A game-theoretic approach for modelling pedestrian-vehicle conflict resolutions in uncontrolled traffic environments.

The interactions of motorised vehicles with pedestrians have always been a concern in traffic safety. The major threat to pedestrians comes from the high level of interactions imposed in uncontrolled traffic environments, where road users have to compete over the right of way. In the absence of traffic management and control systems in such traffic environments, road users have to negotiate the right of way while avoiding conflict. Furthermore, the high level of movement freedom and agility of pedestrians, as one of the interactive parties, can lead to exposing unpredictable behaviour on the road. Traffic interactions in uncontrolled mixed traffic environments will become more challenging by fully/partially automated driving systems' deployment, where the intentions and decisions of interacting agents must be predicted/detected to avoid conflict and improve traffic safety and efficiency. This study aims to formulate a game-theoretic approach to model pedestrian interactions with passenger cars and light vehicles (two-wheel and three-wheel vehicles) in uncontrolled traffic settings. The proposed models employ the most influencing factors in the road user's decision and choice of strategy to predict their movements and conflict resolution strategies in traffic interactions. The models are applied to two data sets of video recordings collected in a shared space in Hamburg and a mid-block crossing area in Surat, India, including the interactions of pedestrians with passenger cars and light vehicles, respectively. The models are calibrated using the identified conflicts between users and their conflict resolution strategies in the data sets. The proposed models indicate satisfactory performances considering the stochastic behaviour of road users - particularly in the mid-block crossing area in India - and have the potential to be used as a behavioural model for automated driving systems.

Driver distraction and in-vehicle interventions: A driving simulator study on visual attention and driving performance.

Driving simulator studies are popular means to investigate driving behaviour in a controlled environment and test safety-critical events that would otherwise not be possible in real-world driving conditions. While several factors affect driving performance, driving distraction has been emphasised as a safety-critical issue across the globe. In this context, this study explores the impact of distraction imposed by mobile phone usage, i.e., writing and reading text messages, on driver behaviour. As part of the greater i-DREAMS project, this study uses a car driving simulator experimental design in Germany to investigate driver behaviour under various conditions: (I) monitoring scenario representing normal driving conditions, (II) intervention scenario in which drivers receive fixed timing in-vehicle intervention in case of unsafe driving manoeuvres, and (III) distraction scenario in which drivers receive in-vehicle interventions based on task completion capability, where mobile phone distraction is imposed. Besides, eye-tracking glasses are used to further explore drivers' attention allocation and eye movement behaviour. This research focuses on driver response to risky traffic events (i.e., potential pedestrian collisions, and tailgating) and the impact of distraction on driving performance, by analysing a set of eye movement and driving performance measures of 58 participants. The results reveal a significant change in drivers' gaze patterns during the distraction drives with significantly higher gaze points towards the i-DREAMS intervention display (the utilised advanced driver assistance systems in this study). The overall statistical analysis of driving performance measures suggests nearly similar impacts on driver behaviour during distraction drives; a higher deviation of lateral positioning was noted irrespective of the event risk levels and lower longitudinal acceleration rates were observed for pedestrian collisions and non-critical events during distracted driving.

Application of naturalistic driving data: A systematic review and bibliometric analysis.

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

Developing a new real-time traffic safety management framework for urban expressways utilizing reinforcement learning tree.

One of the main objectives of an urban traffic control system is to reduce the crash frequency and the loss caused by these crashes on urban expressways. Real-time crash risk prediction (RTCRP) is an essential technique to identify crash precursors so as to take proactive measures to smooth traffic fluctuations. In addition, automatic incident detection (AID) is another important approach to timely detect an incident so as to design countermeasures that reduce any negative impacts on traffic dynamics. With the introduction of disruptive technologies in transport, highly disaggregated large datasets have started to emerge for modelling while existing modelling techniques utilized in RTCRP and AID may not be able to accurately predict traffic crashes in real-time. Therefore, this paper proposes a state-of-the-art reinforcement learning tree (RLT) approach to develop RTCRP model and automatic crash detection (ACD) model similar to AID, and further utilizes it to build a real-time traffic safety management framework for urban expressways with the input of online traffic data streaming. Recorded traffic flow data and historical crash data were extracted and integrated to develop and implement both RTCRP models and ACD models. The prediction results were compared with the frequently used logistic regression (LR), support vector machine (SVM) and deep neural network (DNN) and a sensitivity analysis for variable effects was conducted. The results confirm that RLT outperforms LR, SVM and DNN in developing RTCRP and ACD models. At the cost of 10% false-alarm rate, about 96% of the crashes were predicted or detected correctly by the proposed framework. The results also indicate that: i) collecting more data is helpful to improve the predictive performance and approximatively a minimum sample size of 20 observations per variable is reasonable for training RLT models; and ii) obtaining more factors is beneficial to improve the predictive performance. With the RLT approach, it was demonstrated that selected important variables also have the capability to provide reasonable predictive performance.

