Black Spot Identification Research Articles

Bayesian Latent Class Safety Performance Function for Identifying Motor Vehicle Crash Black Spots

The state of the practice in black spot identification uses safety performance functions based on total crash counts to identify high-risk crash sites. This paper postulates that total crash count is a result of multiple distinct risk-generating processes (RGPs), including geometric characteristics of the road, spatial features of the surrounding environment, and driver behavior factors. However, these multiple sources are ignored in current modeling methodologies that try to explain or predict crash frequencies across sites. Instead, current practice uses models that imply that a single RGP exists. This misspecification may lead to correlation of crashes with incorrect sources of contributing factors (e.g., concluding a crash is predominately caused by a geometric feature when the cause is a behavioral issue), which may ultimately lead to inefficient use of public funds and misidentification of true black spots. This study proposes a latent class model consistent with a multiple risk process theory and investigates the influence this model has on correctly identifying crash black spots. The paper presents the theoretical and corresponding methodological approach in which a Bayesian latent class model is estimated with the assumption that crashes arise from two distinct RGPs, including engineering and unobserved spatial factors. The methodology was applied to state-controlled roads in Queensland, Australia. The results were compared with an empirical Bayesian negative binomial (EB-NB) model. A comparison of goodness-of-fit measures illustrated superiority of the proposed model compared with the NB model. The detection of black spots was improved compared with the EB-NB model. In addition, modeling crashes as the result of two fundamentally separate RGPs reveals more detailed information about unobserved crash causes.

Identification of Hazardous Locations in Regional Road Network – Comparison of Reactive and Proactive Approaches

The study objective is to compare three approaches to identification of hazardous road locations: (1) Traditional reactive accident-based approach, resulting in identification of accident black spots; (2) State-of-the-art empirical Bayes method using accident prediction model, which identifies critical locations, i.e. both real and potential black spots; (3) Proactive “preliminary” road safety inspection, identifying the risk factors, which may potentially increase accident occurrence and severity. Regional rural road network (approx. 1000km) in South Moravia, Czech Republic was used. The methods were applied in identification and ranking of hazardous road locations in the studied network. It was found that black spot approach is not a suitable method, especially in low-volume road network with scattered accident occurrence. On the other hand risk index, based on road safety inspection, is a valid alternative, with ranking performance comparable to state-of-the-art empirical Bayes method. In addition both empirical Bayes and risk index methods are compared with respect to their data requirements. Their mutual application is recommended as a suitable replacement of traditional black spot management, as well as a step forward to a proactive road network safety management.

Modelo de identificación y caracterización de tramos de carreteras sin accidentes (tramos blancos). Aplicación a la red de autopistas de peaje españolas

The traditional approach to infrastructural safety follows the consideration of mitigation tools, such as the identification and management of black spots or road sections with high accident concentration, or preventing tools, as road safety audits and inspections. A complement to these tools is presented in this paper, from a new perspective: the consideration of road sections where, under similar conditions and similar risk exposure, there are no accidents (white road sections). A model has been calibrated to the Spanish toll motorways network, identifying the specific geometric parameters and traffic characteristics which have the highest influence in the occurrence of white sections with sufficient length, without accidents during a significant period of time: heavy vehicles traffic volume, with of the road section and speed.

Black Spot Identification and Audit Analysis for Heterogeneous Traffic Conditions in Hyderabad City � a Case Study

Black Spot Identification and Audit Analysis for Heterogeneous Traffic Conditions in Hyderabad City � a Case Study

Identification and Improvement of Accidents black Spots for Mixed Traffic Streets in Developed Cities-A Case Study

Identification and Improvement of Accidents black Spots for Mixed Traffic Streets in Developed Cities-A Case Study

Unrecorded Accidents Detection on Highways Based on Temporal Data Mining

Automatic traffic accident detection, especially not recorded by traffic police, is crucial to accident black spots identification and traffic safety. A new method of detecting traffic accidents is proposed based on temporal data mining, which can identify the unknown and unrecorded accidents by traffic police. Time series model was constructed using ternary numbers to reflect the state of traffic flow based on cell transmission model. In order to deal with the aftereffects of linear drift between time series and to reduce the computational cost, discrete Fourier transform was implemented to turn time series from time domain to frequency domain. The pattern of the time series when an accident happened could be recognized using the historical crash data. Then taking Euclidean distance as the similarity evaluation function, similarity data mining of the transformed time series was carried out. If the result was less than the given threshold, the two time series were similar and an accident happened probably. A numerical example was carried out and the results verified the effectiveness of the proposed method.

