Development of Hotzone Identification Models for Simultaneous Crime and Collision Reduction

This research contributes to developing macro-level crime and collision prediction models using a new method designed to handle the problem of spatial dependency and over-dispersion in zonal data. A geographically weighted Poisson regression (GWPR) model and geographically weighted negative binomial regression (GWNBR) model were used for crime and collision prediction. Five years (2009-2013) of crime, collision, traffic, socio-demographic, road inventory, and land use data for Regina, Saskatchewan, Canada were used. The need for geographically weighted models became clear when Moran's I local indicator test showed statistically significant levels of spatial dependency. A bandwidth is a required input for geographically weighted regression models. This research tested two bandwidths: 1) fixed Gaussian and 2) adaptive bi-square bandwidth and investigated which was better suited to the study's database. Three crime models were developed: violent, non-violent and total crimes. Three collision models were developed: fatal-injury, property damage only and total collisions. The models were evaluated using seven goodness of fit (GOF) tests: 1) Akaike Information Criterion, 2) Bayesian Information Criteria, 3) Mean Square Error, 4) Mean Square Prediction Error, 5) Mean Prediction Bias, and 6) Mean Absolute Deviation. As the seven GOF tests did not produce consistent results, the cumulative residual (CURE) plot was explored. The CURE plots showed that the GWPR and GWNBR model using fixed Gaussian bandwidth was the better approach for predicting zonal level crimes and collisions in Regina. The GWNBR model has the important advantage that can be used with the empirical Bayes technique to further enhance prediction accuracy. The GWNBR crime and collision prediction models were used to identify crime and collision hotzones for simultaneous crime and collision reduction in Regina. The research used total collision and total crimes to demonstrate the determination of priority zones for focused law enforcement in Regina. Four enforcement priority zones were identified. These zones cover only 1.4% of the Citys area but account for 10.9% of total crimes and 5.8% of total collisions. The research advances knowledge by examining hotzones at a macro-level and suggesting zones where enforcement and planning for enforcement are likely to be most effective and efficient

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies, geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in Sao Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in Sao Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data. Document type: Articl

Safety Investigation of Traffic Crashes Incorporating Spatial Correlation Effects

One main interest in crash frequency modeling is to predict crash counts over a spatial domain of interest (e.g., traffic analysis zones (TAZs)). The macro-level crash prediction models can assist transportation planners with a comprehensive perspective to consider safety in the long-range transportation planning process. Most of the previous studies that have examined traffic crashes at the macro-level are related to high-income countries, whereas there is a lack of similar studies among lower- and middle-income countries where most road traffic deaths (90%) occur. This includes Middle Eastern countries, necessitating a thorough investigation and diagnosis of the issues and factors instigating traffic crashes in the region in order to reduce these serious traffic crashes. Since pedestrians are more vulnerable to traffic crashes compared to other road users, especially in this region, a safety investigation of pedestrian crashes is crucial to improving traffic safety. Riyadh, Saudi Arabia, which is one of the largest Middle East metropolises, is used as an example to reflect the representation of these countries\u27 characteristics, where Saudi Arabia has a rather distinct situation in that it is considered a high-income country, and yet it has the highest rate of traffic fatalities compared to their high-income counterparts. Therefore, in this research, several statistical methods are used to investigate the association between traffic crash frequency and contributing factors of crash data, which are characterized by 1) geographical referencing (i.e., observed at specific locations) or spatially varying over geographic units when modeled; 2) correlation between different response variables (e.g., crash counts by severity or type levels); and 3) temporally correlated. A Bayesian multivariate spatial model is developed for predicting crash counts by severity and type. Therefore, based on the findings of this study, policy makers would be able to suggest appropriate safety countermeasures for each type of crash in each zone

Integrating spatial and temporal approaches for explaining bicycle crashes in high-risk areas in Antwerp (Belgium)

The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle-motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle-motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads

A Methodological Framework to Evaluate Community Perceptions of Economic and Safety Impacts Attributed to Highway Bypass and Widening Projects

