Article
Engineering, Civil
Long Jin
Summary: This study aims to predict the steering and trajectory behavior of vehicles in urban road traffic using deep learning techniques. The research includes the construction of a vehicle trajectory tracking system and a recommendation strategy for steering point behavior. The results provide important theoretical reference and technical support for the digitalization and intelligent upgrade of smart city road traffic systems.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yousra Abdul Alsahib S. Aldeen, Mustafa Musa Jaber, Mohammed Hasan Ali, Sura Khalil Abd, Ahmed Alkhayyat, R. Q. Malik
Summary: Environmentally friendly and sustainable transportation options have been developed to tackle pollution and fuel shortages. However, there are still obstacles to overcome before green transportation goals can be fully achieved. A research study focuses on electric vehicle-centric approach and proposes using IoT-CC to improve EVCS location selection.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Hongbo Gao, Yechen Qin, Chuan Hu, Yuchao Liu, Keqiang Li
Summary: This article presents an interacting multiple model (IMM) for short-term and long-term trajectory prediction of an intelligent vehicle. The model is based on the vehicle's physics model and maneuver recognition model, which address the challenges of long-term trajectory prediction by ensuring the accuracy of both short-term and long-term predictions.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Liang Zhao, Yufei Liu, Ahmed Y. Al-Dubai, Albert Y. Zomaya, Geyong Min, Ammar Hawbani
Summary: The study introduces a vehicle trajectory prediction method based on Generative Adversarial Networks, including vehicle coordinate transformation, neural network prediction model, and vehicle turning model. Experimental results show that GAN-VEEP exhibits higher effectiveness in terms of average accuracy, mean absolute error, and root-mean-squared error compared to other models.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Thermodynamics
Yingxiao Yu, Tri Cuong Do, Yongsoo Park, Kyoung Kwan Ahn
Summary: An innovative hydraulic hybrid excavator with an electrical hydraulic continually variable powertrain and energy regeneration system is proposed to save energy. The equivalent consumption minimization strategy is formulated to calculate control commands and reduce energy consumption. Test bench experiments verify the system’s energy saving efficiency ranging from 36.69% to 45.16%, effectively reducing fuel consumption and emissions of the hydraulic excavator.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Engineering, Chemical
Dapai Shi, Shipeng Li, Kangjie Liu, Yun Wang, Ruijun Liu, Junjie Guo
Summary: Under the dual-carbon goal, research on energy conservation and emission reduction of new energy vehicles has once again become a hot topic. This study proposes an adaptive energy management strategy for plug-in hybrid electric vehicles (PHEVs) to improve fuel economy based on intelligent prediction of driving cycles. Simulation results show that the proposed strategy achieves a 9.85% higher fuel saving rate compared to the rule-based strategy and a 5.30% higher rate compared to the ECMS strategy without prediction, further enhancing the fuel saving potential of PHEVs.
Article
Engineering, Civil
Priyan Malarvizhi Kumar, Charalambos Konstantinou, Shakila Basheer, Gunasekaran Manogaran, Bharat S. Rawal, Gokulnath Chandra Babu
Summary: This article introduces an Agreement-induced Data Verification Model (ADVM) for securing vehicular communication against adversaries. The proposed model utilizes vector classification learning for non-replicated and recommendation-based verifications, enhancing credential validity and communication tolerance amid adversaries.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qingyu Meng, Hongyan Guo, Jialin Li, Qikun Dai, Jun Liu
Summary: Vehicle trajectory prediction is crucial for intelligent driving modules to ensure safe and efficient travel in complex traffic environments. This paper proposes a new multitask parallel joint framework that performs vehicle detection, state assessment, tracking, and trajectory prediction simultaneously based on raw LIDAR data. Experimental results demonstrate that the proposed framework outperforms state-of-the-art methods in vehicle detection and prediction.
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
(2023)
Article
Engineering, Civil
Weiyuan Huang, Yanyan Chen, Xun Zhu
Summary: With the development of network technology and multimedia technology, the multimedia communication system has played an important role in the intelligent transportation system, improving communication efficiency and traffic management, and reducing traffic accidents.
