An Improved Deep Learning Model for Traffic Crash Prediction

Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction. To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer. The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances. The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions. The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively. Document type: Articl

Crash Prediction Models and Methodological Issues

The conducted literature review aimed to provide an overall perspective on the significant findings of past research works related to vehicle crashes and prediction models. The literature review also provided information concerning past road safety research methodology and viable statistical analysis and computing tools. Though the selection of a specific model hinges on the objective of the research and nature of the response, when compared to statistical modeling techniques, Artificial Neural Networks (ANNs), which can model complex nonlinear relationships among dependent and independent parameters, have been witnessed to be very powerful

Bayesian inference for origin-destination matrices of transport networks using the EM algorithm

Information on the origin-destination (OD) matrix of a transport network is a fundamental requirement in much transportation planning. A relatively inexpensive method for updating an OD matrix is to draw inference about the OD matrix based on a single observation of traffic flows on a specific set of network links, where the Bayesian approach is a natural choice for combining the prior knowledge about the OD matrix and the current observation of traffic flows. The existing approaches of Bayesian modeling of OD matrices include using normal approximations to Poisson distributions, which leads to the posterior being intractable even under some simple special cases, and using Markov chain Monte Carlo simulation, which incurs extreme demand of computational efforts. In this article, through the EM algorithm, Bayesian inference is reinvestigated for a transport network for estimating the population means of traffic flows, reconstructing traffic flows, and predicting future traffic flows. It is shown that the resultant estimates have very simple forms with minimal computational costs

Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams

The research questions that motivate transportation safety studies are causal in nature. Safety researchers typically use observational data to answer such questions, but often without appropriate causal inference methodology. The field of causal inference presents several modeling frameworks for probing empirical data to assess causal relations. This paper focuses on exploring the applicability of two such modeling frameworks---Causal Diagrams and Potential Outcomes---for a specific transportation safety problem. The causal effects of pavement marking retroreflectivity on safety of a road segment were estimated. More specifically, the results based on three different implementations of these frameworks on a real data set were compared: Inverse Propensity Score Weighting with regression adjustment and Propensity Score Matching with regression adjustment versus Causal Bayesian Network. The effect of increased pavement marking retroreflectivity was generally found to reduce the probability of target nighttime crashes. However, we found that the magnitude of the causal effects estimated are sensitive to the method used and to the assumptions being violated.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS440 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org