proxy that we used to represent the quality of health care. Policy levers such as Graduated Driver's Licenses (GDL) have improved safety, but to a limited extent. We also found that states with higher urban density and more walking are associated with lower traffic fatality rates. Taken as a whole, our findings suggest that if additional progress is to be made in reducing traffic fatalities, emphasis needs to move beyond simply focusing on policies such as GDL and seat belt laws, which have already been adopted by almost all jurisdictions across the United States. We need to also consider factors that focus on the type of urban form that we are creating to ensure that we are fostering environments that encourage multi-modal transportation such as walking to reduce the VMT and Vehicles per Capita, the two strongest predictors of traffic fatalities.

Relating traffic fatalities to GDP in Europe on the long term

Modeling road safety development can provide important insight into policies for the reduction of traffic fatalities. In order to achieve this goal, both the quantifiable impact of specific parameters, as well as the underlying trends that cannot always be measured or observed, need to be considered. One of the key relationships in road safety links fatalities with risk and exposure, where exposure reflects the amount of travel, which in turn translates to how much travelers are exposed to risk. In general two economic variables: GDP and unemployment rate are selected to analyse the statistical relationships with some indicators of road accident fatality risk. The objective of this research is to provide an overview of relevant literature on the topic and outline some recent developments in macro-panel data analysis that have resulted in ongoing research that has the potential to improve our ability to forecast traffic fatality trends, especially under turbulent financial situations. For this analysis, time series of the number of fatalities and GDP in 30 European countries for a period of 38 years (1975-2012) are used. This process relies on estimating long-term models (as captured by long term time-series models, which model each country separately). Based on these developments, utilizing state-of-the-art modelling and analysis techniques such as the Common Correlated Effects Mean Group estimator (Pesaran), the long-term elasticity mean value equals 0.63, and is significantly different from zero for 10 countries only. When we take away the countries, where the number of fatalities is stationary, the average elasticity takes a higher value of nearly 1. This shows the strong sensitivity of the estimate of the average elasticity over a panel of European countries and underlines the necessity to be aware of the underlying nature of the time series, to get a suitable regression model.

What affects annual changes in traffic safety? A macroscopic perspective in Virginia

INTRODUCTION

Virginia saw a 20% reduction in traffic fatalities in 2008, an unprecedented annual reduction since 1950, and safety stakeholders in Virginia were intrigued about what caused such large a reduction and more generally what affects traffic safety from a macroscopic perspective.

METHOD

This study attempted to find factors associated with such a reduction using historical data of Virginia. Specifically, the study related 18 factors to seven traffic safety measures.

RESULTS

In terms of annual changes, the study found that typical crash exposures were not generally associated with the seven measures, while two economic indicators (unemployment rate and U.S. Consumer Price Index [CPI]) were strongly associated with most of them.

CONCLUSIONS

Annual changes in the CPI and unemployment rate account for about half of the annual changes in total and fatal crash counts, respectively. On average, a 1 point increase in CPI and a 1% increase in the unemployment rate are associated with about 2,500 fewer traffic crashes and about 40 fewer fatal crashes annually in Virginia, respectively.

The turning point in the number of traffic fatalities: two hypotheses about changes in underlying trends

The number of traffic fatalities reached a peak in many highly motorised countries around 1970. Some previous studies have suggested that the turning point in the number of traffic fatalities was inevitable and did not reflect a change in the underlying trends influencing the number of traffic fatalities. Other studies suggest that trends in traffic growth and fatality rate changed from before to after the turning point. This paper proposes two hypotheses about the turning point in the number of traffic fatalities. One hypothesis is that the long-term trends in traffic growth and fatality rate were the same before and after the turning point. The other hypothesis is that the long-term trends in traffic growth and fatality rate were different before and after the turning point was reached, in particular that the annual percentage decline in fatality rate became greater after the turning point than before. Such a change would suggest that road safety policy became more effective. Analysis of data for six countries (Denmark, Great Britain, Netherlands, Norway, Sweden, United States) lends stronger support to the latter hypothesis than to the former. The lesson for policy makers, in particular in countries where the number of traffic fatalities is still growing, is that they should not expect a turning point to be reached without policy interventions.

