A mobile stroke unit (MSU) is an advanced ambulance equipped with specialized technology and trained healthcare personnel to provide on-site diagnosis and treatment for stroke patients. Providing efficient access to healthcare (in a viable way) requires optimizing the placement of MSUs. In this study, we propose a time-efficient method based on a genetic algorithm (GA) to find the most suitable ambulance sites for the placement of MSUs (given the number of MSUs and a set of potential sites). We designed an efficient encoding scheme for the input data (the number of MSUs and potential sites) and developed custom selection, crossover, and mutation operators that are tailored according to the characteristics of the MSU allocation problem. We present a case study on the Southern Healthcare Region in Sweden to demonstrate the generality and robustness of our proposed GA method. Particularly, we demonstrate our method's flexibility and adaptability through a series of experiments across multiple settings. For the considered scenario, our proposed method outperforms the exhaustive search method by finding the best locations within 0.16, 1.44, and 10.09 minutes in the deployment of three MSUs, four MSUs, and five MSUs, resulting in 8.75x, 16.36x, and 24.77x faster performance, respectively. Furthermore, we validate the method's robustness by iterating GA multiple times and reporting its average fitness score (performance convergence). In addition, we show the effectiveness of our method by evaluating key hyperparameters, that is, population size, mutation rate, and the number of generations.

Ambulance travel time estimations play a pivotal role in ensuring timely and efficient emergency medical care by predicting the duration for an ambulance to reach a specific location. Overlooking factors such as local traffic situations, day of the week, hour of the day, or the weather may create a risk of inaccurately estimating the ambulance travel times, which might lead to delayed emergency response times, potentially impacting patient outcomes. In the current paper, we propose a novel framework for accurately estimating ambulance travel times using machine learning paradigms, employing real-world spatiotemporal ambulance data from the Skane region, Sweden. Our framework includes data preprocessing and feature engineering, with a focus on variables significantly correlated with travel time. First, through a comprehensive exploratory data analysis, we highlight the main characteristics, patterns, and underlying trends of the considered ambulance data set. Then, we present an extensive empirical analysis comparing the performance of different machine learning models across different ambulance travel trip scenarios and feature sets, revealing insights into the importance of each feature in improving the estimation accuracy. Our experiments indicate that the aforementioned factors play a significant role when estimating the travel time.

Stroke is a life threatening medical condition that is caused either by a blood clot or a bleeding inside the brain. It is generally agreed that immediate treatment of stroke patients is crucial for their ability to recover; however, before treatment can be initiated, the stroke patient has to be diagnosed using, for example, a computed tomography scan of the brain. This, in turn, requires that the patient is transported to a hospital for diagnosis, which is a procedure that consumes valuable time and resources. In the current paper, we present an agent-based simulation model for assessment of logistical stroke patient triage policies. Using a population of stroke patients as input, the model enables to simulate the activities and the main decisions involved in the logistical operations of stroke patients. In an experimental study covering the southernmost part of Sweden, we used the simulation model in order to assess two different policies regarding where to send patients for diagnosis; the nearest hospital policy and the nearest hospital towards the stroke center policy. Our simulation results show that the patients in need of special treatment favor from being transported in direction towards the stroke center, whereas the patients who are not in need of specialist treatment favor from being sent to the closest hospital.

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.

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. 

Mobile Stroke Units (MSUs) are specialized ambulances that can diagnose and treat stroke patients; hence, reducing the time to treatment for stroke patients. Optimal placement of MSUs in a geographic region enables to maximize access to treatment for stroke patients. We contribute a mathematical model to optimally place MSUs in a geographic region. The objective function of the model takes the tradeoff perspective, balancing between the efficiency and equity perspectives for the MSU placement. Solving the optimization problem enables to optimize the placement of MSUs for the chosen tradeoff between the efficiency and equity perspectives. We applied the model to the Blekinge and Kronoberg counties of Sweden to illustrate the applicability of our model. The experimental findings show both the correctness of the suggested model and the benefits of placing MSUs in the considered regions.

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.

