Time series count data models: An empirical application to traffic accidents

Quddus, Mohammed

doi:10.1016/j.aap.2008.06.011

Cited by 197 publications

(114 citation statements)

References 22 publications

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“…Although time series has been applied on many occasions for road accident studies, (8,(12)(13)(14)(15) our assumptions on the ARIMA model and seasonality were not satisfied by our results and our data appeared to be inappropriate for analysis with time series method.…”

Section: Discussioncontrasting

confidence: 48%

Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study

Razzaghi

Bahrampour

Baneshi

et al. 2013

Int J Health Policy Manag

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Background:Road traffic accidents and their related deaths have become a major concern, particularly in developing countries. Iran has adopted a series of policies and interventions to control the high number of accidents occurring over the past few years. In this study we used a time series model to understand the trend of accidents, and ascertain the viability of applying ARIMA models on data from Taybad city. Methods: This study is a cross-sectional study. We used data from accidents occurring in Taybad between 2007 and 2011. We obtained the data from the Ministry of Health (MOH) and used the time series method with a time lag of one month. After plotting the trend, non-stationary data in mean and variance were removed using Box-Cox transformation and a differencing method respectively. The ACF and PACF plots were used to control the stationary situation. Results: The traffic accidents in our study had an increasing trend over the five years of study. Based on ACF and PACF plots gained after applying Box-Cox transformation and differencing, data did not fit to a time series model. Therefore, neither ARIMA model nor seasonality were observed. Conclusion: Traffic accidents in Taybad have an upward trend. In addition, we expected either the AR model, MA model or ARIMA model to have a seasonal trend, yet this was not observed in this analysis. Several reasons may have contributed to this situation, such as uncertainty of the quality of data, weather changes, and behavioural factors that are not taken into account by time series analysis.

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Section: Discussioncontrasting

confidence: 48%

Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study

Razzaghi

Bahrampour

Baneshi

et al. 2013

Int J Health Policy Manag

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“…because the CRIS does not include traffic flow information on intersection approach movements, one of the most relevant variables to explain intersection crash risk propensity (see Mountain et al, 1998, Noland and Quddus, 2004, Quddus, 2008. So, we had to locate areas in the State that At the same time, the frequency distribution indicates a long right tail, which is also easily accommodated in the proposed count framework by setting…”

Section: Datamentioning

confidence: 99%

A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections

Castro

Paleti

Bhat

2012

Transportation Research Part B: Methodological

136

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This paper proposes a reformulation of count models as a special case of generalized orderedresponse models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variable-based generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in

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“…Ristic et al [5] and Bakouch and Ristic [6] considered crime data, especially sex offence counts data series. Quudus [7] studied monthly car casualties within the congestion zone. It is worth mentioning here that Eq.…”

Section: Introductionmentioning

confidence: 99%

A new model for time series of counts

Khoo¹,

Ong²

2014

AIP Conference Proceedings

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Time series modelling is an active research area in the past few decades with much attention focused in the continuous setting. However, time series data in terms of correlated counts occur in many situations. Thus modelling of time series of count data has attracted the attention many researchers recently. A new univariate non-negative stationary time series model of count data is introduced and is referred to as the mixture of Pegram and thinning on first order integer-valued autoregressive (MPT(1)) process. It is constructed from the combination of the popular thinning and Pegram's operators. The motivation for introducing the model and important statistical properties are discussed, such as autocorrelation, regression and joint distribution. Some simulation results are presented to show the performance of the model. Finally, the advantage of this new model is illustrated with application to a real data set.

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Time series count data models: An empirical application to traffic accidents

Cited by 197 publications

References 22 publications

Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study

Assessment of Trend and Seasonality in Road Accident Data: An Iranian Case Study

A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections

A new model for time series of counts

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