快速路交通流异常数据判断算法研究及实证

对快速路交通流数据进行异常数据判断,有利于避免使用异常数据带来的损失,提高信息利用的有效性。笔者分别根据逻辑推理、交通流的重复性和连续性以及交通流变量之间的机理分析提出了3种判断快速路交通流异常数据的算法,并讨论了这3种算法之间的集成。利用北京快速路实测数据对算法进行了验证,验证结果表明该算法基本是有效的。     

Probabilistic Linkage of Police and Emergency Department Sources of Information on Motor-Vehicle Injury Cases: a Proposal for Improvement

Data on traffic injury cases that includes both the crash circumstances and the health outcome is a key tool for creating appropriate motor-vehicle injury prevention policies. The aim of this study was to explore the feasibility of the probabilistic linkage of police and emergency department data relating to motor-vehicle injury cases in Barcelona. A probabilistic linkage process was performed using local police reports (n = l2,481) and hospital emergency department data (n = 16,733) for all traffic injury cases that occurred in 1997. In almost two out of every three matched pairs for the injured person, at least five of the seven variables coincided, and in 7.3% of the cases all variables coincided. This work has allowed the empirical development of combined methods for the linkage of police and healthcare sources of information at the local level, which are the preliminary steps towards the construction of a data base that would include full information on the circumstances and the consequences of motor-vehicle crashes.     

Crash data analysis: collective vs. individual crash level approach

INTRODUCTION: Traffic safety literature has traditionally focused on identification of location profiles where "more crashes are likely to occur" over a period of time. The analysis involves estimation of crash frequency and/or rate (i.e., frequency normalized based on some measure of exposure) with geometric design features (e.g., number of lanes) and traffic characteristics (e.g., Average Annual Daily Traffic [AADT]) of the roadway location. In the recent past, a new category of traffic safety studies has emerged, which attempts to identify locations where a "crash is more likely to occur." The distinction between the two groups of studies is that the latter group of locations would change based on the varying traffic patterns over the course of the day or even within the hour. METHOD: Hence, instead of estimation of crash frequency over a period of time, the objective becomes real-time estimation of crash likelihood. The estimation of real-time crash likelihood has a traffic management component as well. It is a proactive extension to the traditional approach of incident detection, which involves analysis of traffic data recorded immediately after the incident. The units of analysis used in these studies are individual crashes rather than counts of crashes. RESULTS: In this paper, crash data analysis based on the two approaches, collective and at individual crash level, is discussed along with the advantages and shortcomings of the two approaches.     

灾害多发地域社会关系网络建构路径的实证研究——以云南省大关县翠华镇为例

为了更好地构建典型灾害多发地域的社会关系网络,进而为相关应急管理工作提供支撑,采用灾害多发地域档案数据与问卷数据验证相结合的方法,提出灾害多发地域社会关系网络的建构路径;选取位于古滑坡带上的云南省大关县翠华镇为例进行说明,通过地理信息系统（Arcgis）分析当地档案数据与问卷获取数据的空间对象分布特征,并以问卷调查的局部数据验证相关档案数据的可靠性,最后,在翠华镇社会关系网络基础上,分析该灾害多发地域的应急预警信息传播特点,提出“分区域预警,以户为单位传播预警信息“的措施建议。研究结果表明:通过档案数据与问卷数据相结合方法构建灾害多发地域社会关系网络的方法可行,能够为相关应急管理措施提供参考和支撑。     

Handheld-Assisted Field Data Collection for Occupational Risk Assessment

A computerized handheld procedure is presented in this paper. It is intended as a database complementary tool, to enhance prospective risk analysis in the field of occupational health. The Pendragon forms software (version 3.2) has been used to implement acquisition procedures on Personal Digital Assistants (PDAs) and to transfer data to a computer in an MS-Access format. The data acquisition strategy proposed relies on the risk assessment method practiced at the Institute of Occupational Health Sciences (IST). It involves the use of a systematic hazard list and semi-quantitative risk assessment scales. A set of 7 modular forms has been developed to cover the basic need of field audits. Despite the minor drawbacks observed, the results obtained so far show that handhelds are adequate to support field risk assessment and follow-up activities. Further improvements must still be made in order to increase the tool effectiveness and field adequacy.     