A mobile stroke unit (MSU) is a specialized ambulance, enabling to shorten the time to diagnosis and treatment for stroke patients. In the current paper, we present a simulation-based approach to study the potential impacts of collaborative use of regular ambulances and MSUs in prehospital transportation for stroke patients, denoted as co-dispatching. We integrated a co-dispatch policy in an existing modeling framework for constructing emergency medical services simulation models. In a case study, we applied the extended framework to southern Sweden to evaluate the effectiveness of using the co-dispatch policy for different types of stroke. The results indicate reduced time to diagnosis and treatment for stroke patients when using the co-dispatch policy compared to the situation where either a regular ambulance or an MSU is assigned for a stroke incident.

We present a method, which makes use of historical vehicle data and current vehicle observations in order to estimate 1) the route a vehicle has used and 2) the freight the vehicle carried along the estimated route. The method includes a learning phase and an estimation phase. In the learning phase, historical data about the movement of a vehicle and of the consignments allocated to the vehicle are used in order to build estimation models: one for route choice and one for freight allocation. In the estimation phase, the generated estimation models are used together with a sequence of observed positions for the vehicle as input in order to generate route and freight estimates. We have partly evaluated our method in an experimental study involving a medium-size Swedish transport operator. The results of the study indicate that supervised learning, in particular the algorithm Naive Bayes Multinomial Updatable, shows good route estimation performance even when significant amount of information about where the vehicle has traveled is missing. For the freight estimation, we used a method based on averaging the consignments on the historical known trips for the estimated route. We argue that the proposed method might contribute to building improved knowledge, e.g., in national road administrations, on the movement of trucks and freight.

In this paper, we propose a method for supporting the process of designing Intelligent Transportation System (ITS) services, which utilizes on primarily functional synergies with already existing services. Using synergies between services will enable sharing of resources, such as, information entities, functions and technical resources, which in turn may lead to reduced costs for implementing services. The method is built around an existing service description framework, which is used to describe both existing services and the service to be designed. In order to illustrate the usage of the suggested method, we have applied it for designing a new ITS service, i.e., the Liability Intelligent Transport System (LITS) service. The purpose of the LITS service is to support the process of identifying when, where and why freight has been damaged, and which actor was responsible when the freight was damaged. The LITS service may lead to better quality control of consignments and may also facilitate the identification of which actor was responsible for the freight damage, which is of particular interest in multi-modal transport. By applying our service design method we were able to identify that three out of four functions of the LITS service already exist in other existing ITS services. Therefore, the LITS service can be designed based on synergies with these services.

We present a study on information synergy between an electronic waybill (e-Waybill) and intelligent transportation system (ITS) services. A waybill is an important transport document, and it contains essential information about a consignment. We consider five e-Waybill solutions, which differ in where the e-Waybill information is stored, read, and written. We analyse 20 ITS services, and for each of them, the required input entities that can be provided by an e-Waybill are identified, and the synergy with each of the e-Waybill solutions is determined based on the location of the e-Waybill information. The analysis shows that e-Waybill solutions with storage at the freight-level enable the highest information synergy with ITS services. Our result may support the implementation of practical e-Waybill systems that provide high synergy with ITS services, which may lead to higher utilisation of ITS services and more sustainable transport, e.g., in terms of reduced congestion and emissions

An electronic waybill (e-Waybill) is a service whose purpose is to replace the paper waybill, which is a paper documents that traditionally follows a consignment during transport. An important purpose of the e-Waybill is to achieve a paperless flow of information during freight transport. In this paper, we investigate five e-Waybill solutions, that is, system design specifications for the e-Waybill, regarding their non-functional (technical) requirements. In addition, we discuss how well existing technologies are able to fulfil the identified requirements. We have identified that information storage, synchronization and conflict management, access control, and communication are important categories of technical requirements of the e-Waybill service. We argue that the identified technical requirements can be used to support the process of designing and implementing the e-Waybill service.

