Unraveling urban form and collision risk: The spatial distribution of traffic accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fa-talities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis’ framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotem-poral association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2–26, 27–57, 58–105, 106–192, and 193–364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2–26 m) class concentrated the most intense level of spatiotem-poral association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions

The heartbeat of the city

Human activity is organised around daily and weekly cycles, which should, in turn, dominate all types of social interactions, such as transactions, communications, gatherings and so on. Yet, despite their strategic importance for policing and security, cyclical weekly patterns in crime and road incidents have been unexplored at the city and neighbourhood level. Here we construct a novel method to capture the weekly trace, or "heartbeat" of events and use geotagged data capturing the time and location of more than 200,000 violent crimes and nearly one million crashes in Mexico City. On aggregate, our findings show that the heartbeats of crime and crashes follow a similar pattern. We observe valleys during the night and peaks in the evening, where the intensity during a peak is 7.5 times the intensity of valleys in terms of crime and 12.3 times in terms of road accidents. Although distinct types of events, crimes and crashes reach their respective intensity peak on Friday night and valley on Tuesday morning, the result of a hyper-synchronised society. Next, heartbeats are computed for city neighbourhood 'tiles', a division of space within the city based on the distance to Metro and other public transport stations. We find that heartbeats are spatially heterogeneous with some diffusion, so that nearby tiles have similar heartbeats. Tiles are then clustered based on the shape of their heartbeat, e.g., tiles within groups suffer peaks and valleys of crime or crashes at similar times during the week. The clusters found are similar to those based on economic activities. This enables us to anticipate temporal traces of crime and crashes based on local amenities

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

Improved pattern extraction scheme for clustering multidimensional data

Multidimensional data refers to data that contains at least three attributes or dimensions. The availability of huge amount of multidimensional data that has been collected over the years has greatly challenged the ability to digest the data and to gain useful knowledge that would otherwise be lost. Clustering technique has enabled the manipulation of this knowledge to gain an interesting pattern analysis that could benefit the relevant parties. In this study, three crucial challenges in extracting the pattern of the multidimensional data are highlighted: the dimension of huge multidimensional data requires efficient exploration method for the pattern extraction, the need for better mechanisms to test and validate clustering results and the need for more informative visualization to interpret the “best” clusters. Densitybased clustering algorithms such as density-based spatial clustering application with noise (DBSCAN), density clustering (DENCLUE) and kernel fuzzy C-means (KFCM) that use probabilistic similarity function have been introduced by previous works to determine the number of clusters automatically. However, they have difficulties in dealing with clusters of different densities, shapes and size. In addition, they require many parameter inputs that are difficult to determine. Kernel-nearestneighbor (KNN)-density-based clustering including kernel-nearest-neighbor-based clustering (KNNClust) has been proposed to solve the problems of determining smoothing parameters for multidimensional data and to discover cluster with arbitrary shape and densities. However, KNNClust faces problem on clustering data with different size. Therefore, this research proposed a new pattern extraction scheme integrating triangular kernel function and local average density technique called TKC to improve KNN-density-based clustering algorithm. The improved scheme has been validated experimentally with two scenarios: using real multidimensional spatio-temporal data and using various classification datasets. Four different measurements were used to validate the clustering results; Dunn and Silhouette index to assess the quality, F-measure to evaluate the performance of approach in terms of accuracy, ANOVA test to analyze the cluster distribution, and processing time to measure the efficiency. The proposed scheme was benchmarked with other well-known clustering methods including KNNClust, Iterative Local Gaussian Clustering (ILGC), basic k-means, KFCM, DBSCAN and DENCLUE. The results on the classification dataset demonstrated that TKC produced clusters with higher accuracy and more efficient than other clustering methods. In addition, the analysis of the results showed that the proposed TKC scheme is capable of handling multidimensional data, validated by Silhouette and Dunn index which was close to one, indicating reliable results

Improving automobile insurance ratemaking using telematics : incorporating mileage and driver behaviour data