JOURNAL OF ADVANCED TRANSPORTATION
(2022)
Article
Engineering, Civil
Jerry Chun-Wei Lin, Gautam Srivastava, Jhing-Fa Wang
Summary: With the rapid growth of location sensing in the IoT and IoV techniques, trajectory data can describe diversity and characteristics of moving objects. The analysis and management of trajectory patterns have become important in recent decades, supporting efficient strategies and decisions based on mobility behavior in various fields and applications. The use of AI-based techniques to address applicable issues in ITS has become highly competitive, and this Special Issue provides a forum for researchers to share their insights and developments. There were 52 submissions, with 17 articles accepted for publication in the special issue.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Kang Wang, Lequan Yu, Jinghua Xu, Shuyou Zhang, Jing Qin
Summary: A novel deep network model called 3DPECP-Net is proposed to accurately predict energy consumption in the 3D printing process. The model seamlessly fuses multiple data sources and takes advantage of complementary information for more accurate prediction. Experimental results demonstrate that the 3DPECP-Net outperforms state-of-the-art methods.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Economics
Waqar Ahmed Khan, Hoi-Lam Ma, Xu Ouyang, Daniel Y. Mo
Summary: The proposed CovB-ELM method aims to predict aircraft trajectories and estimate fuel consumption by maximizing the covariance between hidden unit and network errors. This approach improves convergence and accuracy by partially updating randomly generated hidden unit parameters. Experimental results show that CovB-ELM outperforms existing methods in terms of generalization performance.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Thermodynamics
Shemin Sagaria, Rui Costa Neto, Patricia Baptista
Summary: This study assessed the feasibility of various power source configurations for vehicles, finding that electric vehicles perform the best in terms of energy consumption. Combining different power sources can significantly affect vehicle range and energy efficiency, with ultra-capacitors suitable for short frequent trips and fuel cells recommended for long-range coaches and trucks.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Yong Ge, Huayu Li, Alexander Tuzhilin
Summary: The paper explores route recommendations for drivers using vehicle GPS data, proposing a new Route Recommendation with Relaxed Assumptions (RR-RA) problem to recommend locations based on the driver's current position. The study also addresses the destination-oriented route recommendation (DORR) problem to provide a comprehensive approach for route recommendations to drivers.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Mingqiang Wang, Lei Zhang, Zhiqiang Zhang, Zhenpo Wang
Summary: Efficient trajectory planning for intelligent vehicles in dynamic environments is achieved through a hybrid approach combining sampling-based and numerical optimization-based methods. A risk field model is used to evaluate risks with static and moving obstacles. The sampling-based approach generates collision-free trajectory candidates, considering curve smoothness, collision risk, and travel time. The optimization-based method optimizes the behavior trajectory for safety, vehicle dynamics stability, and driving comfort. Simulation results demonstrate the competency of the proposed framework in generating high-quality trajectories in real-time.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Engineering, Civil
Lin Liu, Shuo Feng, Yiheng Feng, Xichan Zhu, Henry X. Liu
Summary: This paper proposes a learning-based stochastic driving model for AV testing, which can reproduce trajectories more similar to human drivers and generate a naturalistic driving environment, showing significant importance for AV testing and evaluation.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Engineering, Civil
Shuo Feng, Yiheng Feng, Haowei Sun, Yi Zhang, Henry X. Liu
Summary: A customized testing scenario library for a specific CAV model is generated through an adaptive process to compensate for the performance dissimilarities and leverage each test of the CAV.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Yiheng Feng, Shihong Ed Huang, Wai Wong, Qi Alfred Chen, Z. Morley Mao, Henry X. Liu
Summary: This study proposes a comprehensive analysis framework to address the cybersecurity problem in the traffic signal control system with connected vehicle technology. A case study is conducted to demonstrate the impact of data spoofing attacks and the corresponding mitigation measures.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Editorial Material
Energy & Fuels
Arno Eichberger, Zsolt Szalay, Martin Fellendorf, Henry Liu
Article
Engineering, Civil
Zachary Jerome, Xingmin Wang, Shengyin Shen, Henry X. Liu
Summary: This paper evaluates the new guidelines for traffic signal intervals published by ITE and proposes a new kinematic equation based on observed vehicle trajectories. The study finds that the previous equation overestimates the required duration for left-turning vehicles due to its assumptions about deceleration timing and rate. The proposed equation provides a more accurate estimation of yellow change and clearance intervals.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Engineering, Civil
Yan Zhao, Wai Wong, Jianfeng Zheng, Henry X. Liu
Summary: This paper proposes a maximum likelihood estimation method for queue length estimation using historical probe vehicle data, solved iteratively by the EM algorithm. Validation results show that the method can accurately estimate the parameters, enabling cycle by cycle estimation of queue lengths.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Operations Research & Management Science
Xiaozheng He, Jian Wang, Srinivas Peeta, Henry X. Liu
Summary: This paper presents a discrete day-to-day signal retiming problem to fine-tune the green splits in a traffic network and reduce congestion and travel time.
