Review of current practices in recording road traffic incident data: with specific reference to spatial analysis and road policing policy

Road safety involves three major components: the road system, the human factor and the vehicle element. These three elements are inter-linked through geo-referenced traffic events and provide the basis for road safety analyses and attempts to reduce the number of road traffic incidents and improve road safety. Although numbers of deaths and serious injuries are back to approximately the 1950s levels when there were many fewer vehicles on the road, there are still over 100 fatalities or serious injuries every day, and this is a considerable waste of human capital. It is widely acknowledged that the location perspective is the most suitable methodology by which to analyse different traffic events, where by in this paper, I will concentrating on the relationship between road traffic incidents and traffic policing. Other methods include studying road and vehicle engineering and these will be discussed later. It is worth noting here that there is some division within the literature concerning the definitions of ‘accident’ and ‘incident’. In this paper I will use ‘incident’ because it is important to acknowledge a vast majority of ‘road accidents’ are in fact crimes. However I will use the term ‘accident’ where it is referred to in the literature or relevant reports. It is important to mention here that a road traffic accident can be defined as ‘the product of an unwelcome interaction between two or more moving objects, or a fixed and moving object’ (Whitelegg 1986). Road safety and road incident reduction relates to many other fields of activity including education, driver training, publicity campaigns, police enforcement, road traffic policing, the court system, the National Health Service and Vehicle engineering. Although the subject of using GIS to analyse road traffic incidents has not received much academic attention, it lies in the field of crime mapping which is becoming increasingly important. It is clear that studies have been attempted to analyse road traffic incidents using GIS are increasingly sophisticated in terms of hypotheses and statistical technique (for example see Austin, Tight and Kirby 1997). However it is also clear that there is considerable blurring of boundaries and the analysis of road accidents sits uncomfortably in crime mapping. This is due to four main reasons: - Road traffic incidents are associated with road engineering, which is concerned with generic solutions while road traffic analysis is about sensitivity to particular contexts. - Not all road traffic incidents are crimes - It is not just the police who have an interest in reducing road traffic incidents, other partners include local authorities, hospitals and vehicle manufacturers - The management of road traffic incidents is not just confined to the police GIS has been used for over thirty years however it has only been recently been used in the field of transportation. The field of transportation has come to embrace Geographical Information Systems as a keytechnology to support its research and operational need. The acronym GIS-T is often employed to refer to the application and adaptation of GIS to research, planning and management in transportation. GIS-T covers a broad arena of disciplines of which road traffic incident detection is just one theme. Others include in vehicle navigation systems. Initially it was only used to ask simple accident enquiries such as depicting the relative incidence of accidents in wet weather or when there is no street lighting, or to flag high absolute or relative incidences of accidents (see Anderson 2002). Recently however there has been increased acknowledgement that there is a requirement to go beyond these simple questions and to extend the analyses. It has been widely claimed by academics and the police alike that knowing where road accidents occur must lead to better road policing, in order to ensure that road policing becomes better integrated with other policing activities. This paper will be used to explore issues surrounding the analysis of road traffic accidents and how GIS analysts, police and policy makers can achieve a better understanding of road traffic incidents and how to reduce them. For the purpose of this study I will be trying to achieve a broader overview of the aspects concerning road accident analysis with a strong emphasis on data quality and accuracy with concern to GIS analysis. Data quality and accuracy are seen as playing a pivotal role in the road traffic management agenda because they assist the police and Local Authorities as to the specific location whereby management can be undertaken. Part one will consider the introduction to road incidents and their relationship with geography and spatial analysis and how this were initially applied to locating ‘hotspots’ and the more recent theory of ‘accident migration’. Part two will address current data issues of the UK collection procedure. This section will pay particular reference to geo-referencing and the implication of data quality on the procedure of analysing road incidents using GIS. Part three addresses issues surrounding the spatial analysis of road traffic incidents, including some techniques such as spatial autocorrelation, time-space geography and the modifiable area unit problem. Finally part four looks at the role of effective road traffic policing and how this can be achieved due to better understanding of the theory and issues arising from analysing road traffic incidents. It will also look at the diffusion and use of GIS within the police and local authorities

Modelling potential movement in constrained travel environments using rough space-time prisms

The widespread adoption of location-aware technologies (LATs) has afforded analysts new opportunities for efficiently collecting trajectory data of moving individuals. These technologies enable measuring trajectories as a finite sample set of time-stamped locations. The uncertainty related to both finite sampling and measurement errors makes it often difficult to reconstruct and represent a trajectory followed by an individual in space-time. Time geography offers an interesting framework to deal with the potential path of an individual in between two sample locations. Although this potential path may be easily delineated for travels along networks, this will be less straightforward for more nonnetwork-constrained environments. Current models, however, have mostly concentrated on network environments on the one hand and do not account for the spatiotemporal uncertainties of input data on the other hand. This article simultaneously addresses both issues by developing a novel methodology to capture potential movement between uncertain space-time points in obstacle-constrained travel environments

Moving Object Trajectories Meta-Model And Spatio-Temporal Queries

In this paper, a general moving object trajectories framework is put forward to allow independent applications processing trajectories data benefit from a high level of interoperability, information sharing as well as an efficient answer for a wide range of complex trajectory queries. Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical space-time. We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4, No.2, April 201

Implanting Life-Cycle Privacy Policies in a Context Database

Ambient intelligence (AmI) environments continuously monitor surrounding individuals' context (e.g., location, activity, etc.) to make existing applications smarter, i.e., make decision without requiring user interaction. Such AmI smartness ability is tightly coupled to quantity and quality of the available (past and present) context. However, context is often linked to an individual (e.g., location of a given person) and as such falls under privacy directives. The goal of this paper is to enable the difficult wedding of privacy (automatically fulfilling users' privacy whishes) and smartness in the AmI. interestingly, privacy requirements in the AmI are different from traditional environments, where systems usually manage durable data (e.g., medical or banking information), collected and updated trustfully either by the donor herself, her doctor, or an employee of her bank. Therefore, proper information disclosure to third parties constitutes a major privacy concern in the traditional studies

A framework for distributed managing uncertain data in RFID traceability networks

The ability to track and trace individual items, especially through large-scale and distributed networks, is the key to realizing many important business applications such as supply chain management, asset tracking, and counterfeit detection. Networked RFID (radio frequency identification), which uses the Internet to connect otherwise isolated RFID systems and software, is an emerging technology to support traceability applications. Despite its promising benefits, there remains many challenges to be overcome before these benefits can be realized. One significant challenge centers around dealing with uncertainty of raw RFID data. In this paper, we propose a novel framework to effectively manage the uncertainty of RFID data in large scale traceability networks. The framework consists of a global object tracking model and a local RFID data cleaning model. In particular, we propose a Markov-based model for tracking objects globally and a particle filter based approach for processing noisy, low-level RFID data locally. Our implementation validates the proposed approach and the experimental results show its effectiveness.Jiangang Ma, Quan Z. Sheng, Damith Ranasinghe, Jen Min Chuah and Yanbo W

Data modelling for emergency response

Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)