Article
Transportation Science & Technology
Yifan Zhang, Xinhong Chen, Jianping Wang, Zuduo Zheng, Kui Wu
Summary: This paper proposes a novel generative hybrid CF model to accurately characterize dynamic human CF behaviors and generate realistic CF behaviors for observed or unobserved driving styles.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Computer Science, Information Systems
Shuoqi Wang, Zhanzhong Wang
Summary: Two methods are proposed to predict the average speed of vehicles on an expressway, based on the number-speed relationship of vehicles on road segments and the traffic situation obtained from online maps API. Intelligent driving strategies are adopted to adjust the driving route and speed according to expressway conditions and desired driving time, overcoming the shortcomings of existing traffic volume prediction. These methods provide a theoretical basis for the design of expressway intelligent driving systems and demonstrate good innovation and practical applications.
Article
Engineering, Civil
Jie Sun, Zuduo Zheng, Jian Sun
Summary: This paper relaxes an unrealistic assumption commonly adopted in the stability analysis of car-following (CF) models, which is the fixed equilibrium state assumption. The influence mechanism of the equilibrium state and its change on the stability of CF models is studied, considering the impact of asymmetry in CF models. The results show significant differences between symmetric and asymmetric CF models: the acceleration process significantly destabilizes the traffic with asymmetric CF models, while the influence of acceleration and deceleration on the stability change is identical and relatively insignificant for symmetric CF models.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Chemistry, Analytical
Jongtae Lim, Songhee Park, Dojin Choi, Kyoungsoo Bok, Jaesoo Yoo
Summary: This paper proposes a machine-learning-based road speed prediction scheme that utilizes road environment data analysis. It accurately predicts both average road speed and rapidly changing road speeds by analyzing speed data from the target road and neighboring roads. It considers historical average speed data and events as weights for prediction and uses the LSTM algorithm for sequential data learning.
Article
Energy & Fuels
Tomasz Krukowicz, Krzysztof Firlag, Jozef Suda, Miroslaw Czerlinski
Summary: This article discusses the impact of signal countdown timers (SCT) on road safety and efficiency, with findings showing that SCTs increase red-light violations, decrease vehicle entries during the amber signal, and increase vehicle speed, among other issues.
Article
Computer Science, Interdisciplinary Applications
Antonio Lucas-Alba, Sharona T. Levy, Oscar M. Melchor, Ana Zarzoso-Robles, Ana M. M. Ferruz, Maria Teresa Blanch, Andres S. Lombas
Summary: This article tackles the issue of traffic congestion from a learning perspective by emphasizing the transformative power of information and communication technologies. The article introduces the WaveDriving Course (WDC), a simulated learning environment that helps drivers transition from the traditional drive-to-keep-distance technique to a new car-following principle better suited for wave-like traffic. The results indicate that the learning process was successful in achieving the desired changes in driver behavior and contributed to the reduction of congestion.
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
(2022)
Article
Engineering, Biomedical
Jielin Chen, Xuefen Lin, Weifeng Ma, Yuchen Wang, Wei Tang
Summary: This study proposes a scheme for EEG acquisition and emotion classification in a simulated driving environment, using graph neural networks to simulate the brain's physiological structure. Through various experiments, high classification accuracy and F1 scores were obtained. The results indicate that the scheme can effectively simulate different dangerous situations during driving and monitor the driver's emotional state.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Civil
Michael Harth, Uzair Bin Amjad, Ronald Kates, Klaus Bogenberger
Summary: In recent years, efforts have been made to incorporate human factors into the modeling of human driver behavior. This paper proposes a methodology based on a long short-term memory architecture to integrate human factors into driver behavior modeling. The methodology is demonstrated using data recorded at an urban signalized intersection and shows superior performance compared to existing models in replicating real-world trajectories and representing different driving strategies.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Chemistry, Analytical
Roozbeh Mohammadi, Claudio Roncoli
Summary: This paper investigates vehicle estimation methods based on CV data, utilizing machine learning models and training with both real and synthetic data to achieve good performance in the presence of incomplete data.