Identification of Water Traffic Black Spot

Through defining Water Traffic Black Spot and analyzing the advantages and disadvantages of identification methods of Road Traffic Black Spot, then choosing Quality Control Method to recognize water area of intensive traffic accidents and applying integrated influential intensity of accident rate based on Systematical Clustering Algorithm into defining the boundary of the area, an effective evaluation method of black spot identification is established, which lays a foundation for the subsequent evaluation work.

Black spots identification through a Bayesian Networks quantification of accident risk index

Traffic accidents constitute a major problem worldwide. One of the principal causes of traffic accidents is adverse driving behavior that is inherently influenced by traffic conditions and infrastructure among other parameters. Probabilistic models for the assessment of road accidents risk usually employs machine learning using historical data of accident records. The main drawback of these approaches is limited coverage of traffic data. This study illustrates a prototype approach that escapes from this problem, and highlights the need to enhance historical accident records with traffic information for improved road safety analysis. Traffic conditions estimation is achieved through Dynamic Traffic Assignment (DTA) simulation that utilizes temporal aspects of a transportation system. Accident risk quantification is achieved through a Bayesian Networks (BNs) model learned from the method’s enriched accidents dataset. The study illustrates the integration of BN with the DTA-based simulator, Visual Interactive Systems for Transport Algorithms (VISTAs), for the assessment of accident risk index (ARI), used to identify accident black spots on road networks.

Safety-Potential-Based Black Spot Safety Management Approach: A Case Study in Ho Chi Minh City

Black Spot Safety Management (BSM) employs identification and treatment of black spots as the two basic stages. Traditionally, these two stages base themselves mainly on recorded accidents rather than safety potential (SAPO). This paper intends to introduce an innovative BSM approach called SAPO-Based BSM which developed is based on the Network Safety Management (BASt & Setra, 2005). There are two basic stages in this approach: SAPO calculation and accident pattern analysis. The innovation lies in the fact that SAPO-Based BSM approach takes SAPO as the main indicator of black spots; and SAPO is determined by means of the traffic volume and the severity of accidents. In order to illustrate the effectiveness of SAPO-Based BSM approach, this paper provides an empirical investigation in which SAPO-Based BSM is practically applied to the local road traffic situations in Ho Chi Minh City (HCMC).

Black spot identification on the expressway network to a guideline of road safety improvement in Bangkok of Thailand

The problem of deaths and injuries is currently acknowledged to be a global phenomenon with authorities virtually in all countries of the world. The study intends to identify the black...

Road Macula Analysis and Evaluation

The subject Introduced the analysis method of the black-spot, established the black-spot discriminant model, And gave its algorithm. According to the characteristics of China's traffic flow, we develope black-spot database, so that the work of the black-spot identification can be quickly carried out。The Road black-spot traffic accidents describes the road sections which have significantly higher accident rate than the average level . Road black-spots identification, analysis and processing are widely considered to be the most efficient way to prevent traffic accidents. The identification of Road accident black-spots is the concern of road design, road safety review, operation management, and security studies , The research studies of road black-spot identification,and determines risk road sections, so that countermeasures can be put forward, and we can achieve the goals of design improvement, strengthen management, improvement of road safety operation environment, decrease of the number of traffic accidents and improvement of the whole road traffic safety performance.

Performance of Safety Indicators in Identification of Black Spots on Two-Lane Rural Roads

Defining a black spot is not as perceptual as it may appear. Unusually high crash counts do not necessarily indicate a real safety problem. When black spots on two-lane rural roads with low and medium traffic volumes are being identified, a reduced number of observed crashes is a critical issue that can emphasize weak results obtained with various safety indicators and checks. It is generally assumed that an empirical Bayesian (EB) estimate represents the best approach for the identification of black spots, but it is difficult to define quantitatively the precision of estimates that are arrived at with different procedures. Because traffic volume, segment length, and crash observation period are critical issues in the identification of black spots and can emphasize the quality of the results obtained with safety indicators and checks, definition is needed of the limits and errors that can be expected when inadequate indicators and procedures are used to identify black spots on two-lane rural roads. A Monte Carlo simulation was used to produce theoretical crash data similar to empirical data, and the data were used to define a priori hazardous sites and, therefore, to assess whether a method could correctly identify such sites. The accuracy and efficiency of procedures are compared on the basis of observed frequency of crashes, crash rate, EB estimation, and the potential for safety improvement (PSI). As a general rule, even if some arrangements can mitigate the lower performance of some indicators, the best practice is to use the indicators based on the EB estimation PSI to identify true positives.

