A Multiple Criteria Decision‐Making Approach for Prioritizing Accident Hotspots in the Absence of Crash Data

Abstract

In an efficient transportation system, traffic safety is an important issue and it is influenced by many factors. In a country like Iran, until now safety improvements are mainly concentrated on road engineering activities, without much attention for vehicle technology or driving behaviour. One important aspect of road safety engineering activities is the so‐called treatment of hotspots or dangerous accident locations. Until recently, accident hotspots were identified and remedied by the esxperts’ personal judgements and a handful of statistics without taking into account other important factors such as geometric and traffic conditions of the road network. This paper therefore aims to define and identify the criteria for accident hotspots, then giving a value to each criterion in order to develop a model to prioritize accident hotspots when traffic accident data is not available. To do this, the ‘Delphi’ method has been adopted and a prioritization model is produced by the use of a ‘Multiple Criteria Decision‐Making’ method. The procedure is illustrated on a collection of 20 road sections in Iran. In addition, the model is validated against an existing database of road sections containing safe locations and hotspots. Finally, a sensitivity analysis is carried out on the proposed method.     

Safety outcomes of internationalization of domestic road haulage: a review of the literature

Abstract

The European Union (EU) promotes gradual lifting of restrictions on foreign hauliers involved in domestic road transport of goods (cabotage), and a major deregulation was scheduled in 2014. Due to complaints from several member states facing competition from new EU-countries with lower labour costs, this process was postponed until 2015. An important aspect related to such a deregulatory reform includes potential consequences for transport safety and accident risk factors. The main aims of the current paper are therefore to examine the potential transport safety outcomes of increasing internationalization of domestic road haulage, and to examine potential accident risk factors of foreign hauliers. This is done by reviewing the research literature. This paper shows that the heavy goods vehicle (HGV) accident risk varies with a factor of up to 10 in European countries, and that the accident risk of foreign HGVs is approximately two times higher than that of domestic HGVs in the studied European countries. The paper points to several risk factors and concludes that better data on accident risk and risk factors must be gathered in order to enable European authorities to correctly analyse and respond to this important traffic safety challenge.     

Feedback control strategy of a new car-following model based on reducing traffic accident rates

ABSTRACT

To reduce the traffic accident death rate effectively and alleviate the traffic congestion phenomenon, this study proposes a new type of car-following model under the influence of drivers’ time-varying delay response time. Based on Lyapunov function theory, this paper reduces the traffic accident rate problem to the stability issues of the new model. By constructing suitable Lyapunov functions and using the linear matrix inequality method, the stability problem of the new car-following model is studied. The model, under the action of the controller, can effectively restrain traffic congestion. Using the traffic accident rate model proposed by Solomon, compared with the car-following model without the controller, the model under the controller shows a stronger convergence. This also means that the traffic congestion phenomenon has been effectively suppressed while greatly reducing the mortality rate of traffic accidents.     

Selatm — a GIS based program for evaluating the safety benefits of local area traffic management schemes

Particular safety problems relate to traffic on local streets. Local Area Traffic Management (LATM) schemes are often implemented with the objective of counteracting these safety problems. One analytical difficulty in appraising the effectiveness of LATM in dealing with safety problems has been the ‘footloose’ nature of accident locations in a local street network. Seldom are there distinct ‘blackspot’ locations. An area‐wide approach is needed and the interaction between the system and arterial road network must be considered. The paper describes the development of a Safety Evaluation Method for Local Area Traffic Management (termed SELATM). It is a GIS‐based program for analysing accident patterns over time and the evaluation of the safety benefits of LATM schemes. The evaluation is perform at different network levels for various accident variables. The thrust of the program involved the integration of network data with data on accidents and the installed devices to generate summary accident statistics for the various network levels allowing for before and after comparison with a control area. This program as developed is applied to a LATM scheme at Enfield, a suburb in metropolitan Adelaide.     