Article
Physics, Multidisciplinary
Trinh Dinh Toan, Soi Hoi Lam, Yiik Diew Wong, Meng Meng
Summary: This paper presents the development and validation of a driving simulator for ramp traffic control on expressways using a traffic simulator and control system. The simulator includes a car-following model and a traffic controller, which are harmonized and integrated in a close-loop control manner. The validation results show that the simulated speed is similar to the actual speed, and the aggregated flow rate discrepancies are within a small range, indicating the reliability of the model for traffic simulation and control applications.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Chemistry, Analytical
Qinglu Ma, Lian Ma, Fengjie Liu, Daniel (Jian) Sun
Summary: This study proposes a traffic noise detection solution based on spectral subtraction, feature fusion, and triangular wave analysis. A simulation platform was built using MATLAB for experimental evaluation. The results show that the proposed method achieves significantly higher accuracy in traffic detection compared to traditional methods, effectively solves the problem of overlapping traffic flow, and improves road operation efficiency.
Article
Engineering, Civil
Christian Liebchen
Summary: This paper presents an integrated mathematical optimization model to maximize the green band widths and lengths of a bidirectional arterial road. The model selects signal programs and computes optimum offsets for each junction. Microscopic traffic flow simulations in Berlin validate the effectiveness of the optimization results.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Engineering, Multidisciplinary
Niklas Wetterberg, Enrico Ronchi, Jonathan Wahlqvist
Summary: This study used a VR experiment to investigate the impact of wildfire smoke on individual driving behavior. The results showed that driving speed decreases with increasing smoke density, but there was no significant difference in lateral position of the driven car on the road at different smoke densities.
Article
Computer Science, Information Systems
David Escuin, Lorena Polo, David Cipres, Carlos Millan, Jorge Carcas
Summary: This article introduces the Smart Driving Service (SDS), which is a customized mobile application and a complex microservices framework designed for both professional drivers and novice drivers who need assistance during long-distance journeys. The system implements the European regulations on driving times, breaks, and rest periods for freight drivers and provides feedback reports and a Route Performance Index (RPI) to improve driver behavior and fuel consumption.
Article
Computer Science, Information Systems
Antonio Luna-Alvarez, Dante Mujica-Vargas, Arturo Rendon-Castro, Manuel Matuz-Cruz, Jean Marie Vianney Kinani
Summary: In the field of self-driving vehicles, steering control is the process of transforming sensor information into commands to steer the vehicle and avoid obstacles. This research proposes a data fusion approach using a neurofuzzy aggregation deep learning layer, which combines fuzzy measures, the Choquet fuzzy integral, and a fuzzy backpropagation algorithm to process data from different sources. A self-driving neural model based on the aggregation of steering control and obstacle detection models is also implemented and tested. The proposed approach achieves an average autonomy of 95% and improves driving smoothness by 9% compared to other state-of-the-art methods, as shown in the experiments conducted in a simulation environment and with a scale prototype.
Article
Engineering, Civil
Tao Wang, Tie-Qiao Tang, Jian Zhang, Peng Li
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2020)
Article
Energy & Fuels
Jian Zhang, Tie-Qiao Tang, Yadan Yan, Xiaobo Qu
Summary: This paper proposes a novel wireless charging scheme for electric vehicles which, combined with an eco-driving control strategy, can increase driving range and decrease travel cost. The effectiveness of deploying wireless charging lanes at intersections has been verified through field tests.
Article
Transportation Science & Technology
Tao Wang, Tie-Qiao Tang, Hai-Jun Huang, Xiaobo Qu
Summary: This study investigates the impact of electric vehicles (EVs) on morning commute traffic. The results show that EVs can lead to extra traffic congestion, but interventions can be implemented to mitigate this issue. Such findings shed light on policymaking for promoting EVs.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2021)
Article
Engineering, Civil
Ying-Xu Rui, Tie-Qiao Tang, Jian Zhang
Summary: This study investigates group behavior in bicycle flow, specifically the effects of shoulder group behavior and following group behavior on bicycle motion. The findings suggest that shoulder group behavior has negative impacts on bicycle motion, while following group behavior has positive impacts. Additionally, increasing group size and group probability can influence the extent of these impacts.
JOURNAL OF ADVANCED TRANSPORTATION
(2021)
Article
Transportation
Tie-Qiao Tang, Xiao-Ting Yuan, Peng-Cheng Hu, Tao Wang
Summary: In this paper, an improved cellular automaton (CA) model is proposed to describe the motion of different types of pedestrians in a hospital registration hall. The simulation results show that different types of pedestrians exhibit different motion behaviors during non-emergency evacuation, which have varying impacts on the efficiency of the evacuation process. This research provides valuable insights for understanding the non-emergency evacuation process of heterogeneous pedestrians and designing reasonable evacuation strategies for unexpected emergencies in hospital registration halls.
