Hourly Bridge Freeway Traffic Crash Prediction Using Log Count Data Poisson Time Series INGARCH Model and Its Comparisons With Regression-based Methods

Traffic crash prediction is vital for relevant agencies to take precautionary measures to minimize the economic and social losses from traffic accidents. Currently, the popularity of machine learning, deep learning, and traditional regression-based models in crash predictions eclipsed the use of count data time series models. Count data model has many intrinsic advantages over machine learning based methods in crash analysis. It is an extension of conventional time series regression by extending normal distribution to Poisson and Negative binomial. Meanwhile, covariate variables can get properly incorporated and their influence on dependent variable is well interpreted. This study attempts to compare and examine the performances of the count data time series model with the regression-based models in hourly crash prediction, utilizing traffic crash data from the Sutong Yangtze River Bridge in China. Log linear extension of Poisson distribution integer valued generalized autoregressive conditional heteroscedasticity models (INGARCH), as a type of count data model, is adopted and compared with the zero-inflated Poisson model (ZIP), as well as the cumulative link model for ordinal regression (CLM). The performances of ZIP and log linear extension of INGARCH count data model are similar and superior to the performances of CLM. Results showed that previous traffic accidents influence the crash occurrence in the near future and the employment of count data time series model in hourly crash prediction can appropriately capture this influence, with an average model sensitivity rate of 77.5%.

Analysis of Road Traffic Accidents in the Punjab by Using Panel Count Data Models

Each year more than three thousand people die and get serious injuries in traffic accidents. Count data model provide more precise tools for planners and decision makers to conduct proactive road safety planning.We analyzed the exploratory research of Road Traffic Accidents (RTAs) and furthermore explores the factors affecting the RTAs frequency in 36 districts of the Punjab over a time period of three years (July 1, 2013 June 30, 2016) with monthly data using panel count data models. Among the models considered, the random parameters Poisson panel count data model is found to fit the data best. The exploratory analysis shows that highly dense populated districts with large number of registered vehicles causes more accidents as compared to low density populated districts. It is found that, most of the variables used to control the variation in the frequency of RTAs counts play vital role with higher significance levels. The application of regression analysis and modeling of RTAs at district level in Punjab will help to identification of districts with high RTAs rates and this could help more efficient road safety management in the Punjab.