Comparing prediction methods for maritime accidents

ABSTRACT

The purpose of maritime accident prediction is to reasonably forecast an accident occurring in the future. In determining the level of maritime traffic management safety, it is important to analyze development trends of existing traffic conditions. Common prediction methods for maritime accidents include regression analysis, grey system models (GM) and exponential smoothing. In this study, a brief introduction is provided that discusses the aforementioned prediction models, including the associated methods and characteristics of each analysis, which form the basis for an attempt to apply a residual error correction model designed to optimize the grey system model. Based on the results, in which the model is verified using two different types of maritime accident data (linear smooth type and random-fluctuation type, respectively), the prediction accuracy and the applicability were validated. A discussion is then presented on how to apply the Markov model as a way to optimize the grey system model. This method, which proved to be correct in terms of prediction accuracy and applicability, is explored through empirical analysis. Although the accuracy of the residual error correction model is usually higher than the accuracy of the original GM (1,1), the effect of the Markov correction model is not always superior to the original GM (1,1). In addition, the accuracy of the former model depends on the characteristics of the original data, the status partition and the determination method for the status transition matrix.     

Agricultural vehicles and sustainable safe road traffic: solving conflicts on arterial highways

Addressing the issues of traffic safety in rural areas presents a constant challenge. The mix of light and heavy vehicles and the considerable differences in speed among these traffic participants result in high risks and delays for the faster vehicles. Agricultural vehicles (AVs) in particular have such an impact on traffic, especially when using arterial highways. This paper reviews the problems of safety and delays that AVs cause on arterial highways, and the appropriate mitigation. The concept of 'sustainable safety' in The Netherlands focuses on these problems, because of the proposed construction of parallel roads alongside all arterial highways. However, Dutch accident statistics cannot justify the high costs for the construction of parallel roads alongside 7000 km of arterial highways. Delays experienced by fast traffic are another reason for separating AVs from other road users with parallel roads. Alternative measures alongside the arterial highway, such as passing bays, restricting AVs to travelling at off-peak only and improving the conspicuity of the AVs, may be more cost-effective ways of reducing delays and/or improving traffic safety on arterial highways. Another solution may be to eliminate the need for AVs to use the arterial highway by altering their routes. For this purpose, land reallocation projects (as practised in Holland) can provide a useful tool.     

Vehicle Positioning for Improving Road Safety

Abstract

Vehicle positioning is a key requirement for many safety applications. Active safety systems require precise vehicle positioning in order to assess the safety threats accurately, especially for those systems which are developed for warning/intervention in safety critical situations. When warning drivers of a local hazard (e.g. an accident site), accurate vehicle location information is important for warning the right driver groups at the right time. Global positioning system and digital maps have become major tools for vehicle positioning providing not only vehicle location information but also geometry preview of the road being used. Advances in wireless communication have made it possible for a vehicle to share its location information with other vehicles and traffic operation centres which greatly increases the opportunities to apply vehicle positioning technologies for improving road safety. This paper presents a state‐of‐the‐art review of vehicle positioning requirements for safety applications and vehicle positioning technologies. The paper also examines key issues relating to current and potential future applications of vehicle positioning technologies for improving road safety.     

Formal scientific research of traffic collision data utilizing GIS

This paper explores the tenuous link between speeding behavior and accident causation, one that has not been well established in the international literature to date. Taking advantage of established engineering conventions and formulae, we were able to set up an a priori hypothesis suitable for testing. Utilizing this formal scientific method (which GIS researchers have been criticised for not using) we establish a statistical link for this relationship. Our methodology can be used to support all police intervention strategies, including the controversial photo radar systems. The results from our research have been entered into a GIS in order to create a map for spatial display. This map illustrates the relative probability or risk of collision occurrence resulting from speeding at all intersections and interchanges within the scope of the study. It is suggested that this methodology could easily be maintained with periodic updates of data, thus creating a dynamic model from which to monitor traffic safety within the city. Furthermore, this model can be utilized to study specific strategies, allowing for the scrutiny of before, during and after effects. The study area is the entire city of Calgary, Alberta, Canada, and includes all traffic collisions occurring during the year of 1994.     

