Bus bays inventory using a terrestrial laser scanning system

This article presents the use of laser scanning technology for the assessment of bus bay geo-location. Ground laser scanning is an effective tool for collecting three-dimensional data. Moreover, the analysis of a point cloud dataset can be a source of a lot of information. The authors have outlined an innovative use of data collection and analysis using the TLS regarding information on the flatness of bus bays. The results were finalized in the form of colour three-dimensional maps of deviations and pavement type

The use of explanatory variables in time series modelling : applications to transport demand and road risk

L’objet de la thèse est d’exposer une démarche méthodologique qui vise à prendre en compte, dans les modèles de séries temporelles, des effets exogènes mesurés à l’aide de variables additionnelles, et de l’illustrer par un certain nombre d’applications au secteur des transports. Dans ces applications, le pas de temps est le jour, le mois, le trimestre voire le semestre : il s’est agi de prendre en compte des effets exogènes, de nature transitoire ou de nature durable, qui se manifestent dans le court terme. La première partie de la thèse traite de la modélisation des séries temporelles. Nous situons le cadre formel des modèles auxquels nous nous intéressons, nous exposons la démarche suivie dans le cadre des modèles ARMA avec variables explicatives, puis dans le cadre des modèles markoviens avec variables explicatives en y détaillant le cas particulier des modèles structurels. Les deuxième et troisième parties de la thèse regroupent deux ensembles d’applications. Le premier porte sur des données de trafics, de voyageurs et de marchandises, agrégées par mode de transport ou par grande catégorie de réseau, et le second sur des données d’accidents corporels et de victimes de la circulation routière, agrégées par grande catégorie de réseau routier. La période couverte la plus large est 1970-2000. La plupart des applications intègre la prise en compte des effets transitoires, de nature climatique et calendaire, sur la demande de transport et sur le risque routier, et nous donnons dans la thèse les premiers résultats détaillés démontrant pour la France la significativité du facteur climatique sur le bilan routier national, mesuré en nombres d’accidents corporels et de tuésThe aim of the thesis is to set out a methodology that includes in time-series modelling exogenous effects measured by additional variables. This methodology is illustrated by a number of applications relating to transport. In these applications, time is measured in days, months, quarters and semesters (half years). We aim to take account of exogenous effects which are either transitory or durable lasting and which manifest themselves in the short term The first part of the thesis deals with time-series modelling. We provide a typology of timeseries models and place our approach within it. We describe the approach used in ARMA modelling with explanatory variables and then in state space modelling with explanatory variables, paying special attention to structural time-series modelling. The second and third parts bring together two groups of applications. The first group considers traffic datasets, for passengers and for freight, aggregated by mode and by main network type. The second group considers numbers of road injury accidents and casualties, aggregated by main network type. The largest period covered is 1970-2000. Most of the applications address the transitory effects on transport demand and road risk of weather and calendar factors. We provide the first detailed results that demonstrate the significance of weather factor on road safety in France, measured by numbers of injury accidents and fatalitie

The use of explanatory variables in time series modelling : applications to transport demand and road risk

L’objet de la thèse est d’exposer une démarche méthodologique qui vise à prendre en compte, dans les modèles de séries temporelles, des effets exogènes mesurés à l’aide de variables additionnelles, et de l’illustrer par un certain nombre d’applications au secteur des transports. Dans ces applications, le pas de temps est le jour, le mois, le trimestre voire le semestre : il s’est agi de prendre en compte des effets exogènes, de nature transitoire ou de nature durable, qui se manifestent dans le court terme. La première partie de la thèse traite de la modélisation des séries temporelles. Nous situons le cadre formel des modèles auxquels nous nous intéressons, nous exposons la démarche suivie dans le cadre des modèles ARMA avec variables explicatives, puis dans le cadre des modèles markoviens avec variables explicatives en y détaillant le cas particulier des modèles structurels. Les deuxième et troisième parties de la thèse regroupent deux ensembles d’applications. Le premier porte sur des données de trafics, de voyageurs et de marchandises, agrégées par mode de transport ou par grande catégorie de réseau, et le second sur des données d’accidents corporels et de victimes de la circulation routière, agrégées par grande catégorie de réseau routier. La période couverte la plus large est 1970-2000. La plupart des applications intègre la prise en compte des effets transitoires, de nature climatique et calendaire, sur la demande de transport et sur le risque routier, et nous donnons dans la thèse les premiers résultats détaillés démontrant pour la France la significativité du facteur climatique sur le bilan routier national, mesuré en nombres d’accidents corporels et de tuésThe aim of the thesis is to set out a methodology that includes in time-series modelling exogenous effects measured by additional variables. This methodology is illustrated by a number of applications relating to transport. In these applications, time is measured in days, months, quarters and semesters (half years). We aim to take account of exogenous effects which are either transitory or durable lasting and which manifest themselves in the short term The first part of the thesis deals with time-series modelling. We provide a typology of timeseries models and place our approach within it. We describe the approach used in ARMA modelling with explanatory variables and then in state space modelling with explanatory variables, paying special attention to structural time-series modelling. The second and third parts bring together two groups of applications. The first group considers traffic datasets, for passengers and for freight, aggregated by mode and by main network type. The second group considers numbers of road injury accidents and casualties, aggregated by main network type. The largest period covered is 1970-2000. Most of the applications address the transitory effects on transport demand and road risk of weather and calendar factors. We provide the first detailed results that demonstrate the significance of weather factor on road safety in France, measured by numbers of injury accidents and fatalitie

