Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning

Planning & Scheduling Applications in Urban Traffic Management

Local authorities that manage traffic-related issues in urban areas have to optimise the use of available resources, in order to minimise congestion and delays. In this context, Automated Planning and Scheduling can be fruitfully exploited, in order to provide dynamic plans that help managing the urban road network. In this paper we provide a review of existing planning and scheduling approaches that have been designed for dealing with different aspects of traffic management, with the aim of gaining insights on the limits of current applications, and highlighting the open challenges

The 2014 International Planning Competition: Progress and Trends

We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems

Knowledge engineering techniques for automated planning

Formulating knowledge for use in AI Planning engines is currently some-thing of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. Also, there is little published research to inform engineers on which method and tools to use in order to effectively engineer a new planning domain model. This is of growing importance, as domain independent planning engines are now being used in a wide range of applications, with the consequence that op-erational problem encodings and domain models have to be developed in a standard language. In particular, at the difficult stage of domain knowledge formulation, changing a statement of the requirements into something for-mal - a PDDL domain model - is still somewhat of an ad hoc process, usually conducted by a team of AI experts using text editors. On the other hand, the use of tools such as itSIMPLE or GIPO, with which experts gen-erate a high level diagrammatic description and automatically generate the domain model, have not yet been proven to be more effective than hand coding. The major contribution of this thesis is the evaluation of knowledge en-gineering tools and techniques involved in the formulation of knowledge. To support this, we introduce and encode a new planning domain called Road Traffic Accidents (RTA), and discuss a set of requirements that we have derived, in consultation with stakeholders and analysis of accident management manuals, for the planning part of the management task. We then use and evaluate three separate strategies for knowledge formulation, encoding domain models from a textual, structural description of require-ments using (i) the traditional method of a PDDL expert and text editor (ii) a leading planning GUI with built in UML modelling tools (iii) an object-based notation inspired by formal methods. We evaluate these three ap-proaches using process and product metrics. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning. In addition, we discuss a range of state-of-the-art modelling tools to find the types of features that the knowledge engineering tools possess. These features have also been used for evaluating the methods used. We bench-mark our evaluation approach by comparing it with the method used in the previous International Competition for Knowledge Engineering for Plan-ning & Scheduling (ICKEPS). We conclude by providing a set of guide-lines for building future knowledge engineering tools

Trends and Evolution of Road User behaviour Research: A Bibliometric Review

Every single person is entitled to equal space on the roads or sidewalks, so they rely on each other’s empathy and compassion and not be self-centered. Therefore, it is essential to promote the ethics of road safety and road users’ exemplary behavior upmost. This review analyzed the publication trends and thematic evolution of road user behaviour over 47 years from 1973 to 2020. The assessment uses the Scopus database and various bibliometric indicators, such as output growth trends, eminent countries, research hotspots, and author keywords. Also, this study presented a graphical visualization of bibliometric indicators using a VOSviewer. Another bibliometric software tool, known as SciMAT, was used to inspect road user behaviour research’s thematic evolution. The verdicts revealed that the number of publications increased exponentially, starting in 2005 with a hike in publications in 2020. Road user behaviour researches were diverse by examining the various research hotspots. This review also focuses on several themes and dimensions of road user behaviour research. The essential motor theme during the first period (2005-2012) was “schools”. Other motor themes, such as “cross-sectional studies,” “car”, and “space-temporal-analysis”, became the most significant number of publications in the second period (2013-2020). These four themes may be beneficial as a benchmark for researchers focusing on the art of road user behaviour. This bibliometric study provides a comprehensive and in-depth view of road users’ behaviour that may help future researchers advance potential knowledge in this field.https://dorl.net/dor/20.1001.1.20088302.2022.20.3.5.2  

Electric scooter safety: An integrative review of evidence from transport and medical research domains

Safe mobility is a prerequisite in the paradigm shift toward sustainable cities and societies. Yet, the serious safety concerns associated with the practice of emerging modes such as electric scooters (e-scooters) are a major challenge for a smooth adoption of these transport modes. We have systematically reviewed peer-reviewed e-scooter safety papers with a primary focus on transport and a secondary focus on medical research domains. Our findings suggest a dire need for analysing interactions of e-scooters with other road users, and, subsequently, adopting surrogate safety measures for e-scooters. Also, it is determined that head and face injuries are the most common injury types for e-scooter riders involved in collisions. The absence of uniform regulations for the practice of e-scooters could potentially affect their safe adoption. The findings highlight the importance of providing uniform regulations for safety gears as well as the prevention of riding under the influence