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This paper presents a methodology to locate vehicle base stations using a scenario based optimisation to address daily traffic and demand changes, which are due to what we define as city dynamics. The model allows us to understand better how these daily changes affect an urban emergency medical service (uEMS) response system. The methodology incorporates two steps. The first step uses scenario-based optimisation and survival function theory to locate vehicle base stations, whereas the second step uses agent-based simulation to assess the solution performance and compare it with average-period and non-survival prone solutions. The proposed models are tested for different situations using real data from the city of Porto. The results of the sensitivity analyses show the importance of understanding the dynamics of cities and how they impact uEMS response systems. Useful insights regarding the number of stations and the average response time are addressed together with the minimum number of stations required for different maximum response time limits and different survival coefficients. Finally, we conclude that a multi-period solution improves response time because it accounts for city dynamics and that a heterogeneous survival-based approach benefits victims' by properly measuring the system response concerning the victims' outcome. Keywords Emergency medical service; Scenario-based optimisation; Simulation; City dynamics; Survival functions; Multiperiod approach

Besides fatalities, road traffic crashes also cause a large number of nonfatal injuries with a high impact on economic and human costs to society. In order to allow a deep knowledge of the nonfatal injuries, efforts have been made to improve injury severity classification. Recently, and in line with the OECD working group proposal, the European Commission adopted the maximum abbreviated injury scale (MAIS) classification. This injury severity classification is based on medical diagnosis, which is reported by the international classification of diseases (ICD). Therefore, the adoption of MAIS classification will open the door to a new source of information based on hospital data. Furthermore, the type of medical treatment is commonly described using the international classification named diagnosis-related group (DRG), which is a system codification usually used as a reimbursement mechanism as well as to perform comparisons across hospitals. Tools and methods to easily use this clinical metrics to traffic injury analysis are critical to advance safety knowledge. In this study, we seek to explore the most used clinical metrics that are ICD and DRG to describe the diagnosis and the medical treatment, respectively. The ICD is converted to abbreviated injury scale (AIS), which in time provides the MAIS, i.e., the severity of the injury but also the anatomical description such as the type of body region and anatomical structure. On the other hand, DRG is used to estimate the health care costs (HCC) applying a national standard methodology. Together with the length of hospital stay (LHS), statistical analyses are applied using generalized linear models (GLM) selected depending on the type of response variable, i.e. discrete or continuous. Due to an evident correlation between body region and MAIS, we firstly analyse the relationship between both variables. Also, the combination of body region and type of anatomical structure is set as other alternative variable. The ordinal logit model is applied, showing that for instance, head is the region of the body associated with high severity, particularly regarding loss of consciousness. Secondly, the relationship between HCC and MAIS, and HCC and body region were analysed separately by using a log link and gamma distribution GLM. The results clearly show that increasing severity, the HCC increase with an evident leap between score 2 to 3 and between score 4 to 5. The head is the body region associated to higher medical treatment costs. Because LHS is still widely used has a measure of injury severity, we apply the same model to the LHS as response variable. Despite the type of relationship was found to be more or less the same, differences were found between LHS model and HCC model. Finally, a discussion is presented with reference to the long term costs estimated by disability-adjusted life-year (DALY), identifying the future needs to a huge implementation of this metric to traffic injuries.

This chapter presents an alternative approach to research regarding hotspot definition and identification based on a probabilistic model that defines the dependent variable as an indicator of a discrete choice. A binary choice model was used considering a binary dependent variable that differentiates a hotspot (category 1) from a safe (category 0) site set by the number of accidents per kilometer. The proposed methodology consists of two main steps. First, a threshold value for the number of accidents is set to distinguish hotspots from safe sites (category 1 or 0, respectively). A strategy to more accurately define a hot spot was developed based on an epidemiological criteria constraint. Based on this classification, a binary model is applied that allows the construction of an ordered site list using the probability of a site being a hotspot. To apply this approach risk factors including traffic volume, the number of minor intersections per kilometer, the road function classification and land use from an urban segment data set collected over a five-year period from Porto, Portugal, were used. The probabilities were estimated by the binary choice model, and a performance evaluation was then applied. The second step involves the definition of four classes to classify a site in terms of safety using the uncertainty in a site being a hot spot, setting for each class a range of probability values. Thus, the classification of a site through one of these classes may be a tool to define priorities and types of engineering intervention, allowing for the efficient management of site intervention. The gains of using this method are related to the simplicity of its application, while critical issues such as prior distribution effect assumptions and the regression-to-the-mean phenomenon are overcome. Further, the proposed model provides a realistic and intuitive perspective and supports easy practical application. Once a site is identified, the diagnosis of the problems and potential treatments will be straightforward.

