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

Modelling drivers’ braking behaviour and comfort under normal driving

The increasing growth of population and a rising number of vehicles, connected to an individual, demand new solutions to reduce traffic delays and enhance road safety. Autonomous Vehicles (AVs) have been considered as an optimal solution to overcome those problems. Despite the remarkable research and development progress in the area of (semi) AVs over the last decades, there is still concern that occupants may not feel safe and comfortable due to the robot-like driving behaviour of the current technology. In order to facilitate their rapid uptake and market penetration, ride comfort in AVs must be ensured.Braking behaviour has been identified to be a crucial factor in ride comfort. There is a dearth of research on which factors affect the braking behaviour and the comfort level while braking and which braking profiles make the occupants feel safe and comfortable. Therefore, the primary aim of this thesis is to model the deceleration events of drivers under normal driving conditions to guide comfortable braking design. The aim was achieved by exploiting naturalistic driving data from three projects: (1) the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project, (2) the Field Operational Test (FOT) conducted by Loughborough University and Original Equipment Manufacturer (OEM), and (3) the UDRIVE Naturalistic Driving Study.A total of about 35 million observations were examined from 86 different drivers and 644 different trips resulting in almost 10,000 deceleration events for the braking features analysis and 21,600 deceleration events for the comfort level analysis. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. The examined factors were kinematics, situational, driver and trip characteristics with the first two categories to affect the most the deceleration features. More specifically, the initial speed and the reason for braking play a significant role, whereas the driver’s characteristics, i.e. the age and gender do not affect the deceleration features, except for driver’s experience which significantly affects the deceleration duration.An algorithm was developed to calculate the braking profiles, indicating that the most used profile follows smooth braking at the beginning followed by a harder one. Moreover, comfort levels of drivers were analysed using the Mixed Multinomial Logit models to identify the effect of the explanatory factors on the comfort category of braking events. Kinematic factors and especially TTC and time headway (THW) were found to affect the most the comfort level. Particularly, when TTC or THW are increased by 1 second, the odds of the event to be “very comfortable” are respectively 1.03 and 4.5 times higher than being “very uncomfortable”. Moreover, the driver’s characteristic, i.e. age and gender affect significantly the comfort level of the deceleration event. Findings from this thesis can support vehicle manufacturers to ensure comfortable and safe braking operations of AVs.</div

Evaluating Impacts of Shared E-scooters from the Lens of Sustainable Transportation

As the popularity of shared micromobility is increasing worldwide, city governments are struggling to regulate and manage these innovative travel technologies that have several benefits, including increasing accessibility, reducing emissions, and providing affordable travel options. This dissertation evaluates the impacts of shared micromobility from the perspective of sustainable transportation to provide recommendations to decision-makers, planners, and engineers for improving these emerging travel technologies. The dissertation focuses on four core aspects of shared micromobility as follows: 1) Safety: I evaluated police crash reports of motor vehicle involving e-scooter and bicycle crashes using the most recent PBCAT crash typology to provide a comprehensive picture of demographics of riders crashing and crash characteristics, as well as mechanism of crash and crash risk, 2) Economics: I estimated the demand elasticity of e-scooters deployed, segmented by weekday type, land use, category of service providers based on fleet size using negative binomial fixed effect regression model and K-means clustering, 3) Expanding micromobility to emerging economies: Using dynamic stated preference pivoting survey and panel data mixed logit model, I assessed the intentions to adopt shared micromobility in mid-sized cities of developing countries, where these innovative technology could be the first wave of decarbonizing transportation sector, and 4) Micromobility data application: I identified five usage-clusters of shared e-scooter trips using combination of Principal Component Analysis (PCA) and K-means clustering to propose a novel framework for using micromobility data to inform data-driven decision on broader policy goals. Based on the key findings of the research, I provide five recommendations as follows: 1) decision-makers should be proactive in incorporating new travel technologies like shared micromobility, 2) city governments should leverage shared micromobility usage and operation data to empower the decision-making process, 3) each shared micromobility vehicles should be approached uniquely for improving road safety, 4) city governments should consider regulating the number of service providers and their fleet sizes, and 5) decision-makers should prioritize expanding shared micromobility in emerging economies as one of the first efforts to the decarbonizing transportation sector

Application of big data in transportation safety analysis using statistical and deep learning methods

The emergence of new sensors and data sources provides large scale high-resolution big data from instantaneous vehicular movements, driver decision and states, surrounding environment, roadway characteristics, weather condition, etc. Such a big data can be served to expand our understanding regarding the current state of the transportation and help us to proactively evaluate and monitor the system performance. The key idea behind this dissertation is to identify the moments and locations where drivers are exhibiting different behavior comparing to the normal behavior. The concept of driving volatility is utilized which quantifies deviation from normal driving in terms of variations in speed, acceleration/deceleration, and vehicular jerk. This idea is utilized to explore the association of volatility in different hierarchies of transportation system, i.e.: 1) Instance level; 2) Event level; 3) Driver level; 4) Intersection level; and 5) Network level. In summary, the main contribution of this dissertation is exploring the association of variations in driving behavior in terms of driving volatility at different levels by harnessing big data generated from emerging data sources under real-world condition, which is applicable to the intelligent transportation systems and smart cities. By analyzing real-world crashes/near-crashes and predicting occurrence of extreme event, proactive warnings and feedback can be generated to warn drivers and adjacent vehicles regarding potential hazard. Furthermore, the results of this study help agencies to proactively monitor and evaluate safety performance of the network and identify locations where crashes are waiting to happen. The main objective of this dissertation is to integrate big data generated from emerging sources into safety analysis by considering different levels in the system. To this end, several data sources including Connected Vehicles data (with more than 2.2 billion seconds of observations), naturalistic driving data (with more than 2 million seconds of observations from vehicular kinematics and driver behavior), conventional data on roadway factors and crash data are integrated

