GIS-based Economic Cost Estimation of Traffic Accidents in St. Louis, Missouri

AbstractThe economic loss due to total traffic accidents in St. Louis remains high every year. This paper presents an effective approach to spatially identifying potential casualty areas and their economic losses. In this study, five years of traffic accident data, from 2007 to 2011, collected in the City of St. Louis and the adjacent counties, is used. Using Geographic Information System (GIS)-based techniques, e.g. Kernel Density Estimation (KDE), two maps are generated and compared: 1) traffic accident rate map based on the number of traffic accidents per year and 2) the economic costs map. The locations with high economic costs but with low accident rates are identified and shown in a 3-D visualization format. The results can be used as a foundation for the traffic accident cost estimation related research and serves as a guideline for practitioners to investigate the areas with high traffic accident severity levels

Análisis espacial con agrupamientos kernel ponderados para determinar sectores de riesgo por accidentes de tráfico en zona urbana. Análisis Tunja, Colombia

A method is presented to identify and determine groups with risk sectors due to the greater occurrence of traffic accidents in urban areas as an integral component in road safety management. The methodology was framed in Spatial Analysis with geographic statistics based on Exploratory Data Analysis (AED), Kernel Density Estimation (KDE), and the application of correlation and geoprocessing techniques. The accident data collected between 2015 and 2018 from the urban area of ​​Tunja, Boyacá, Colombia, were the basis for the study of the distribution of events, characterization of clusters, occurrence dynamics and pattern modeling. The definition and delimitation of risks depended on the dispersion or grouping (Hotspots) found with weighted Kernel together with the socio-spatial interrelation of underlying processes due to the territorial dynamics of the sector. The results reveal patterns of events in concentration foci with different levels of risk, in which land uses of opposite characteristics coexist according to their activities [commercial and residential], socioeconomic sectors of low strata with a mixture of arterial road network that by its functionality mobilizes high vehicular and pedestrian flows. Although the analysis is limited to a case study, the findings show a promising perspective in road safety by delimiting risk sites for traffic accidents through the incidence of territorial variables.Se presenta un método para identificar y determinar agrupaciones con sectores de riesgo por mayor ocurrencia de accidentes de tránsito en áreas urbanas como un componente integral en la gestión de seguridad vial. La metodología se enmarcó en el Análisis Espacial con estadística geográfica fundamentada sobre el Análisis Exploratorio de Datos (AED), la estimación Densidad Kernel (KDE), y la aplicación de técnicas de correlación y geoprocesamiento. Los datos de accidentes recopilados entre 2015 a 2018 de la zona urbana de Tunja, Boyacá, Colombia, fueron la base para el estudio de la distribución de eventos, caracterización de agrupaciones, dinámica de ocurrencia y la modelación de patrones. La definición y delimitación de riesgos dependió de la dispersión o agrupamiento (Hotspots) hallados con Kernel ponderado junto con la interrelación socioespacial de procesos subyacentes por la dinámica territorial del sector. Los resultados revelan patrones de eventos en focos de concentración con diferentes niveles de riesgo, en el que coexisten usos de suelo de características opuestas de acuerdo con sus actividades [comercial y residencial], sectores socioeconómicos de estratos bajos con mezcla de red vial arterial que por su funcionalidad moviliza altos flujos vehiculares y peatonales. A pesar de que el análisis se limita a un estudio de caso, los hallazgos muestran una perspectiva prometedora en seguridad vial al delimitar sitios de riesgo por accidentes de tráfico a través de la incidencia de variables territoriales

Spatiotemporal analysis of traffic crashes involving pedestrians and cyclists in Jefferson County, Kentucky.

Walking and cycling are health-conscious, environmentally friendly modes of transportation, yet very few American trips are accomplished using these methods. A major factor behind this is the fear of being involved in a crash with an automobile. From 2009-2019 there were over 5,200 automobile crashes involving either pedestrians or cyclists in Louisville/ Jefferson County, Kentucky. Researchers have found that these kinds of crashes exhibit spatiotemporal patterns in different cities across the globe. The objective of this study was to determine if there exist any spatial and/or temporal patterns regarding these kinds of crashes. Data for this study came from the Kentucky State Police and encompassed all pedestrian and cyclist crashes from 2009-2019. GISsystems were used to perform a network-based kernel density estimation for the spatial analysis. For the temporal analysis, the scales of time, day and month were observed and plotted. Hot-spots were found to exist within the study area, with some locations being hot-spots for both pedestrian and cyclist crashes. These shared hot-spot locations were analyzed in detail, using the original Kentucky State Police data, as well as Google Earth and Street View imagery

