Sylvain Lassarre

Analysis of progress in road safety in ten European countries

Classic models for the long-term forecasting of the number of fatalities in road accidents are based on a decreasing exponential form of the rate of fatalities per vehicle x km. We decided to extend this simple model to incorporate intervention functions connected with the major safety measures introduced and to replace the deterministic trend by a stochastic one. Harveys structural model, known as the local linear trend model, is applied to ten European countries. The relationship between the slope of this trend and the elasticity in terms of the number of vehicles x km yields an indicator of the rate of progress in road safety made in the different countries. The average rate is around -6% per annum, with a minimum of -4.5% and a maximum of -13.5% for Spain in 1994. Europes road systems can thus absorb a 6% increase in traffic per annum while maintaining the number of fatalities constant.

The introduction of the variables “traffic volume,” “speed” and “belt-wearing” into a predictive model of the severity of accidents

Three stages of modelling the monthly number of accidents and deaths are possible, ranked in order of increasing complexity according to the Box-Jenkins methodology: the univariate model; the univariate model with dummy variable; the multivariate model. By means of the transformation of the number of accidents by logarithmic function, these additive types of model become multiplicative with elasticity; the latter is much easier to handle at the interpretation stage. The greatly improved analytical capacity that more complex models allow is illustrated by means of an analysis of the evolution of the monthly number of deaths in France during the period from 1970 to 1977. The stochastic components of the time series are estimated by the univariate model, the effects of the speed-limiting and belt-wearing countermeasures by the univariate model with dummy variable and the influence of speeds and belt-wearing taking account of traffic volume by the multivariate model. These models have made it possible (i) to estimate the safety gain achieved by more widespread belt-wearing and that achieved by a narrowing of the range of speeds practised, and (ii) to conclude that there is only a small influence upon safety brought about by changes in the average speeds of private cars and other vehicles of similar mass.

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.

A stochastic jump process applied to traffic flow modelling

The paper presents the main aspects of a stochastic conservative model of the evolution of the number of vehicles per road section. The model, defined in continuous time on a discrete space, follows a misanthrope Markovian process. It is a mesoscopic traffic model in the following sense: the vehicles are individually considered, but their dynamics are aggregated per section. The model parameters are supply and demand functions in equilibrium (i.e. a fundamental diagram). In order to model flows on a traffic network, different schemes of junction dynamics are proposed. The model properties in transient and stationary states are investigated analytically in simple cases and by simulation. The results show that the process presents classical properties of deterministic macroscopic model such as the propagation of shock or rarefaction wave for Riemann initial condition. On the other hand, one observes phenomena usually related to high order models, such as a wide scattering of the flow performances or the propagation (backward or forward according to the density level) of local perturbations, due to the stochasticity.

A GIS-based methodology for identifying pedestrians’ crossing patterns

A pedestrian trip is a spatiotemporal process going through different states and related to different decisions made at certain times and locations on the urban network. The analysis of pedestrian trips in terms of crossing patterns is a complex task, which is often further limited by a lack of appropriate and detailed data. The objective of this research is the development and testing of appropriate indicators of pedestrian crossing behavior along urban trips, and a methodology for collecting and processing the data required for the analysis of this behavior. First, a comprehensive set of indicators for the assessment of pedestrian behavior in urban areas is proposed (i.e. average trip length, number, type and location of crossings). Then, a GIS tool is developed for the storage and integration of information on pedestrian trips, and the crossings made during the trips, with other geographical information (e.g. road network function and geometry, traffic control and pedestrian facilities). The proposed approach is then tested at network level on a sample of pedestrian trips collected by a field survey. The results suggest specific patterns of pedestrian crossing behavior, such as the tendency to cross at the beginning of the trip and the tendency to cross at mid-block locations when signalized junctions are not available. The results are further discussed in terms of urban planning and management implications. It is concluded that the proposed approach is very efficient for the analysis of pedestrian crossing behavior in urban areas.