An alternative methodology is presented for hot-spot identification based on a probabilistic model. In this method, the ranking criterion for hot-spot identification conveys the probability of a site's being a hot spot or not being a hot spot. A binary choice model is used to link the outcome to a set of factors that characterize the risk of the sites under analysis on the basis of two categories (0/1) for the dependent variable. The proposed methodology consists of two main steps. After a threshold value for the number of accidents is set to distinguish hot spots from safe sites (Category 1 or 0, respectively), a binary model based on this classification is applied. This model allows the construction of a site list ordered by using the probability of a site's being a hot spot. In the second step, the selection strategy can target a fixed number of sites with the greatest probability or all sites exceeding a specific probability, such as .5. To demonstrate the proposed methodology, simulated urban intersection data from Porto, Portugal, covering 5 years are used. The results of the binary model show a good fit. To evaluate and compare the probabilistic method with other commonly used methods, the performance of each method is tested by its power to detect true hot spots. The test results indicate the superiority of the proposed method. This method is simple to apply, and critical issues such as assumptions of a prior distribution effect and the regression-to-the-mean phenomenon are overcome. Further, the model provides a realistic and intuitive perspective.

This paper presents a probabilistic approach to measure the potential to reduce crash frequencyof urban segments. This new approach leads to a hotspot definition and identification using aprobabilistic model defining the dependent variable as an indicator of a discrete choice. A binarychoice model is used considering a binary dependent variable that differentiates a hotspot from asafe site set by the number of crashes per kilometre. The explanatory variables to set similarsegments are based on average annual daily traffic, segment length, density of minorintersections. A threshold value for the number of crashes per kilometre is set to distinguishhotspots from safe sites. Based on this classification, a binary model is applied that allows theconstruction of an ordered site list using the probability of a site being a hotspot. Ademonstration of the proposed methodology is provided using simulated data. For the simulationdesign, urban segment data from Porto, Portugal, covering a five-year period are used. Theresults of the binary model show a good fit. To evaluate and compare the probabilistic methodwith other used methods described in the Highway Safety Manual, measures are used to test theperformance of each method in terms of its power to detect the "true" hotspots. As alreadydemonstrated through the application of the binary model to urban intersections, the test resultsindicate that the binary model performs better than the other two models. The gains of using thismethod are the simplicity, the reliability, and the efficiency.

Portugal, as a European country, has set a target for cutting injuries. However, the real magnitude of the road safety and the scale of long-term impairment and associated socio-economic costs are not well known given the common problems of under-reporting and misreporting under current arrangements. The problem is that current figures on serious injuries are generalized, and consequently reference values are usually estimates. Thus, in presence of these problems, comparison across European countries has been a complex task Knowing that no single database will provide enough information to give a complete picture of road traffic injuries and to fully understand underlying injury mechanisms, the use of health sector data to complement police data is recognized as being the key for future improvements in road safety analysis. Therefore, an accident record linkage is the first step towards a wide road safety analysis, comprising the knowledge of the accident site and the consequences of the accident covering the period between the time immediately after the accident and the discharge time of the accident victim(s). In this context, this paper will provide the first assessment of the needs of a linked data system, to be applied in Portugal, in response to EU strategy on serious road injuries. This kind of data may be particularly important to analyze, understand, and define the vulnerable road users' (VRU) traffic safety issues core, and to propose and evaluate practical accident countermeasures. In fact the progress in the safe mobility has not been as consistent as desirable in part because of the lack of knowledge of the real situation.

An optimization model for a rail freight network was developed as asupport tool for planning and policy decisions involved in the improvementof rail networks on a regional and national level. The model wasbased on a strategic traffic assignment model, which was designed tomodel macronetworks with a high aggregation level, and was exclusivelydesigned for freight traffic. The model contemplates road and railtransport modes and considers two types of cargo: intermodal cargo,which is generally transported in containers and is easily interchangedbetween different modes at intermodal terminals, and general cargo,which represents all the remaining cargo. The optimization process wasbased on a local search heuristic that delivers good solutions in a reasonablecomputing time. The quality of each network improvement solutionwas assessed on the basis of the reductions in total generalized costsand carbon dioxide emissions. This optimization model for a freightnetwork is innovative because it is not limited: both the improvementof existing links and the construction of new ones are allowed, and themodel does not have a limit on the number or variety of possibilitiesfor network improvement. The adaptability to different conditions wasemphasized when the model was applied to two networks under differentinvestment scenarios because the model delivered considerablydifferent solutions adapted to the conditions of each scenario.

