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This paper develops an empirical model of the relationship between road traffic accidents and traffic flows. The analysis focuses on the accident externality which is mainly determined by the difference between the marginal and average risks. The model is estimated using a new dataset which combines hourly London traffic count data from automated vehicle recorders together with police records of road accidents. The accident-flow relationship is seen to vary considerably between different road classes and geographical areas. More importantly, even having controlled for these and other differences, the accident externality is shown to vary significantly with traffic flows. In particular, while the accident externality is typically close to zero for low to moderate traffic flows, it increases substantially at high traffic flows.

Trabajo presentado al "FUZZ-IEEE 2008" celebrado en Hong Kong del 1 al 6 de Junio de 2008. ; We address, by means of fuzzy linguistic summaries, two related problems: summarizing network flow statistics and making these statistics human-readable. Two complementary summarization methods are developed. First, a fixed set of protoforms of interest is defined, and the ones with a higher truth value are shown to the user as simple on-line summaries. This first method is suitable for real-time monitoring. Then, an association rules mining process is carried out in order to find hidden relations in flow records. Both approaches are implemented in a tool capable of real-time and off-line processing of network flow records. Experimental results for a number of heterogeneous NetFlow records show the usefulness of linguistic summaries to both network practitioners and users. ; This work has been supported in part by projects TEC2005-04359/MIC from the Spanish Ministry of Education and Science and project TIC2006-635 from the Andalusian regional Government. ; Peer Reviewed

This paper develops an empirical model of the relationship between road traffic accidents and traffic flows. The analysis focuses on the accident externality, which is determined mainly by the difference between the marginal and average risks. The model is estimated using a new data‐set which combines hourly London traffic count data from automated vehicle recorders together with police records of road accidents. The accident‐flow relationship is seen to vary considerably between different road classes and geographical areas. More importantly, even having controlled for these and other differences, the accident externality is shown to vary significantly with traffic flows. In particular, while the accident externality is typically close to zero for low to moderate traffic flows, it increases substantially at high traffic flows.

In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle ows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being able to work with a traffic distribution close to reality. We have compared the results of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%. ; Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by project number 8.06/5.47.4142 in collaboration with the VSB-Technical University of Ostrava and Universidad de Málaga UMA/FEDER FC14-TIC36, programa de fortalecimiento de las capacidades de I+D+i en las universidades 2014-2015, de la Consejería de Economía, Innovación, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER). Also, partially funded by the Spanish MINECO project TIN2014-57341-R (http://moveon.lcc.uma.es). The authors would like to thank the FEDER of European Union for financial support via project Movilidad Inteligente: Wi-Fi, Rutas y Contaminación (maxCT) of the "Programa Operativo FEDER de Andalucía 2014-2020. We also thank all Agency of Public Works of Andalusia Regional Government staff and researchers for their dedication and professionalism. Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports.

This study analyses the EU policies related to the assessment and management of traffic noise in urban areas. The analysis is based on the application of noise maps using Cadna A software for the central part of Kaunas city, Lithuania. Data on the traffic flows, height of buildings, speed limits combined with actual noise measurements are used as an input to the dispersion model. The assessment of noise is based on the guidelines of the method 'XPS 31-133' defined in the European Commission Recommenda-tion C(2003)2807. The formation of noise maps has raised the need for qualitative determination of traffic flow type and the elaboration of acoustical equivalent concept for different modes of transport. An analysis of alternative noise maps (scenarios) should be considered as an impor-tant component of urban traffic flows management.

Cruise tourism as a propulsive branch of tourism is being increasingly affirmed in the field of river transport. Accordingly, the main research problem in this paper is the analysis of the possibility of further affirmation and growth of passenger flows on river cruises on the Danube as the backbone of river traffic in the European Union. In the context of the defined research problem, the paper analyzes: relevant geo–traffic and socio–economic characteristics of the Danube, relevant indicators of passenger flows on cruises on the Danube – intensity, structure, dynamics and distribution of passenger traffic flows. Based on the aforementioned content, the further dynamics of passenger flows on cruises is concluded and forecasted, and the guidelines and factors of valorization of cruise tourism along the Danube Corridor are highlighted.