Latent risk and trend models for the evolution of annual fatality numbers in 30 European countries

In this paper a unified methodology is presented for the modelling of the evolution of road safety in 30 European countries. For each country, annual data of the best available exposure indicator and of the number of fatalities were simultaneously analysed with the bivariate latent risk time series model. This model is based on the assumption that the amount of exposure and the number of fatalities are intrinsically related. It captures the dynamic evolution in the fatalities as the product of the dynamic evolution in two latent trends: the trend in the fatality risk and the trend in the exposure to that risk. Before applying the latent risk model to the different countries it was first investigated and tested whether the exposure indicator at hand and the fatalities in each country were in fact related at all. If they were, the latent risk model was applied to that country; if not, a univariate local linear trend model was applied to the fatalities series only, unless the latent risk time series model was found to yield better forecasts than the univariate local linear trend model. In either case, the temporal structure of the unobserved components of the optimal model was established, and structural breaks in the trends related to external events were identified and captured by adding intervention variables to the appropriate components of the model. As a final step, for each country the optimally modelled developments were projected into the future, thus yielding forecasts for the number of fatalities up to and including 2020.

Road safety forecasts in five European countries using structural time series models

OBJECTIVE

Modeling road safety development is a complex task and needs to consider both the quantifiable impact of specific parameters as well as the underlying trends that cannot always be measured or observed. The objective of this research is to apply structural time series models for obtaining reliable medium- to long-term forecasts of road traffic fatality risk using data from 5 countries with different characteristics from all over Europe (Cyprus, Greece, Hungary, Norway, and Switzerland).

METHODS

Two structural time series models are considered: (1) the local linear trend model and the (2) latent risk time series model. Furthermore, a structured decision tree for the selection of the applicable model for each situation (developed within the Road Safety Data, Collection, Transfer and Analysis [DaCoTA] research project, cofunded by the European Commission) is outlined. First, the fatality and exposure data that are used for the development of the models are presented and explored. Then, the modeling process is presented, including the model selection process, introduction of intervention variables, and development of mobility scenarios.

RESULTS

The forecasts using the developed models appear to be realistic and within acceptable confidence intervals. The proposed methodology is proved to be very efficient for handling different cases of data availability and quality, providing an appropriate alternative from the family of structural time series models in each country.

CONCLUSIONS

A concluding section providing perspectives and directions for future research is presented.

State-space based analysis and forecasting of macroscopic road safety trends in Greece

In this paper, macroscopic road safety trends in Greece are analyzed using state-space models and data for 52 years (1960-2011). Seemingly unrelated time series equations (SUTSE) models are developed first, followed by richer latent risk time-series (LRT) models. As reliable estimates of vehicle-kilometers are not available for Greece, the number of vehicles in circulation is used as a proxy to the exposure. Alternative considered models are presented and discussed, including diagnostics for the assessment of their model quality and recommendations for further enrichment of this model. Important interventions were incorporated in the models developed (1986 financial crisis, 1991 old-car exchange scheme, 1996 new road fatality definition) and found statistically significant. Furthermore, the forecasting results using data up to 2008 were compared with final actual data (2009-2011) indicating that the models perform properly, even in unusual situations, like the current strong financial crisis in Greece. Forecasting results up to 2020 are also presented and compared with the forecasts of a model that explicitly considers the currently on-going recession. Modeling the recession, and assuming that it will end by 2013, results in more reasonable estimates of risk and vehicle-kilometers for the 2020 horizon. This research demonstrates the benefits of using advanced state-space modeling techniques for modeling macroscopic road safety trends, such as allowing the explicit modeling of interventions. The challenges associated with the application of such state-of-the-art models for macroscopic phenomena, such as traffic fatalities in a region or country, are also highlighted. Furthermore, it is demonstrated that it is possible to apply such complex models using the relatively short time-series that are available in macroscopic road safety analysis.

Are road traffic crash fatality rates converging among OECD countries?

This article examines the trends of road traffic crash (RTC) fatality rates in OECD countries over the past four decades. Based on recent developments in the economic growth literature we propose and test the hypothesis that RTC fatality rates initially increase with economic development, peak, and then gradually decrease. The theory predicts that, as a result, the RTC fatality rates of different countries will tend to converge over time. Our results for the period 1961-2007 reveal no evidence of the convergence of RTC fatality rates across the OECD as a whole for that time period. Nevertheless, there is evidence of convergence among sub-groups of countries. This evidence may assist policymakers as an additional way of benchmarking their country's performance against that of its peers and to identify the next-closest peer in country sub-groups with superior road safety performance.