License to Supervise:Influence of Driving Automation on Driver Licensing

To use highly automated vehicles while a driver remains responsible for safe driving, places new – yet demanding, requirements on the human operator. This is because the automation creates a gap between drivers’ responsibility and the human capabilities to take responsibility, especially for unexpected or time-critical transitions of control. This gap is not being addressed by current practises of driver licensing. Based on literature review, this research collects drivers’ requirements to enable safe transitions in control attuned to human capabilities. This knowledge is intended to help system developers and authorities to identify the requirements on human operators to (re)take responsibility for safe driving after automation

Relationship between Road Network Characteristics and Traffic Safety

The Transportation and Capital Improvement of the City of San Antonio, Texas Department of Transportation (TxDOT) and other related agencies often make several efforts based on traffic data to improve safety at intersections, but the number of intersection crashes is still on the high side. There is no one size fits all solution for intersections and the City is often usually confronted with doing best value option analysis on different solutions to choose the least expensive yet more advancements. The goal of this project was to obtain the relationship between road network characteristics and public safety with a focus on intersections; perform a thorough analysis of critical intersections with high crash incidents and crash rates within the city of San Antonio, Texas, and analyze key factors that lead to crashes and recommend effective safety countermeasures. Researchers conducted the following tasks: literature review, crash data analysis, factors affecting crashes at intersections, and the development of possible solutions to some of the identified challenges. Several variables and factors were analyzed, including driver characteristics, like age and gender, road-related factors and environmental factors such as weather conditions and time of day ArcGIS was used to analyze crash frequency at different intersections, and hotspot analysis was carried out to identify high-risk intersections. The crash rates were also calculated for some intersections. The research outcome shows that there are more male drivers than female drivers involved in crashes, even though we have more licensed female drivers than male drivers. The highest number of crashes involved drivers within the age range of 15 – 34 years; this is an indication that intersection crash is one of the top threats to the young generation. The study also shows that the most common crash type is the angle crash which represents over 23% of the intersection crashes. Driver’s inattention ranked first among all the contributing factors recorded. The high-risk intersections based on crash frequency and crash rate show that the intersection along the Bandera Road and Loop 1604 is the worst in the city, with 399 crashes and 8.5 crashes per million entering vehicles. The research concluded with some suggested countermeasures, which include public enlightenment and road safety audit as a proactive means of identifying high-risk intersections

2000 Transportation Scholars Conference: Compendium of Papers, 2000

Compendium of papers presented at the Transportation Scholars Conference in 2000

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCube

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCub

Investigating the transition from normal driving to safety-critical scenarios

Investigation of the correlation between factors associated with crash development has enabled the implementation of methods aiming to avert and control crash causation at various points within the crash sequence (Evans, 2006). Partitioning the crash sequence is important because intricated crash causation sequences can be deconstructed and effective prevention strategies can be suggested (Wu & Thor, 2015). Towards this purpose, Tingvall et al. (2009) documented the so-called integrated safety chain which described the change of crash risk on the basis of a developing sequence of events that led to a collision. This thesis examines the crash sequence development and thus, the transition from normal driving to safety critical scenarios. [Continues.

Understanding the physical and social environmental determinants of road traffic injury in South Africa

Abstract Road traffic injuries (RTIs) are a major public health challenge, accounting for significant injury, economic and psycho-social burden to societies across the world. While decreases are projected for many high-income countries (HICs) over the next decade or so, staggering increases in the burden of mortality and morbidity are forecast for low- and middle-income countries (LMICs). The unique contextual influences on RTIs in LMICs are, however, not well understood. Conceptual frameworks applied mostly to HICs also do not provide adequate recognition of the unique contextual influences of LMICs. Accordingly, the research in this thesis adopts a predominantly geographical approach to incorporate a large range of physical and social environmental effects, and which are aggregated at different spatial and spatial-temporal scales to understand the contextual influences to road traffic injuries (RTIs) in the South African (S.A) setting. In this regard, four studies are presented; these include: a geographical epidemiology and risk analysis at the district council level and for time, space and population aggregations; an integrated spatialtemporal analysis at the province-week level; a fine-scale geographical analysis at the police area level; and a small area analysis at the suburb level for the city of Durban. In addition to important effects relating to alcohol and travel exposure, findings have shown most environmental influences on RTIs in S.A to be development-related, including effects relating to social and area deprivation, violence and crime, and rurality. With the exception of rurality, the above effects showed a positive association with the occurrence of RTIs in S.A. The findings have implications for alignment and possible integration of road safety policies and practices with other developmental policies in the country. In addition, this research has shown that geographical approaches may provide a useful analytical framework for understanding the complexity and interacting influences within broader systems-based approaches; and especially those of the contextual environment that are particularly relevant for LMIC settings