Evaluation of Safety Identification Methods for Low-Volume Roads Using Monte Carlo Simulation

Two-lane rural roads characterized by low traffic volume (fewer than 3,000 vehicles per day) represent a specific road typology of interest in safety analysis. Correct identification of black spots (sites in which the expected number of crashes is abnormally high) is crucial for the road administration when the location and ranking of improvements are chosen. Simply observing unusually high crash counts does not necessarily indicate a true safety problem. In black spot identification on two-lane rural roads with low traffic volume, the reduced number of observed crashes is a critical issue that can emphasize weak results carried out with different safety indicators and control checks. Generally it is assumed that the empirical Bayesian estimate represents the best approach for black spot identification, but the precision of the estimation among different procedures is difficult to define quantitatively. The simulation approach is able to identify crash data similar to empirical data with the unique possibility to define a priori hazardous sites and therefore assess whether a method can correctly identify them. The Monte Carlo simulation presented here was able to define the accuracy and efficiency of the procedures on the basis of observed frequency of crashes, crash rate, empirical Bayes estimation, and potential for safety improvement. The results show that an appropriate selection of exposure values can mitigate the lower performance of some indicators, but the best practice is to use the empirical Bayes approach to identify true positives.

Fully Bayesian Approach to Investigate and Evaluate Ranking Criteria for Black Spot Identification

The fully Bayesian (FB) approach for identification of collision black spots has been available for some time. However, little research has been conducted on the performance of the FB method, especially on criteria for ranking sites. A study was done to fill this void by a thorough evaluation of the FB method for black spot identification. First, an investigation compared the FB approach with the now-traditional empirical Bayesian method. It was confirmed that the FB method was superior for key ranking criteria [the posterior Poisson mean (PM) of crash frequency and potential for safety improvement] based on evaluation criteria, including sensitivity and specificity, and the sum of the PM. Next, eight ranking criteria, which included PM, posterior expected, mode and median ranks, and probability of being the worst, were proposed and evaluated for the best of several FB model variations explored. The mode rank of the posterior distribution of the Poisson mean proved to be the most promising because it tended to provide the best results, especially for top-ranked sites. The sum of the Poisson mean was also found to be a solid evaluation criterion, especially for limited numbers of top-ranked sites.

Understanding Crash Mechanisms and Selecting Interventions to Mitigate Real-Time Hazards on Urban Expressways

The concept of dynamic black spot identification has matured substantially over the past decade. Rather than being confined to improving prediction accuracy, the concept has expanded to understanding the crash mechanism by using sensor data and devising real-time countermeasures. A major drawback of most studies has been considering a crash as a generic mechanism throughout the expressway. Consequently, proposed countermeasures, such as warning messages, variable speed limits, and ramp metering, have been independent of location. Some studies have incorporated ramps as variables, but little progress has been made in investigating variation in the crash mechanism or evaluating the effectiveness of countermeasures separately for the basic freeway segments and ramp vicinities. This paper divides expressways into five segments: basic freeway, upstream and downstream of exits, and entrance ramps. The paper uses high-resolution detector data and advanced ensemble learning methods such as random forest and classification and regression trees to identify important factors and understand the crash mechanism. The outcome advocates lane-based speed management to improve the safety of basic freeway segments. Although ramp flows were found to contribute to crashes in ramp vicinities, the flows caused little concern for basic freeway segments. The findings will be valuable for researchers in predicting real-time crashes, investigating crash mechanisms, and designing proactive countermeasures.