A spatial decision support system for traffic accident prevention in different weather conditions

Natural conditions play an important role as determinants and cocreators of the spatiotemporal road traffic accident Hot Spot footprint; however, none of the modern commercial, or open-source, navigation systems currently provides it for the driver. Our findings, based on a spatiotemporal database recording 11 years of traffic accidents in Slovenia, proved that different weather conditions yield distinct spatial patterns of dangerous road segments. All potentially dangerous road segments were identified and incorporated into a mobile spatial decision support system (SLOCrashInfo), which raises awareness among drivers who are entering or leaving the predefined danger zones on the street network. It is expected that such systems could potentially increase road traffic safety in the future

An integrated GIS-based and spatiotemporal analysis of traffic accidents: a case study in Sherbrooke

Abstract: Road traffic accidents claim more than 1,500 lives each year in Canada and affect society adversely, so transport authorities must reduce their impact. This is a major concern in Quebec, where the traffic-accident risks increase year by year proportionally to provincial population growth. In reality, the occurrence of traffic crashes is rarely random in space-time; they tend to cluster in specific areas such as intersections, ramps, and work zones. Moreover, weather stands out as an environmental risk factor that affects the crash rate. Therefore, traffic-safety engineers need to accurately identify the location and time of traffic accidents. The occurrence of such accidents actually is determined by some important factors, including traffic volume, weather conditions, and geometric design. This study aimed at identifying hotspot locations based on a historical crash data set and spatiotemporal patterns of traffic accidents with a view to improving road safety. This thesis proposes two new methods for identifying hotspot locations on a road network. The first method could be used to identify and rank hotspot locations in cases in which the value of traffic volume is available, while the second method is useful in cases in which the value of traffic volume is not. These methods were examined with three years of traffic-accident data (2011–2013) in Sherbrooke. The first method proposes a two-step integrated approach for identifying traffic-accident hotspots on a road network. The first step included a spatial-analysis method called network kernel-density estimation. The second step involved a network-screening method using the critical crash rate, which is described in the Highway Safety Manual. Once the traffic-accident density had been estimated using the network kernel-density estimation method, the selected potential hotspot locations were then tested with the critical-crash-rate method. The second method offers an integrated approach to analyzing spatial and temporal (spatiotemporal) patterns of traffic accidents and organizes them according to their level of significance. The spatiotemporal seasonal patterns of traffic accidents were analyzed using the kernel-density estimation; it was then applied as the attribute for a significance test using the local Moran’s I index value. The results of the first method demonstrated that over 90% of hotspot locations in Sherbrooke were located at intersections and in a downtown area with significant conflicts between road users. It also showed that signalized intersections were more dangerous than unsignalized ones; over half (58%) of the hotspot locations were located at four-leg signalized intersections. The results of the second method show that crash patterns varied according to season and during certain time periods. Total seasonal patterns revealed denser trends and patterns during the summer, fall, and winter, then a steady trend and pattern during the spring. Our findings also illustrated that crash patterns that applied accident severity were denser than the results that only involved the observed crash counts. The results clearly show that the proposed methods could assist transport authorities in quickly identifying the most hazardous sites in a road network, prioritizing hotspot locations in a decreasing order more efficiently, and assessing the relationship between traffic accidents and seasons.Les accidents de la route sont responsables de plus de 1500 décès par année au Canada et ont des effets néfastes sur la société. Aux yeux des autorités en transport, il devient impératif d’en réduire les impacts. Il s’agit d’une préoccupation majeure au Québec depuis que les risques d’accidents augmentent chaque année au rythme de la population. En réalité, les accidents routiers se produisent rarement de façon aléatoire dans l’espace-temps. Ils surviennent généralement à des endroits spécifiques notamment aux intersections, dans les bretelles d’accès, sur les chantiers routiers, etc. De plus, les conditions climatiques associées aux saisons constituent l’un des facteurs environnementaux à risque affectant les taux d’accidents. Par conséquent, il devient impératif pour les ingénieurs en sécurité routière de localiser ces accidents de façon plus précise dans le temps (moment) et dans l’espace (endroit). Cependant, les accidents routiers sont influencés par d’importants facteurs comme le volume de circulation, les conditions climatiques, la géométrie de la route, etc. Le but de cette étude consiste donc à identifier les points chauds au moyen d’un historique des données d’accidents et de leurs répartitions spatiotemporelles en vue d’améliorer la sécurité routière. Cette thèse propose deux nouvelles méthodes permettant d’identifier les points chauds à l’intérieur d’un réseau routier. La première méthode peut être utilisée afin d’identifier et de prioriser les points chauds dans les cas où les données sur le volume de circulation sont disponibles alors que la deuxième méthode est utile dans les cas où ces informations sont absentes. Ces méthodes ont été conçues en utilisant des données d’accidents sur trois ans (2011-2013) survenus à Sherbrooke. La première méthode propose une approche intégrée en deux étapes afin d’identifier les points chauds au sein du réseau routier. La première étape s’appuie sur une méthode d’analyse spatiale connue sous le nom d’estimation par noyau. La deuxième étape repose sur une méthode de balayage du réseau routier en utilisant les taux critiques d’accidents, une démarche éprouvée et décrite dans le manuel de sécurité routière. Lorsque la densité des accidents routiers a été calculée au moyen de l’estimation par noyau, les points chauds potentiels sont ensuite testés à l’aide des taux critiques. La seconde méthode propose une approche intégrée destinée à analyser les distributions spatiales et temporelles des accidents et à les classer selon leur niveau de signification. La répartition des accidents selon les saisons a été analysée à l’aide de l’estimation par noyau, puis ces valeurs ont été assignées comme attributs dans le test de signification de Moran. Les résultats de la première méthode démontrent que plus de 90 % des points chauds à Sherbrooke sont concentrés aux intersections et au centre-ville où les conflits entre les usagers de la route sont élevés. Ils révèlent aussi que les intersections contrôlées sont plus à risque par comparaison aux intersections non contrôlées et que plus de la moitié des points chauds (58 %) sont situés aux intersections à quatre branches (en croix). Les résultats de la deuxième méthode montrent que les distributions d’accidents varient selon les saisons et à certains moments de l’année. Les répartitions saisonnières montrent des tendances à la densification durant l’été, l’automne et l’hiver alors que les distributions sont plus dispersées au cours du printemps. Nos observations indiquent aussi que les répartitions ayant considéré la sévérité des accidents sont plus denses que les résultats ayant recours au simple cumul des accidents. Les résultats démontrent clairement que les méthodes proposées peuvent: premièrement, aider les autorités en transport en identifiant rapidement les sites les plus à risque à l’intérieur du réseau routier; deuxièmement, prioriser les points chauds en ordre décroissant plus efficacement et de manière significative; troisièmement, estimer l’interrelation entre les accidents routiers et les saisons