The focus of this paper is to present potential electronic waybill (e-waybill) solutions for a traditional waybill with the potential for supporting different Intelligent Transport System (ITS) services, such as, identification of freight, automating the exchange of content-related data for regulatory or commercial purposes, etc. At present there are some initiatives, for instance, by the International Air Transport Association (IATA) and the e-Freight framework, for achieving solutions that can handle e-waybills. Both these solutions focus on actor-to-actor communication, however we hypothesize that the storage of a waybill’s data both locally and centrally (i.e. actor-to-actor as well as goods-to-actor communication) can support more services than only central storage of the waybill’s data. Further we look at the information required and possible communication links between different actors in order to identify different ITS services that can be achieved through an e-waybill solution. We then map this information on to the e-waybill solutions that we have proposed, which will allow us to identify which services are supported by the different e-waybill solutions.

Traffic flow estimates play a key role in traffic network management and planning of transportation networks. Commonly it is the average daily traffic (ADT) flow for different road segments that constitute the data. This paper shows how an advanced and detailed analysis based on hourly flow measurements over the day can contribute to a deeper understanding of how hourly flows together reflect the vehicles’ routes. The proposed method identifies the shortest travel time paths between all possible origins and destinations in a transportation network, and thereafter it identifies the most significant routes in the network by performing statistical tests. For this purpose, the paper presents a mathematical model, a vehicle simulator based on this model, and a statistical framework that is able to find the most probable underlying routes. The paper contains a real test scenario based on 24-hour traffic flows (hour by hour) to demonstrate the applicability of the method.

It is widely known that the time to treatment is vital for patients who suffer from a stroke. Therefore, in different places in the world, the value of acquiring mobile stroke units has been identified. A mobile stroke unit is a specially developed ambulance for stroke treatment, which has special equipment and trained personnel who can perform intravenous stroke treatment (thrombolysis). It is clear that the involvement of mobile stroke units will aid at least a certain proportion of the stroke patients; however, mobile stroke units are often purchased and placed without any developed method or analysis of where and how it should be placed. In the current paper, we use expected value optimization in order to identify, depending on what perspective is of interest, efficiency or equality, the optimal placement of a mobile stroke unit. In an experimental study, considering the Skåne County in Sweden, we show that the placement of a mobile stroke unit may vary considerably depending on which of the two perspectives is adopted.

The use of agreement technologies in the planning and execution of goods transports is analyzed. We have previously suggested an approach called Plug and Play Transport Chain Management (PnP TCM) that provides agent-based support for key tasks, such as, finding the best sequence of transport services for a particular goods transport, monitoring the execution of the transport, and managing the interaction between the involved actors. In this paper we analyze five agreement technologies in the context of PnP TCM, i.e., semantics, norms, organizations, argumentation and negotiation, and trust. We conclude that all five technologies play a critical role in the realization of PnP TCM.

We investigate the opportunities and challenges of the forth wave of digitalization, also referred to as the Internet of Things (IoT), with respect to public transport and how it can support sustainable development of society. Environmental, economical, and social perspectives are considered through analysis of the existing literature and explorative studies. We conclude that there are great opportunities for both transport operators and planners, as well as for the travelers. We describe and analyze a number of concrete opportunities for each of these actors. However, in order to realize these opportunities, there are also a number of challenges that needs to be addressed. There are both technical challenges, such as data collection issues, interoperability, scalability and information security, and non-technical challenges such as business models, usability, privacy issues, and deployment.

A novel approach to efficiently plan and execute effective transport solutions is presented. It provides agent-based support for key tasks, such as, finding the best sequence of transport services for a particular goods transport, monitoring the execution of the transport, as well as the interaction between the involved actors. The approach is based on the FREIGHTWISE framework in which a minimal set of information packages is defined. The purpose is to capture all the information that needs to be communicated between the actors involved in a transport, such as, transport users, transport providers, and infrastructure managers, during the complete process from planning to termination. The approach is inspired by the concepts of virtual enterprises and breeding environments. We analyse the requirements of such an approach and describe a multi-agent system architecture meeting these requirements.

Demand Responsive Transport (DRT) is seen as a means to providing mobility for passengers living in lowdensity population areas and impaired passengers with a reasonable cost. Conventional public transport istoo expensive to provide a desired level of mobility for these categories of passengers. Hence DRT hasbeen introduced in order to replace or supplement existing transportation schemes. However, multiple DRTschemes were discontinued due to a high cost or poor patronage. In this work we argue that a simulationtool is required to analyze DRT applicability in given conditions before implementing it. As a first steptowards this tool, we describe the requirements that DRT impose on a simulator.

