Constructing simulation models can be a complex and time-consuming task, in particular if the models are constructed from scratch or if a general-purpose simulation modeling tool is used. In this paper, we propose a model construction framework, which aims to simplify the process of constructing discrete event simulation models for emergency medical service (EMS) policy analysis. The main building blocks used in the framework are a set of general activities that can be used to represent different EMS care chains modeled as flowcharts. The framework allows to build models only by specifying input data, including demographic and statistical data, and providing a care chain of activities and decisions. In a case study, we evaluated the framework by using it to construct a model for the simulation of the EMS activities related to acute stroke. Our evaluation shows that the predefined activities included in the framework are sufficient to build a simulation model for the rather complex case of acute stroke.

A mobile stroke unit (MSU) is a special type of ambulance, where stroke patients can be diagnosed and provided intravenous treatment, hence allowing to cut down the time to treatment for stroke patients. We present a discrete event simulation (DES) model to study the potential benefits of using MSUs in the southern health care region of Sweden (SHR). We included the activities and actions used in the SHR for stroke patient transportation as events in the DES model, and we generated a synthetic set of stroke patients as input for the simulation model. In a scenario study, we compared two scenarios, including three MSUs each, with the current situation, having only regular ambulances. We also performed a sensitivity analysis to further evaluate the presented DES model. For both MSU scenarios, our simulation results indicate that the average time to treatment is expected to decrease for the whole region and for each municipality of SHR. For example, the average time to treatment in the SHR is reduced from 1.31h in the baseline scenario to 1.20h and 1.23h for the two MSU scenarios. In addition, the share of stroke patients who are expected to receive treatment within one hour is increased by a factor of about 3 for both MSU scenarios.

Traffic flows play a very important role in transportation engineering. In particular, link flows are a source of information about the traffic state, which is usually available from the authorities that manage road networks. Link flows are commonly used in both short-term and long-term planning models for operation and maintenance, and to forecast the future needs of transportation infrastructure. In this paper, we propose a model to study how traffic flow in one location can be expected to reflect the traffic flow in a nearby region. The statistical basis of the model is derived from link flows to find estimates of the distribution of traffic flows in junctions. The model is evaluated in a numerical study, which uses real link flow data from a transportation network in southern Sweden. The results indicate that the model may be useful for studying how large departing flows from a node reflect the link flows in a neighboring geographic region.

For acute medical conditions, for instance strokes, the time until the start of the treatment is a crucial factor to prevent a fatal outcome and to facilitate the recovery of the patient’s health. Hence, the planning and optimization of patient logistics is of high importance to ensure prompt access to healthcare facilities in case of medical emergencies. Computer simulation can be used to investigate the effects of different stroke logistics policies under realistic conditions without jeopardizing the health of the patients. The success of such policies greatly depends on the behavior of the individuals. Hence, agent-based simulation is particularly well-suited as it imitates human behavior and decision-making by means of artificial intelligence, which allows for investigating the effects of policies under different conditions. Agent-based simulation requires the generation of a realistic synthetic population, that adequately represents the population that shall be investigated such that reliable conclusions can be drawn from the simulation results. In this article, we propose a process for generating an artificial population of potential stroke patients that can be used to investigate the effects of stroke logistics policies using agent-based simulation. To illustrate how this process can be applied, we present the results from a case study in the region of Skåne in southern Sweden, where a synthetic population of stroke patients with realistic mobility behavior is simulated. 

For patients suffering from a stroke, the time until the start of the treatment is a crucial factor with respect to the recovery from this condition. In rural regions, transporting the patient to an adequate hospital typically delays the diagnosis and treatment of a stroke, worsening its prognosis. To reduce the time to treatment, different policies can be applied. This includes, for instance, the use of Mobile Stroke Units (MSUs), which are specialized ambulances that can provide adequate care closer to where the stroke occurred. To simulate and assess different stroke logistics policies, such as the use of MSUs, a major challenge is the realistic modeling of the patients. In this article, we present an approach for generating an artificial population of stroke patients to simulate when and where strokes occur. We apply the model to the region of Skåne, where we investigated the relevance of travel behavior on the spatial distribution of stroke patients.