Investigation of powered 2-wheeler accident involvement in urban arterials by considering real-time traffic and weather data

Objective: Understanding the various factors that affect accident risk is of particular concern to decision makers and researchers. The incorporation of real-time traffic and weather data constitutes a fruitful approach when analyzing accident risk. However, the vast majority of relevant research has no specific focus on vulnerable road users such as powered 2-wheelers (PTWs). Moreover, studies using data from urban roads and arterials are scarce. This study aims to add to the current knowledge by considering real-time traffic and weather data from 2 major urban arterials in the city of Athens, Greece, in order to estimate the effect of traffic, weather, and other characteristics on PTW accident involvement.

Methods: Because of the high number of candidate variables, a random forest model was applied to reveal the most important variables. Then, the potentially significant variables were used as input to a Bayesian logistic regression model in order to reveal the magnitude of their effect on PTW accident involvement.

Results: The results of the analysis suggest that PTWs are more likely to be involved in multivehicle accidents than in single-vehicle accidents. It was also indicated that increased traffic flow and variations in speed have a significant influence on PTW accident involvement. On the other hand, weather characteristics were found to have no effect.

Conclusions: The findings of this study can contribute to the understanding of accident mechanisms of PTWs and reduce PTW accident risk in urban arterials.     

A comprehensive assessment system for the maritime traffic environment

It is desirable when drawing up a maritime traffic environmental project or a project for consolidating port and harbour facilities, that the safety of the maritime environment for shipping-traffic is assessed quantitatively, and that the rationale of a given project and the proposed safety measures are evaluated in an objective manner.In this report, the system flow and procedures to integrally assess the safety of the maritime traffic environment by systematically combining various simulation techniques are discussed first.Subsequently, the quantitative risk evaluation procedure for collisions of ships in waters with a high traffic density, among the assessment procedures above, and an outline of the shipping traffic flow simulation capable of reproducing ships′ movements, are described. In evaluating the results of simulations, a method introducing Subjective Judgement values (SJ-values) as indexes manifesting the subjective degree of danger felt by shiphandlers, is shown. The SJ-values were experimentally determined from simulator experiments using parameters representing the relation ship with other ships proceeding in the vicinity.Finally, some of the results of studies conducted for the recent construction of new LNG berths in Tokyo Bay are introduced as an example of practical safety assessment in this field.     

A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks

Introduction

This study describes a method for reducing the number of variables frequently considered in modeling the severity of traffic accidents. The method's efficiency is assessed by constructing Bayesian networks (BN).

Method

It is based on a two stage selection process. Several variable selection algorithms, commonly used in data mining, are applied in order to select subsets of variables. BNs are built using the selected subsets and their performance is compared with the original BN (with all the variables) using five indicators. The BNs that improve the indicators’ values are further analyzed for identifying the most significant variables (accident type, age, atmospheric factors, gender, lighting, number of injured, and occupant involved). A new BN is built using these variables, where the results of the indicators indicate, in most of the cases, a statistically significant improvement with respect to the original BN.

Conclusions

It is possible to reduce the number of variables used to model traffic accidents injury severity through BNs without reducing the performance of the model.

Impact on Industry

The study provides the safety analysts a methodology that could be used to minimize the number of variables used in order to determine efficiently the injury severity of traffic accidents without reducing the performance of the model.     

Disaggregated traffic conditions and road crashes in urban signalized intersections

Introduction: Road safety studies in signalized intersections have been performed extensively using annually aggregated traffic variables and crash frequencies. However, this type of aggregation reduces the strength of the results if variables that oscillate over the course of the day are considered (speed, traffic flow, signal cycle length) because average indicators are not able to describe the traffic conditions preceding the crash occurrence. This study aims to explore the relationship between traffic conditions aggregated in 15-min intervals and road crashes in urban signalized intersections. Method: First, an investigation of the reported crash times in the database was conducted to obtain the association between crashes and their precursor conditions. Then, 4.1 M traffic condition intervals were consolidated and grouped using a hierarchical clustering technique. Finally, charts of the frequency of crashes per cluster were explored. Results: The main findings suggest that high vehicular demand conditions are related to an increase in property damage only (PDO) crashes, and an increase in the number of lanes is linked to more PDO and injury crashes. Injury crashes occurred in a wide range of traffic conditions, indicating that a portion of these crashes were due to speeding, while the other fraction was associated with the vulnerability of road users. Traffic conditions with: (a) low vehicular demand and a long cycle length and (b) high vehicular demand and a short cycle length were critical in terms of PDO and injury crashes. Practical Applications: The use of disaggregated data allowed for a stronger evaluation of the relationship between road crashes and variables that oscillate over the course of the day. This approach also permits the development of real-time risk management strategies to mitigate the frequency of critical traffic conditions and reduce the likelihood of crashes.     