For the provision of efficient and high-quality public transport services in rural areas with a low population density, the introduction of Demand Responsive Transport (DRT) services is reasonable. The optimal design of such services depends on various socio-demographical and environmental factors, which is why the use of simulation is feasible to support planning and decision-making processes. A key challenge for sound simulation results is the generation of realistic demand, i.e., requests for DRT journeys. In this paper, a method for modelling and simulating commuting activities is presented, which is based on statistical real-world data. It is applied to Sjöbo and Tomelilla, two rural municipalities in southern Sweden.

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.

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.

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations without numerical difficulties.

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an ``iterative route cover optimization method'' to suggest  charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each  route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. 

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.

Travel time reliability is vital for both road agencies and road users. Expected travel time reliability can be used by road agencies to assess the state of a transportation system, and by road users, to schedule their trips. Road network deficiencies, such as insufficient traffic flow capacity of a road segment or poor road design, have a negative impact on the reliability of travel times. Thus, to maintain robust and reliable travel times, the detection of road network deficiencies is vital. By continuously analyzing travel times and using appropriate travel time reliability measurements, it is possible to detect existing deficiencies or deficiencies that may eventually occur unless necessary actions are taken. In many cases, indices and measurements of travel time reliability are related to the distribution of the travel times, specifically the skewness and width of the distribution. The current paper introduces a median-based misery index for travel time reliability. The index is robust and handles travel times that follow a skewed distribution well. The index measures the relative difference between the slow travel speeds and the free-flow travel speed. The index is inspired by the median absolute deviation, and its primary application is to detect routes or road segments with potential road network deficiencies. To demonstrate the applicability of the index, we conducted an empirical case study using real travel speed data from the European route E4 in Sweden. The results from the empirical case study indicate that the index is capable of detecting road segments with slow travel speeds regardless of the travel speed distribution.

In this paper, we introduce a new approach for collecting data for transport simulation models that is using on-line services in order to outsource parts of the modeling and computation of simulation models. We describe our approach of using on-line services as part of a simulation model and we present our experiences of applying the approach to a case study using the ASIMUT model, where the travelers between two neighbour cities in Southern Sweden are modeled. The results from our case study shows that the use of on-line services for data collection in transport simulation can bring advantages to the simulation model, for example, in terms of reduced needs for modeling of the transport system as well as computation inside the simulation model and improved access to the most recent information. We also noticed some limitations, such as the inability to access to information regarding the future such as timetables and no control over data provided by third-party services. However, we argue that there are solutions for each of the identified limitations, and therefore we believe that the suggested approach might provide a unique opportunity for future transportation simulation models.

An agent-based simulation model for supporting the decision making in urban transport planning is presented. The model can be used to investigate how different transport infrastructure investments and policy instruments will affect the travel choices of passengers. We identified four main categories of factors influencing the choice of travel: cost, time, convenience, and social norm. However, travelers value these factors differently depending on their in-dividual characteristics, such as age, income, work flexibility and environmen-tal engagement, as well as on external factors, such as the weather. Moreover, instead of modeling the transport system explicitly, online web services are used to generate travel options. The model can support transport planners by providing estimations of modal share, as well as economical and environmental consequences. As a first step towards validation of the model, we have con-ducted a simple case study of three scenarios where we analyze the effects of changes to the public transport fares on commuters’ travel choices in the Malmö-Lund region in Sweden.

We present a study, where we used regression in order to predict the number of bicycles registered by a bicycle counter (located in Malmö, Sweden). In particular, we compared two regression problems, differing only in their target variables (one using the absolute number of bicycles as target variable and the other one using the deviation from a long-term trend estimate of the expected number of bicycles as target variable). Our results show that using the trend curve deviation as target variable has potential to improve the prediction accuracy (compared to using the absolute number of bicycles as target variable). The results also show that support vector regression (using 2nd and 3rd degree polynomial kernels) and regression trees perform best for our problem.