NETWORKS & SPATIAL ECONOMICS
(2022)
Article
Transportation Science & Technology
Xingmin Wang, Yafeng Yin, Yiheng Feng, Henry X. Liu
Summary: This study proposes a new framework for max pressure control using reinforcement learning algorithms, considering phase switching loss and optimizing parameters. Simulation results show that the proposed control method outperforms traditional max pressure control. This research is of great significance for real-world implementations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Civil
Rusheng Zhang, Zhengxia Zou, Shengyin Shen, Henry X. Liu
Summary: This paper introduces a newly developed and deployed roadside cooperative perception system with an edge-cloud structure and multiple kinds of sensors. The performance of the system is analyzed using data collected from the field, and its potential in applications such as traffic volume monitoring and road safety warning is demonstrated.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Engineering, Civil
Xingmin Wang, Zachary Jerome, Chenhao Zhang, Shengyin Shen, Vivek Vijaya Kumar, Henry X. Liu
Summary: This paper proposes a trajectory data processing pipeline for urban traffic network applications, which includes matching, splitting, and distance extraction steps, as well as smoothing and filtering algorithms to reduce noise and errors. The processed data is used to calculate various mobility performance indices for comprehensive evaluations. The proposed methods are efficient, robust, and scalable, and can be applied to large-scale urban traffic networks.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Multidisciplinary Sciences
Shuo Feng, Haowei Sun, Xintao Yan, Haojie Zhu, Zhengxia Zou, Shengyin Shen, Henry X. Liu
Summary: A critical bottleneck for autonomous vehicle development and deployment is the high costs required to validate safety in real-world driving. Researchers have developed an intelligent testing environment using AI-based agents to accelerate the safety validation process without bias. Their approach reduces testing time by orders of magnitude and can also be applied to other safety-critical autonomous systems.
