On the Exploitation of Automated Planning for Efficient Decision Making in Road Traffic Accident Management

University of Huddersfield Repository Chrpa, Lukáš and Vallati, Mauro On the Exploitation of Automated Planning for Efficient Decision Making in Road Traffic Accident Management Original Citation Chrpa, Lukáš and Vallati, Mauro (2016) On the Exploitation of Automated Planning for Efficient Decision Making in Road Traffic Accident Management. In: 55th IEEE Conference on Decision and Control, 12-14 December 2016, Las Vegas, USA. This version is available at http://eprints.hud.ac.uk/id/eprint/29730/ The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. 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For more information, including our policy and submission procedure, please contact the Repository Team at: [email protected]. http://eprints.hud.ac.uk/ On the Exploitation of Automated Planning for Efficient Decision Making in Road Traffic Accident Management Luka´sˇ Chrpa and Mauro Vallati Abstract— Automated Planning can be fruitfully exploited as focuses on the problem of determining a (nearly) optimal a Decision Support toolkit that, given a specification of available coverage of emergency services [6] where various techniques actions (elementary decisions to be taken), an initial situation such as genetic programming [7] or fuzzy reasoning [8] and goals to be achieved, generates a plan that represents a (partially ordered) sequence of such elementary decisions that have been used. Models predicting the likeliness of medical once performed the required goals are achieved. Road Traffic incident occurrence, which can assist the emergency response Accident Management is a life-critical task that deals with controllers in their decision making, have been developed [9]. effective planning of emergency response when accidents occur, For simulating ambulance deployment in urban areas sev- in order to mitigate negative effects, especially saving human eral systems have been developed [10], [11]. However, the lives that might be in imminent danger. In this paper, we exploit Automated Planning in the Road aforementioned approaches give a little assistance in decision Traffic Accident Management domain. We specifically focus making for emergency response controllers. on providing necessary treatment for victims injured during Automated planning, which deals with the problem of accidents. This involves coordination of medical teams re- finding a plan (a sequence of actions) that transforms the sponsible for providing medical treatment to the victims and environment from an initial state to some desired goal fire brigades that are required to release victims trapped in damaged vehicles. An empirical analysis, based in the region state [12], is an effective tool for decision making. Plans of West Yorkshire (UK) with a number of real accidents recently consist of information about which action and at which time occurred there, shows the suitability of the proposed Automated it has to be executed, in order to achieve given goals. In Planning approach to be used in time-critical conditions, and traffic accident management, for example, a plan embodies a confirms the effectiveness of the generated plans. We also procedure which if correctly followed will ensure that the all demonstrated its usefulness as a tool for evaluating the impact of additional resources, in order to provide guidance for future accident victims have received appropriate treatment while investments. taking into account constraints (e.g. the number of medical staff). Moreover, with numerous generic planning engines I. INTRODUCTION that accept the description of planning problems in a standard Managing emergency response to traffic accidents is cru- language such as PDDL [13], it is easy to apply Automated cial to mitigate their consequences, especially to save human Planning as part of larger intelligent systems (e.g., the recent lives that might be in danger after an accident occurs, work in marine robotics [14]). and to reduce the economical impact on the society. Ef- In this paper, we aim to use Automated Planning as an fective managing of emergency response involves coordi- effective Decision Support tool in the Road Traffic Accident nating medical staff that is responsible for providing first- Management domain. Automated Planning provides a power- aid to accident victims, ambulances that are necessary for ful toolkit for decision support that has already been used in transporting seriously injured victims to hospitals, and fire real-world applications, including Urban Traffic Control [15], brigades that have to assist in cases where a victim is [16]. Applying Automated Planning for decision support trapped in damaged vehicle. Traffic accident management in Traffic Accident Management has been considered by is also subject to specific local regulations [1] with strictly Ozbay¨ et al. [17], where probabilistic models have been defined (medical) response times [2] and procedures. This used for planning operations –but not for reacting to actual provides a challenge for accident management coordinators reported accidents–, and by Shah et al. [18], which con- because they have to take a number of critical decisions in sidered “classical” domain-independent planning. It should a very short time. Currently, most of these decisions are be noted that in the work of Shah et al., the traffic accident taken by humans. However, humans in charge of taking management domain was mainly investigated as a case study decisions are usually under a huge pressure –particularly in for comparing Knowledge Engineering techniques; one of case of multiple accidents– and must react quickly: chances the resulting domain models was included in the temporal of making errors are therefore high. track of the last International Planning Competition [19]. Remarkably, there has been work in the area of au- In terms of modelling of real-world scenarios, it was quite tonomous systems for supporting human experts. However, simplistic. Recently, the work has been also adapted for it is primarily focused to the Search and Rescue domain [3], general incident management [20]. [4], mainly thanks to the RoboCup Rescue Robot and Simu- Inspired by the work of Shah et al. [18], here we spec- lation competitions [5]. In emergency response, the research ify and develop a planning domain model that complies L. Chrpa and M. Vallati are with School of Computing & Engineering, with standards provided by the National Health Services University of Huddersfield, HD1 3DH Queensgate, Huddersfield, UK (NHS) in the UK [2]. The domain model is encoded in Planning Domain Definition Language (PDDL) [13] that is time as metrics for evaluating the local services. Nationally, widely supported by the large number of existing domain- the target is to reach the 75% of category A patients within independent planning engines. Our approach is empirically the 8 minutes limit, and the 95% of category B patients evaluated on several scenarios that involve a part of the area within the 19 minutes limit. of West Yorkshire (UK) with actual locations of emergency In this work, we consider patients in categories A and B, as services and frequent traffic accident spots. The results are well as those who are trapped in damaged vehicles. Notice thoroughly discussed in order to understand strengths and that patients in category C are not of interest in our case, drawbacks of using Automated Planning in Traffic Accident given the not strict (up to 1 hour) time limits for treating Management as well as to indicate promising avenues for them. The goal is to have all the victims given first-aid future research. to their injuries and been untrapped (if they were initially trapped in damaged vehicles), and the seriously injured II. BACKGROUND victims been transported into hospitals by ambulances. The Automated Planning can deal with different levels of plan achieving such a goal provides the emergency response expressiveness. In this work we focus on Temporal Planning, coordinators a procedure that distributes tasks to teams of which is a subclass of Automated Planning that reasons medics and fire brigades, and provides an allocation of with actions whose execution takes time (so called “durative available resources which are required for the execution of actions”) in a deterministic and fully observable environment. the tasks (e.g. ambulances). The environment is described by first-order logic predicates and numeric fluents. Actions are specified via their execution A. Domain Model Specification duration, preconditions which are logical expressions that Given the “high-level” problem specification, one might must hold in order to make the action executable, and effects have an impression about actions that have to be taken in which are sets of literals or fluent assignments that take place order to achieve the goal (rescue and treat traffic accident when the action is executed. In PDDL 2.1, preconditions victims). To be precise, the conceptualisation of the problem can take place just before the action is executed, during requires to specify object types, which stand for classes execution of the action and just before finishing execution of of objects considered in the planning process, predicates the action. Similarly, effects can take place just after stating and numeric fluents, which describe the environment, and execution of the action, or just after finishing execution actions, which modify the environment [18]. of the action [13]. A Planning Domain Model consists of Object types in our model are divided into four main predicates, numeric fluents and actions. A Planning Problem categories: Assets, Agents, Locations and Victims.

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