A theoretical definition of a hotspot is any location that has a higher expected number of accidents than other similar locations as a result of local risk factors present at the location. This study presents an alternative approach to research regarding hot spot definition and identification based on a probabilistic model that defines the dependent variable as an indicator of a discrete choice. A binary choice model was used considering a binary dependent variable that differentiates a hot spot (category 1) from a safe (category 0) site set by the number of accidents per kilometer. To define these two categories, it is necessary to use a strategy that selects a hot spot as accurately as possible. Based on a threshold strategy, various hypotheses were analyzed to obtain the most appropriate value to balance sensitivity and specificity criteria (epidemiological criteria). To apply this approach, risk factors including traffic volume, the number of minor intersections per kilometer, the road function classification and land use from an urban segment data set collected over a five-year period from Porto, Portugal, were used. The probabilities were estimated by the binary choice model, and a performance evaluation was then applied. In addition, considering the probabilistic nature of this approach, it is also possible to define four classes to classify a site in terms of safety using the uncertainty in a site being a hot spot, setting for each class a range of probability values. Furthermore, a comparative analysis was considered to test the performance of the qualitative response (QR) method relative to two commonly applied methods - accident frequency (AF) and empirical Bayes (EB). Considering the probabilistic nature of this approach, two probability threshold values were determined based on the "correct proportion" of sensitivity and specificity. These two values, along with the 0.5 probability value that the model defines to separate a hot spot from a non-hot spot, were used to define four classes. Using these classes, priorities and types of engineering intervention can be determined and thus, site interventions can be efficiently managed. In addition, the QR approach was compared with the AF and EB, against four robust and informative quantitative evaluation criteria tests. These tests showed that the QR method performs better than the two other methods. In this paper, a strategy to more accurately classify hot spots from a set of sites is proposed using four classes that may define priorities and types of engineering intervention, allowing for the efficient management of site intervention. Above all, this analysis proves that the application of a probabilistic model is an alternative approach to hot spot research.

Neste trabalho apresenta-se um novo método de definição e identificação de zonas de acumulação (ZAA) de acidentes considerando um modelo de regressão binário. Este modelo permite calcular a probabilidade de um local ser ou não ZAA tendo em conta as características geométricas e funcionais do mesmo. Este novo método foi aplicado ao caso das interseções tendo-se, para tal, gerado uma base de dados fictícia considerando as características da sinistralidade e das interseções da cidade do Porto. Através da simulação de dados é possível conhecer a priori as "verdadeiras" ZAA. O desempenho do método foi analisado com base nos erros resultantes da classificação dos locais em ZAA ou não-ZAA, e comparado com dois dos métodos mais aplicados e analisados - o método de ranking pelo número de acidentes e o método Bayesiano-empírico. Desta análise verificou-se que o método binário proposto tem claramente melhor desempenho. This paper presents a new methodology to define and to identify hot spot based on a binary regression model. Considering the geometric and functional site characteristics, the model estimates the probability of a site being a hot spot. This method was applied to intersections using a simulation-based approach to data generated considering the characteristics of the Porto city data base. Using simulation, it is possible to establish sites that are a priori "true" hot spot. The performance of the new method was evaluated using the errors of the classification outcomes of the sites and compared with two commonly implemented methods that are the simple ranking of sites and the empirical Bayesian technique. This analysis has set that the proposed binary model performed better.

A análise dos acidentes rodoviários, sobretudo pelo forte impacto destes na sociedade, tem merecido especial atenção dos principais agentes e organizações com responsabilidades na garantia da segurança dos cidadãos. Neste sentido, revela-se premente a aplicação de uma análise de risco e o desenvolvimento de limites expectáveis de aceitação para o caso dos acidentes rodoviários ocorridos em estrada a céu aberto. Com a presente comunicação pretende-se definir alguns critérios e procedimentos relativos à análise de risco em vias rodoviárias, conduzindo à formulação e especificação dos limites de aceitação do risco de colisão em vias rodoviárias. Assim, identificaram-se os indicadores de referência e os respectivos limites de tolerância, sendo ainda propostos valores limites nacionais por tipo de via. Tendo como base o estudo das curvas F-N a investigação ambiciona ainda a definição de valores limite para o risco social associado aos acidentes por colisão.

O planeamento do sistema de transportes é uma actividade importante que permiteantecipar as consequências de determinadas acções avaliando os impactes daí decorrentes,nomeadamente ao nível da sinistralidade.Com este trabalho pretende-se analisar diferentes formas de integrar a segurança rodoviáriano processo de planeamento e de tomada de decisão em meio urbano, propondo-se umaestrutura para a sua análise e avaliação. Dado que nesta fase os dados disponíveis são aindapouco precisos, dificultando a estimação dos efeitos das soluções estudadas, apresenta-se,ainda neste trabalho, modelos econométricos que atendem à limitação dos dados, emparticular, os relativos às características geométricas e de tráfego.

This paper provides the estimation of an aggregate freight railway demand function to analyze effects arising from a change of a variety of economic, technological and demographic characteristics. Thus, the paper presents estimates of demand elasticities with respect to price, income, quality of service and a range of exogenous characteristics. The results reveal that freight transport exhibit a slight increase of traffic density but significant returns to network length. In contrast to previous studies, this study reveals that the aggregate own and cross price elasticities of the rail freight demand are quite inelastic. Demographic/environmental characteristic elasticities confirm expectations that rail freight transport is particularly competitive for long run movements between major city agglomerations. Moreover, the elasticities related to the variation in the supply of alternative transport modes indicate that there are complementarities between sea and inland waterway modes and the rail mode. With respect to road transport, rail demand response is considered to be as sensitive to price as to supply. In global terms, it is clear that the railway transport mode gains greater competitiveness from road transport rather than from air and maritime transport modes.