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%.

New Intersection Crash Prediction Models for the Second Edition of the Highway Safety Manual

The objective of this research was to develop new intersection crash prediction models for consideration in the second edition of the Highway Safety Manual (HSM), consistent with existing methods in HSM Part C and comprehensive in their ability to address a wide range of intersection configurations and traffic control types in rural and urban areas. The focus of the research was on developing safety performance functions (SPFs) for intersection configurations and traffic control types not currently addressed in HSM Part C. SPFs were developed for the following general intersection configurations and traffic control types: rural and urban all-way stop-controlled intersections; rural three-leg intersections with signal control; intersections on high-speed urban and suburban arterials (i.e., arterials with speed limits greater than or equal to 50 mph); urban five-leg intersections with signal control; three-leg intersections where the through movements make turning maneuvers at the intersections; crossroad ramp terminals at single-point diamond interchanges; and crossroad ramp terminals at tight diamond interchanges. Development of severity distribution functions (SDFs) for use in combination with SPFs to estimate crash severity as a function of geometric design elements and traffic control features was explored; but owing to challenges and inconsistencies in developing and interpreting the SDFs, it was recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition, without use of SDFs.

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.