Police officer dynamic positioning for incident response and community presence

Police Forces are under a constant struggle to provide the best service possible with limited and decreasing resources. One area where service cannot be compromised is incident response. Resources which are assigned to incident response must provide attendance to the scene of an incident in a timely manner to protect the public . To ensure the possible demand is met maximum coverage location planning can be used so response officers are located in the most effective position for incident response. This is not the only concern of response officer positioning. Location planning must also consider targeting high crime areas, hotspots, as an officer presence in these areas can reduce crime levels and hence reduce future demand on the response officers. In this work hotspots are found using quadratic kernel density estimation with historical crime data. These are then used to produce optimal dynamic patrol routes for response officers to follow. Dynamic patrol routes result in reduced response times and reduced crime levels in hotspot areas resulting in a lower demand on response officers

Predictive police patrolling to target hotspots and cover response demand

Police forces are constantly competing to provide adequate service whilst faced with major funding cuts. The funding cuts result in limited resources hence methods of improving resource efficiency are vital to public safety. One area where improving the efficiency could drastically improve service is the planning of patrol routes for incident response officers. Current methods of patrolling lack direction and do not consider response demand. Police patrols have the potential to deter crime when directed to the right areas. Patrols also have the ability to position officers with access to high demand areas by pre-empting where response demand will arise. The algorithm developed in this work directs patrol routes in real-time by targeting high crime areas whilst maximising demand coverage. Methods used include kernel density estimation for hotspot identification and maximum coverage location problems for positioning. These methods result in more effective daily patrolling which reduces response times and accurately targets problem areas. Though applied in this instance to daily patrol operations, the methodology could help to reduce the need for disaster relief operations whilst also positioning proactively to allow quick response when disaster relief operations are required

Anticipatory routing of police helicopters

We have developed a decision support application for the Dutch Aviation Police and Air Support unit for routing their helicopters in anticipation of unknown future incidents. These incidents are not known in advance, yet do require a swift response. A response might include the dispatch of a police helicopter to support the police on the ground. If a helicopter takes too long to arrive at the crime scene, it might be too late to assist. Hence, helicopters have to be proximate when an incident happens to increase the likelihood of being able to support the police on the ground in apprehending suspects. We propose the use of a forecasting technique, followed by a routing heuristic to maximize the number of incidents where a helicopter provides a successful assist. We have implemented these techniques in a decision support application in collaboration with the Dutch Aviation Police and Air Support. Using numerical experiments, we show that our application has the potential to improve the success rate with a factor nine. The Dutch Air Support and Aviation Police are now using the application

STRATEGIES TO IMPROVE THE EFFICIENCY OF EMERGENCY MEDICAL SERVICE (EMS) SYSTEMS UNDER MORE REALISTIC CONDITIONS

Emergency medical service (EMS) systems provide medical care to pre-hospital patients who need rapid response and transportation. This dissertation proposes a new realistic approach for EMS systems in two major focuses: multiple unit dispatching and relocation strategies. This work makes recommendations for multiple-unit dispatch to multiple call priorities based on simulation optimization and heuristics. The objective is to maximize the expected survival rate. Simulation models are proposed to determine the optimization. A heuristic algorithm is developed for large-scale problems. Numerical results show that dispatching while considering call priorities, rather than always dispatching the closest medical units, could improve the effectiveness of EMS systems. Additionally, we extend the model of multiple-unit dispatch to examine fairness between call priorities. We consider the potentially-life-threatening calls which could be upgraded to life-threatening. We formulate the fairness problem as an integer programming model solved using simulation optimization. Taking into account fairness between priorities improves the performance of EMS systems while still operating at high efficiency. As another focus, we consider dynamic relocation strategy using a nested-compliance table policy. For each state of the EMS systems, a decision must be made regarding exactly which ambulances will be allocated to which stations. We determine the optimal nested-compliance table in order to maximize the expected coverage, in the binary sense, as will be later discussed. We formulate the nested-compliance table model as an integer program, for which we approximate the steady-state probabilities of EMS system to use as parameters to our model. Simulation is used to investigate the performance of the model and to compare the results to a static policy based on the adjusted maximum expected covering location problem (AMEXCLP). Additionally, we extend the nested-compliance table model to consider an upper bound on relocation time. We analyze the decision regarding how to partition the service area into smaller sub-areas (districts) in which each sub-area operates independently under separate relocation strategies. We embed the nested-compliance table model into a tabu search heuristic algorithm. Iteration is used to search for a near-optimal solution. The performance of the tabu search heuristic and AMEXCLP are compared in terms of the realized expected coverage of EMS systems