We show how data collected from a GPS device can be incorporated in motor insurance ratemaking. The calculation of premium rates based upon driver behaviour represents an opportunity for the insurance sector. Our approach is based on count data regression models for frequency, where exposure is driven by the distance travelled and additional parameters that capture characteristics of automobile usage and which may affect claiming behaviour. We propose implementing a classical frequency model that is updated with telemetrics information. We illustrate the method using real data from usage-based insurance policies. Results show that not only the distance travelled by the driver, but also driver habits, significantly influence the expected number of accidents and, hence, the cost of insurance coverage. Telemetry should facilitate the inclusion within insurance pricing of those factors that traffic authorities identify as being associated with risky drivers, including, for example, traffic violations

BEYOND HUMAN FACTORS : EXAMINING THE UNDERLYING DETERMINANTS OF RECREATIONAL BOATING ACCIDENTS WITH SPATIAL ANALYSIS AND MODELING

Recreational boating has grown in popularity in recent decades, accompanied with increased accidents resulting in property damage and personal injury. Some 5,000 recreational boating accidents are reported annually, ranking recreational boating as a leading cause of transportation accidents, second only to automotive. Recent research suggests that recreational boating accidents stem from multiple factors. In contrast, public perception and public policy overwhelmingly attribute boating accidents to human error, e.g., operator drug or alcohol use or lack of experience. This dissertation offers a comprehensive perspective on recreational boating accidents by exploring human, technological, and environmental factors that most influence these accidents. This level of inclusiveness is absent from previous research. The conceptual model developed in this dissertation is derived from general accident theory that integrates spatial and temporal qualities of recreational boating (and boating accidents) from satellite imagery, on-the-water boater surveys, and federal boating accident data. Data were assembled for two distinctive research sites, Sandusky, OH and Tampa, FL. Analyses of these data depended, in part, upon various forms of spatial statistics, e.g., hot spot analyses. The boating accident model developed here uses the multivariate negative binomial model to analyze accident count data aggregated to 0.25 mi² grid cells. The result is a synthetic model with improved parameter estimates and predictive capability compared to previous boating accident research. Key risk factors contained in the final model clearly represent human (operator experience), technological (boat speed and length), and environmental (boat density and channel character) dimensions. This research has important societal impact, i.e., to public officials faced with the allocation of limited resources. In particular, this research emphasizes the concentrated nature of boating risk in time (seasonality, day of week, time of day) and in space (shoals, channels, fixed facilities). These features should guide the timing and the placement of mobile law enforcement capacity as well as the location of operation centers near high risk boating sites. Finally, this work emphasizes the need for investigations of additional sites and the importance of including remotely sensed data to complement survey data in studies of recreational boating accidents.  Ph.D

Profiling Spanish Prospective Buyers of Electric Vehicles Based on Demographics

p. 1-22As traffic congestion and air pollution rise at alarming rates in many cities worldwide, new smart technologies are emerging to meet the urban mobility challenge. In addition, utomotive firms have transformed their business models to make them more sustainable and to adjust to demand response. Electric vehicles (EVs) represent a viable option to reduce ecological damage and improve public health. However, in the previous literature, no consensus has been reached on the profile of prospective buyers of EVs. Based on a large-scale sample of Spanish citizens and using cluster analysis, our study provides a better understanding of the demographics of such prospective buyers. We identified four types of EV prospective buyers. Our results show that although men have a strong preference for EVs, low-income older women prove to be the most EV-aware group; their automotive driving experience and concern for sustainability could be among the underlying causes of this particular interest. Another valuable insight is the greater partiality of older people for EVs. These findings have many implications for managers, especially in the automotive industry, policymakers, and sustainability strategists. They show that EV prospective buyers should not be approached as a homogeneous group but as a heterogeneous group with different socio-demographic characteristics that might help decision-makers make better business decision.S

In the mood: online mood profiling, mood response clusters, and mood-performance relationships in high-risk vocations

