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Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method
We propose a multi-view learning method to estimate the missing values for traffic-related time series data by combining temporal and spatial views. Expand
Prevention of taxi accidents in Xi'an, China: what matters most?
OBJECTIVES Since the city of Xi'an has been extremely concerned with the serious problem of taxi involved crashes, injuries and fatalities, the primary purpose of this study is to identify the riskExpand
Can variations in visual behavior measures be good predictors of driver sleepiness? A real driving test study
ABSTRACT Objective: The primary purpose of this study was to examine the association between variations in visual behavior measures and subjective sleepiness levels across age groups over time toExpand
Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm
In this paper, a deep feature leaning approach is proposed to predict short-term traffic flow in the following multiple steps using supervised learning techniques. Expand
Professional drivers’ views on risky driving behaviors and accident liability: a questionnaire survey in Xining, China
Abstract This study examines the correlation among the attitudes, behaviors, and other characteristics of professional drivers involved in accidents in China, using a questionnaire-based surveyExpand
The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China.
Although the taxi industry is playing an important role in Chinese everyday life, little attention has been posed towards occupational health issues concerning the taxi drivers' working conditions,Expand
Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model
Metro passenger flow forecasting is an essential component of intelligent transportation system. To enhance the forecasting accuracy and explainable of traditional models, a hybrid model combiningExpand
Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model
We propose a multimodal deep learning model to enable heterogeneous traffic data imputation that can accurately impute the continuously missing data. Expand
Traffic speed prediction for intelligent transportation system based on a deep feature fusion model
We propose a deep feature fusion model to predict space–mean–speed using heterogeneous data and compare its performance with data–level fusion method. Expand
How eye movement and driving performance vary before, during, and after entering a long expressway tunnel: considering the differences of novice and experienced drivers under daytime and nighttime
IntroductionDriving environment in tunnels is quite different from the ordinary roadway sections, especially the entrance locations, which causes great difficulty in obtaining and interpreting information through fixations and saccades that are relevant to driving safety. Expand