Ces quinze dernières années, le transport aérien a connu une expansion sans précédent au Canada. Cette étude fournit des prévisions de court et moyen terme du nombre de passagers embarqués\débarqués au Canada en utilisant divers modèles de séries chronologiques : la régression harmonique, le lissage exponentiel de Holt-Winters et les approches dynamiques ARIMA et SARIMA. De plus, elle examine si la combinaison des prévisions issues de ces modèles permet d'obtenir une meilleure performance prévisionnelle. Cette dernière partie de l'étude se fait à l'aide de deux techniques de combinaison : la moyenne simple et la méthode de variance-covariance. Nos résultats indiquent que les modèles étudiés offrent tous une bonne performance prévisionnelle, avec des indicateurs MAPE et RMSPE inférieurs à 10% en général. De plus, ils capturent adéquatement les principales caractéristiques statistiques des séries de passagers. Les prévisions issues de la combinaison des prévisions des modèles particuliers sont toujours plus précises que celles du modèle individuel le moins performant. Les prévisions combinées se révèlent parfois plus précises que les meilleures prévisions obtenues à partir d'un seul modèle. Ces résultats devraient inciter le gouvernement canadien, les autorités aéroportuaires et les compagnies aériennes opérant au Canada à utiliser des combinaisons de prévisions pour mieux anticiper l'évolution du traffic de passager à court et moyen terme. Mots-Clés : Passsagers aériens, Combinaisons de prévisions, Séries temporelles, ARIMA, SARIMA, Canada. ; This master's thesis studies the Canadian air transportation sector, which has experienced significant growth over the past fifteen years. It provides short and medium term forecasts of the number of enplaned/ deplaned air passengers in Canada for three geographical subdivisions of the market: domestic, transborder (US) and international flights. It uses various time series forecasting models: harmonic regression, Holt-Winters exponential smoothing, autoregressive-integrated-moving average (ARIMA) and seasonal autoregressive-integrated-moving average (SARIMA) regressions. In addition, it examines whether or not combining forecasts from each single model helps to improve forecasting accuracy. This last part of the study is done by applying two forecasting combination techniques: simple averaging and a variety of variance-covariance methods. Our results indicate that all models provide accurate forecasts, with MAPE and RMSPE scores below 10% on average. All adequately capture the main statistical characteristics of the Canadian air passenger series. Furthermore, combined forecasts from the single models always outperform those obtained from the single worst model. In some instances, they even dominate the forecasts from the single best model. Finally, these results should encourage the Canadian government, air transport authorities, and the airlines operating in Canada to use combination techniques to improve their short and medium term forecasts of passenger flows. Key Words: Air passengers, Forecast combinations, Time Series, ARIMA, SARIMA, Canada.

The analysis of origin-destination traffic flows may be useful in many contexts of application (e.g., urban planning, tourism economics) and have been commonly studied through the gravity model, which states that flows are proportional to ''masses" of both origin and destination, and inversely proportional to distance between them. Using data on the flow of mobile phone SIM among different aree di censimento, recorded hourly basis for several months and provided by FasterNet in the context of MoSoRe project, in this work we characterize and model the dynamic of such flows over the time in the strongly urbanized and flood-prone area of the Mandolossa (western outskirts of Brescia, northern Italy), with the aim of predicting the traffic flow during flood episodes. Whereas a traditional "static" mass explanatory variable is represented by residential population (Pop), or by gross domestic product (GDP), here we propose to use a most accurate set of explanatory variables in order to better account for the dynamic over the time. First, we employ a time-varying mass variable represented by the number of city-users by area and by time period, which has been estimated from mobile phone data (provided by TIM) using functional data approach and already adopted to derive crowding maps for flood exposure. Secondly, we include in the model a proper set of factors such as areal and time dummies, and a novel set of indices related to (e.g.) the number and the type of streets, the number of offices, restaurants or cinemas, which may be retrieved from OpenStreetMap. The joint use of these two novel sets of explanatory variables should allow us to obtain a better linear fitting of the gravity model and a better traffic flow prediction for the flood risk evaluation.