The relationship between mood and performance has long attracted the attention of researchers. Typically, research on the mood construct has had a strong focus on psychometric tests that assess transient emotions (e.g., Profile of Mood States [POMS]; McNair, Lorr, & Dropplemann, 1971, 1992; Terry, Lane, Lane, & Keohane, 1999). Commonly referred to as mood profiling, many inventories have originated using limited normative data (Terry et al., 1999), and cannot be generalised beyond the original population of interest. With brevity being an important factor when assessing mood, Terry et al. (1999) developed a 24-item version of the POMS, now known as the Brunel Mood Scale (BRUMS). Including six subscales (i.e., tension, depression, anger, vigour, fatigue, and confusion), the BRUMS has undergone rigorous validity testing (Terry, Lane, & Fogarty, 2003) making it an appropriate measure in several performance environments. Mood profiling is used extensively for diverse purposes around the world, although Internet-delivered interventions have only recently been made available, being in conjunction with the proliferation of the World Wide Web. Developed by Lim and Terry in 2011, the In The Mood website (http://www.moodprofiling.com) is a web-based mood profiling measure based on the BRUMS and guided by the mood-performance conceptual framework of Lane and Terry (2000). The focus of the website is to facilitate a prompt calculation and interpretation of individual responses to a brief mood scale, and link idiosyncratic feeling states to specific mood regulation strategies with the aim of facilitating improved performance. Although mood profiling has been a popular clinical technique since the 1970s, currently there are no published investigations of whether distinct mood profiles can be identified among the general population. Given this, the underlying aim of the present research was to investigate clusters of mood profiles. The mood responses (N = 2,364) from the In The Mood website were analysed using agglomerative, hierarchical cluster analysis which distinguished six distinct and theoretically meaningful profiles. K-means clustering with a prescribed six-cluster solution was used to further refine the final parameter solution. The mood profiles identified were termed the iceberg, inverse iceberg, inverse Everest, shark fin, surface, and submerged profiles. A multivariate analysis of variance (MANOVA) showed significant differences between clusters on each dimension of mood, and a series of chi-square tests of goodness-of-fit indicated that gender, age, and education were unequally distributed. Further, a simultaneous multiple discriminant function analysis (DFA) showed that cluster membership could be correctly classified with a high degree of accuracy. Following this, a second (N = 2,303) and third (N = 1,865) sample each replicated the results. Given that certain vocations are by nature riskier than others (Khanzode, Maiti, & Ray, 2011) highlighting the importance of performance in the workplace, the present research aimed to further generalise the BRUMS to high-risk industries using a web-based delivery method. Participants from the construction and mining industries were targeted, and the relationship between mood and performance in the context of safety was investigated, together with associated moderating variables (i.e., gender, age, education, occupation, roster, ethnicity, and location)

Evaluating changes in driver behaviour for road safety outcomes: a risk profiling approach

Road safety continues to be an important issue with road crashes among the leading causes of death. Considerable effort has been put into improving our understanding of the factors that influence driving behaviour with a view to devising more effective road safety strategies. Within the literature, demographics, social norms, personality, enforcement and the road environment have all been identified as influencers of risky driving behaviour. What is missing is an integrated empirical approach which examines the relationship between these factors and drivers’ awareness of their speeding behaviour to a measure of day-to-day driving behaviour. This research employs demographic, psychological, vehicle, trip and Global Positioning System (GPS) driving data collected from 106 drivers in Sydney, Australia during a pay-as-you-drive study. The main contributions are three-fold. First, a methodology is developed to control for the influence of spatiotemporal characteristics on driver behaviour. This deals with the inherent variability introduced from road environment factors external to the driver which would otherwise lead to misleading results. Second, the creation of a composite measure of driver behaviour allows driver behaviour to be described using a single measure whilst accounting for the variability and multitude of aspects within the driving task. This allows drivers to be compared to each other and for the same driver to be compared across time and space permitting empirical testing of interventions in a before and after study. Lastly, this research reveals the potential for reducing the extent and magnitude of risky driving behaviour by making drivers aware of their own behaviour. The results indicate that drivers can be placed in three groups: drivers requiring a monetary incentive to change speeding behaviour, drivers requiring information alone to change their speeding behaviour and drivers that appear unresponsive to both monetary incentives and information