Development of a conflict risk evaluation model to assess pedestrian safety in interaction with vehicles.

Interactions of motorised vehicles with pedestrians have always been a concern in traffic safety. The major threat to pedestrians comes from the high level of interactions imposed in uncontrolled traffic environments, where road users have to compete over the right of way. The interactions become more complex with the variety of user types and their available conflict resolution strategies. In this research, a conflict risk evaluation model is developed to assess the safety level of pedestrian conflict with other road users. Surrogate safety indicators are employed to measure road users' temporal and spatial proximity during a conflict. The thresholds are determined through the application of various methods (i.e., intersection point, p-tile, maximum between-class variance, and minimum cross-entropy method) to separate potential critical conflicts against normal traffic conditions, on the basis of the conflict risk evaluation model. An F-score method is used to select the optimal threshold given by various applied methods. Two data sets of shared space and mid-block were used to develop and validate conflict risk evaluation models for the interaction of pedestrians with vehicles (passenger cars) and light vehicles (two- or three-wheel vehicles) separately. The proposed model can potentially be used as a real-time conflict risk evaluation model to improve traffic safety.

Prediction of rear-end conflict frequency using multiple-location traffic parameters.

Traffic conflicts are heavily correlated with traffic collisions and may provide insightful information on the failure mechanism and factors that contribute more towards a collision. Although proactive traffic management systems have been supported heavily in the research community, and autonomous vehicles (AVs) are soon to become a reality, analyses are concentrated on very specific environments using aggregated data. This study aims at investigating -for the first time- rear-end conflict frequency in an urban network level using vehicle-to-vehicle interactions and at correlating frequency with the corresponding network traffic state. The Time-To-Collision (TTC) and Deceleration Rate to Avoid Crash (DRAC) metrics are utilized to estimate conflict frequency on the current network situation, as well as on scenarios including AV characteristics. Three critical conflict points are defined, according to TTC and DRAC thresholds. After extracting conflicts, data are fitted into Zero-inflated and also traditional Negative Binomial models, as well as quasi-Poisson models, while controlling for endogeneity, in order to investigate contributory factors of conflict frequency. Results demonstrate that conflict counts are significantly higher in congested traffic and that high variations in speed increase conflicts. Nevertheless, a comparison with simulated AV traffic and the use of more surrogate safety indicators could provide more insight into the relationship between traffic state and traffic conflicts in the near future.

A global horizon scan of the future impacts of robotics and autonomous systems on urban ecosystems.

Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems (RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human-nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits.

Explaining demand patterns during COVID-19 using opportunistic data: a case study of the city of Munich.

Background: The COVID-19 pandemic is a new phenomenon and has affected the population's lifestyle in many ways, such as panic buying (the so-called "hamster shopping"), adoption of home-office, and decline in retail shopping. For transportation planners and operators, it is interesting to analyze the spatial factors' role in the demand patterns at a POI (Point of Interest) during the COVID-19 lockdown viz-a-viz before lockdown. Data and Methods: This study illustrates a use-case of the POI visitation rate or popularity data and other publicly available data to analyze demand patterns and spatial factors during a highly dynamic and disruptive event like COVID-19. We develop regression models to analyze the correlation of the spatial and non-spatial attributes with the POI popularity before and during COVID-19 lockdown in Munich by using lockdown (treatment) as a dummy variable, with main and interaction effects. Results: In our case-study for Munich, we find consistent behavior of features like stop distance and day-of-the-week in explaining the popularity. The parking area is found to be correlated only in the non-linear models. Interactions of lockdown with POI type, stop-distance, and day-of-the-week are found to be strongly significant. The results might not be transferable to other cities due to the presence of different city-specific factors. Conclusion: The findings from our case-study provide evidence of the impact of the restrictions on POIs and show the significant correlation of POI-type and stop distance with POI popularity. These results suggest local and temporal variability in the impact due to the restrictions, which can impact how cities adapt their transport services to the distinct demand and resulting mobility patterns during future disruptive events.

