New Criteria for Evaluating Methods of Identifying Hot Spots

Identification of crash hot spots is the critical component of the highway safety management process. Errors in hot spot identification (HSID) may result in the inefficient use of resources for safety improvements. One HSID method that is based on the empirical Bayesian (EB) method has been widely used as an effective approach for identifying crash-prone sites. For the EB method, the negative binomial (NB) model is usually needed to obtain the EB estimates. Recently, some studies have shown that the Sichel (SI) model can be easily used in the EB modeling framework and potentially yield better EB estimates. The objective of this study was to compare the performance of the two crash prediction (SI and NB) models in identifying hot spots with the EB method. To accomplish the objective of this study, empirical crash data collected at highway segments in Texas were used to generate simulated crash counts. Three commonly used HSID methods (simple ranking, confidence interval, and EB) were applied with the use of simulated data. False positives, false negatives, and false identifications were calculated and compared across the methods. The simulation results in this study suggested that the SI-based EB method could consistently provide a better HSID result than the NB-based EB method. Moreover, EB methods yielded the lowest error percentage of the three HSID methods. This study confirmed that the EB technique was an effective method for identifying hazardous sites. On the basis of the findings in this study, it is recommended that transportation safety researchers consider the SI model as an alternative crash prediction model when the EB approach is used.

Experimental evaluation of hotspot identification methods

Road fatalities are among the major causes of mortality for Strigiformes species and may affect the population's survival. The use of mitigation strategies must be considered to overcome this problem. However, because mitigation along the total length of all roads is not financially feasible, the locations where Strigiformes roadkills are more frequent (i.e., road fatality hotspots) must be identified. In addition to hotspot identification, factors that influence the occurrence of such fatalities should be recognized to allow mitigation measures to be delineated. We used road fatality data collected from 311 km of southern Portugal roads over a 2‐year period to compare the performance of five hotspot identification methods: binary logistic regression (BLR), ecological niche factor analysis (ENFA), Kernel density estimation, nearest neighbor hierarchical clustering (NNHC), and Malo's method. BLR and ENFA modelling were also used for recognizing roadkill deterministic factors. Our results suggest that Malo's method should be preferred for hotspot identification. The main factors driving owl roadkillings are those associated with good habitat conditions for species occurrence and specific conditions that promote hunting behavior near roads. Based on these factors, several mitigation measures are recommended.

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

Traffic Modeling and Safety Analysis on Motorway Ramps

Meso-level hotspot identification for suburban arterials

ABSTRACTTraditionally, crash frequency analyses have been undertaken at the macro- and micro-levels, independently. This study proposes a Bayesian integrated spatial crash frequency model, which links the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considers the spatial autocorrelation of the different types of road entities (i.e. segments and intersections) at the micro-level with a joint structure. The modelling results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model could simultaneously identify both macro- and micro-level factors contributing to the crash occurrence. Subsequently, a novel hotspot identification method was suggested, which enables us to detect hotspots for both macro- and micro-levels with comprehensive information from the two levels.

Evaluating the environmental impact of agricultural food production systems is a relatively recent research activity and the present methods for assessing the environmental impact associated with production-consumption systems are input-output accounting, ecological footprint analysis, life cycle assessment, financial evaluation of environmental externalities, farm cost and food miles. Complete environmental impact analyses require considerable amounts of data and time and are very likely to be expensive. Preliminary identification of the most-evident environmental hot spots is beneficial in aiding the determination of the analysis scope and goal and in setting the borders of the studied system. In the present study a reconstruction of the production schemes was performed and the identification of evident environmental impact hot spots was performed expert-wise for four selected model food products (hard cooked cheese, dry-cured ham, beer and cauliflower), traditional in Europe. This preliminary hot spot identification was preformed specifically for a conceptual life cycle assessment (LCA) technique in prosecution. In our opinion, these results can also be employed as a basis for many other environmental impact assessment approaches.

Hot spot identification is a very relevant problem in a wide variety of areas such as health care, energy or transportation. A hot spot is defined as a region of high likelihood of occurrence of a particular event. To identify hot spots, location data for those events is required, which is typically collected by telematics devices. These sensors are constantly gathering information, generating very large volumes of data. Current state-of-the-art solutions are capable of identifying hot spots from big static batches of data by means of variations of clustering or instance selection techniques that pre-process the original input data, providing the most relevant locations. However, these approaches neglect to address changes in hot spots over time. This paper presents a dynamic bio-inspired approach to detect hot spots in big data streams. This computational intelligence method is designed and applied to the transportation sector as a case study to identify incidents in the roads caused by heavy goods vehicles. We adapt an immune-based algorithm to account for the temporary aspect of hot spots inspired by the idea of pheromones, which is then subsequently implemented using Apache Spark Streaming. Experimental results on real datasets with up to 4.5 million data points—provided by a telematics company—show that the algorithm is capable of quickly processing large streaming batches of data, as well as successfully adapting over time to detect hot spots. The outcome of this method is twofold, both reducing data storage requirements and demonstrating resilience to sudden changes in the input data (concept drift).