Economic growth, motorization, and road traffic injuries in the Sultanate of Oman, 1985-2009

BACKGROUND

Recent affluence, assisted by exploitation of hydrocarbon, has sparked unprecedented economic growth and influx of all façades of modernity in Oman. Different statistical models have examined the relationship between economic growth, motorization rates, and road traffic fatalities. However, such a relationship in Oman has never been described.

OBJECTIVE

To describe and analyze the trend of road traffic injuries (RTIs) in relation to motorization rates and economic growth during the period from 1985 to 2009 using Smeed's (1949) model and Koren and Borsos's (2010) model.

METHODS

The study is based on national data reported between 1985 and 2009. Data on the population and gross domestic product (GDP) per capita in U.S. dollars were gathered from the Ministry of National Economy reports. Data on the number of vehicles and road traffic crashes, fatalities, and injuries were gathered from the Royal Oman Police (ROP) reports. Crash, fatality, and injury rates per 1000 vehicles and per 100,000 population were computed. Linear regression analysis was carried out to estimate the average annual changes in the rates. Smeed's (1949) and Koren and Borsos's (2010) models were used to predict the relations between motorization and road traffic fatalities in Oman. In addition, a cross-sectional analysis of year 2007 data for a number of Arab countries was carried out.

RESULTS

The GDP per capita increased from US$6551 in 1985 to US$25,110 in 2009 with an annual increase of UR$547 per capita. The motorization rates increased by 36 percent from 1745 per 10,000 population in 1985 to 2382 per 10,000 population in 2009. Both Smeed's (1949) and Koren and Borsos's (2010) models had a high goodness of fit, with R(2) greater than 0.70. This indicated that road traffic fatalities in Oman may have a direct relationship with increased motorization. The cross-sectional analysis showed that the relation between crash fatalities and motorization rates in Oman and the United Arab Emirates can be better explained by Koren and Borsos's (2010) model than other countries.

CONCLUSION

Recent economic growth in Oman was associated with an increase in motorization rates, which in turn has resulted in an increased burden of road traffic fatalities and injuries.

Are we there yet? Australian road safety targets and road traffic crash fatalities

BACKGROUND

Road safety targets are widely used and provide a basis for evaluating progress in road safety outcomes against a quantified goal. In Australia, a reduction in fatalities from road traffic crashes (RTCs) is a public policy objective: a national target of no more than 5.6 fatalities per 100,000 population by 2010 was set in 2001. The purpose of this paper is to examine the progress Australia and its states and territories have made in reducing RTC fatalities, and to estimate when the 2010 target may be reached by the jurisdictions.

METHODS

Following a descriptive analysis, univariate time-series models estimate past trends in fatality rates over recent decades. Data for differing time periods are analysed and different trend specifications estimated. Preferred models were selected on the basis of statistical criteria and the period covered by the data. The results of preferred regressions are used to determine out-of-sample forecasts of when the national target may be attained by the jurisdictions. Though there are limitations with the time series approach used, inadequate data precluded the estimation of a full causal/structural model.

RESULTS

Statistically significant reductions in fatality rates since 1971 were found for all jurisdictions with the national rate decreasing on average, 3% per year since 1992. However the gains have varied across time and space, with percent changes in fatality rates ranging from an 8% increase in New South Wales 1972-1981 to a 46% decrease in Queensland 1982-1991. Based on an estimate of past trends, it is possible that the target set for 2010 may not be reached nationally, until 2016. Unsurprisingly, the analysis indicated a range of outcomes for the respective state/territory jurisdictions though these results should be interpreted with caution due to different assumptions and length of data.

CONCLUSIONS

Results indicate that while Australia has been successful over recent decades in reducing RTC mortality, an important gap between aspirations and achievements remains. Moreover, unless there are fairly radical ("trend-breaking") changes in the factors that affect the incidence of RTC fatalities, deaths from RTCs are likely to remain above the national target in some areas of Australia, for years to come.

When may road fatalities start to decrease?

INTRODUCTION

The comparative analysis of macroscopic trends in road safety has been a popular research topic. The objective of this research is to propose a simple and, at the same time, reliable multiple regime model framework for international road safety comparisons, allowing for the identification of slope changes of personal risk curves and respective breakpoints.