TRANSPORTMETRICA A-TRANSPORT SCIENCE
(2023)
Article
Transportation
Yun-Qi Gao, Tie-Qiao Tang, Jian Zhang, Feng You
Summary: Different aircraft have different load capacity and fuel consumption, and this study finds that smaller aircraft tend to have better fuel efficiency than larger aircraft at the same payload level. The relationship between fuel consumption and aircraft payload is positively correlated, which can help with energy conservation and fleet planning for airlines.
TRANSPORTMETRICA B-TRANSPORT DYNAMICS
(2022)
Article
Engineering, Civil
Yanyan Chen, Shiwei Li, Yuyan Pan, Jian Zhang
Summary: In this paper, a data-plus-model framework-based expressway congestion forewarning method is proposed to accurately and quickly predict traffic congestion by analyzing historical traffic flow data and obtaining critical congestion forewarning parameters.
JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS
(2022)
Article
Engineering, Civil
Tie-Qiao Tang, Yong Gui, Jian Zhang
Summary: This paper proposes a car-following model based on automating entropy adjustment for level 3 autonomous driving, trains the model through reinforcement learning, and validates its advantages in safe, efficient, and comfortable driving.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Jia He, Jie Qu, Jian Zhang, Zhengbing He
Summary: This paper proposes an analytic method based on trajectory data to investigate the impact of discretionary lane-changing on surrounding traffic, and demonstrates the effectiveness of the method using a high-precision trajectory dataset.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Environmental Studies
Jia He, Na Yan, Jian Zhang, Tao Wang, Yan-Yan Chen, Tie-Qiao Tang
Summary: This paper presents a battery electric bus energy consumption model based on characteristic data and proposes a method to extract the characteristic data from unordered data. A practical BEB charging plan optimization model is developed based on the departure plan timetable and trip electricity consumption estimation of the bus fleet. Real-world numerical tests show that the energy consumption model has an average relative error of 7.5-9.2% and the optimized charging plan can reduce charging costs by approximately 16%.
TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT
(2023)
Article
Economics
Tao Wang, Peng Liao, Tie-Qiao Tang, Hai-Jun Huang
Summary: This study investigates the effects of deterministic capacity drop on commuter's departure time choice, the evolution of traffic flow, and traffic congestion in the morning commute process. It reveals a new manifestation of the capacity expansion paradox caused or influenced by the deterministic capacity drop.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Physics, Multidisciplinary
Liang Chen, Zhi-Liang Guo, Tao Wang, Chuan-Yao Li, Tie-Qiao Tang
Summary: This study proposes an evacuation guidance model for heterogeneous populations to investigate the effects of guidance strategies on large-scale pedestrian evacuation. The model integrates an exit choice model and a microscopic pedestrian simulation model. Simulation results show that better evacuation performance can be achieved by balancing the time interval for sending evacuation instruction and the frequency of the target exit changes. The performance of the guidance strategy is closely related to pedestrian density level and the heterogeneity of pedestrian speeds.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Thermodynamics
Feng Cao, Tie-Qiao Tang, Yunqi Gao, Feng You, Jian Zhang
Summary: A study at Xining International Airport in China compares five taxiing methods and finds that new methods can reduce fuel consumption and pollutant emissions compared to traditional methods. Onboard systems perform the best in terms of energy saving and emissions reduction. The study also explores the carbon abatement potential of each method over the next decade.
Article
Transportation Science & Technology
Chuanyao Li, Dexin Huang, Tao Wang, Jin Qin
Summary: This study proposes a rule to improve vehicles' lane-changing decisions by utilizing accurate information from surrounding vehicles. The rule suggests that connected and autonomous vehicles should change lanes in advance if there is severe flow reduction, but maintain the car-following state if the traffic flow variations are similar across all lanes. Numerical simulations and tests show that this approach can significantly enhance traffic flow throughput, especially in congested conditions.
TRANSPORTATION SAFETY AND ENVIRONMENT
(2023)
Article
Mathematics
Ying Luo, Yanyan Chen, Kaiming Lu, Jian Zhang, Tao Wang, Zhiyan Yi
Summary: The driver's stochastic nature can cause traffic oscillation. By proposing a stochastic full velocity difference model (SFVDM) and a stable speed guidance model (S-SFVDM), the impact of the driver's stochastic characteristics on car-following behavior can be better described, and traffic oscillation caused by driving stochasticity can be mitigated. Numerical tests show the effectiveness of the proposed models.
ELECTRONIC RESEARCH ARCHIVE
(2022)
