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Special Issue "Promotion of Big Data and Intelligent Transportation to Traffic Safety and Environment"

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601).

Deadline for manuscript submissions: 31 December 2019

Special Issue Editors

Guest Editor
Dr. Feng Chen

College of Transportation Engineering, Tongji University, 1239 Siping Road, Shanghai, China
E-Mail
Phone: +86 021 6958 5721
Fax: +86 021 6958 3813
Interests: traffic injury prevention; statistical analysis of Crash Data; vehicle dynamic model; reliability analysis; driving simulator
Guest Editor
Dr. Kun Xie

Department of Civil and Natural Resources Engineering, University of Canterbury, 20 Kirkwood Ave, Christchurch 8041, New Zealand
Website | E-Mail
Interests: transportation safety, connected and autonomous vehicles, big data analytics,statistics and machine learning, resilience in multimodal transportation systems
Guest Editor
Dr. Xiaoxiang Ma

College of Transportation Engineering, Tongji University, 4800 Cao'an Road, Shanghai, China
E-Mail

Special Issue Information

Dear Colleagues,

Metropolitan areas face serious traffic-related problems. Road traffic accidents cause a large number of deaths and disabilities every day. Moreover, traffic congestion has been increasingly severe around the world, causing enormous pollutant emissions to degrade air quality. Both traffic accidents and vehicle pollutions have become major public health issues. The recent development of new technologies such as big data, automated driving, and connected vehicle and cooperative vehicle infrastructure systems show great potential to enhance traffic safety and mitigate traffic congestion. By harnessing the power of these emerging technologies, a better understanding of data-driven traffic systems can be achieved, which is of practical importance to traffic safety and traffic operation.

This Special Issue aims to report on recent advances in interdisciplinary research related to understanding associated risks and the improvement of traffic safety and environment problems in transportation networks around the world. It is open to any subject area of the related theme, and research articles encompassing multiple fields, such as big data, ITS, automated driving, connected vehicle, cooperative vehicle infrastructure systems, etc., are particularly welcome. The International Journal of Environmental Research and Public Health is indexed by SCI-E, PubMed, and other databases.

Dr. Feng Chen
Dr. Kun Xie
Dr. Xiaoxiang Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data and traffic safety
  • big data and traffic environment
  • intelligent transportation and traffic safety
  • intelligent transportation and traffic environment
  • automatic driving
  • cooperative vehicle infrastructure system
  • connected car

Published Papers (3 papers)