METHOD

The trends of road traffic fatalities in several EU countries have been examined through the temporal evolution of elementary socioeconomic indicators, namely motorized vehicle fleet and population, at the country level.

RESULTS

Piece-wise linear regression models have been fitted, using a methodology that allows the simultaneous estimation of all slopes and breakpoints. The number and location of breakpoints, as well as the slope of the connecting trends, vary among countries, thus indicating different road safety evolution patterns.

IMPACT ON INDUSTRY

Macroscopic analysis of road accident trends may be proved beneficial for the identification of best examples and the implementation of appropriate programmes and measures, which will lead to important benefits for the society and the economy through the reduction of road fatalities and injuries. Best performing countries and the related programmes and measures adopted may concern several safety improvements at the processes of the road, the vehicle and the insurance industries.

CONCLUSIONS

Lessons from the analysis of the past road safety patterns of developed countries provide some insight into the underlying process that relates motorization levels with personal risk and can prove to be beneficial for predicting the road safety evolution of developing countries that may have not yet reached the same breakpoints. Furthermore, the presented framework may serve as a basis to build more elaborate models, including more reliable exposure indicators (such as vehicle-km driven).

Stop versus yield on pedestrian-involved fatal crashes in the United States

In an effort to improve pedestrian safety, several states in the United States changed their pedestrian laws by changing the requirement that drivers yield to pedestrians in crosswalks to a requirement that drivers stop for pedestrians in crosswalks. This study examined whether this change had an effect on pedestrian safety in the United States, with its focus on low-speed roads. To examine the association between changes in pedestrian laws and changes in pedestrian-involved fatal crashes, three approaches were employed: before-after analysis, time-series analysis, and cross-sectional analysis. Pedestrian-involved fatal traffic crashes on low-speed roads were extracted from the U.S. national fatal crash database, the Fatality Analysis Reporting System (FARS), from 1980 through 2005. This study found no statistically significant reduction in pedestrian-involved fatal crashes attributable to changes in the laws, yet this finding is not definitive because of study limitations such as the omission of relevant exposure data.

A statistical model to compare road mortality in OECD countries

The objective of this paper is to compare safety levels and trends in OECD countries from 1980 to 1994 with the help of a statistical model and to launch international discussion and further research about international comparisons. Between 1980 and 1994, the annual number of fatalities decreased drastically in all the selected countries except Japan (+ 12%), Greece (+ 56%) and ex-East Germany (+ 50%). The highest decreases were observed in ex-West Germany (- 48%), Switzerland (- 44%), Australia (- 40%), and UK (- 39%). In France, the decrease in fatalities over the same period reached 34%. The fatality rate, an indicator of risk, decreased in the selected countries from 1980 to 1994 except in the east-European countries during the motorization boom in the late 1980s. As fatality rates are not sufficient for international comparisons, a statistical multiple regression model is set up to compare road safety levels in 21 OECD countries over 15 years. Data were collected from IRTAD (International Road Traffic and Accident Database) and other OECD statistical sources. The number of fatalities is explained by seven exogenous (to road safety) variables. The model, pooling cross-sectional and time series data, supplies estimates of elasticity to the fatalities for each variable: 0.96 for the population; 0.28 for the vehicle fleet per capita; -0.16 for the percentage of buses and coaches in the motorised vehicle fleet; 0.83 for the percentage of youngsters in the population; - 0.41 for the percentage of urban population; 0.39 for alcohol consumption per capita; and 0.39 for the percentage of employed people. The model also supplies a rough estimate of the safety performance of a country: the regression residuals are supposed to contain the effects of essentially endogenous and unobserved variables, independent to the exogenous variables. These endogenous variables are safety performance variables (safety actions, traffic safety policy, network improvements and social acceptance). A new indicator, better than the mortality rate, is then set upon the residuals. Mean estimates of this indicator for the years 1980-1982 and the years 1992-1994 rank the countries in the beginning and at the end of the study period. Countries showing the best ranks (and thus the best performance) in 1980 and 1994 are Sweden, the Netherlands and Norway. The UK and Switzerland reach the top 5 in 1994. Greece, Belgium, Portugal and Spain are the last countries in the classification along with, surprisingly, the USA. France was ranked 18th in 1980 and 15th in 1994 but is ranked amongst the five countries that most improved from 1980 to 1994. This model remains non definitive because it is not able to distinguish between safety performance and unobserved exogenous variables although these exogenous variables could explain more about the differences in levels and trends between the countries. More complex models, particularly highly sophisticated models regarding the number of fatalities with breakdowns by road users or road classes would be needed to give a precise and profound ranking of safety levels and safety improvements between countries.