We present TAPAS-Z, which is an agent-based freight transport analysis model for simulation of decision-making and transport activities. TAPAS-Z is a further development of a simulation model called TAPAS, and it has improved support for simulation of transport in large geographical regions. It is based on the principles that shipments are simulated for chosen supplier-consumer relations in a geographic region, and that the geographic locations of suppliers and consumers are randomly varied for each shipment. In TAPAS-Z, one supplier represents all real-world suppliers in a geographic zone, and one consumer represents all real-world consumers in a zone. In that way, TAPAS-Z is able to capture some of the diversity in freight transport that is caused by the varying geographic locations of senders and receivers, and which is important when assessing the impact of transport policy and infrastructural measures.

We present the Transportation And Production Agent-based Simulator (TAPAS), which is an agent-based model for simulation of transport chains that can be used, e.g., for analysis of transport-related policy and infrastructure measures. TAPAS is more powerful than traditional approaches to freight transport analysis, as it explicitly models production and customer demand, and it captures the interaction between individual transport chain actors, their heterogeneity and decision making processes, as well as time aspects. Whereas traditional approaches rely on assumed statistical correlation, TAPAS relies on causality, i.e., the focus is on the decisions and negotiations that lead to activities. TAPAS is composed of two connected layers, one that simulates the physical activities, e.g., production and transportation, and one that simulates the decision making and interaction between actors. We illustrate TAPAS with a scenario in which the consequences of three transport policy and infrastructure measures are studied.

The process of collecting traffic data is a key component to evaluate the current state of a transportation network and to analyze movements of vehicles. In this paper, we argue that both active stationary and mobile measurement devices should be taken into account for high-quality traffic data with sufficient geographic coverage. Stationary devices are able to collect data over time at certain locations in the network and mobile devices are able to gather data over large geographic regions. Hence, the two types of measurement devices have complementary properties and should be used in conjunction with each other in the data collection process. To evaluate the complementary characteristics of stationary and mobile devices for traffic data collection, we present a traffic simulation model, which we use to study the share of successfully identified vehicles when using both types of devices with varying identification rate. The results from our simulation study, using freight transport in southern Sweden, shows that the share of successfully identified vehicles can be significantly improved by using both stationary and mobile measurement devices. 

In this paper, we study the complementary characteristics of stationary and mobile devices for traffic data collection. Since stationary devices continuously collect traffic data at fixed locations in a network, they can give insight of the traffic at particular locations over a longer period of time. Mobile devices have wider range and are able to collect traffic data over a larger geographic region. Thus, we argue that both types of technology should be considered to obtain high-quality information about vehicle movements. We present a traffic simulation model, which we use to study the share of successfully identified vehicles when considering both stationary and mobile technologies with varying identification rate. The results of our study, where we focus on freight transport in southern Sweden, confirms that it is possible to identify the majority of vehicles, even when the identification rate is low, and that the share of identified vehicles can be increased by using both stationary and mobile measurement devices.

We propose an optimization model to tackle the problem of determining how projects are assigned to student groups based on a bidding procedure. In order to improve student experience in project-based learning we resort to actively involving them in a transparent and unbiased project allocation process. To evaluate our work, we collected information about the students' own views on how our approach influenced their level of learning and overall learning experience and provide a detailed analysis of the results. The results of our evaluation show that the large majority of students (i.e., 91%) increased or maintained their satisfaction ratings with the proposed procedure after the assignment was concluded, as compared to their attitude towards the process before the project assignment occurred.

As an alternative to the car, the bicycle is considered important for obtaining more sustainable urban transport. The bicycle has many positive effects; however, bicyclists are more vulnerable than users of other transport modes, and the number of bicycle related injuries and fatalities are too high. We present a clustering analysis aiming to support the identification of the locations of bicyclists' perceived unsafety in an urban traffic network, so-called bicycle impediments. In particular, we used an iterative k-means clustering approach, which is a contribution of the current paper, and DBSCAN. In contrast to standard k-means clustering, our iterative k-means clustering approach enables to remove outliers from the data set. In our study, we used data collected by bicyclists travelling in the city of Lund, Sweden, where each data point defines a location and time of a bicyclist's perceived unsafety. The results of our study show that 1) clustering is a useful approach in order to support the identification of perceived unsafe locations for bicyclists in an urban traffic network and 2) it might be beneficial to combine different types of clustering to support the identification process. (C) 2020 The Authors. Published by Elsevier B.V.

