Article
Engineering, Civil
Baihe Ma, Xu Wang, Wei Ni, Ren Ping Liu
Summary: This paper explores a method to protect the location privacy of vehicles in road networks, proposes a dual-obfuscation algorithm for personalized location privacy-preserving scheme, and conducts experiments to validate its effectiveness, demonstrating high capability in location privacy protection and data utility.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Zhouhao Wu, Yaxiang Li, Xin Wang, Juan Su, Liu Yang, Yu Nie, Yuanqing Wang
Summary: The study revealed that in Shenzhen, approximately 23.5% of taxi trips involved detours of more than 2.1 kilometers, and the level of detour intensity and ratio are influenced by road features and dynamics.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Civil
Jingyu Chen, Xin Feng, Shiyun Xiao
Summary: This paper proposes a leakage zone identification method based on alarm levels and pattern identification. By converting residual values into alarm levels and optimizing the model through the Euclidean distance method and enumeration method, the leakage nodes can be accurately identified, and the size of candidate leakage zones can be effectively reduced.
JOURNAL OF CIVIL STRUCTURAL HEALTH MONITORING
(2022)
Article
Geosciences, Multidisciplinary
Chengyu Hong, Guangbin Luo, Weibin Chen
Summary: This study proposes a method for safety monitoring of a foundation ditch using distributed fiber optic sensors and micro-electro-mechanical sensors, and predicts the monitored parameters using various artificial neural network methods. The monitoring results show that these sensors are effective for monitoring settlement, horizontal displacement, and axial force change of the foundation ditch.
Article
Environmental Sciences
Lanting Fang, Ze Kou, Yuzhang Zhou, Yudong Zhang, George Y. Yuan
Summary: The widespread use of mobile devices has generated vast amounts of spatial data, which can be used to address real-life problems through data-driven approaches. This paper introduces the max coverage region (MCR) problem in road networks and provides efficient solutions.
Article
Engineering, Civil
Sayan Sen Sarma, Koushik Sinha, Chayanon Sub-r-pa, Goutam Chakraborty, Bhabani P. Sinha
Summary: This study focuses on optimizing traffic distribution during disasters to reduce travel time to evacuation centers. Various algorithms were proposed and simulations showed superior results compared to existing methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2021)
Article
Computer Science, Information Systems
Sunwoo Kim, Luong T. Nguyen, Junhan Kim, Byonghyo Shim
Summary: This study proposes a deep learning-based technique for IoT network localization, which estimates the Euclidean distance matrix D using multiple deep neural networks and exploits its symmetry and specific element properties during recovery. Experimental results demonstrate that this approach shows significant performance improvement over traditional methods in noiseless scenarios and competitive performance in noisy scenarios.
IEEE WIRELESS COMMUNICATIONS LETTERS
(2021)
Article
Computer Science, Interdisciplinary Applications
Xi Zhang, Amitava Mukherjee, Chenglong Li
Summary: This paper introduces distance-based quasi-distribution-free approaches for Phase-I analysis of multivariate and high-dimensional processes. It aims to bridge the gap in the limited existing literature on multivariate nonparametric statistical process monitoring (NSPM) in Phase-I analysis. The proposed alternative multivariate schemes are capable of detecting changes in the multivariate location vector, scale matrix, or both without knowledge of the process distribution.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Information Systems
Haining Yu, Lailai Yin, Hongli Zhang, Dongyang Zhan, Jiaxing Qu, Guangyao Zhang
Summary: Two secure road distance computation approaches are proposed in this study, which can efficiently compute road distance over encrypted data. An approximate computation approach uses Partially Homomorphic Encryption and road network set embedding, while an exact computation approach utilizes Somewhat Homomorphic Encryption and road network hypercube embedding. Evaluation on real cityscale road network demonstrates the accuracy and efficiency of these approaches.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Environmental Sciences
Lianbi Yao, Changcai Qin, Qichao Chen, Hangbin Wu
Summary: Automatic extraction and vectorization of road markings were studied in this paper using vehicle-borne laser point cloud data. The process involved extracting scan lines, pavement, transforming point clouds into raster images, segmenting into binary images, and identifying solid lines and guidelines using a deep learning network framework. The F-scores for identifying different road markings ranged from 0.66 to 1.
Article
Computer Science, Artificial Intelligence
Xin Guo, Wen-jing Li, Jun-fei Qiao
Summary: To address the difficulty of accurately modeling and predicting time series, a novel self-organizing modular neural network based on empirical mode decomposition with sliding window mechanism (SWEMD-MNN) is proposed, which can effectively decompose time series and improve the prediction accuracy of classical modular neural networks.