A conceptual framework to understand the role of built environment on traffic safety

IntroductionMany U.S. cities have adopted the Vision Zero strategy with the specific goal of eliminating traffic-related deaths and injuries. To achieve this ambitious goal, safety professionals have increasingly called for the development of a safe systems approach to traffic safety. This approach calls for examining the macrolevel risk factors that may lead road users to engage in errors that result in crashes. This study explores the relationship between built environment variables and crash frequency, paying specific attention to the environmental mediating factors, such as traffic exposure, traffic conflicts, and network-level speed characteristics. Methods: Three years (2011–2013) of crash data from Mecklenburg County, North Carolina, were used to model crash frequency on surface streets as a function of built environment variables at the census block group level. Separate models were developed for total and KAB crashes (i.e., crashes resulting in fatalities (K), incapacitating injuries (A), or non-incapacitating injuries (B)) using the conditional autoregressive modeling approach to account for unobserved heterogeneity and spatial autocorrelation present in data. Results: Built environment variables that are found to have positive associations with both total and KAB crash frequencies include population, vehicle miles traveled, big box stores, intersections, and bus stops. On the other hand, the number of total and KAB crashes tend to be lower in census block groups with a higher proportion of two-lane roads and a higher proportion of roads with posted speed limits of 35 mph or less. Conclusions: This study demonstrates the plausible mechanism of how the built environment influences traffic safety. The variables found to be significant are all policy-relevant variables that can be manipulated to improve traffic safety. Practical Applications: The study findings will shape transportation planning and policy level decisions in designing the built environment for safer travels.     

Society's aggression level as a predictor of traffic fatality rate

This research investigated the relationship of violence/aggression and other societal variables to traffic accidents. In the first of two studies, multiple regression was applied to 1977 data from each of the 50 states and the District of Columbia. Traffic fatalities per registered vehicle was the dependent variable. The independent variables were homicide rate, suicide rate, fatality rate from other causes, unemployment rate, personal income, density of physicians, alcohol consumption, motor vehicles per capita, road mileage per vehicle, sex and age distribution of drivers, and attained education. The main finding was that the homicide rate (but not the suicide rate) predicted the traffic fatality rate; additional significant predictors were the proportion of young drivers and the fatality rate from non-motor-vehicle accidents. The two primary predictors (homicides and young drivers) accounted for 64 % of the variance of traffic fatalities. In the second study, validation was performed by using the 1977 regression coefficients to estimate 1978 traffic fatalities. The results indicate that when the 1977 regression coefficients were applied to the 1978 values for homicides and young drivers, they accounted for 49 % of the variance of the 1978 traffic fatalities. The findings are discussed in terms of how society's violence/aggression may contribute to traffic accidents.     

Safety assessment of automated vehicles: how to determine whether we have collected enough field data?

Abstract

Objective: The amount of collected field data from naturalistic driving studies is quickly increasing. The data are used for, among others, developing automated driving technologies (such as crash avoidance systems), studying driver interaction with such technologies, and gaining insights into the variety of scenarios in real-world traffic. Because data collection is time consuming and requires high investments and resources, questions like “Do we have enough data?,” “How much more information can we gain when obtaining more data?,” and “How far are we from obtaining completeness?” are highly relevant. In fact, deducing safety claims based on collected data—for example, through testing scenarios based on collected data—requires knowledge about the degree of completeness of the data used. We propose a method for quantifying the completeness of the so-called activities in a data set. This enables us to partly answer the aforementioned questions.

Method: In this article, the (traffic) data are interpreted as a sequence of different so-called scenarios that can be grouped into a finite set of scenario classes. The building blocks of scenarios are the activities. For every activity, there exists a parameterization that encodes all information in the data of each recorded activity. For each type of activity, we estimate a probability density function (pdf) of the associated parameters. Our proposed method quantifies the degree of completeness of a data set using the estimated pdfs.

Results: To illustrate the proposed method, 2 different case studies are presented. First, a case study with an artificial data set, of which the underlying pdfs are known, is carried out to illustrate that the proposed method correctly quantifies the completeness of the activities. Next, a case study with real-world data is performed to quantify the degree of completeness of the acquired data for which the true pdfs are unknown.