Article
Engineering, Civil
Zhen Yang, Rusheng Zhang, Gaurav Pandey, Neda Masoud, Henry X. Liu
Summary: This work proposes a hierarchical vehicle behavior prediction framework that incorporates traffic signal information and models the interaction between vehicles. The framework predicts vehicle behaviors in two stages: discrete intention prediction and continuous trajectory prediction. It is designed to capture the difference among human drivers with parameterized driver characteristics.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Civil
Zhen Yang, Jun Ying, Junjie Shen, Yiheng Feng, Qi Alfred Chen, Z. Morley Mao, Henry X. Liu
Summary: This paper proposes a method to detect GPS spoofing attacks on connected vehicles (CVs) and autonomous vehicles (AVs) using domain knowledge in transportation and vehicle engineering. A computational-efficient driving model is constructed by learning from historical trajectories, and a statistical method is developed to measure the dissimilarities between observed and predicted trajectories for anomaly detection. The proposed method is validated on real-world datasets and shown to detect almost all attacks with low false positive and false negative rates.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Yujie Feng, Jiangtao Wang, Yasha Wang, Xu Chu
Summary: As a crucial part of public health system, population health monitoring plays a significant role in shaping health policies. However, the high cost of traditional data collection methods has led to the proposal of sparse-sampling-completion algorithms. Existing data-completion methods primarily focus on adjacent-spatial correlations, which may not accurately infer missing prevalence data in neighboring areas due to cost constraints. To address this problem, we propose a novel deep-learning-based prevalence inference model, SDA-GAIN (Spatial-attention and Demographic-augmented Generative Adversarial Imputation Network), which improves accuracy by learning health semantic space similarities between cross-space areas. SDA-GAIN utilizes a Transformer-based model to learn healthy semantic similarities and a GAN-based model for high-accuracy completion, with the addition of demographic data to enhance the model's ability in learning better health semantic representation through CNN. Extensive experiments demonstrate that SDA-GAIN outperforms other state-of-the-art approaches at low sampling rates (<30%), leading to significant cost savings. Moreover, the visualization of health semantic similarity learned by SDA-GAIN closely resembles real-life situations.
IEEE TRANSACTIONS ON BIG DATA
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jingxuan Yang, Honglin He, Yi Zhang, Shuo Feng, Henry X. Liu
Summary: This paper proposes an adaptive testing method using sparse control variates, which evaluates the performance of CAVs by adaptively utilizing testing results. It reduces estimation variance by adjusting testing results based on multiple linear regression techniques and optimizes regression coefficients for the CAV under test. The method applies sparse control variates to critical variables of testing scenarios and has been validated in high-dimensional overtaking scenarios, achieving a 30 times acceleration in the evaluation process.
2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
(2022)
Article
Transportation Science & Technology
Yue Zhao, Liujiang Kang, Huijun Sun, Jianjun Wu, Nsabimana Buhigiro
Summary: This study proposes a 2-population 3-strategy evolutionary game model to address the issue of subway network operation extension. The analysis reveals that the rule of maximum total fitness ensures the priority of evolutionary equilibrium strategies, and proper adjustment minutes can enhance the effectiveness of operation extension.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Hongtao Hu, Jiao Mob, Lu Zhen
Summary: This study investigates the challenges of daily storage yard management in marine container terminals considering delayed transshipment of containers. A mixed-integer linear programming model is proposed to minimize various costs associated with transportation and yard management. The improved Benders decomposition algorithm is applied to solve the problem effectively and efficiently.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Zhandong Xu, Yiyang Peng, Guoyuan Li, Anthony Chen, Xiaobo Liu
Summary: This paper studied the impact of range anxiety among electric vehicle drivers on traffic assignment. Two types of range-constrained traffic assignment problems were defined based on discrete or continuous distributed range anxiety. Models and algorithms were proposed to solve the two types of problems. Experimental results showed the superiority of the proposed algorithm and revealed that drivers with heightened range anxiety may cause severe congestion.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Chuanjia Li, Maosi Geng, Yong Chen, Zeen Cai, Zheng Zhu, Xiqun (Michael) Chen
Summary: Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Benjamin Coifman, Lizhe Li
Summary: This paper develops a partial trajectory method for aligning views from successive fixed cameras in order to ensure high fidelity with the actual vehicle movements. The method operates on the output of vehicle tracking to provide direct feedback and improve alignment quality. Experimental results show that this method can enhance accuracy and increase the number of vehicles in the dataset.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
Article
Transportation Science & Technology
Mohsen Dastpak, Fausto Errico, Ola Jabali, Federico Malucelli
Summary: This article discusses the problem of an Electric Vehicle (EV) finding the shortest route from an origin to a destination and proposes a problem model that considers the occupancy indicator information of charging stations. A Markov Decision Process formulation is presented to optimize the EV routing and charging policy. A reoptimization algorithm is developed to establish the sequence of charging station visits and charging amounts based on system updates. Results from a comprehensive computational study show that the proposed method significantly reduces waiting times and total trip duration.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2024)
