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Data-Driven Analysis and Control Methods in ITS and Accident Prevention

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (18 August 2022) | Viewed by 13498

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Special Issue Editors

Prof. Changxi Ma

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Guest Editor

School of Traffic and Transportation Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: intelligent transportation systems; traffic control and traffic safety

Dr. Xuecai Xu

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Guest Editor

School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: intelligent transportation systems; traffic control and traffic safety

Special Issue Information

Dear Colleagues,

Emerging techniques such as big data, Internet of Things (IoT), artificial intelligence, blockchain, and hypercomputation have been deeply integrated into the transportation field, enabling data-driven methods to become a potential approach in intelligent transportation systems (ITS). Meanwhile, based on data and driven by new techniques, accident prevention always plays an important role in conventional and intelligent transportation systems. Accordingly, the data have become significant and it is critical to collect, process, and apply data from different sources for intelligent transportation systems and accident prevention.

This Special Issue, “Data-Driven Analysis and Control Methods in ITS and Accident Prevention” will concentrate on the theories, methodologies, and applications of data-driven methods for analysis, modeling, optimization, and control in ITS and accident prevention. Submissions to this Special Issue are encouraged to employ deep learning, reinforcement learning, and other machine learning methods as well as interdisciplinary approaches for data preprocessing, data mining, and data postprocessing. The aim of this Special Issue is to reveal the emerging techniques and the most recent developments of data-driven analysis, modeling, optimization, and control in ITS and accident prevention.

Potential topics include but are not limited to the following:

Data-driven analysis methods in ITS and accident prevention;
Data-driven modeling methods in ITS and accident prevention;
Data-driven optimization methods in ITS;
Data-driven control methods in ITS and accident prevention;
Emerging and advanced data analysis methods in ITS and accident prevention;
Deep learning and reinforcement learning in ITS and accident prevention;
Big data and IoT in ITS and accident prevention;
Artificial intelligence methods and applications in ITS and accident prevention;
Blockchain methods and applications in ITS and accident prevention;
Hypercomputation methods and applications in ITS and accident prevention;
Data processing and data mining methods in ITS and accident prevention;
Other related topics and interdisciplinary approaches in ITS and accident prevention.

Prof. Changxi Ma
Dr. Xuecai Xu
Guest Editors

Manuscript Submission Information

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Keywords

intelligent transportation systems
data-driven analysis
system optimization
traffic control
accident prevention

Published Papers (6 papers)

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Research

16 pages, 4953 KiB

Open AccessArticle

Acceleration and Deceleration Rates in Interrupted Flow Based on Empirical Digital Tachograph Data

by Junhyung Lee

Sustainability 2022, 14(18), 11165; https://0-doi-org.brum.beds.ac.uk/10.3390/su141811165 - 06 Sep 2022

Cited by 3 | Viewed by 1461

Abstract

The acceleration and deceleration rates are crucial road design reference values in terms of traffic safety. The purpose of acceleration and deceleration lanes is to reduce the speed difference between the mainline and ramp to minimize rear-end collision. Thus, in traffic safety and traffic flow operational aspects, the acceleration and deceleration lane lengths need to be long. Moreover, excessively long minimum acceleration or deceleration lane length regulations may cause hesitancy to connect new facilities with the road. Despite their relevance, acceleration and deceleration rates have not been updated for many decades. In this study, we analyze the digital tachograph data of vehicles stopped at signalized intersections at red lights and empirically deduce the acceleration and deceleration rates that reflect recent vehicle performance and driver behavior. Finally, we suggest a new corner clearance distance to safely connect the new road near a signalized intersection in urban and rural areas derived from our empirical acceleration and deceleration rates. Full article

(This article belongs to the Special Issue Data-Driven Analysis and Control Methods in ITS and Accident Prevention)

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21 pages, 6613 KiB

Open AccessArticle

Quantitative Study on Road Traffic Environment Complexity under Car-Following Condition

by Wenlong Liu, Yixin Chen, Hongtao Li and Hui Zhang

Sustainability 2022, 14(10), 6251; https://0-doi-org.brum.beds.ac.uk/10.3390/su14106251 - 20 May 2022

Cited by 1 | Viewed by 2330

Abstract

With the development of the drive of electronic communication technology, the driving assistance system that perceives the external traffic environment has developed rapidly. However, when quantifying the complexity of the road traffic environment without fully considering the driving characteristics and subjective feelings, the false alarm rate of the driving warning system increases and affects the early warning effect. In order to more accurately quantify the complexity of the road traffic environment, we analyzed the impact of road traffic environment changes on drivers under the condition of car-following. Firstly, we selected the influencing factors of the traffic environment complexity, such as the driving operation indicators, the vehicle driving status indicators and the road environmental indicators. The weight calculation model of each influence factor is established based on the principal component analysis method. Secondly, the driver’s reaction time during car-following is used as the quantitative index of road traffic environment complexity. The quantitative model of road traffic environment complexity is constructed combined with the weight of road traffic environment complexity. Finally, the driving simulation experiment is designed to verify the complexity quantification model of the road traffic environment. The road traffic environment complexity value calculated in our study is better than the TTC, and the early-warning threshold is raised by 2–5%. The research conclusion can provide a basis for the design of the car alarm system. Full article