Hotspots identification (HSID), a reactive crash prediction based on the historical accident counts, is crucial to transport authorities for evaluating the risk level of the object road sites. The objective of the research is to identify unidentified hotspots that should have been treated. Numerous conventional HSID approaches have been developed and applied for decades, none of which takes daily variability of traffic flow and crash record into account. In this regard, we categorize the time of day into four groups: (1) morning peak hours, (2) afternoon peak hours, (3) daytime, and (4) night off-peak hours. The authors further apply this proposed methodology to Pacific Motorway Southeast Queensland section linking Brisbane to Gold Coast based on an Empirical Bayesian (EB) approach. Finally, the applications of these proposed EB-based methods and the conventional EB method are discussed through an aggregated view.

Identification of hot spots, a small portion of protein-protein interface residues that contribute the majority of the binding free energy, can provide crucial information for understanding the function of proteins and studying their interactions. Based on our previous method (PredHS), we propose a new computational approach, PredHS2, that can further improve the accuracy of predicting hot spots at protein-protein interfaces. Firstly we build a new training dataset of 313 alanine-mutated interface residues extracted from 34 protein complexes. Then we generate a wide variety of 600 sequence, structure, exposure and energy features, together with Euclidean and Voronoi neighborhood properties. To remove redundant and irrelevant information, we select a set of 26 optimal features utilizing a two-step feature selection method, which consist of a minimum Redundancy Maximum Relevance (mRMR) procedure and a sequential forward selection process. Based on the selected 26 features, we use Extreme Gradient Boosting (XGBoost) to build our prediction model. Performance of our PredHS2 approach outperforms other machine learning algorithms and other state-of-the-art hot spot prediction methods on the training dataset and the independent test set (BID) respectively. Several novel features, such as solvent exposure characteristics, second structure features and disorder scores, are found to be more effective in discriminating hot spots. Moreover, the update of the training dataset and the new feature selection and classification algorithms play a vital role in improving the prediction quality.

Studies on relationships between angiogenesis and tumour cell proliferation have provided conflicting results. This study has therefore investigated the relationships between the number and location of fully automatically identified CD31-positive microvessels and interactively segmented mitoses and necrotic compartments by image processing. These features were studied in ten invasive breast cancers, in the 'hot spots' and in whole tumour sections. Microvessel and mitosis hot spots were topographically close or overlapping and were always located at the periphery of the tumour. The numbers of mitoses and microvessels per mm(2) in the hot spot were strongly correlated with the respective numbers in the whole tumour section, as well as mutually. The ratio of mitoses in the hot spot to the whole tumour section was significantly higher than the corresponding microvessel ratio. Mitoses were preferentially located at a distance of 50-150 microm from microvessels. No significant difference was found between the average distance between mitoses and microvessels in the whole tumour sections and in the hot spot (79 vs. 72 microm), although considerable inter-tumour differences were found (hot spot 43-101 microm, tumour 47-111 microm). The presence of necrotic areas correlated with the number of mitoses per mm(2) and necrosis was in general observed at a distance of more than 150 microm from the microvessels, suggesting that necrotic areas have outgrown their vascular system. These results indicate the usefulness of image processing of whole tumour sections for the identification of proliferation and vascularization hot spots, which are strong prognostic factors in breast cancer. The results also support a close relationship between tumour necrosis and microvessels.

This study explored potential use of a traffic conflict-based surrogate safety assessment as an alternative way of evaluating the safety performance of roads and identifying potential sites for safety improvement. The study compared two major safety assessment streams (a statistical modeling method and a traffic conflict-based method) in their evaluation of crash risk to investigate the performance of the traffic conflict-based method as an alternative to the crash-based method in identification of hot spots. The empirical Bayes (EB) method coupled with the safety performance function (SPF), called the EB-SPF method, was used as a benchmark, and the conventional crash frequency method was used as a comparison supplement. These two methods were viewed as the crash-based methods because they relied on crash data. Traffic conflicts were estimated through the microscopic traffic simulation model VISSIM. The safety evaluation was performed separately for 24 signalized intersections and 86 segments in the Tyson...

Determining the hot spots and hot moments of soil N2O emissions and mineral N leaching in a mixed landscape under subtropical monsoon climatic conditions