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.@en

The objective of this research is the comparative analysis of observed and declared behaviour of pedestrians as regards road crossing in urban areas. A field survey was carried out, in which a panel of 75 young and middle-aged pedestrians (out of which 40 males) were asked to take 8 short walking trips (each one corresponding to a different walking and crossing scenario and involving one road crossing) in the Athens city centre in Greece. This allowed to record their crossing behaviour in different road and traffic conditions, including residential roads, main urban roads and major urban arterials. The same individuals were asked to fill in a questionnaire on their crossing behaviour and preferences at different road and traffic environments, as well as other related questions concerning their travel motivations, their mobility characteristics, their risk perceptions and preferences etc. A comparative analysis of their declared and observed crossing behaviour was carried out. More specifically, for each pedestrian, the rate of mid-block crossing and diagonal crossing during the walking tasks was calculated for the different road and traffic conditions. These were compared to their questionnaire responses on their crossing behaviour in different road and traffic conditions. The results suggest that, overall, pedestrians observed behaviour is in accordance with their declared behaviour. However, there is a non-negligible share of pedestrians, whose observed and declared behaviour were discordant, either at specific road and traffic conditions or overall. For instance, there were pedestrians who declared that they never cross at mid-block on major urban road but did so during the survey. Moreover, there were pedestrians who declared high frequency of mid-block crossing, but did not implement these crossing practices during the survey. The degree of discordance between pedestrian observed and declared behaviour was further analysed in relation to pedestrian demographics. A weak tendency was identified for female pedestrians to have more discordance between observed and declared crossing behaviour on residential roads, and the same was the case for young pedestrians in all road and traffic conditions. Overall, the results suggest that, while most pedestrians appear to have consistent declared and observed behaviour, there may all deviate from their general “profile” under specific conditions.@en

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.@en

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. @en

Pedestrians are mainly exposed to the risk of road accident when crossing a road in urban areas. Traditionally in the road safety field, the risk of accident for pedestrian is estimated as a rate of accident involvement per unit of time spent on the road network. The objective of this research is to develop an approach of accident risk based on the concept of risk exposure used in environmental epidemiology, such as in the case of exposure to pollutants. This type of indicator would be useful for comparing the effects of urban transportation policy scenarios on pedestrian safety. The first step is to create an indicator of pedestrians’ exposure, which is based on motorised vehicles’ “concentration” by lane and also takes account of traffic speed and time spent to cross. This is applied to two specific micro-environments: junctions and mid-block locations. A model of pedestrians’ crossing behaviour along a trip is then developed, based on a hierarchical choice between junctions and mid-block locations and taking account of origin and destination, traffic characteristics and pedestrian facilities. Finally, a complete framework is produced for modelling pedestrians’ exposure in the light of their crossing behaviour. The feasibility of this approach is demonstrated on an artificial network and a first set of results is obtained from the validation of the models in observational studies.@en

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.@en

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.@en

The analysis of spatial dependences among road safety outcomes (road accidents and fatalities) has received increased attention in recent research with interesting findings at national or European level. The objective of this research is the modelling of the spatial variation of road accidents and fatalities in Greece. Greece is selected as a country for which spatial effects have not been previously explored, and also as a country presenting some spatial particularity (i.e. loose spatial structure with many clusters of islands), making such an analysis interesting. For that purpose, Nuts-3 road accident and fatalities risk rates are used for the development of conditional auto-regressive (Car) and Car-convolution spatial models. Moreover, two types of neighbourhood structures of regional road safety data are tested: a basic structure defined according to the road connections between counties and an extended structure defined on the basis of both road and ferry connections between counties. The results suggest that the basic spatial structure accounts for an important part of the variation in road accident rates in the Greek counties, revealing a pattern of risk increase from northern to southern Greece. Spatial effects are also identifiable when considering the extended spatial structure, without, however explaining a larger part of the overall variation compared to the basic structure. Finally, it is shown that the effect of a key explanatory variable of road safety in Greece, namely alcohol enforcement, would have been quite overestimated if spatial effects were not taken into account in the models.@en

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.@en

The objective of this research is the development of pedestrian crossing choice models on the basis of road, traffic and human factors. For that purpose, a field survey was carried out, in which a panel of 75 pedestrians were asked to take 8 short walking trips (each one corresponding to a different walking and crossing scenario) in the Athens city centre in Greece, allowing to record their crossing behaviour in different road and traffic conditions. The same individuals were asked to fill in a questionnaire on their travel motivations, their mobility characteristics, their risk perceptions and preferences with respect to walking and road crossing, their opinion on drivers, etc. The walking and crossing scenarios’ data were used to develop mixed sequential logit models of pedestrian behaviour on the basis of road and traffic characteristics. The modelling results showed that pedestrian crossing choices are significantly affected by road type, traffic flow and traffic control. The questionnaire data were used to estimate human factors (components) of pedestrian crossing behaviour by means of principal component analysis. The results showed that three components of pedestrian crossing behaviour emerge, namely a “risk-taking and optimisation” component reflecting the tendency to cross at mid-block in order to save time, etc., a “conservative” component, concerning individuals with increased perceived risk of mid-block crossing, who also appear to be frequent public transport users, and a “pedestrian for pleasure” component, bringing together frequent pedestrians, walking for health or pleasure, etc. The introduction of these components as explanatory variables into the choice models resulted in improvement of the modelling results, indicating that human factors have additional explanatory power over road and traffic factors of pedestrian behaviour. Therefore, the development of integrated choice and latent variables models appears to be an appropriate field for further research.@en