La prise en compte de variables explicatives dans les modèles de séries temporelles : application à la demande de transport et au risque routier

The aim of the thesis is to set out a methodology that includes in time-series modelling exogenous effects measured by additional variables. This methodology is illustrated by a number of applications relating to transport. In these applications, time is measured in days, months, quarters and semesters (half years). We aim to take account of exogenous effects which are either transitory or durable lasting and which manifest themselves in the short term The first part of the thesis deals with time-series modelling. We provide a typology of timeseries models and place our approach within it. We describe the approach used in ARMA modelling with explanatory variables and then in state space modelling with explanatory variables, paying special attention to structural time-series modelling. The second and third parts bring together two groups of applications. The first group considers traffic datasets, for passengers and for freight, aggregated by mode and by main network type. The second group considers numbers of road injury accidents and casualties, aggregated by main network type. The largest period covered is 1970-2000. Most of the applications address the transitory effects on transport demand and road risk of weather and calendar factors. We provide the first detailed results that demonstrate the significance of weather factor on road safety in France, measured by numbers of injury accidents and fatalitiesL’objet de la thèse est d’exposer une démarche méthodologique qui vise à prendre en compte, dans les modèles de séries temporelles, des effets exogènes mesurés à l’aide de variables additionnelles, et de l’illustrer par un certain nombre d’applications au secteur des transports. Dans ces applications, le pas de temps est le jour, le mois, le trimestre voire le semestre : il s’est agi de prendre en compte des effets exogènes, de nature transitoire ou de nature durable, qui se manifestent dans le court terme. La première partie de la thèse traite de la modélisation des séries temporelles. Nous situons le cadre formel des modèles auxquels nous nous intéressons, nous exposons la démarche suivie dans le cadre des modèles ARMA avec variables explicatives, puis dans le cadre des modèles markoviens avec variables explicatives en y détaillant le cas particulier des modèles structurels. Les deuxième et troisième parties de la thèse regroupent deux ensembles d’applications. Le premier porte sur des données de trafics, de voyageurs et de marchandises, agrégées par mode de transport ou par grande catégorie de réseau, et le second sur des données d’accidents corporels et de victimes de la circulation routière, agrégées par grande catégorie de réseau routier. La période couverte la plus large est 1970-2000. La plupart des applications intègre la prise en compte des effets transitoires, de nature climatique et calendaire, sur la demande de transport et sur le risque routier, et nous donnons dans la thèse les premiers résultats détaillés démontrant pour la France la significativité du facteur climatique sur le bilan routier national, mesuré en nombres d’accidents corporels et de tué

La prise en compte de variables explicatives dans les modèles de séries temporelles (application à la demande de transport et au risque routier)

L objet de la thèse est d exposer une démarche méthodologique qui vise à prendre en compte, dans les modèles de séries temporelles, des effets exogènes mesurés à l aide de variables additionnelles, et de l illustrer par un certain nombre d applications au secteur des transports. Dans ces applications, le pas de temps est le jour, le mois, le trimestre voire le semestre : il s est agi de prendre en compte des effets exogènes, de nature transitoire ou de nature durable, qui se manifestent dans le court terme. La première partie de la thèse traite de la modélisation des séries temporelles. Nous situons le cadre formel des modèles auxquels nous nous intéressons, nous exposons la démarche suivie dans le cadre des modèles ARMA avec variables explicatives, puis dans le cadre des modèles markoviens avec variables explicatives en y détaillant le cas particulier des modèles structurels. Les deuxième et troisième parties de la thèse regroupent deux ensembles d applications. Le premier porte sur des données de trafics, de voyageurs et de marchandises, agrégées par mode de transport ou par grande catégorie de réseau, et le second sur des données d accidents corporels et de victimes de la circulation routière, agrégées par grande catégorie de réseau routier. La période couverte la plus large est 1970-2000. La plupart des applications intègre la prise en compte des effets transitoires, de nature climatique et calendaire, sur la demande de transport et sur le risque routier, et nous donnons dans la thèse les premiers résultats détaillés démontrant pour la France la significativité du facteur climatique sur le bilan routier national, mesuré en nombres d accidents corporels et de tuésThe aim of the thesis is to set out a methodology that includes in time-series modelling exogenous effects measured by additional variables. This methodology is illustrated by a number of applications relating to transport. In these applications, time is measured in days, months, quarters and semesters (half years). We aim to take account of exogenous effects which are either transitory or durable lasting and which manifest themselves in the short term The first part of the thesis deals with time-series modelling. We provide a typology of timeseries models and place our approach within it. We describe the approach used in ARMA modelling with explanatory variables and then in state space modelling with explanatory variables, paying special attention to structural time-series modelling. The second and third parts bring together two groups of applications. The first group considers traffic datasets, for passengers and for freight, aggregated by mode and by main network type. The second group considers numbers of road injury accidents and casualties, aggregated by main network type. The largest period covered is 1970-2000. Most of the applications address the transitory effects on transport demand and road risk of weather and calendar factors. We provide the first detailed results that demonstrate the significance of weather factor on road safety in France, measured by numbers of injury accidents and fatalitiesPARIS-EST-Université (770839901) / SudocSudocFranceF

