Journal Publications
📅 $\color{red} {2026} $
[4]From street-view sensing to maintenance decisions: A knowledge-based engineering informatics framework for urban pavement defect assessment
Developments in the Built Environment, 2026, 26, 100920.
Li, Linchao, Li, Bangxing, Liu, Jiazhen and Du, Bowen
Highlight
- Uses widely available street-view images to support scalable pavement condition sensing.
- Builds a customized defect dataset for cracks and potholes in complex urban road scenes.
- Enhances YOLOv8 for small, subtle, and low-resolution pavement defects through stronger feature representation and multi-scale fusion.
- Connects detection outputs with pavement assessment and maintenance prioritization, moving from recognition to actionable engineering decisions.
[3]Automated construction monitoring based on computer vision: A comprehensive review
Developments in the Built Environment, 2026, 25, 100832.
Li, Linchao, Huang, Zijian, Wang, Junzhen, Du, Bowen and Dailin, Fabao
Highlight
- Systematically reviews equipment, methods, datasets, metrics, and applications for automated construction monitoring.
- Compares data collection setups including single cameras, UAVs, mobile phones, and multi-camera systems.
- Identifies real-world barriers such as environmental variability, limited dataset generalization, metric inconsistency, and deployment integration.
- Provides guidance for selecting monitoring configurations and future directions such as hybrid models, large-scale datasets, and integrated platforms.
[2]Resilience assessment of urban mobility flow networks from different scales: A case study in Shenzhen
Reliability Engineering & System Safety, 2026, 274, 112374.
Li, Linchao, Li, Bangxing and Zhong, Liangjian
Highlight
- Moves beyond single-scale analysis by evaluating resilience at citywide, district/corridor, and street/intersection levels.
- Models both failure and recovery processes, instead of only measuring robustness after disruption.
- Uses centrality-based strategies to identify critical nodes and links that strongly shape mobility network performance.
- Translates complex-network results into planning insights for resilience-oriented urban mobility management.
[1]Automated Tracking of Worker and Heavy Equipment on Tunnel Construction Sites: Deep-Learning Framework
Journal of Construction Engineering and Management, 2026, 152(2), 04025237.
Li, Linchao, Cui, Xiaodong, Wang, Junzheng, Jin, Hao and Xu, Hongbin
Highlight
- Builds a tunnel-specific dataset covering workers and multiple categories of machinery in narrow, low-light, cluttered environments.
- Enhances YOLOv8 with self-calibrated illumination to improve visibility and detection under poor lighting.
- Introduces multiscale attention and a lightweight GhostNet backbone to improve dense small-target detection while reducing model complexity.
- Combines improved YOLOv8 with Deep SORT to support real-time multi-object tracking for safety monitoring and construction management.
📅 $\color{red} {2025} $
[5]Micro-mobility behavior resilience analysis in extreme weather events based on a knowledge-informed machine learning approach
Reliability Engineering & System Safety, 2025, 264, 111285.
Zhong, Liangjian, Gan, Zuoxian, Yu, Qing and Li, Linchao
[4]An adaptive high-order singular value decomposition for spatial and temporal monitoring data imputation for tunnel digital twin
Advanced Engineering Informatics, 2025, 68, 103566.
Li, Linchao, Lin, Xiang, Gong, Qi, Xu, Hongbin and Du, Yanliang
[3]Can public transportation improve equity in high-level healthcare time accessibility?
Transportation Research Part D: Transport and Environment, 2025, 145, 104804.
Gan, Zuoxian, Li, Linchao, Miao, Hongzhi, Zhao, Ruijia and Yang, Min
[2]Urban Traffic Accident Frequency Modeling: An Improved Spatial Matrix Construction Method
Journal of Advanced Transportation, 2025, 2025, 1923889.
Gan, Jing, Su, Qing, Li, Linheng, Ju, Yanni and Li, Linchao
[1]Investigating Factors Contributing to Urban Traffic Incident Risk Using High-Resolution Heterogeneous Data
Journal of Advanced Transportation, 2025, 2025, 5065270.
Zhu, Dongpeng, Zhang, Yuzhi, Wen, Dilin and Li, Linchao
📅 $\color{red} {2024} $
[6]Tensor decomposition of transportation temporal and spatial big data: A brief review
Fundamental Research, 2024, Online.
Li, Linchao, Lin, Xiang, Ran, Bin and Du, Bowen
[5]Spatial-temporal graph convolution network model with traffic fundamental diagram information informed for network traffic flow prediction
Expert Systems with Applications, 2024, 249, 123543.
Liu, Zhao, Ding, Fan, Dai, Yunqi, Li, Linchao, Chen, Tianyi and Tan, Huachun
[4]Factors affecting college students’ attitudes towards carpooling
Transportation Safety and Environment, 2024, 6(2), tdad025.