Traffic accident risk perception among drivers: a latent variable approach

ABSTRACT

Governments require decision tools to deal with road traffic accidents, a pandemic resulting in millions of deaths around the world. Evidence shows that human factors are one of the major causes of road accidents, and there is much interest in identifying variables that may have an impact on drivers’ perception of risk. To this aim, we design a stated choice experiment with eight hypothetical driving scenarios considering attributes that have been strongly associated with increased accident risks: (i) driving speed, (ii) driving the wrong way in a one-way street, (iii) overtaking on a bend, and (iv) driving under the influence of alcohol or drugs. Data from a sample of survey respondents are used to estimate a hybrid discrete choice model incorporating two latent variables, Driver Concentration and Safe Driving. Our results may contribute to the design of public policies geared to prevent accidents by encouraging safer driving behaviour.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

基于WOS数据库的文献计量分析的城市交通事故态势研究

为从宏观上了解交通事故的研究态势,利用文献计量法对WOS数据库收录的474篇文献进行数据可视化分析。研究发现,发文量历经了零阶段、稳定阶段和上升阶段;中国研究机构数量和发文量都位于世界第一;研究领域形成了由122位作者组成的核心作者群体;研究方向经历了以交通参与者、道路交通事故、交通事故安全为研究目的的变化;关键词分析得出该领域未来的研究热点将集中在交通事故安全、交通事故严重程度及交通事故影响三方面。     

道路交通事故发生机理与实例分析

交通事故发生机理是认识道路交通事故发生过程、交通事故预防和改善交通安全的基础。文章以道路交通系统为研究对象,分析道路交通事故的形成过程,将交通事故发生机理分为驾驶行为差错类事故发生机理、外部因素突变类事故发生机理、综合性事故发生机理三类,并在此基础上绘制了道路交通事故发生机理图,同时结合国道109线兰州八盘村路段进行了实例分析。     

A Microscopic Simulation Model for Incident Modeling in Urban Networks

Abstract

This paper reviews the main modules of an integrated system for incident management in real-time, -sim. A core to such a system is a microscopic simulator with extended abilities to model the temporal and spatial evolution of specified non-recurrent traffic conditions. The paper reviews the mathematical formulation of the car-following and lane-changing modules. The model is validated using a simulation-based approach. Concluding comments on the general validation process of the model are provided. The paper finally presents a sample of the accident patterns replicated by the model together with their implications for real world validation.     

Development of a delay model for unsignalized intersections applicable to traffic assignment

Abstract

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models.

Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods.

A comparison between delay models performed using different time intervals shows that the coefficients of determination, R ², increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.     

Locating inspection facilities in traffic networks: an artificial intelligence approach

Abstract

In order for traffic authorities to attempt to prevent drink driving, check truck weight limits, driver hours and service regulations, hazardous leaks from trucks, and vehicle equipment safety, we need to find answers to the following questions: (a) What should be the total number of inspection stations in the traffic network? and (b) Where should these facilities be located? This paper develops a model to determine the locations of uncapacitated inspection stations in a traffic network. We analyze two different model formulations: a single-objective optimization problem and a multi-objective optimization problem. The problems are solved by the Bee Colony Optimization (BCO) method. The BCO algorithm belongs to the class of stochastic swarm optimization methods, inspired by the foraging habits of bees in the natural environment. The BCO algorithm is able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are found to be acceptable.     

The authors in this issue

This paper considers the main road-traffic parameters that determine air pollution, i.e. the total volume of traffic, road speeds and the composition of the vehicle fleet. Changes in the amounts of pollutants emitted, and the importance of each of the three parameters, have been computed by using a traffic assignment model, which also represents emission factors. The types of policies that may be implemented to reduce the environmental impact of transport are then considered. The study demonstrates, for example, that the impact of a deterioration in traffic conditions is limited in comparison with the effect of forecast increases in traffic and improvements in the environmental performance of vehicles. As a consequence, if cities and urban transport are to achieve sustainable development, urban expansion must take place in a controlled way.     