Understanding and Developing Equitable and Fair Transportation Systems

The transportation system is an interplay between infrastructure, vehicles, and policy. During the past century, the rapid expansion of the road network, blended with increasing vehicle production and mobility demands, has been stressing the system's capacity and resulting in a shocking amount of annual costs. To alleviate these costs while providing passengers with safe and efficient travel experiences, we need to better design and plan our transportation system. To start with, not only the design of our road network is topologically flawed but also our infrastructure likely facilitates inequality: roads and bridges are found to better connect affluent sectors while excluding the poor. While technological advancements such as connected and autonomous vehicles (CAVs) and novel operation modes such as shared economy have offered new opportunities, questions remain. First, what is the relationship between the road network, community development, demographics, and mobility behaviors? Second, by leveraging the insights from studying the first question, can we better plan, coordinate, and optimize vehicles in different modalities such as human-driven and autonomous to construct safe, efficient, and resilient traffic flows? Third, how can we build an intelligent transportation system to promote equity and fairness in our community development? This proposal is the first step toward answering these questions

Discovering Spatial and Temporal Patterns of Traffic Accidents in Stillwater, Oklahoma

The purpose of this study was to establish a greater understanding towards spatial and temporal patterns of traffic accidents in Stillwater, Oklahoma through the use of a GIS. Traffic accidents from 2001 through 2008 were represented through various visualization methods in ArcGIS to support the idea that geovisualization influences an increased understanding of patterns with accidents in the city. Traffic accidents are not random. Spatiotemporal patterns of accidents exist within the city of Stillwater, Oklahoma. Accidents are influenced by patterning of daily and seasonal social activities, as well as weather events. Deeper insight of traffic accidents in the city is gained through crash mapping and using different 3-D visual representations of the data in a GIS. Hot spots in the city reveal when and where concentrations of accidents cluster at specific locations. Geovisualization of traffic accidents acts as a compass to help navigate police and transportation planners to road areas needing safety reform, a benefit to the city.Department of Geograph