[Trends in traffic accident mortality in Spain, 1962-1994]

OBJECTIVE

To assess the evolution of the traffic accident mortality rate in Spain from 1962 to 1994, and the role played by its four theoretical components: motorization index (vehicles/population), accidentability index (accidents/vehicles), harmfulness index (victims/accidents) and fatality index (deaths/victims).

METHODS

Data from the National Population Census and the Bulletin of the Dirección General de Tráfico were collected to estimate the above mentioned indicators for all accidents and accidents in road and urban zones. Simple and multiple partial correlation coefficients among variables were calculated. Poisson regression models were also obtained.

RESULTS

An increasing trend during the whole period was observed for the national traffic accident mortality rate, especially from 1982 to 1989 in the younger age groups, followed by a decrease since 1990. The aforementioned four components were significatively associated with the mortality rate. The strength of this association was especially high for the motorization index and for the harmfulness index when all accidents and road accidents were considered. For urban accidents, the fatality index rate is the component most strongly associated with mortality rate. The role played by the accidentability index in the magnitude of the mortality rate seems less important.

CONCLUSIONS

The growing exposure rate to traffic accidents observed in Spain (measured by the motorization index) is not directly influenced by public heath strategies. Therefore, it seems advisable to emphasize the development of measures focused to control the other three components of traffic accident mortality rate, especially those related with harmfulness and fatality.

Future health scenarios as a tool in the surveillance of unintentional injuries

Health policy needs effective public health surveillance systems. In order to support injury control in the Netherlands we established a surveillance system which monitors trends in the epidemiology of injuries due to traffic accidents, occupational accidents and home and leisure accidents. We introduced the combination of traditional monitoring methods with a new tool: the use of future health scenarios. Trends in the epidemiology of injuries since 1985 were compared with observations from the preceding period (1970-1985) and to future health scenarios (1985-2000). These scenarios were based on the opinions of 80 Dutch experts, which had been collected with the help of a Delphi-study. We identified interruptions of the actual injury mortality trends in the Netherlands. In the second half of the 1980s the rapidly declining injury mortality trends of the period 1970-1985 slowed down (traffic accidents), slightly reversed (occupational accidents) or stabilised (home and leisure accidents). The transitions into less favourable developments were already foreseen by Dutch experts. The future health scenarios appear to have added value for health policy. They offer a well-defined conceptual framework for monitoring and facilitate the early detection of trend interruptions. Moreover, they provide information on the most likely future development and on the feasibility of health policy goals. The results of our study show that health policy goals of the Dutch government will not be reached and that new initiatives in injury control are needed.

What is happening to the number of fatalities in road accidents? A model for forecasts and continuous monitoring of development up to the year 2000

A model for successively forecasting and monitoring the development in the number of fatalities in traffic is presented. The model has been created through time series analysis covering the years 1977-1991. The model is simple, with the number of fatalities as the dependent variable and with time and traffic as the only predictors. The time factor describes the cumulative effect of changes such as better roads, vehicles, drivers, etc. The model is multiplicative and permits a nonproportional relationship with traffic volume. Taking into account the purely random fluctuations in the number of fatalities, the historical fit for the period 1977-1991 is very good. Also the forecasts for 1992 and 1993 have proved very accurate. The model will be revised as new annual data are received. At present, the model points to a favorable development in the reduction of the number of fatalities up to the year 2000, assuming a moderate increase in traffic.

Application of Smeed's formula to assess development of traffic safety in Jordan

Traffic safety in Jordan has been debated in the last few years. A high percentage of the citizens still think that the country is ranked among the most dangerous countries in the world. This study traces the development of traffic safety in Jordan during the last two decades. Examining changes in accident rate over time and comparing with those in Middle Eastern and developing countries, it is concluded that safety levels in Jordan are among the best in Middle Eastern and developing countries.