APPLIED SOFT COMPUTING
(2023)
Article
Chemistry, Multidisciplinary
Athita Onuean, Hanmin Jung, Krisana Chinnasarn
Summary: The air quality monitoring network plays a crucial role in managing air pollution, but setting up an initial network in a city can be challenging due to lack of necessary information. Existing networks may not adequately represent the spatial coverage of air pollution issues, especially in rapidly urbanizing areas. By using Euclidean distance and the k-nearest neighbor algorithm, new methods have been proposed to find stations and improve spatial coverage, showing promising results for future expansions in air monitoring.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Moustapha Diaw, Agnes Delahaies, Jerome Landre, Florent Retraint, Frederic Morain-Nicolier
Summary: This study presents a new method for image pair comparison and classification based on the modeling of the Local Dissimilarity Map (LDM). The method uses a statistical model for the LDM and applies classifiers to compute the classification scores. It is capable of effectively differentiating and classifying similar and dissimilar image pairs, and is robust against geometric transformations such as translation.
Article
Engineering, Mechanical
Lisha Liang, Xibing Li, Quanqi Zhu, Siyu Peng, Xuefeng Si
Summary: Understanding the stress distribution and failure characteristics around a U-shaped cavern is important for the stability analysis of rock structures. This study analyzed the stress distribution based on complex variable theory and conducted numerical simulations to study the failure process and energy evolution of a U-shaped cavern with different height-to-width ratios. The results show that the height-to-width ratio affects the stress concentration and failure process of the cave walls, vault, and floor. The direction of the maximum principal stress also influences the stability and failure characteristics of the cavern. Therefore, the design of the support system for U-shaped caverns should consider both the height-to-width ratio and the direction of the maximum principal stress.
ENGINEERING FAILURE ANALYSIS
(2024)
Article
Mathematics, Interdisciplinary Applications
Xiangyi Meng, Bin Zhou
Summary: Complex networks are often considered to be scale-free, characterized by a power-law distribution of the nodes' degree. However, in real-world networks, the distribution of the degree-degree distance, a metric similar to degree, shows a stronger power-law distribution. We investigate the relationship between the two distributions and introduce network models that have a power-law distribution of degree-degree distance but not degree. Our findings suggest that degree-degree distance is a more suitable indicator of scale-freeness.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Transportation Science & Technology
Yaqian Zhou, Hai Yang, Jintao Ke
Summary: This paper proposes a general model for describing the equilibrium state of a ride-sourcing market with multiple competing platforms. The market changes from monopoly to different levels of oligopoly or perfect competition as the number of platforms increases, leading to two opposing effects on system efficiency. To capture these effects, a game-theoretical model is developed to find the Nash equilibrium solutions of a competitive ride-sourcing market. The price of competition and fragmentation is quantified by establishing an upper bound of the inefficiency ratio. The market equilibrium, including the inefficiency ratio, is jointly determined by the degree of market fragmentation and competition among platforms.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Economics
Jintao Ke, Xiqun (Michael) Chen, Hai Yang, Sen Li
Summary: This paper proposes an equilibrium model to describe a ride-sourcing market with both ride-pooling (RP) and non-pooling (NP) services considering traffic congestion externality. It suggests that the market can exist in one or multiple equilibria, depending on the fare difference between RP and NP services. The study also reveals a smiling curve relationship between average sojourn time and vehicle fleet size. The research discusses the impacts of labor supply and background traffic on the platform's operating strategy and profit.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2022)
Article
Economics
Yining Di, Meng Xu, Zheng Zhu, Hai Yang, Xiqun Chen
Summary: Based on real-world ride-sourcing data, this study analyzes the working patterns of ride-sourcing drivers and finds significant differences in performance and income based on different start locations and time windows. Drivers in suburban and rural areas have lower working efficiency compared to those in urban and suburban areas, while frequent switching between working patterns may lead to a decrease in income. The platform can introduce differentiated policies to handle the spatiotemporal inequality and improve service performance and overall benefits.
Article
Engineering, Civil
Zheng Zhu, Meng Xu, Yining Di, Hai Yang
Summary: This paper proposes a physics regularized multi-output grid Gaussian Process Model (PRMGGP) for fast and accurate fitting of large-scale spatial-temporal processes in transportation systems. The PRMGGP model adopts a grid input structure, uses Kronecker algebra for accelerated computation, and incorporates physics laws using a shadow GP. Experimental results demonstrate the efficiency and accuracy of the proposed model.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Economics
Yahan Lu, Lixing Yang, Hai Yang, Housheng Zhou, Ziyou Gao
Summary: This paper systematically investigates the joint optimization of passenger flow control strategy and train timetable on a congested metro line. A deterministic model is developed to balance operation efficiency and service fairness, and three integer linear programming models are formulated to derive robust passenger flow control strategies. Real-world case studies on the Beijing metro Batong line are conducted to verify the performance and effectiveness of the proposed approaches.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Operations Research & Management Science
Ren-Yong Guo, Hai Yang, Hai-Jun Huang
Summary: We study a departure time choice model for commuters in a bottleneck system with heterogeneity in travel time and schedule delays. A Walrasian toll charge scheme is used to control traffic flows. The scheme is anonymous and does not require information on travel time and schedule delays. The theoretical analysis proves that the toll charge scheme can achieve the system optimum flow pattern. The distributions of traffic flows and toll charges at the system optimum state are shown analytically, and the scheme's effectiveness is examined through numerical analyses.