Exploring the temporal stability of global road safety statistics.

Given the importance of rigorous quantitative reasoning in supporting national, regional or global road safety policies, data quality, reliability, and stability are of the upmost importance. This study focuses on macroscopic properties of road safety statistics and the temporal stability of these statistics at a global level. A thorough investigation of two years of measurements was conducted to identify any unexpected gaps that could highlight the existence of inconsistent measurements. The database used in this research includes 121 member countries of the United Nation (UN-121) with a population of at least one million (smaller country data shows higher instability) and includes road safety and socioeconomic variables collected from a number of international databases (e.g. WHO and World Bank) for the years 2010 and 2013. For the fulfillment of the earlier stated goal, a number of data visualization and exploratory analyses (Hierarchical Clustering and Principal Component Analysis) were conducted. Furthermore, in order to provide a richer analysis of the data, we developed and compared the specification of a number of Structural Equation Models for the years 2010 and 2013. Different scenarios have been developed, with different endogenous variables (indicators of mortality rate and fatality risk) and structural forms. The findings of the current research indicate inconsistency phenomena in global statistics of different instances/years. Finally, the results of this research provide evidence on the importance of careful and systematic data collection for developing advanced statistical and econometric techniques and furthermore for developing road safety policies.

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.

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.

Explaining the road accident risk: weather effects.

This research aims to highlight the link between weather conditions and road accident risk at an aggregate level and on a monthly basis, in order to improve road safety monitoring at a national level. It is based on some case studies carried out in Work Package 7 on "Data analysis and synthesis" of the EU-FP6 project "SafetyNet-Building the European Road Safety Observatory", which illustrate the use of weather variables for analysing changes in the number of road injury accidents. Time series analysis models with explanatory variables that measure the weather quantitatively were used and applied to aggregate datasets of injury accidents for France, the Netherlands and the Athens region, over periods of more than 20 years. The main results reveal significant correlations on a monthly basis between weather variables and the aggregate number of injury accidents, but the magnitude and even the sign of these correlations vary according to the type of road (motorways, rural roads or urban roads). Moreover, in the case of the interurban network in France, it appears that the rainfall effect is mainly direct on motorways--exposure being unchanged, and partly indirect on main roads--as a result of changes in exposure. Additional results obtained on a daily basis for the Athens region indicate that capturing the within-the-month variability of the weather variables and including it in a monthly model highlights the effects of extreme weather. Such findings are consistent with previous results obtained for France using a similar approach, with the exception of the negative correlation between precipitation and the number of injury accidents found for the Athens region, which is further investigated. The outlook for the approach and its added value are discussed in the conclusion.

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.

On statistical inference in time series analysis of the evolution of road safety.

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research.

Study and development of reversed-phase HPLC systems for the determination of 2-imidazolines in the presence of preservatives in pharmaceutical preparations.

Different HPLC chromatographic systems were investigated on a C18 ACE 5 pm, 150 x 4.6 mm id column for the determination of tymazoline, tramazoline, and antazoline, with either naphazoline or xylometazoline, in commercial preparations. For the development and optimization of the systems, a Response Surface Method (r=0.925-0.980) was used to illustrate the changes in k as a function of pH values and different salt concentrations. The simultaneous separation of 2-imidazolines was accomplished at 40 degrees C with 0.01 M ammonium acetate-methanol (50+50, v/v, pH 6.0) mobile phase at a flow rate of 1.2 mL/min. In order to deal with the usual coexistence of 2-imidazolines with benzethonium and benzalkonium chloride preservatives, it was necessary to use another chromatographic system, 0.01 M ammonium acetate-methanol (50+50, v/v) mobile phase on a cyano ACE 5 pm, 150 x 4.6 mm id column. As part of a more thorough theoretical investigation, a partial least-squares (PLS) technique was used for modeling the RP-HPLC retention data. The model was based on molecular structure descriptors of the analytes' X variables and on their retention time (Log K) Y. The goodness of fit was estimated by the PLS correlation coefficient (r2) and root mean square error of estimation values, which were 0.994 and 0.0479, respectively.

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).