Review
Transportation Science & Technology
Yibing Wang, Xianghua Yu, Jinqiu Guo, Ioannis Papamichail, Markos Papageorgiou, Lihui Zhang, Simon Hu, Yongfu Li, Jian Sun
Summary: This paper presents a comprehensive review of the latest works on macroscopic model calibration and validation and proposes a benchmarking framework for traffic flow modeling. By conducting numerous case studies using the macroscopic traffic flow model METANET on the urban expressway network in Shanghai, the paper provides more comprehensive results on model calibration and reports many model validation results. The results demonstrate that METANET is capable of accurately modeling complex traffic flow dynamics in large-scale freeway networks.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Civil
Shuming Du, Saiedeh Razavi
Summary: The study proposes an interacting multiple model approach with a pseudo-model set for achieving fault-tolerant variable speed limit control in freeway work zones, addressing recurrent sensor faults. The system ensures reliable diagnosis of sensor faults and consistent improvements in mobility, safety, and sustainability despite faults.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Pan Wang, Shunying Zhu, Xiaoyue Zhao
Summary: In order to address the complex process of identifying rear-end collisions and lane change collisions in work zones, this study explored an appropriate identification method for traffic conflicts in the merge area of freeway work zones. Vehicle running tracking data from multiple work zones were collected using an unmanned aerial vehicle video technique and analyzed using MATLAB 2018b extension tools. Based on the behavior characteristics of vehicle conflict avoidance, a new identification method for evading severe traffic conflicts was proposed. Statistical analysis was performed to analyze the spatial distribution characteristics and influencing factors of traffic conflicts in typical merge areas. The research results can be used as a reference for freeway management departments to improve the safety levels of merge areas during road work.
Article
Engineering, Multidisciplinary
Fahad Aljuaydi, Benchawan Wiwatanapataphee, Yong Hong Wu
Summary: This paper presents multivariate machine learning-based prediction models for freeway traffic flow under non-recurrent events. Five different model architectures, including MLP, CNN, LSTM, CNN-LSTM, and Autoencoder LSTM networks, are developed to predict traffic flow under road crashes and rainfall. The models' performance is evaluated using an input dataset with five features (flow rate, speed, density, road incident, and rainfall) and two standard metrics (Root Mean Square error and Mean Absolute error).
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Review
Chemistry, Multidisciplinary
Dominik Cvetek, Mario Mustra, Niko Jelusic, Leo Tisljaric
Summary: Traffic congestion occurs when traffic demand exceeds network capacity, resulting in slower speeds, longer travel times, and increased pollution. To alleviate congestion, accurate data is required to estimate traffic flow status.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Civil
Ying Shang, Xingang Li, Bin Jia, Zhenzhen Yang, Zheng Liu
Summary: This study proposes a dynamic traffic state estimation method based on multisource data, which can accurately estimate the traffic state by collecting data on an arbitrary cross section of the freeway, showing superior performance, especially in cases of congestion.
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2022)
Article
Engineering, Civil
Chong Wang, Yang Xu, Jian Zhang, Bin Ran
Summary: This research proposes a centralized traffic control system that utilizes the latest deep reinforcement learning methods to improve freeway traffic mobility. The results show that the actor-critic-based methods are superior and can save more than 20% of the total travel time.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhuo Chen, Xiaoyue Cathy Liu
Summary: This study proposes a new travel time reliability measurement for identifying freeway bottlenecks with high probability. By using statistical distance measurements, it can effectively identify both recurrent and non-recurrent bottlenecks, as demonstrated in a case study on the I-15 freeway corridor in Salt Lake City. The recurrent bottlenecks show clustering characteristics, while locations with high probability of non-recurrent bottlenecks scatter spatially and temporally, aligning with the random nature of non-recurrent congestion.
TRANSPORTATION RESEARCH RECORD
(2021)
Article
Ergonomics
Miao Guo, Xiaohua Zhao, Ying Yao, Pengwei Yan, Yuelong Su, Chaofan Bi, Dayong Wu
Summary: The study developed a traffic crash risk prediction model based on risky driving behavior and traffic flow data, using random forest and SMOTE techniques for data processing, which ultimately improved the model's prediction accuracy and reduced the false alarm rate.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Engineering, Civil
Ignasi Echaniz Soldevila, Victor L. Knoop, Serge Hoogendoorn
Summary: Traffic engineers rely on microscopic traffic models to design, plan, and operate various traffic applications. This study aims to gain new empirical insights into driving behavior and proposes a model that can benefit from challenging data sources. The proposed method integrates parametric and non-parametric formulations to predict individual driver acceleration. This data-driven model utilizes a large training data set and traditional car-following models to predict acceleration in different scenarios.
TRANSPORTATION RESEARCH RECORD
(2021)
Article
Green & Sustainable Science & Technology
Andrea Pompigna, Raffaele Mauro
Summary: This paper introduces the methods of measuring traffic quality and congestion level in highway engineering and discusses in depth the application of reliability approach. By simulating and estimating the ARIMA models of speed random processes, combined with the Product Limit Method, it is possible to assess traffic reliability and congestion probability.