How did the economic recession (2008–2010) influence traffic fatalities in OECD-countries?

This paper presents analyses of how the economic recession that started in 2008 has influenced the number of traffic fatalities in OECD countries. Previous studies of the relationship between economic recessions and changes in the number of traffic fatalities are reviewed. Based on these studies, a causal diagram of the relationship between changes of the business cycle and changes in the number of traffic fatalities is proposed. This causal model is tested empirically by means of multivariate analyses and analyses of accident statistics for Great Britain and Sweden. Economic recession, as indicated both by slower growth of, or decline of gross national product, and by increased unemployment is associated with an accelerated decline in the number of traffic fatalities, i.e. a larger decline than the long-term trend that is normal in OECD countries. The principal mechanisms bringing this about are a disproportionate reduction of driving among high-risk drivers, in particular young drivers and a reduction of fatality rate per kilometre of travel, probably attributable to changes in road user behaviour that are only partly observable. The total number of vehicle kilometres of travel did not change very much as a result of the recession. The paper is based on an ITF-report that presents the analyses in greater detail.

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.

Simulation of Pedestrians and Motorised Traffic: Existing Research and Future Challenges

The objective of this paper is the analysis of the state of the art in pedestrian simulation models and the identification of key issues for further research, with particular focus on the modelling of pedestrians and motorised traffic. A review and a comparative assessment of pedestrian simulation models are carried out, including macroscopic models, earlier meso-and miscosimulation models mostly in Cellular Automata and more recent Multi-Agent simulation models. The reviewed models cover a broad range of research topics: pedestrian flow and level of service, crowd dynamics and evacuations, route choice etc. However, pedestrian movement in urban areas and the interactions between pedestrians and vehicles have received notably less attention. A number of challenges to be addressed in future research are outlined: first, the need to and account for the hierarchical behavioural model of road users strategic / tactical / operational behaviour; second, the need for appropriate description and parameterization of vehicle and pedestrian networks and their crossing points; third, the need to exploit in the simulation models the results of statistical and probabilistic models, which offer valuable insight in the determinants of pedestrian behaviour. In each case, recent studies towards addressing these challenges are outlined.

Jam avoidance with autonomous systems

Many car-following models have been developed for jam avoidance in highways. Two mechanisms are used to improve the stability: feedback control with autonomous models and increasing of the interaction within cooperative ones. In this paper, we compare the linear autonomous and collective optimal velocity (OV) models. We observe that the stability is significantly increased by adding predecessors in interaction with collective models. Yet, autonomous and collective approaches are close when the speed difference term is taken into account. In the linear OV models tested, the autonomous models including speed difference are sufficient to maximise the stability.

Road, Traffic, and Human Factors of Pedestrian Crossing Behavior: Integrated Choice and Latent Variables Models

This study analyzed road, traffic, and human factors of pedestrian crossing behavior through the development of integrated choice and latent variables models. The analysis used recent research as a starting point, in which a two-stage approach was successfully tested, including a separate estimation of human factors and choice models. Data from a dedicated field survey were used: pedestrian field observations of road crossing behavior in different road and traffic scenarios were combined with a questionnaire on pedestrian attitudes, perceptions, motivations, and declared behaviors. The integrated choice and latent variables models were developed for four road types: major urban arterials, main roads, secondary roads, and residential roads. Results suggest that the effect of traffic conditions on pedestrian crossing choices was more important on main and secondary urban roads, whereas on major urban arterials and on residential roads it was nonsignificant. In regard to the effects of human factors, a risk latent variable was found to enhance the explanatory power of most of the models. This variable was estimated on the basis of different indicators in each case, reflecting a clear risk-taking tendency on major and main roads and an optimization tendency on minor roads. Overall, it is indicated that the integration of human factors in pedestrian crossing models provides meaningful and insightful results, and they may be advantageous compared with the two-stage approach.