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.

It has been shown in previous research that regression modeling can be used in order to predict the number of bicycles registered by a bicycle counter. To improve the prediction accuracy, it has also been suggested that a long-term trend curve estimate can be incorporated in a regression problem formulation. A long-term trend curve estimate aims to capture those factors that are difficult, or even impossible, to explicitly model as input variables in the regression model. In the current paper, we present a regression-based approach for evaluating long-term trend curve estimates regarding their possibility to improve the regression prediction accuracy of bicycle counter data. We illustrate our approach by applying it on a time series recorded by a bicycle counter in Malmö, Sweden. For the considered data set, our experimental results indicate that a polynomial of degree two, which has been fitted to the time series, gives the best prediction.

We present an agent-based optimization approach that is built upon the principles of Dantzig-Wolfe column generation, which is a classic reformulation technique. We show how the approach can be used to optimize production, inventory, and transportation, which may result in improved planning for the involved supply chain actors. An important advantage is the possibility to keep information locally when possible, while still enabling global optimization of supply chain activities. In particular, the approach can be used as strategic decision support to show how the involved actors may benefit from applying Vendor Managed Inventory (VMI). In a case study, the approach has been applied to a real-world integrated production, inventory and routing problem, and the results from our experiments indicate that an increased number of VMI customers may give a significant reduction of the total cost in the system. Moreover, we analyze the communication overhead that is caused by using an agent-based, rather than a traditional (non agent-based) approach to decomposition, and some advantages and disadvantages are discussed.

We present a novel approach for increasing the operating room efficiency by significantly reducing the turnover time in elective surgical case scheduling. Reduced turnover time typically leads to increased surgeon utilization and increased patient throughput. Our main contribution is an optimization model for generating operating room schedules in a surgery department, where the pre-procedure of a surgical case is allowed to overlap with the procedure and post-procedure activities of an ongoing surgical case assigned to the same operating room. In addition, we present a computational experiment, where we create and compare 1-day schedules for a surgery department consisting of three operating rooms. The results of the experiment clearly show the potential of the idea of allowing overlapping surgical activities. This feature allows for significantly improved schedules compared to when no overlaps are allowed. The experiment also verifies the correctness of the optimization model.

We contribute an extension of the agent-based freight transport analysis model TAPAS-Z, whose aim is to simulate decision- making and other activities in transport chains. TAPAS-Z is a further development of the TAPAS model, and it enables stochastic variation, based on statistical distributions, of the locations of senders and receivers of freight, hence providing improved support for simulation of transport between larger regions. The model extension presented in this paper enables stochastic sampling of sender and receiver locations from historically known data, which we argue is beneficial in those situations where such data exist. We also contribute a case study where we used our extended TAPAS-Z model to simulate transport of timber from forest felling places to a Swedish paper mill. The case study illustrates how we recommend conducting a case study using the extended TAPAS-Z model. The aim of the study was to assess the possible implications of a structural change from a time-based to a distance-based Eurovignette system for heavy freight trucks in Sweden, which will lead to increased costs for most road users. As a mitigation action for the increased costs, we studied the possibility to use heavier trucks than the currently allowed 60 tonne trucks. The simulation results suggest that an introduction of a distance-based Eurovignette is expected to cause a small modal shift from road to rail for the studied type of transport. Furthermore, an introduction of larger freight trucks is expected to lead to a complete shift to road transport.

We present a role-based method for structured and uniform analysis of supply chain models. The method is based on a framework of supply chain roles, responsibilities, and interactions, which can be used to represent different types of organizations involved in providing and using products and transport services. To show the applicability, validity and generality of the method, we have used it to analyze five different supply chain models; three agent-based models, one model based on discrete event simulation, and one based on system dynamics.