(This article belongs to the Special Issue Data-Driven Analysis and Control Methods in ITS and Accident Prevention)

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16 pages, 4591 KiB

Open AccessArticle

A Nonintrusive and Real-Time Classification Method for Driver’s Gaze Region Using an RGB Camera

by Huili Shi, Longfei Chen, Xiaoyuan Wang, Gang Wang and Quanzheng Wang

Sustainability 2022, 14(1), 508; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010508 - 04 Jan 2022

Cited by 6 | Viewed by 1724

Abstract

Driver distraction has become a leading cause of traffic crashes. Visual distraction has the most direct impact on driving safety among various driver distractions. If the driver’s line of sight deviates from the road in front, there will be a high probability of visual distraction. A nonintrusive and real-time classification method for driver’s gaze region is proposed. A Multi-Task Convolutional Neural Network (MTCNN) face detector is used to collect the driver’s face image, and the driver’s gaze direction can be detected with a full-face appearance-based gaze estimation method. The driver’s gaze region is classified by the model trained through the machine learning algorithms such as Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The simulated experiment and the real vehicle experiment were conducted to test the method. The results show that it has good performance on gaze region classification and strong robustness to complex environments. The models in this paper are all lightweight networks, which can meet the accuracy and speed requirements for the tasks. The method can be a good help for further exploring the visual distraction state level and exert an influence on the research of driving behavior. Full article

(This article belongs to the Special Issue Data-Driven Analysis and Control Methods in ITS and Accident Prevention)

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20 pages, 1690 KiB

Open AccessArticle

Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

by Shanshan Wei, Xiaoyan Shen, Minhua Shao and Lijun Sun

Sustainability 2021, 13(22), 12773; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212773 - 18 Nov 2021

Cited by 7 | Viewed by 1788

Abstract

With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads. Full article

(This article belongs to the Special Issue Data-Driven Analysis and Control Methods in ITS and Accident Prevention)

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19 pages, 2482 KiB

Open AccessArticle

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

by Bo Qiu and Wei (David) Fan

Sustainability 2021, 13(13), 7454; https://0-doi-org.brum.beds.ac.uk/10.3390/su13137454 - 03 Jul 2021

Cited by 19 | Viewed by 2921

Abstract

Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability. Full article

(This article belongs to the Special Issue Data-Driven Analysis and Control Methods in ITS and Accident Prevention)

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17 pages, 5684 KiB

Open AccessArticle

Data-Driven Detection Methods on Driver’s Pedal Action Intensity Using Triboelectric Nano-Generators

by Qian Cheng, Xiaobei Jiang, Haodong Zhang, Wuhong Wang and Chunwen Sun

Sustainability 2020, 12(21), 8926; https://0-doi-org.brum.beds.ac.uk/10.3390/su12218926 - 27 Oct 2020

Cited by 11 | Viewed by 1870

Abstract

Driver’s driving actions on pedals can be regarded as an expression of driver’s acceleration/deceleration intention. Quickly and accurately detecting driving action intensity on pedals can have great contributions in preventing road traffic accidents and managing the energy consumption. In this paper, we report a pressure-sensitive and self-powered material named triboelectric nano-generators (TENGs). The generated voltage data of TENGs, which is associated with the pedal action, can be collected easily and stored sequentially. According to the characteristics of the voltage data, we have employed a hybrid machine learning method. After collecting signals from TENGs and driving simulator simultaneously, an unsupervised Gaussian mixture model is used to cluster the pedal events automatically using data from simulator. Then, multi-feature candidates of the voltage data from TENGs are extracted and ranked. A supervised random forest model that treats voltage data of TENGs as input data is trained and tested. Results show that data from TENGs can have a high accuracy of more than 90% using the random forest algorithm. The evaluating results demonstrate the accuracy of the proposed data-driven hybrid learning algorithm for recognition of driver’s pedal action intensity. Furthermore, technical and economic characteristics of TENGs and some common sensors are compared and discussed. This work may demonstrate the feasibility of using these data-driven methods on the detection of driver’s pedal action intensity. Full article

(This article belongs to the Special Issue Data-Driven Analysis and Control Methods in ITS and Accident Prevention)

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