This article demonstrates an approach to the simulation of Demand Responsive Transport (DRT) – a flexible transport mode that typically operates as a combination of taxi and bus modes. Travellers request individual trips and DRT is capable of adjusting its routes or schedule to the needs of travellers. It has been seen as a part of the public transport network, which has the potential to reduce operational costs of public transport services, to provide better service quality for population groups with limited mobility and to improve transport fairness. However, a DRT service needs to be thoroughly planned to target the intended user groups, attract a sufficient demand level and maintain reasonable operational costs. As the demand for DRT is dynamic and heterogeneous, it is difficult to simulate it with a macro approach. To address this problem, we develop and evaluate an individual-based simulation comprising models of traveller behaviour for both supply and demand sides. Travellers choose a trip alternative with a mode choice model and DRT vehicle routing utilises a model of travellers’ mode choice behaviour to optimise routes. This allows capturing supply-side operational costs and demand-side service quality for every individual, what allows for designing a personalised service that can prioritise needy groups of travellers improving transport fairness. By simulating different setups of DRT services, the simulator can be used as a decision support tool.

A mobile stroke unit (MSU) is an ambulance, where stroke patients can be diagnosed and treated. Recently, placement of MSUs has been studied focusing on either maximum population coverage or equal service for all patients, termed efficiency and equity, respectively. In this study, we propose an unconstrained optimization model for the placement of MSUs, designed to introduce a tradeoff between efficiency and equity. The tradeoff is based on the concepts of weighted average time to treatment and the time difference between the expected time to treatment for different geographical areas. We conduct a case-study for Sweden’s Southern Health care Region (SHR), generating three scenarios (MSU1, MSU2, and MSU3) including 1, 2, and 3 MSUs, respectively. We show that our proposed optimization model can tune the tradeoff between the efficiency and equity perspectives for the MSU(s) allocation. This enables a high level of equal service for most inhabitants, as well as reducing the time to treatment for most inhabitants of a geographic region. In particular, placing three MSUs in the SHR with the proposed tradeoff, the share of inhabitants who are expected to receive treatment within an hour potentially improved by about a factor of 14 in our model.

Electric vehicles (EVs) are environmentally friendly and are considered to be a promising approach toward a green transportation infrastructure with lower greenhouse gas emissions. However, the limited driving range of EVs demands a strategic allocation of charging facilities, hence providing recharging opportunities that help reduce EV owners' anxiety about their vehicles' range. In this paper, we study a set covering method where self-avoiding walks are utilized to find the most significant locations for charging stations. In the corresponding optimization problem, we derive a lower bound of the number of charging stations in a transportation network to obtain full coverage of the most probable routes. The proposed method is applied to a transportation network of the southern part of Sweden.

We propose a method, whose purpose is to combine a set of GPS traces collected by bicyclists with a set of notifications of problematic situations to determine an optimal action plan for solving safety related problems in a traffic network. In particular, we use optimization to determine which problem locations to resolve under a given budget constraint in order to maximize the number of impediment free trips. The method aims to suggest a priority of impediments to resolve, which would be manually infeasible. The proposed method consists of two steps. First, problematic locations are clustered, where each cluster corresponds to a so-called impediment. Each impediment is associated with trips nearby using a distance function. The trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments associated with such a part induces a cost and makes the associated part of trips impediment free. The second step aims to find the set of impediments that can be solved with a given budget and that makes the maximum number of trips impediment free. A branch-and-bound optimizer for the second step is presented and evaluated. The clustering parameters affect the set of identified impediments and the extent of each of them. In order to evaluate the sensitivity of the result to the clustering parameters a technique is proposed to consistently estimate the impediment resolution cost. Our study aims to support the interactive urban designer to improve the urban bicycle road infrastructure. By providing a method to prioritize between impediments to resolve, it also aims to contribute to a safer and more attractive traffic situation for bicyclists.

The bicycle has many positive effects; however, bicyclists are more vulnerablethan users of other transport modes, andthe number of bicycle related injuries and fatalities are toohigh.We present a clustering analysis aiming to support the identification of the locations ofbicyclists' perceived unsafety in an urban trafficnetwork, so-called bicycle impediments.In  particular,  we presentan  iterative  k-means  clustering approach,  which  in  contrast  to  standard  k-means  clustering, enables to remove outliers and solitary points from the data set. In our study, we used data collected by bicyclists travelling inthe city of Lund, Sweden, where each data point defines a location andtime of a bicyclist's perceived unsafety.The results of our study show that 1) clustering is a usefulapproach in order to support the identification of perceived unsafelocations forbicyclists in an urban traffic networkand2) it might bebeneficial to combine different types of clustering to support theidentification process. Furthermore, using the adjusted Rand index, our results indicate highrobustness of our iterative k-means clustering approach.