The aim of this paper is to present a study on freight transport analysis models. The purpose is to identify different stakeholders’ perceptions of existing models, e.g., strengths and weaknesses, and of their requirements and views on future models. The study is based on a questionnaire and interviews with representatives of public authorities, consultancy companies, and universities in Sweden and Denmark. The study shows that there is a need for freight analysis models for supporting the transport planning in public authorities, including impact assessment of actions and estimation of freight flows. The respondents work mainly with macro-level models, whose main strength is their large geographic scopes, which allow comparative studies on, e.g., the national level using one model. Weaknesses include poor quality, missing functionality, and inadequate user-friendliness. In order to achieve improved freight transport analysis, the respondents wish to include more detailed logistics aspects in their analyses, which could possibly be achieved by combining macro-level and agent-based models. The outcome of our study might be used by researchers and public authorities in order to, e.g., guide the decision-making on future model development: the views of the model users and clients are important to consider in order to assure that the development and research efforts lead to fulfilling their needs. The presented work provides insight into the needs and attitudes of model users and clients involved in freight transport analysis. This knowledge is important, e.g., for researchers involved in model development. According to the best of our knowledge, there is no previous study like the one presented.

To predict which supply chain effects will appear when applying governmental control policies, infrastructure investments, and business strategies, multi-agent-based simulation (MABS) can be used. In this paper, we identify abstract supply chain responsibilities, roles and interactions that are argued to be sufficient for representing all types of organizations involved in the processes of buying and selling products and transport services. The identified responsibilities, roles and interactions are organized into a framework together with a set of modeling guidelines, which we relate to the GAIA methodology to simplify the process of developing multi-agent-based supply chain simulation models. To illustrate the usage of the framework, we provide two case studies where we apply it to two different MABS models.

Macro-level models is the dominating type of freight transport analysis models for supporting the decision-making in public authorities. Recently, also agent-based models have been used for this purpose. These two model types have complementing characteristics: macro-level models enable to study large geographic regions in low level of detail, whereas agent-based models enable to study entities in high level of detail, but typically in smaller regions. In this paper, we suggest and discuss three approaches for combining macro-level and agent-based modeling: exchanging data between models, conducting supplementary sub-studies, and integrating macro-level and agent-based modeling. We partly evaluate these approaches using two case studies and by elaborating on existing freight transport analysis approaches based on executing models in sequence.

In this paper we elaborate on the usage of multi-agent-based simulation (MABS) for quantitative impact assessment of transport policy and infrastructure measures. We provide a general discussion on how to use MABS for freight transport analysis, focusing on issues related to input data management, validation and verification, calibration, output data analysis, and generalization of results. The discussion is built around an agent-based transport chain simulation tool called TAPAS (Transportation And Production Agent-based Simulator) and a simulation study concerning a transport chain around the Southern Baltic Sea.

A set of GPS traces for bicyclists and a set of notifications by bicyclists of problematic situations (spots identified by GPS records) had been collected independently. The data collection periods did not coincide but overlapped and none was contained in the other one. The aim is to use both datasets to determine an optimal action plan for problem solving given a limited budget. First, problematic locations are clustered. Each cluster corresponds to an impediment. Impediments are then associated with trips using a distance function. The aim is to find out which impediments to solve under a given budget constraint in order to maximize the number of impediment free trips. Thereto the trip set is partitioned by matching each trip with the largest set of its affecting impediments. Solving all impediments in such set induces a cost and makes the associated part of trips impediment free. An optimizer is presented and evaluated. (C) 2020 The Authors. Published by Elsevier B.V.

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.

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.

The efficiency of road transport is typically influenced by factors such as, weather, choice of road, and time of day, and day of the week. Knowledge about interactions between different traffic- and transport related factors and their influence on the execution of transport is important in transport planning. The purpose of this paper is to study the impact of different factors on the performance of road transport. We aim to contribute to improved transport planning by analysing traffic and transport data obtained from different sources in order to support data driven decision making. Through a review of existing literature and discussions with a Swedish road transport operator, we identified factors that could be relevant to consider when planning a transport, e.g., related to weather, location of roads where the transport will take place, and planned time of the transport. As a result of variation in size, type and volume of the data representing these factors, suitable machine learning algorithms were selected, such as Decision Stump, M5 model tree, M5 regression tree, RepTree, M5 rules, and linear regression in order to study the data. Our experimental results illustrate the complexity associated to the performance of road transport systems mainly because of the dependency between the choices of influencing factors and geographic location of the road segment.