Transportation practitioners have proposed the construction of highway bypass and widening projects in rural communities to address traffic-related problems that include noise pollution and congestion, among others. In the past, the construction of bypass projects has led community residents to raise concerns about potential decreases in business activity for businesses located along the bypassed road. For transportation organizations, it is essential to understand the economic, social, and safety impacts of transportation projects in terms of public perceptions as public input is a required part of the project planning phase. Moreover, it is recommended that agencies perform retrospective analyses of project economic and safety impacts to better inform future project planning. Yet, a step-by-step framework to aid transportation agencies in gathering retrospective public perceptions of project impacts has not been documented. Moreover, for safety analysis, there are few tools and models to identify causes of crashes at planning area levels, as most focus on analyses of specific segments. This thesis contributes to these methodological gaps by (1) developing and applying a systematic framework to assess the public perceptions of transportation project impacts on local economies and highway safety; and (2) quantifying the factors attributed to crash occurrence at a zonal level. The components of the framework include (i) design of a semi-structured phone interview survey protocol with data-driven questions; (ii) methods to select participants, and (iii) survey content analysis. While typical crash studies examine crash causal factors at a segment level, understanding the factors at a larger zonal level more closely aligns with the needs for performance-based planning required by federal transportation legislation. Specifically, in this work, a Random Effect Negative Binomial model (RENB) model is developed to estimate the effect of crash causal factors on crash count at the Traffic Analysis Zone (TAZ) level. By accounting for serial and spatial correlation in longitudinal crash data, the impact of various factors like weather conditions, roadway characteristics, and built-environment can be assessed. Then, planners, engineers, and other traffic safety professionals can identify what countermeasures and programs may be most appropriate for mitigating crashes in a zone

Spatial Analysis of the Variables Involved in the Frequency and Severity of Traffic Accidents on Rural Highways in Pernambuco

Traffic safety depends on a lot of factors associated with traffic accidents and where it takes place. Analyzing how variables related to traffic accidents influences on its frequency and severity may help on the proposition of significant improvement to the effective reduction of said accidents. The goal of this research is to analyze the impact of contributing factors to traffic accidents of any kind, reducing the number of variables related to the statistic model, adjusting it to the brazilian reallity. The methodology was applied in a case study in a 255km patch of a simple lane countryside highway in the state of Pernambuco. Statistics trials were taken to quantify its possible effects on the frequency and severity of traffic accidents. The analysis showed significant factors that contribute to the frequency and severity of the observed accidents. These factors were the amount of traffic (VDMA), radius of the horizontal curve, greide, age range and day of the week. Even though most of the accidents happened in tangent patches, the most severe accidents take place in turns. It also shows that young people between 18 and 30 years old are 22,7% more likely to get involved in fatal accidents than adults over 50 years old, and that in the weekends the chances of an accident occuring is 67% higher than during a week day. The analysis may be used to provide information on future reviews of parameter selection guidelines, especially regarding turns, based on the main parameters of the highway design to reduce risk of accidents in turns

Environmental factors influencing the distribution of pedestrian traffic accidents in Iran

Background: This study aimed to determine the environmental factors affecting the frequency of accidents leading to injury and death related to pedestrians in 31 provinces of Iran. Methods: Data necessary for the study were extracted from databases of traffic police, statistics center, ministry of roads and urban development, and Iran meteorological organization. Hot spots analysis was used based on Getis-Ord G statistics in geographically weighted regression models. Goodness of fit of models was evaluated using the Bayesian information criterion, Akaike's information criterion and Deviance statistics. Results: In this study, 49,409 incidents were investigated. Of these, 48,382 (98) cases were injuries and 1027 (2) cases were fatal accidents. The incidence of fatal accidents does not follow a specific pattern; however, the incidence of accidents leading to injuries is higher in the central and the northeastern provinces of the country and lower in the southern and southeast provinces of the country. The final models showed that the relationship between different variables, including demographic characteristics, road network, and distance from the capital, traffic volume, and rainfall with dependent variables (number of accidents in geographic units), was statistically significant. Conclusion: In order to better design preventive plans for traffic accidents and promote the safety of passageways for pedestrians inside and outside the cities, these factors need to be considered more carefully, and practical solutions should be developed and implemented for their correction

