Driving Simulator, a powerful simulation tool, has already been used in safety evaluation of roadway geometric design during the pre-construction design stage. Conventional ways of estimating proper sample size include the empirical method, the resource equation, power analysis, and the Bayesian method. However, significant boundaries and prior distributions of operational indices are hard to identify in simulator studies, which makes it difficult to use conventional ways in choosing the acceptable sample size. This study proposes an empirical method to infer proper sample size. The Tongji University eight-degree-of-freedom driving simulator was utilized to collect continuous driving behavior data from a simulated mountainous freeway. Vehicle speed and lane departure events were selected as the indices to measure the influence of geometric design features on operational efficiency and safety. A mixed linear model and a mixed logistic regression model were used to assess the relationships between geometric design features and vehicle speed and lane departure. Random sampling was used to choose 10 samples of 5 to 50 drivers from a total of 55 drivers. Acceptable sample size was determined based on the parameter coefficient convergence elbow points of the mean squared error (MSE) curves of significant variables. The clear elbow points of the MSE curves indicate that 30 is an acceptable sample size.
A traffic crash is becoming one of the major factors that leads to unexpected death in the world. Short window traffic crash prediction in the near future is becoming more pragmatic with the advancements in the fields of artificial intelligence and traffic sensor technology. Short window traffic prediction can monitor traffic in real time, identify unsafe traffic dynamics, and implement suitable interventions for traffic conflicts. Crash prediction being an important component of intelligent traffic systems, it plays a crucial role in the development of proactive road safety management systems. Some near future crash prediction models were put forward in recent years; further improvements need to be implemented for actual applications. This paper utilizes traffic accident data from the study Freeway in China to build a time series-based count data model for daily crash prediction. Lane traffic flow, weather information, vehicle speed, and truck to car ratio were extracted from the deployment of non-intrusive detection systems with support of the Bridge Management Administration study and were input into the model as independent variables. Different types of prediction models in machine learning and time series forecasting methods such as boosting, ARIMA, time-series count data model, etc. are compared within the paper. Results show that integrating time series with a count data model can capture traffic accident features and account for the temporal structure for variable serial correlation. A prediction error of 0.7 was achieved according to Root Mean Squared Deviation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.