TRANSPORTATION SCIENCE
(2023)
Article
Economics
Yang Xia, Wenjia Zeng, Canrong Zhang, Hai Yang
Summary: This paper addresses the vehicle routing problem with load-dependent drones (VRPLD). A facility called the docking hub is introduced to enhance the collaboration between trucks and drones. A mixed-integer model is proposed, and a branch-and-price-and-cut algorithm is developed to solve the problem efficiently. The computational results demonstrate the effectiveness of the proposed algorithm compared to existing methods.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Economics
Hongxing Ding, Hai Yang, Hongli Xu, Ting Li
Summary: Based on the status quo-dependent route choice model in Xu et al. (2017), this study integrates the model into traffic assignment modeling and establishes a Status quo-dependent User Equilibrium (SDUE) model. The SDUE model considers cognitive limitations, satisficing behavior, inertial behavior, and variation in value of time (VOTs) in route choice behavior. The study also demonstrates that equilibrium solutions from previous UE models can be included in the SDUE solution set by varying VOTs among users and scenarios.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Transportation Science & Technology
Zheng Zhu, Meng Xu, Jintao Ke, Hai Yang, Xiqun (Michael) Chen
Summary: In this paper, a Bayesian clustering ensemble Gaussian process (BCEGP) model is proposed for network-wide traffic flow clustering and prediction. The model combines hard clustering and Gaussian process learning methods to effectively tackle limitations of machine learning models in traffic flow prediction, such as interpretability, generalization, and reliance on image data processing. Experimental results show that the BCEGP model performs well in predictive accuracy, computational speed, and applicability.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Transportation Science & Technology
Yun Wang, Yu Zhou, Hai Yang, Xuedong Yan
Summary: This paper systematically investigates the bus bridging service design problem in urban rail transit, aiming to minimize operator and passenger costs while effectively addressing service disruptions. A column generation-based approach is proposed to quickly generate high-quality emergency response plans for public transit operators. Our method has been tested and proven effective in real case studies.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Economics
Yue Bao, Hai Yang, Ziyou Gao, Hongli Xu
Summary: This study investigates the impact of pre-event activities on attendees' departure-time choices and traffic congestion near a venue. A bottleneck model is proposed to analyze the heterogeneous pre-event utility of attendees, considering the attractiveness of the venue. Different distributions of pre-event utility sensitivity are used to analyze the equilibrium at the bottleneck and determine the conditions to eliminate queues. The study also examines the impact of venue attractiveness on attendees' pre-event utility sensitivity and determines optimal pricing and facility levels to maximize venue profit and attendees' trip utilities.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Economics
Xiaoshu Ding, Qi Qi, Sisi Jian, Hai Yang
Summary: Mobility-as-a-Service (MaaS) is a new transport model that offers multiple travel modes through a single platform. The MaaS operator acts as a middleman, purchasing resources from different service providers and offering seamless transport services to meet travelers' needs. The challenge lies in matching travelers to providers, ensuring profitability for the providers and efficiency for the system.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Transportation Science & Technology
Kehua Chen, Jindong Han, Siyuan Feng, Meixin Zhu, Hai Yang
Summary: This article studies the issue of driver profiling in ride-hailing services and proposes a Hierarchical Graph Contrastive Learning (HGCL) framework that automatically learns low-dimensional embeddings from raw GPS data to encode driver behaviors. Experimental results demonstrate the efficacy of the proposed framework in driver profiling.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
Article
Economics
Hongxing Ding, Hai Yang, Xiaoran Qin, Hongli Xu
Summary: This study proposes a credit charge-cum-reward (CCR) scheme to promote green mobility and alleviate congestion and emissions by regulating travelers' periodic mode usage behavior. The scheme minimizes individual travel costs by taking into account travelers' heterogeneity in value of time. The government designs CCR schemes without and with revenue constraints, and investigates their Pareto-improvement and revenue-neutrality. The proposed CCR scheme flexibly facilitates multi-modal traffic demand management through charging and rewarding rates and differentiated charging and redemption prices.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2023)
Article
Transportation Science & Technology
Bolong Zhou, Wei Liu, Hai Yang
Summary: This study examines the multi-depot location-routing problems of unmanned aerial vehicles (UAVs) for urban monitoring (MDLRP-UM). The proposed solution method combines an iterative algorithm with a tailored adaptive large neighborhood search (ALNS) based heuristic algorithm to solve the master and sub-problems, resulting in an efficient and effective approach for solving MDLRP-UM.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2023)
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)