Article
Computer Science, Interdisciplinary Applications
Zhongmin Huang, M. N. Smirnova, Jiarui Bi, N. N. Smirnov, Zuojin Zhu
Summary: This paper proposes a macroscopic traffic model to analyze the effects of roadway work zones on freeway traffic flow. The numerical results show that roadway work zones generate traffic shocks when the initial density reaches a certain value, and ramp flows have a significant impact on the root mean square value of travel time.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2023)
Article
Computer Science, Interdisciplinary Applications
Xingyu Lu, Li Fei, Huibing Zhu, Wangjun Cheng, Zijie Wang
Summary: The new traffic model proposed can effectively alleviate traffic congestion in the work zone and eliminate the inducement of traffic accidents under traffic light control, but traffic lights are not necessary in the work zone when the traffic density is low.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2021)
Article
Green & Sustainable Science & Technology
Bingsheng Huang, Fusheng Zhang
Summary: With the increase in travel demands, the air pollution caused by transportation, especially traffic congestion, is becoming more serious. This paper proposes a method for effectively analyzing and identifying oversaturation states, which has important implications for alleviating traffic congestion and reducing vehicle carbon emissions.
Article
Engineering, Civil
Xingju Wang, Rongqun Zhang, Yang Gou, Jiayu Liu, Lin Zhao, Yanting Li
Summary: This paper proposes a model for alleviating traffic congestion in freeway bottleneck areas using a variable speed limit control method in an intelligent connected environment. The results show that the VSL online control method in an intelligent connected environment has better control effect, especially with an increasing penetration rate of intelligent connected vehicles (ICV).
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
Article
Engineering, Civil
Younshik Chung, Wilfred W. Recker
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2015)
Article
Engineering, Civil
Younshik Chung, Yoon-Hyuk Choi, Byoung-Jo Yoon
INTERNATIONAL JOURNAL OF CIVIL ENGINEERING
(2018)
Article
Green & Sustainable Science & Technology
Younshik Chung
Article
Economics
Rahul Swamy, Jee Eun Kang, Rajan Batta, Younshik Chung
SOCIO-ECONOMIC PLANNING SCIENCES
(2017)
Article
Environmental Sciences
Younshik Chung, Tai-Jin Song
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2018)
Article
Green & Sustainable Science & Technology
Younshik Chung, Minsu Won
Article
Management
Laiyun Wu, Jee Eun Kang, Younshik Chung, Alexander Nikolaev
Summary: Collecting individual travelers' Origin-Destination (OD) information is crucial for public transit systems to provide calculated services; accurate estimation of route choice model parameters can help assess service levels. Knowledge of OD links and route choice logic is especially important for emerging mobility services.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2021)
Article
Chemistry, Analytical
Hoe Kyoung Kim, Younshik Chung, Minjeong Kim
Summary: The study found that while the enhanced functions of the ADAS camera did not significantly impact the accuracy of traffic state estimates, the distance for vehicle identification and number of lanes did not always guarantee better estimations. It is recommended that relevant parameters and their range be carefully selected to ensure a certain level of accuracy for traffic state estimates.
Article
Transportation
Heewon Lee, Jisun Lee, Younshik Chung
Summary: This study aims to analyze the potential of using vehicle sensors to estimate traffic density in future traffic environments through simulation experiments. The research developed software modules, conducted traffic simulation experiments, and evaluated the performance of front and rear radar sensors compared to camera sensors. The results showed that front and rear radar sensors performed best, especially in low probe vehicle ratio situations, while camera sensors could be an important alternative in high sensor-equipped vehicle ratios and congested traffic conditions.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Green & Sustainable Science & Technology
Laiyun Wu, Samiul Hasan, Younshik Chung, Jee Eun Kang
Summary: The study analyzed a large-scale Automatic Fare Collection dataset of Seoul, South Korea's transit system, identifying variations in mobility patterns across user characteristics and modal preferences. The findings showed the heterogeneity of mobility patterns among demographic user groups, which will significantly impact future mobility models based on trajectory datasets.
Article
Ergonomics
Younshik Chung
Summary: This study analyzed the injury severity of two-wheeler (TW) riders in taxi-TW crashes using accurate crash data collected by taxis equipped with IVVR devices. Seven variables were found to significantly affect injury severity, including crash speed, second collision, and Delta-V.
ACCIDENT ANALYSIS AND PREVENTION
(2022)
Article
Transportation
Younshik Chung, Sanggi Nam
Summary: This study explores the concept of travel time expenditure using mobile phone signaling data and achieves better spatiotemporal precision than traditional household survey data. However, collecting and cleaning the raw data requires tremendous effort.
TRAVEL BEHAVIOUR AND SOCIETY
(2024)
Proceedings Paper
Transportation
Younshik Chung, Tai-Jin Song, Juyoung Kim
ADVANCES IN HUMAN ASPECTS OF TRANSPORTATION
(2018)
Article
Ergonomics
Younshik Chung
ACCIDENT ANALYSIS AND PREVENTION
(2018)
Article
Green & Sustainable Science & Technology
Jinwoo (Brian) Lee, Sunhyung Yoo, Hyun Kim, Younshik Chung
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2018)