Li, Linchao, Zhang, Huali and Gan, Zuoxian
[3]Road pothole detection based on crowdsourced data and extended Mask R-CNN
IEEE Transactions on Intelligent Transportation Systems, 2024, 25(9), 12504-12516.
Li, Linchao, Liu, Jiazhen, Xing, Jiabao, Liu, Zhiyang, Lin, Kai and Du, Bowen
[2]Analysis of the relationship between metro ridership and built environment: A machine learning method considering combinational features
Tunnelling and Underground Space Technology, 2024, 144, 105564.
Li, Linchao, Zhong, Liangjian, Ran, Bin and Du, Bowen
[1]Hierarchical context representation and self-adaptive thresholding for multivariate anomaly detection
IEEE Transactions on Knowledge and Data Engineering, 2024, 36(7), 3139-3150.
Lin, Chunming, Du, Bowen, Sun, Leilei and Li, Linchao
📅 $\color{red} {2023} $
[5]A physical law constrained deep learning model for vehicle trajectory prediction
IEEE Internet of Things Journal, 2023, 10(24), 22775-22790.
Li, Hanchu, Liao, Ziyi, Rui, Yikang, Li, Linchao and Ran, Bin
[4]Anomaly detection of high-frequency sensing data in transportation infrastructure monitoring system based on fine-tuned model
IEEE Sensors Journal, 2023, 23(8), 8630-8638.
Liu, Hanlin and Li, Linchao
[3]Metro tunnel accessorial facilities and lining diseases detection method based on improved Yolov5 (基于改进 Yolov5 的地铁隧道附属设施与衬砌表观病害检测方法)
Journal of Railway Science & Engineering (铁道科学与工程学报), 2023, 20(3).
Zhu, Jiasong, Zheng, Ao, Lei, Zhanzhan, Lian, Minqing, Yang, Junwu and Li, Linchao
[2]Heterogeneous structural responses recovery based on multi-modal deep learning
Structural Health Monitoring, 2023, 22(2), 799-813.
Du, Bowen, Wu, Liyu, Sun, Leilei, Xu, Fei and Li, Linchao
[1]TCN-SA: A Social Attention Network Based on Temporal Convolutional Network for Vehicle Trajectory Prediction
Journal of Advanced Transportation, 2023, 2023, 1286977.
Li, Qin, Ou, Bingguang, Liang, Yifa, Wang, Yong, Yang, Xuan and Li, Linchao
📅 $\color{red} {2022} $
[9]DeepVIP: Deep Learning-Based Vehicle Indoor Positioning Using Smartphones
IEEE Transactions on Vehicular Technology, 2022, 71(12), 13299-13309.
Zhou, Baoding, Gu, Zhining, Gu, Fuqiang, Wu, Peng, Yang, Chengjing, Liu, Xu, Li, Linchao, Li, Yan and Li, Qingquan
[8]Smartphone-based road manhole cover detection and classification
Automation in Construction, 2022, 140, 104344.
Zhou, Baoding and Zhao, Wenjian and Guo, Wenhao and Li, Linchao and Zhang, Dejin and Mao, Qingzhou and Li, Qingquan
[7]A temporal and spatial denoising method for intelligent settlement sensing system
IEEE Sensors Journal, 2022, Early Access.
Li, Linchao and Yi, Junnan and Xu, Fei and Liu, Hanlin
[6]Bus travel time prediction based on ensemble learning methods
IEEE Intelligent Transportation Systems Magazine, 2022, 14(2), 174-189.
Zhong, Gang and Yin, Tingting and Li, Linchao and Zhang, Jian and Zhang, Honghai and Ran, Bin
[5]Missing Data Imputation in GNSS Monitoring Time Series Using Temporal and Spatial Hankel Matrix Factorization
Remote Sensing, 2022, 14(6), 1500.
Liu, Hanlin and Li, Linchao*
[4]Real-time traffic incident detection based on a hybrid deep learning model
Transportmetrica A: transport science, 2022, 18(1), 78-98.
Li, Linchao and Lin, Yi and Du, Bowen and Yang, Fan and Ran, Bin
[3]Response prediction based on spatial-temporal deep learning model for intelligent structural health monitoring
IEEE Internet of Things Journal, 2022, 9(15), 13364-13375.
Du, Bowen and Lin, Chunming and Sun, Leilei and Zhao, Yangping and Li, Linchao*
[2]Displacement Data Imputation in Urban Internet of Things System Based on Tucker Decomposition with L2 Regularization
IEEE Internet of Things Journal, 2022, 9(15), 13315-13326.
