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

Commandeur, Jacques J.F.; Bijleveld, F D; Bergel-Hayat, Ruth; Antoniou, Constantinos; Yannis, George; Papadimitriou, Eleonora

doi:10.1016/j.aap.2012.11.006

Cited by 53 publications

(19 citation statements)

References 8 publications

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“…Researchers have mainly focused on structural modelling introduced by Harvey (1989), whether on an annual, quarterly or monthly basis (Lassarre, 2001;Commandeur, Bijleveld, & Bergel, 2007;Bergel-Hayat, Debbarh, Antoniou, & Yannis, 2013). Although structural modelling was presented as more appropriate than the autoregressive integrated and moving average (ARIMA) modelling commonly used on a quarterly or monthly basis (Harvey, & Durbin, 1986), it is worth noting that these two approaches are coherent as some forms of structural models may be written with an equivalent ARIMA model form (Commandeur et al, 2013). As regards the appropriateness of the time-series models used to fit aggregate road safety indicators, a review of the techniques and resulting applications has been provided and recommendations for using dynamic time-series models which explicitly take time dependency into account, such as ARIMA or state space models, have been given 4 (Dupont & Martensen, 2007).…”

Section: Time-series Analysis Techniquesmentioning

confidence: 98%

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

Bergel-Hayat¹,

Żukowska

2015

Transport Reviews

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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 2000-12 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.

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Section: Time-series Analysis Techniquesmentioning

confidence: 98%

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

Bergel-Hayat¹,

Żukowska

2015

Transport Reviews

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“…There are different statistical methods to forecast future conditions. This study uses time series analysis; its main purpose is modeling and forecasting (8). Modeling and forecasting traffic fatalities can provide insight for policy-makers to help them adjust their policies and implement effective countermeasures (8).…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Trend of Traffic Accident Mortality in West Iran

et al. 2016

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Background: Traffic accidents are the main cause of deaths in developing countries. Fatalities due to traffic accidents are assessed through a three-year time series forecast. Objectives: The aim of this study is to use trend assessment to predict traffic accident fatalities for January 2013 to December 2015 in Kermanshah province, Iran. Materials and Methods: This is a historical longitudinal study using time series analysis to identify the best fit model. The criteria of MSE (mean square of error) were used to determine the model with the best goodness of fit. The model that had the smaller MSE value was introduced as a suitable model. The selected model was used to forecast the number of deaths related to traffic accidents in the next three years. Results: A decreasing trend was observed in accident mortality. The highest and lowest deaths were seen annually in the spring and

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“…Some techniques applied use explanatory variables [5], [6], [7], they could be internal or external variables, disaggregated data to simplify the process were used by [8] and [9].…”

Section: Introductionmentioning

confidence: 99%

Forecast of Traffic Accidents Based on Components Extraction and an Autoregressive Neural Network with Levenberg-Marquardt

Barba

Rodríguez

2014

Mining Intelligence and Knowledge Exploration

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Abstract. In this paper is proposed an improved one-step-ahead strategy for traffic accidents and injured forecast in Concepción, Chile, from year 2000 to 2012 with a weekly sample period. This strategy is based on the extraction and estimation of components of a time series, the Hankel matrix is used to map the time series, the Singular Value Decomposition(SVD) extracts the singular values and the orthogonal matrix, and the components are forecasted with an Autoregressive Neural Network (ANN) based on Levenberg-Marquardt (LM) algorithm. The forecast accuracy of this proposed strategy are compared with the conventional process, SVD-ANN-LM achieved a MAPE of 1.9% for the time series Accidents, and a MAPE of 2.8% for the time series Injured, in front of 14.3% and 21.1% that were obtained with the conventional process.

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

Cited by 53 publications

References 8 publications

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

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

Forecasting the Trend of Traffic Accident Mortality in West Iran

Forecast of Traffic Accidents Based on Components Extraction and an Autoregressive Neural Network with Levenberg-Marquardt

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