Road Safety Trends at National Level in Europe: A Review of Time-series Analysis Performed during the Period 2000-12

This paper presents a review of time-series analysis of road safety trends, aggregated at a national level, which has been performed in the period 2000-2012 and applied to European national data sets covering long time periods. It provides a guideline and set of best practices in the area of time-series modelling and identifies the latest methods and applications of national road safety trend analysis in Europe. The paper begins with the methodological framework adopted for aggregate time-series modelling that will be considered, and then discusses a number of relevant applications to long-period data aggregated at the national level, whether for countries alone, or for groups of countries. Some analyses, which were performed at the disaggregated level, are also provided, as they are being used more and more. Finally, the paper summarizes and discusses the significant changes in aggregate road safety trend analysis which occurred during the period and provides recommendations for continuing these research efforts

Added risk by rainy weather on the roads of Normandie-Centre region in France : Some elements for the French IRCAD-SARI traffic monitoring and information system for drivers and operators

The project IRCAD-SARI, funded by the French Ministry of Transport, will experiment on some rural roads in France a warning sign system during adverse weather conditions. The safety stake is assessed by computing the added-risk in case of adverse weather conditions, and especially in case of rain on bends. The method for estimating rain exposure takes into account several types of meteorological information, and the additional weather information of the accident database. The traffic database, when available, enables to drop the approximate assumption that traffic is not correlated with rain. A method for computing the added risk due to rain is proposed and some related results are given in this paper

Searching for road deformations using mobile laser scanning

Millions of people use roads every day all over the world. Roads, like many other structures, have an estimated durability. In Poland a lot of the roads were built at the turn of the 20th and 21st c., especially for light cars. Many of these roads carry traffic and heavy goods vehicles which were not predicted when the traffic was first estimated. It creates a lot of problems with technical conditions and the infrastructure must be improved. Treatments can be problematic, because restoring the original properties of the road requires workers to restrict traffic as cars may cause a lot of damage to the construction. In the article the authors present a method for estimating the condition of a road using the MLS (Mobile Laser Scanning) measurement technique. It is based on a mobile platform and equipped with the Riegl VMZ-400 scanning system. Post-processing of the data constrain to extract the scan lines and road’s condition analysis in addition to estimate its parameters. In conclusion, the authors present the advantages and disadvantages of Mobile Laser Scanning in addition to the paths. Moreover, tell the possibility of the factors determine which describe the security level of the roads

On statistical inference in time series analysis of the evolution of road safety

Data collected for building a road safety observatory usually include observations made sequentially through time. Examples of such data, called time series data, include annual (or monthly) number of road traffic accidents, traffic fatalities or vehicle kilometers driven in a country, as well as the corresponding values of safety performance indicators (e.g., data on speeding, seat belt use, alcohol use, etc.). Some commonly used statistical techniques imply assumptions that are often violated by the special properties of time series data, namely serial dependency among disturbances associated with the observations. The first objective of this paper is to demonstrate the impact of such violations to the applicability of standard methods of statistical inference, which leads to an under or overestimation of the standard error and consequently may produce erroneous inferences. Moreover, having established the adverse consequences of ignoring serial dependency issues, the paper aims to describe rigorous statistical techniques used to overcome them. In particular, appropriate time series analysis techniques of varying complexity are employed to describe the development over time, relating the accident-occurrences to explanatory factors such as exposure measures or safety performance indicators, and forecasting the development into the near future. Traditional regression models (whether they are linear, generalized linear or nonlinear) are shown not to naturally capture the inherent dependencies in time series data. Dedicated time series analysis techniques, such as the ARMA-type and DRAG approaches are discussed next, followed by structural time series models, which are a subclass of state space methods. The paper concludes with general recommendations and practice guidelines for the use of time series models in road safety research. © 2013 Published by Elsevier Ltd