An Improved Deep Belief Network Model for Road Safety Analyses

by   Guangyuan Pan, et al.

Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper introduces a new machine learning (ML) based approach as an alternative to the traditional techniques. The proposed ML model is called regularized deep belief network, which is a deep neural network with two training steps: it is first trained using an unsupervised learning algorithm and then fine-tuned by initializing a Bayesian neural network with the trained weights from the first step. The resulting model is expected to have improved prediction power and reduced need for the time-consuming human intervention. In this paper, we attempt to demonstrate the potential of this new model for crash prediction through two case studies including a collision data set from 800 km stretch of Highway 401 and other highways in Ontario, Canada. Our intention is to show the performance of this ML approach in comparison to various traditional models including negative binomial (NB) model, kernel regression (KR), and Bayesian neural network (Bayesian NN). We also attempt to address other related issues such as effect of training data size and training parameters.



There are no comments yet.


page 1


A Kernel-Expanded Stochastic Neural Network

The deep neural network suffers from many fundamental issues in machine ...

Justification-Based Reliability in Machine Learning

With the advent of Deep Learning, the field of machine learning (ML) has...

Improving Botnet Detection with Recurrent Neural Network and Transfer Learning

Botnet detection is a critical step in stopping the spread of botnets an...

Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data

Machine learning (ML) techniques are increasingly applied to decision-ma...

A robust low data solution: dimension prediction of semiconductor nanorods

Precise control over dimension of nanocrystals is critical to tune the p...

Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

Model-based methods and deep neural networks have both been tremendously...

A New Spatial Count Data Model with Bayesian Additive Regression Trees for Accident Hot Spot Identification

The identification of accident hot spots is a central task of road safet...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.