WOODY LANDSCAPE PLANT BREEDING IN MINNESOTA (45°N): IT'S NOT ALL ABOUT COLD HARDINESS

The Woody Landscape Plant Breeding project at the University of Minnesota has been in existence since 1954. The project was initiated largely to develop an extended palette of cold-hardy woody landscape plants for northern landscapes. Since its inception, it has been responsible for the release of 49 woody plant cultivars including large stature shade trees, small stature flowering trees, shrubs, roses, and deciduous azaleas. Given the project’s success at building a cold-hardy germplasm pool for a number of woody taxa, recent breeding efforts have been directed towards other traits such as disease resistance in several plant taxa. Projects include an examination of the nature of resistance to powdery mildew disease (Microspharae spp.) in deciduous azalea (Rhododendron subg. Pentanthera (G. Don) Pojark) species and cultivars in both field and growth chamber studies; a physiological race and an Amplified Fragment Length Polymorphism (AFLP) molecular characterization of the rose black spot pathogen (Diplocarpon rosae Wolf) and identification of resistance genes to the disease in cultivated rose (Rosa ×hybrida). Recently, a high-throughput screening protocol to facilitate the identification of resistance to golden canker disease incited by the fungal pathogen (Cryptodiaporthe corni (Wehm.) Petrak) in a tree native to the upper midwestern U.S., Pagoda dogwood (Cornus alternifolia L.) has been initiated.

Identification of black spots for traffic injury in road intersections dependence of injury definition

IntroductionIn Denmark it is common practice that road administrations use black spot identification methods in planning. Assumed black spots are further reviewed based on traffic volume, modes of transportation and...

Total costs of bicycle injuries in Norway: Correcting injury figures and indicating data needs

Bicycle injuries and fatalities are reported by the police to Statistics Norway. Fatality records from the police are then corrected with Vital Statistics records. However, there is no complete hospital recording that could provide more correct data for bicycle injuries. Bicycle injuries are underreported in official data. There is a nearly complete omission of single bicycle accidents. This disguises societal accident costs and curtails the identification of black spots and effective infrastructure improvements. This paper provides an estimate of total bicycle injuries in Norway and the total costs of these injuries. Application of case study hospital data from Norwegian towns enabled an estimation of the relationship between these data and the official data, including the distribution of injuries by severity. Costs were then assessed by applying official monetary values for given levels of injury severity. Total annual bicycle injury costs are huge, but these costs must be balanced against the benefits of bicycling, related to health and environment. Accident reporting and data should be enhanced to enable a reduction of bicycle injuries.

Identification and Ranking of Black Spots: Sensitivity Analysis

In Flanders, Belgium, approximately 1,014 accident locations are considered dangerous. These dangerous accident sites, or black spots, are selected by means of historic accident records for the period 1997 to 1999. A combination of weighting values, respectively, 1 for each light injury, 3 for each serious injury, and 5 for each deadly injury (1_3_5), is used to rank and select the most dangerous accident locations. A sensitivity analysis was performed to investigate the effect on the identification and ranking of black spots as based on three different weighting value combinations, representing a different attitude toward the traffic safety problem: avoiding all accidents (1_1_1), all deadly accidents (1_1_10), and all accidents with serious or deadly injuries (1_10_10). Furthermore, effects of use of the expected number of accidents, estimated from a hierarchical Bayesian model, instead of the historic count data, to rank and select the accidents sites, were evaluated. Results show that a different attitude toward the traffic safety problem and the choice of the corresponding injury weighting values on the one hand and use of estimates instead of count values on the other hand do have important consequences for the selection and ranking of black spots. This is important not only for the number of accident locations that will receive a different ranking order but also for the effect on the type of accident locations that are selected as dangerous and accordingly for the resulting future traffic safety decisions.

Profiling of High-Frequency Accident Locations by Use of Association Rules

In Belgium, traffic safety is one of the government's highest priorities. The identification and profiling of black spots and black zones (geographical locations with high concentrations of traffic accidents) in terms of accident-related data and location characteristics must provide new insights into the complexity and causes of road accidents, which, in turn, provide valuable input for governmental actions. Association rules were used to identify accident-related circumstances that frequently occur together at high-frequency accident locations. Furthermore, these patterns were analyzed and compared with frequently occurring accident-related characteristics at low-frequency accident locations. The strength of this approach lies with the identification of relevant variables that make a strong contribution toward obtaining a better understanding of accident circumstances and the discerning of descriptive accident patterns from more discriminating accident circumstances to profile black spots and black zones. This data-mining algorithm is particularly useful in the context of large data sets for road accidents, since data mining can be described as the extraction of information from large amounts of data. The results showed that human and behavioral aspects are of great importance in the analysis of frequently occurring accident patterns. These factors play an important role in identifying traffic safety problems in general. However, the accident characteristics that were the most discriminating between high-frequency and low-frequency accident locations are mainly related to infrastructure and location.