Classification of driver-assistance systems according to their impact on road safety and traffic efficiency

The aim was to examine driver-assistance systems that seem to have a considerable potential for road safety and traffic efficiency improvement, and to propose an impact-oriented classification of these systems. A broad overview of a series of driver-assistance systems under development or in some cases already available is presented and it identifies the basic characteristics of each system and its expected impact on traffic efficiency and road safety. The latter is assessed on the basis of appropriate evaluation criteria. Expert judgement and literature evidence available are used in this context. This impact approach, in contrast with the usually adopted user or system-oriented approaches, allows for more appropriate identification of the priorities in the field of future research, development and promotion of driver-assistance systems. The proposed classification allocates the driver-assistance systems in four different categories on the basis of whether traffic efficiency and safety impact are high or low. This categorization reveals that 40% of the systems considered are expected to have a high safety and low traffic-efficiency impact, while only 15% is expected to have both impacts high.     

Cycle Network Planning: Towards a Holistic Approach Using Temporal Topology

ABSTRACT

Current cycle-network planning (CNP) at the local level tends to be dominated by a subjective-pragmatic approach in which it is only possible to consider a limited number of route alignment possibilities and development-sequencing scenarios. Although this method may produce acceptable results, it may also be true that the final design could be improved – and the construction efficiency could be enhanced – by a more comprehensive review of the available options in relation to the objectives of the intervention. Such objectives may include accident reduction, modal shift in favour of cycling, health benefits or strategic expansion of a network. This article presents work undertaken to demonstrate a holistic approach to CNP, based on a logically defined spatio-temporal model and allowing some semi-automated multiobjective optimization of network designs in a GIS-based decision-support system. The model is introduced briefly before a case study applying this model to development of a cycle network in a small town in northeast England is described. The results obtained from this case study and the implications of this research for cycle network planners are discussed.     

基于灰色理论的事故黑点改造效果分析

文章分析了汉宜高速公路黑点路段事故原因,提出黑点路段的具体整治措施,并结合汉宜高速公路改造后的事故特征,运用灰色预测模型,预测交通事故进一步的变化态势,结果表明:交通事故逐年减少,改造效果明显.     

Urban single-lane roundabouts: A new analytical approach using multi-criteria and simultaneous multi-objective optimization of geometry design,efficiency and safety

Building safe and effective roundabouts requires optimizing traffic (operational) efficiency (TE) and traffic safety (TS) while taking into account geometric factors, traffic characteristics and local constraints. Most existing simulation-based optimization models do not simultaneously optimize all these factors. To capture the relationship among geometry, efficiency and safety, we put forward a model formulation in this paper. We present a new multi-criteria and simultaneous multi-objective optimization (MOO) model approach to optimize geometry, TE and TS of urban unsignalized single-lane roundabouts. To the best of our knowledge, this is the first model that uses the multi-criteria decision-making method known as analytic hierarchy process to evaluate and rank traffic parameters and geometric elements of urban single-lane roundabouts. The model was built based on comprehensive review of the research literature and existing roundabout simulation software, a field survey of 61 civil and traffic expert engineers in Croatia, and field studies of roundabouts in the Croatian capital city of Zagreb. We started from the basis of Kimber’s capacity model, HCM2010 serviceability model, and Maycock and Hall's accident prediction model, which we extended by adding sensitivity analysis and powerful MOO procedures of the bounded objective function method and interactive optimization. Preliminary validation of the model was achieved by identifying the optimal and most robust of three geometric alternatives (V.1-V.3) for an unsignalized single-lane roundabout in Zagreb, Croatia. The geometric parameters in variant V.1 had significantly higher values than in the existing design V.0, while approaches 1 and 3 in variant V.2 were enlarged as much as possible within allowed spatial limits and Croatian guidelines, reflecting their higher traffic demand. Sensitivity analysis indicated that variant V.2 showed the overall highest TE and TS across the entire range of traffic flow demand and pedestrian crossing flow demand at approaches. At the same time, the number of predicted traffic accidents was similar for all three variants, although it was lowest overall for V.2. The similarity in predicted accident frequency for the three variants suggests that V.2 provides the greatest safety within the predefined constraints and parameter ranges explored in our study. These preliminary results suggest that the proposed model can optimize geometry, TE and TS of urban single-lane roundabouts.