Deadly designs : the impact of road design on road crash patterns along Jamaica’s North Coast Highway

Jamaica has struggled to curb the number of road crash fatalities, having had on average 25 fatalities per month between 2010 and 2014, while many more persons have been injured. The causes of crashes are multidimensional, however this study focused on understanding one aspect of reducing crashes - safe road design. The aim of this study was to determine the relationships between road design characteristics and fatal road crash distribution along the North Coast Highway (NCH) in Jamaica. The Anselin Local Moran’s I and the Getis-Ord Gi* models were employed to look at the distribution of crash hotspots. This paper also utilised Esri’s Weighted Sum Analysis tool to devise a scoring method for determining how safe or dangerous road segments were based on the presence, absence and type of road design features. The design variables selected for this study included bus stops, pedestrian crossings, traffic lights, intersections, places of interest, sidewalks, speed limit, soft shoulders, medians, lanes and roadside barriers. This study also used the zero-inflated negative binomial (ZINB) regression model to identify the empirical relationships between crash counts, crash types, road design features and safety scores. Results The ZINB model identified road segments with many places of interest (POIs), single lane, medians and many intersections as being significantly related to the segments with the most crash counts (irrespective of crash type). This study demonstrates how the spatial analysis of road design features and crash distribution can be used to determine how effective road design features are in advancing road safety and where to implement road safety plans.Jamaica has struggled to curb the number of road crash fatalities, having had on average 25 fatalities per month between 2010 and 2014, while many more persons have been injured. The causes of crashes are multidimensional, however this study focused on understanding one aspect of reducing crashes - safe road design. The aim of this study was to determine the relationships between road design features and fatal road crash distribution along the North Coast Highway (NCH) in Jamaica. The distribution of crash hotspots was determined using two spatial analysis methods, the Anselin Local Moran’s I and the Getis-Ord Gi* models. A scoring method was also used to determine how safe or dangerous road segments were based on the presence, absence and type of road design features found along each road segment. The Weighted Sum Analysis tool was utilized to determine these scores. The design variables selected for this study included bus stops, pedestrian crossings, traffic lights, intersections, places of interest (entities other than houses, such as hotels, gas stations, schools and churches), sidewalks, speed limit, soft shoulders, medians, lanes and roadside barriers (guardrails). To determine relationships or associations between crashes and road design features the zero-inflated negative binomial (ZINB) regression model was used. Results The ZINB model identified road segments with many places of interest (POIs), single lane, medians and many intersections as being associated with segments with the most crash counts. This research showed how the analysis of road design features and crash distribution can be used to determine how effective road design features are in advancing road safety and where to implement road safety plans