Conclusion: The presented case studies illustrate that the proposed method is able to quantify the degree of completeness of a small set of field data and can be used to deduce whether sufficient data have been collected for the purpose of the field study. Future work will focus on applying the proposed method to larger data sets. The proposed method will be used to evaluate the level of completeness of the data collection on Singaporean roads, aimed at defining relevant test cases for the autonomous vehicle road approval procedure that is being developed in Singapore.     

Investigating the effect of geometric dimensions of median traffic barriers on crashes: Crash analysis of interstate roads in Wyoming using actual crash datasets

IntroductionDespite the numerous safety studies done on traffic barriers’ performance assessment, the effect of variables such as traffic barrier’s height has not been identified considering a comprehensive actual crash data analysis. This study seeks to identify the impact of geometric variables (i.e., height, post-spacing, sideslope ratio, and lateral offset) on median traffic barriers’ performance in crashes on interstate roads.MethodGeometric dimensions of over 110 miles median traffic barriers on interstate Wyoming roads were inventoried in a field survey between 2016 and 2018. Then, the traffic barrier data collected was combined with historical crash records, traffic volume data, road geometric characteristics, and weather condition data to provide a comprehensive dataset for the analysis. Finally, an ordered logit model with random-parameters was developed for the severity of traffic barrier crashes. Based on the results, traffic barrier’s height was found to impact crash severity.ResultsCrashes involving cable barriers with a height between 30″ and 42″ were less severe than other traffic barrier types, while concrete barriers with a height shorter than 32″ were more likely involved with severe injury crashes. As another important finding, the post-spacing of 6.1–6.3 ft. was identified as the least severe range in W-beam barriers.Practical applicationsThe results show that using flare barriers should reduce the number of crashes compared to parallel barriers.     

Development of conflict techniques for left-turn and cross-traffic at protected left-turn signalized intersections

To design comprehensive traffic safety measures, the risk of the occurrence of traffic accidents must be objectively evaluated. It is difficult, however, to evaluate the effects of traffic measures in terms of the change in the number of traffic accidents, because traffic accidents are unpredictable and rare events. Therefore, this study used a traffic conflict technique to objectively evaluate traffic safety at intersections. In this study, traffic conflicts that occur at the time of a signal violation were differentiated according to type, and a traffic conflict technology was developed that would consider the severity of each type of conflict. To apply this method, traffic images were collected from the Jungja intersection and the Naejung intersection in Sungnam City, Korea and were analyzed using traffic data by means of image procedure technology. The analyses showed that of the four levels of differentiated conflict conditions, the Level 1 condition occurred most often, followed by Level 2, which occurred much less frequently than Level 1. Level 3 (serious conflicts) and Level 4 (dangerous conflicts: accidents) conditions were not detected.     

Predicting reduced visibility related crashes on freeways using real-time traffic flow data

ObjectivesThe main objective of this paper is to investigate whether real-time traffic flow data, collected from loop detectors and radar sensors on freeways, can be used to predict crashes occurring at reduced visibility conditions. In addition, it examines the difference between significant factors associated with reduced visibility related crashes to those factors correlated with crashes occurring at clear visibility conditions.MethodRandom Forests and matched case-control logistic regression models were estimated.ResultsThe findings indicated that real-time traffic variables can be used to predict visibility related crashes on freeways. The results showed that about 69% of reduced visibility related crashes were correctly identified. The results also indicated that traffic flow variables leading to visibility related crashes are slightly different from those variables leading to clear visibility crashes.Impact on IndustryUsing time slices 5–15 minutes before crashes might provide an opportunity for the appropriate traffic management centers for a proactive intervention to reduce crash risk in real-time.     

Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials

IntroductionThe effective treatment of road accidents and thus the enhancement of road safety is a major concern to societies due to the losses in human lives and the economic and social costs. The investigation of road accident likelihood and severity by utilizing real-time traffic and weather data has recently received significant attention by researchers. However, collected data mainly stem from freeways and expressways. Consequently, the aim of the present paper is to add to the current knowledge by investigating accident likelihood and severity by exploiting real-time traffic and weather data collected from urban arterials in Athens, Greece.MethodRandom Forests (RF) are firstly applied for preliminary analysis purposes. More specifically, it is aimed to rank candidate variables according to their relevant importance and provide a first insight on the potential significant variables. Then, Bayesian logistic regression as well finite mixture and mixed effects logit models are applied to further explore factors associated with accident likelihood and severity respectively.ResultsRegarding accident likelihood, the Bayesian logistic regression showed that variations in traffic significantly influence accident occurrence. On the other hand, accident severity analysis revealed a generally mixed influence of traffic variations on accident severity, although international literature states that traffic variations increase severity. Lastly, weather parameters did not find to have a direct influence on accident likelihood or severity.ConclusionsThe study added to the current knowledge by incorporating real-time traffic and weather data from urban arterials to investigate accident occurrence and accident severity mechanisms.Practical applicationThe identification of risk factors can lead to the development of effective traffic management strategies to reduce accident occurrence and severity of injuries in urban arterials.     

Identifying crash propensity using specific traffic speed conditions

INTRODUCTION: In spite of recent advances in traffic surveillance technology and ever-growing concern over traffic safety, there have been very few research efforts establishing links between real-time traffic flow parameters and crash occurrence. This study aims at identifying patterns in the freeway loop detector data that potentially precede traffic crashes. METHOD: The proposed solution essentially involves classification of traffic speed patterns emerging from the loop detector data. Historical crash and loop detector data from the Interstate-4 corridor in the Orlando metropolitan area were used for this study. Traffic speed data from sensors embedded in the pavement (i.e., loop detector stations) to measure characteristics of the traffic flow were collected for both crash and non-crash conditions. Bayesian classifier based methodology, probabilistic neural network (PNN), was then used to classify these data as belonging to either crashes or non-crashes. PNN is a neural network implementation of well-known Bayesian-Parzen classifier. With its superb mathematical credentials, the PNN trains much faster than multilayer feed forward networks. The inputs to final classification model, selected from various candidate models, were logarithms of the coefficient of variation in speed obtained from three stations, namely, station of the crash (i.e., station nearest to the crash location) and two stations immediately preceding it in the upstream direction (measured in 5 minute time slices of 10-15 minutes prior to the crash time). RESULTS: The results showed that at least 70% of the crashes on the evaluation dataset could be identified using the classifiers developed in this paper.     

Mental Symptoms and the Use of New Technical Equipment

The aim of this paper was to present how mental symptoms are connected to the use of desktop, portable or minicomputers (communicators and hand-held computers), mobile phones, and background information such as age and gender in the Finnish working-age population. The study was carried out as a cross-sectional study by posting a questionnaire to 15 000 working-age (18-65) Finns. The mental symptoms of 6121 respondents were analysed using the model factors age, gender, the use of computers and the use of mobile phones. In all data, the use of desktop computers was related to mental symptoms. However, the results of our data are not highly reliable, because the nonresponse rate was over 50%. Nevertheless, it may be essential to take into account in the future that working with computers can increase workers’ mental symptoms, and it is important to observe their mental health.     

云南省交通事故数据的分析研究

通过对云南省1981—2003年的交通事故统计数据的分析研究,给出了交通事故死亡人数的预测模型。通过与发达国家类似的交通事故历史数据的对比分析,给出以时间和机动车拥有量为自变量、交通事故死亡人数为因变量的简单预测模型,该模型对2004年的交通事故死亡人数的预测是准确的;同时采用该模型预测了云南省交通事故死亡人数的峰值及其年份。结论指出:基于目前的人、车、路和管理水平及发展趋势,云南省的交通事故死亡人数在2013—2018年之间将达到高峰,高峰时的交通事故死亡人数在5528～7369人之间。     

Data fusion,ensemble and clustering to improve the classification accuracy for the severity of road traffic accidents in Korea

Increasing amount of road traffic in 1990s has drawn much attention in Korea due to its influence on safety problems. Various types of data analyses are done in order to analyze the relationship between the severity of road traffic accident and driving environmental factors based on traffic accident records. Accurate results of such accident data analysis can provide crucial information for road accident prevention policy. In this paper, we use various algorithms to improve the accuracy of individual classifiers for two categories of severity of road traffic accident. Individual classifiers used are neural network and decision tree. Mainly three different approaches are applied: classifier fusion based on the Dempster–Shafer algorithm, the Bayesian procedure and logistic model; data ensemble fusion based on arcing and bagging; and clustering based on the k-means algorithm. Our empirical study results indicate that a clustering based classification algorithm works best for road traffic accident classification in Korea.