Time Response Ambulatory Calls Evaluation

Time response (the time the patient dials 911 to the time any medical assistance reaches the site) is an important aspect of emergency medical care. With the rapid growth in population all around the world, emergency medical assistance needs to be at its best to be able to serve the ever demanding society. With early care and pre-hospital in- ambulance treatment, many lives can be saved. A thorough analysis was conducted on the EMS systems and time response data in the US and the UK. Possible technological solutions and advances were considered, analysed, and recommended for ameliorating time response. Virtual implementations proved to be successful. The solutions were again researched for viability and improvements in the United States, especially in Massachusetts

Disruption Management of ASAE's Inspection Routes

The Rapid development and the emergence of technologies capable of producing real-time data opened new horizons to both planning and optimization of vehicle routes [4]. In this dissertation, the Autoridade de Segurança Alimentar e Económica (ASAE) operation's scenario will be explored and analyzed as a case study to the problem. ASAE is a Portuguese administrative authority specialized in food security and economic auditing and is responsible to regulate thousands of economic entities in the Portuguese territory. ASAE inspections are usually done by brigades using vehicles to inspect economic operators, taking into account their timetables. Previous work on this topic led to the implementation of an inspection route optimization module capable of defining and assigning routes to inspect economic operators, seeking to maximize a utility function. Using optimization algorithms, inspection routes are calculated for each brigade, with information regarding specific map paths and inspection schedules. The approach used does not take into consideration the dynamic properties of real-life scenarios, as the precalculated operation plan is not reviewed in real-time. This work aims to study the dynamic properties of ASAE's operational environment and proposes a solution to efficiently review the precalculated inspection routes and apply the required changes in an appropriate time frame. Vehicle routing problems (VRP) are optimization problems where the aim is to calculate the set of optimized routes for a vehicle fleet, from a starting point to several interesting locations. Dynamic vehicle routing problem (DVRP) is a variant of VRP that makes use of real-time information to calculate the most optimized set of routes at a certain moment [39]. DVRP is a challenging problem because its scope is real-time, meaning that decisions sometimes must be made in short time windows, preventing the use of complex algorithms that require long computational times [10]. The typical approach to this problem is to initially calculate the routes for the whole fleet and dynamically revise the defined operations plan in real-time, once a disruption occurs. This work will model the problem as a DVRP and will compare the performance of heuristics and other modern optimization techniques, proposing a solution that will reduce the impact of disruptions on inspection routes. An optimized operations plan will reduce the time required for inspections, allowing massive economic savings, while reducing a company's ecological footstep. The work can eventually be scaled and used in other institutions, such as GNR or PSP in Portugal, that operate similarly

Optimized routing of unmanned aerial systems to address informational gaps in counterinsurgency

Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 129-132).Recent military conflicts reveal that the ability to assess and improve the health of a society contributes more to a successful counterinsurgency (COIN) than direct military engagement. In COIN, a military commander requires maximum situational awareness not only with regard to the enemy but also to the status of logistical support concerning civil security operations, governance, essential services, economic development, and the host nation's security forces. Although current Brigade level Unmanned Aerial Systems (UAS) can provide critical unadulterated views of progress with respect to these Logistical Lines of Operation (LLO), the majority of units continue to employ UASs for strictly conventional combat support missions. By incorporating these LLO targets into the mission planning cycle with a collective UAS effort, commanders can gain a decisive advantage in COIN. Based on the type of LLO, some of these targets might require more than a single observation to provide the maximum benefit. This thesis explores an integer programming and metaheuristic approach to solve the Collective UAS Planning Problem (CUPP). The solution to this problem provides optimal plans for multiple sortie routes for heterogeneous UAS assets that collectively visit these diverse secondary LLO targets while in transition to or from primary mission targets. By exploiting the modularity of the Raven UAS asset, we observe clear advantages, with respect to the total number of targets observed and the total mission time, from an exchange of Raven UASs and from collective sharing of targets between adjacent units. Comparing with the status quo of decentralized operations, we show that the results of this new concept demonstrate significant improvements in target coverage. Furthermore, the use of metaheuristics with a Repeated Local Search algorithm facilitates the fast generation of solutions, each within 1.72% of optimality for problems with up to 5 UASs and 25 nodes. By adopting this new paradigm of collective Raven UAS operations and LLO integration, Brigade level commanders can maximize the use of organic UAS assets to address the complex information requirements characteristic of COIN. Future work for the CUPP to reflect a more realistic model could include the effects of random service times and high priority pop-up targets during mission execution.by Andrew C. Lee.S.M.in Transportatio

A mathematical programming approach for dispatching and relocating EMS vehicles.

We consider the problem of dispatching and relocating EMS vehicles during a pandemic outbreak. In such a situation, the demand for EMS vehicles increases and in order to better utilize their capacity, the idea of serving more than one patient by an ambulance is introduced. Vehicles transporting high priority patients cannot serve any other patient, but those transporting low priority patients are allowed to be rerouted to serve a second patient. We have considered three separate problems in this research. In the first problem, an integrated model is developed for dispatching and relocating EMS vehicles, where dispatchers determine hospitals for patients. The second problem considers just relocating EMS vehicles. In the third problem only dispatching decisions are made where hospitals are pre-specified by patients not by dispatchers. In the first problem, the objective is to minimize the total travel distance and the penalty of not meeting specific constraints. In order to better utilize the capacity of ambulances, we allow each ambulance to serve a maximum of two patients. Considerations are given to features such as meeting the required response time window for patients, batching non-critical and critical patients when necessary, ensuring balanced coverage for all census tracts. Three models are proposed- two of them are linear integer programing and the other is a non-linear programing model. Numerical examples show that the linear models can be solved using general-purpose solvers efficiently for large sized problems, and thus it is suitable for use in a real time decision support system. In the second problem, the goal is to maximize the coverage for serving future calls in a required time window. A linear programming model is developed for this problem. The objective is to maximize the number of census tracts with single and double coverage, (each with their own weights) and to minimize the travel time for relocating. In order to tune the parameters in this objective function, an event based simulation model is developed to study the movement of vehicles and incidents (911 calls) through a city. The results show that the proposed model can effectively increase the system-wide coverage by EMS vehicles even if we assume that vehicles cannot respond to any incidents while traveling between stations. In addition, the results suggest that the proposed model outperforms one of the well-known real time repositioning models (Gendreau et al. (2001)). In the third problem, the objective is to minimize the total travel distance experienced by all EMS vehicles, while satisfying two types of time window constraints. One requires the EMS vehicle to arrive at the patients\u27 scene within a pre-specified time, the other requires the EMS vehicle to transport patients to their hospitals within a given time window. Similar to the first problem, each vehicle can transport maximum two patients. A mixed integer program (MIP) model is developed for the EMS dispatching problem. The problem is proved to be NP-hard, and a simulated annealing (SA) method is developed for its efficient solution. Additionally, to obtain lower bound, a column generation method is developed. Our numerical results show that the proposed SA provides high quality solutions whose objective is close to the obtained lower bound with much less CPU time. Thus, the SA method is suitable for implementation in a real-time decision support system