INCORPORATING SPEED INTO CRASH MODELING FOR RURAL TWO-LANE HIGHWAYS

Rural two-lane highways account for 76% in mileages of the total paved roads in the US. In Kentucky, these roads represent 85 % of the state-maintained mileages. Crashes on these roads account for 40% of all crashes, 47% of injury crashes, and 66% of fatal crashes on state-maintained roads. These statistics draw attention to the need to investigate the crashes on these roads. Several factors such as road geometries, traffic volume, human behavior, etc. contribute to crashes on a road. Recently, studies have identified speed as one of the key factors of crashes as well as the severity associated with them and indicated the need to incorporate speed into predicting crashes and severity. Such studies are limited for rural two-lane highways due to the lack of measured speed data in the past. This study fills this gap by utilizing widely available measured speed data on these roads and investigates the relationship between speed and crashes on rural two-lane highways. This study collected crash, speed, traffic, and road geometric data for rural two-lane highways in Kentucky. Particularly for the speed, this study utilized GPS-based probe data. The speed data was integrated with the crash data and road attributes for the rural two-lane highways. This study utilized the speed measures directly calculated from the measured speed data and evaluated the effect of speed on the crashes of these roads. At first, this study investigated the effect of speed by incorporating average speed along with traffic volume and length in the crash prediction model for total number of crashes. A zero-inflated negative binomial model was utilized to account for the overdispersion from excess zero crashes in the dataset. From the model, a negative relationship was identified between average speed and number of crashes. One possible explanation is that rural two-lane roads with higher speeds tend to be those main corridors with better geometric conditions. Furthermore, the significance of speed in the model varies with the operating speed on these roads. This suggested considering speed as a categorizer to develop separate models for different speed ranges. Separating models based on speed provided improved prediction performance compared to an overall model. Operating speed often reflects geometric conditions. Therefore, this study also evaluated how the change in the 85th percentile speed from one section to another road section affects the crashes of a road. The analysis showed that more crashes tend to occur when the 85th percentile speed differential between consecutive segments increases. However, further investigation showed that speed differential may not be a suitable indicator of identifying the locations with a high risk of crashes, rather it can be applied for design improvement of the roads. Later, this study investigated spatial heterogeneity of the effect of speed in addition to other factors utilizing a geographically weighted regression model. The model accounted for the geographical location of the data and helped to investigate the spatially varying effect of speed. The results from this model showed that the significance of speed can vary at different locations, which is not observed in the global model. In some regions, speed actually reflects the local geometric conditions of the roads. On the road with poor geometric conditions, crashes tend to be higher. The safety improvement strategies for these roads can focus on improving the geometric conditions such as providing shoulders, realigning the sharp curves, etc. Furthermore, speed seemed to increase crashes in some locations with good geometric conditions and low traffic volume. Speed was indeed a critical factor for these locations and safety countermeasures should be recommended considering the operating condition. Utilizing measured speed data, this study also explored the effect of speed separately on KABC and PDO crashes for these roads. Separate models were developed for KABC and PDO crashes using a zero-inflated Poisson model form. Results from the models showed that speed had a positive relationship with KABC crashes, but a negative relationship with PDO crashes. For the KABC crashes, more KABC crashes tend to occur on high-speed roads. In contrast, PDO crashes tend to be higher on low-speed roads with poor geometric conditions. Furthermore, this study separated the models for each severity level using speed as a categorizer. The models developed at individual speed ranges revealed a varying effect of speed over the different speed ranges of these roads. For example, speed had a positive effect on KABC crashes of low and medium-speed roads, whereas it had a negative influence on crashes of high-speed roads. Further investigation of the study data showed that most of the low and medium-speed roads had poor geometric conditions (narrow shoulder and lane widths with the presence of sharp curves), whereas, high-speed roads had standard geometric conditions. Especially on low-speed roads, it is understandable that a crash can be severe when speed goes up under such restrictive geometric conditions of the roads. In contrast, on high-speed roads, the number of severe crashes tends to be low under standard geometric conditions. Additionally, separating models considering speed ranges provided 19% and 6.5% improvement respectively for KABC and PDO crashes compared to the overall models. Such models can help the agencies to adopt strategies for minimizing crashes at different severity levels based on the speed condition of the road. This study further looked at the effect of speed using Random Forest model since it can deal with multicollinearity between explanatory variables and requires no assumptions on the functional form. After including all the traffic and geometric variables in the model, speed showed 11.5% importance. Compared to the traditional count model, the model provided a better fit with an improved performance of 13%. For better predictability, planning level safety analysis can utilize such machine learning model

A decade of car-cyclist collisions in Louisville: a spatio-temporal analysis.

This thesis has considered factors of the built environment to discover if cay-cyclist collisions display any patterns that could be used to improve cycling safety. This thesis contains an introduction, a literature review, an overview of the study area and data, a description of the methods, results, and discussion and conclusion section. This thesis is significant because it has been the first study to consider cyclist volume as an explanatory variable of the spatiality of car-cyclist dependence for Louisville, Kentucky. Through descriptive and spatial statistics, trends in car-cyclists were identified. Collisions occur more frequently in the summer, during commute hours, at signalized intersections, and near bus stops. It also evaluated the use of third-party sources as exposure measure and explanatory variables. This thesis also put forward recommendations to better the information available to study cyclist collisions, and ways to improve the safety of cyclists in Louisville