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Research

Open AccessArticle Predicting Future Driving Risk of Crash-Involved Drivers Based on a Systematic Machine Learning Framework
Int. J. Environ. Res. Public Health 2019, 16(3), 334; https://doi.org/10.3390/ijerph16030334
Received: 3 December 2018 / Revised: 20 January 2019 / Accepted: 20 January 2019 / Published: 25 January 2019
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Abstract
The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing [...] Read more.
The objective of this paper is to predict the future driving risk of crash-involved drivers in Kunshan, China. A systematic machine learning framework is proposed to deal with three critical technical issues: 1. defining driving risk; 2. developing risky driving factors; 3. developing a reliable and explicable machine learning model. High-risk (HR) and low-risk (LR) drivers were defined by five different scenarios. A number of features were extracted from seven-year crash/violation records. Drivers’ two-year prior crash/violation information was used to predict their driving risk in the subsequent two years. Using a one-year rolling time window, prediction models were developed for four consecutive time periods: 2013–2014, 2014–2015, 2015–2016, and 2016–2017. Four tree-based ensemble learning techniques were attempted, including random forest (RF), Adaboost with decision tree, gradient boosting decision tree (GBDT), and extreme gradient boosting decision tree (XGboost). A temporal transferability test and a follow-up study were applied to validate the trained models. The best scenario defining driving risk was multi-dimensional, encompassing crash recurrence, severity, and fault commitment. GBDT appeared to be the best model choice across all time periods, with an acceptable average precision (AP) of 0.68 on the most recent datasets (i.e., 2016–2017). Seven of nine top features were related to risky driving behaviors, which presented non-linear relationships with driving risk. Model transferability held within relatively short time intervals (1–2 years). Appropriate risk definition, complicated violation/crash features, and advanced machine learning techniques need to be considered for risk prediction task. The proposed machine learning approach is promising, so that safety interventions can be launched more effectively. Full article
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Open AccessArticle Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data
Int. J. Environ. Res. Public Health 2019, 16(2), 219; https://doi.org/10.3390/ijerph16020219
Received: 29 November 2018 / Revised: 4 January 2019 / Accepted: 4 January 2019 / Published: 14 January 2019
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Abstract
This study attempts to investigate spatial autocorrelation and spillover effects in micro traffic safety analysis. To achieve the objective, a Poisson-based count regression with consideration of these spatial effects is proposed for modeling crash frequency on freeway segments. In the proposed hybrid model, [...] Read more.
This study attempts to investigate spatial autocorrelation and spillover effects in micro traffic safety analysis. To achieve the objective, a Poisson-based count regression with consideration of these spatial effects is proposed for modeling crash frequency on freeway segments. In the proposed hybrid model, the spatial autocorrelation and the spillover effects are formulated as the conditional autoregressive (CAR) prior and the exogenous variables of adjacent segments, respectively. The proposed model is demonstrated and compared to the models with only one kind of spatial effect, using one-year crash data collected from Kaiyang Freeway, China. The results of Bayesian estimation conducted in WinBUGS show that significant spatial autocorrelation and spillover effects simultaneously exist in the freeway crash-frequency data. The lower value of deviance information criterion (DIC) and more significant exogenous variables for the hybrid model compared to the other alternatives, indicate the strength of accounting for both spatial autocorrelation and spillover effects on improving model fit and identifying crash contributing factors. Moreover, the model results highlight the importance of daily vehicle kilometers traveled, and horizontal and vertical alignments of targeted segments and adjacent segments on freeway crash occurrences. Full article
Open AccessArticle Investigation of the Contributory Factors to the Guessability of Traffic Signs
Int. J. Environ. Res. Public Health 2019, 16(1), 162; https://doi.org/10.3390/ijerph16010162
Received: 16 November 2018 / Revised: 29 December 2018 / Accepted: 2 January 2019 / Published: 8 January 2019
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Abstract
Traffic signs play an important role in traffic management systems. A variety of studies have focused on drivers’ comprehension of traffic signs. However, the travel safety of prospective users, which has been rarely mentioned in previous studies, has attracted considerable attention from relevant [...] Read more.
Traffic signs play an important role in traffic management systems. A variety of studies have focused on drivers’ comprehension of traffic signs. However, the travel safety of prospective users, which has been rarely mentioned in previous studies, has attracted considerable attention from relevant departments in China. With the growth of international and interregional travel demand, traffic signs should be designed more universally to reduce the potential risks to drivers. To identify key factors that improve prospective users’ sign comprehension, this study investigated eight factors that may affect users’ performance regarding sign guessing. Two hundred and one Chinese students, all of whom intended to be drivers and none of whom had experience with daily driving after obtaining a license or visits to Germany, guessed the meanings and rated the sign features of 54 signs. We investigated the effects of selected user factors on their sign guessing performance. Additionally, the contributions of four cognitive design features to the guessability of traffic signs were examined. Based on an analysis of the relationships between the cognitive features and the guessability score of signs, the contributions of four sign features to the guessability of traffic signs were examined. Moreover, by exploring Chinese users’ differences in guessing performance between Chinese signs and German signs, cultural issues in sign design were identified. The results showed that vehicle ownership and attention to traffic signs exerted a significant influence on guessing performance. As expected, driver’s license training and the number of years in college were dominant factors for guessing performance. With regard to design features, semantic distance and confidence in guessing were two dominant factors for the guessability of signs. We suggest improving the design of signs by including vivid, universal symbols. Thus, we provide several suggestions for designing more user-friendly signs. Full article
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Int. J. Environ. Res. Public Health EISSN 1660-4601 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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