Li, Linchao and Lin, Xiang and Liu, Hanlin and Lu, Wenqi and Zhou, Baoding and Zhu, Jiasong
[1]Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost
Wireless Communications and Mobile Computing, 2022, 2022.
Zou, Liang and Shu, Sisi and Lin, Xiang and Lin, Kaisheng and Zhu, Jiasong and Li, Linchao*
📅 $\color{red} {2021} $
[4]ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems
Applied Soft Computing, 2021, 112, 107847.
Lin, Yi and Yang, Bo and Li, Linchao and Guo, Dongyue and Zhang, Jianwei and Chen, Hu and Zhang, Yi
[3]A hybrid method coupling empirical mode decomposition and a long short-term memory network to predict missing measured signal data of SHM systems
Structural Health Monitoring, 2021, 20(4), 1778-1793.
Li, Linchao and Zhou, Haijun and Liu, Hanlin and Zhang, Chaodong and Liu, Junhui
[2]A data-driven inertial navigation/Bluetooth fusion algorithm for indoor localization
IEEE Sensors Journal, 2021, 22(6), 5288-5301.
Chen, Jianfan and Zhou, Baoding and Bao, Shaoqian and Liu, Xu and Gu, Zhining and Li, Linchao and Zhao, Yangping and Zhu, Jiasong and Li, Qingquan
[1]Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm
Journal of Sensors, 2021, 2021.
Liu, Hanlin and Yang, Linqiang and Li, Linchao*
📅 $\color{red} {2020} $
[8]Missing data estimation method for time series data in structure health monitoring systems by probability principal component analysis
Advances in Engineering Software, 2020, 149, 102901.
Li, Linchao and Liu, Hanlin and Zhou, Haijun and Zhang, Chaodong
[7]Ranking contributors to traffic crashes on mountainous freeways from an incomplete dataset: A sequential approach of multivariate imputation by chained equations and random forest classifier
Accident Analysis & Prevention, 2020, 146, 105744.
Li, Linchao and Prato, Carlo G and Wang, Yonggang
[6]A data-driven approach to trip generation modeling for urban residents and non-local travelers
Sustainability, 2020, 12(18), 7688.
Yang, Fan and Li, Linchao and Ding, Fan and Tan, Huachun and Ran, Bin
[5]Automated traffic incident detection with a smaller dataset based on generative adversarial networks
Accident Analysis & Prevention, 2020, 144, 105628.
Lin, Yi and Li, Linchao* and Jing, Hailong and Ran, Bin and Sun, Dongye
[4]A deep fusion model based on restricted Boltzmann machines for traffic accident duration prediction
Engineering Applications of Artificial Intelligence, 2020, 93, 103686.
Li, Linchao and Sheng, Xi and Du, Bowen and Wang, Yonggang and Ran, Bin
[3]Coupled application of deep learning model and quantile regression for travel time and its interval estimation using data in different dimensions
Applied Soft Computing, 2020, 93, 106387.
Li, Linchao and Ran, Bin and Zhu, Jiasong and Du, Bowen
[2]Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data
Transportation Research Part A: Policy and Practice, 2020, 136, 282-292.
Li, Linchao and Zhu, Jiasong and Zhang, Hailong and Tan, Huachun and Du, Bowen and Ran, Bin
[1]Estimation of missing values in heterogeneous traffic data: application of multimodal deep learning model
Knowledge-Based Systems, 2020, 194, 105592.
Li, Linchao and Du, Bowen and Wang, Yonggang and Qin, Lingqiao and Tan, Huachun
📅 $\color{red} {2019} $
[7]Traffic speed prediction for intelligent transportation system based on a deep feature fusion model
Journal of Intelligent Transportation Systems, 2019, 23(6), 605-616.
Linchao Li, Xu Qu, Jian Zhang, Yonggang Wang, Bin Ran
[6]Self-reports of workloads and aberrant driving behaviors as predictors of crash rate among taxi drivers: A cross-sectional study in China
Traffic injury prevention, 2019, 20(7), 738-743.
Yonggang Wang, Yong Zhang, Linchao Li, Guohua Liang
[5]Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm
Knowledge-Based Systems, 2019, 172, 1-14.
Linchao Li, Lingqiao Qin, Xu Qu, Jian Zhang, Yonggang Wang, Bin Ran
[4]The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China
Accident Analysis & Prevention, 2019, 126, 17-24.
Yonggang Wang, Linchao Li, Carlo G Prato
[3]A new solution for freeway congestion: Cooperative speed limit control using distributed reinforcement learning
IEEE Access, 2019, 7, 41947-41957.
Chong Wang, Jian Zhang, Linghui Xu, Linchao Li, Bin Ran
[2]Revealing the varying impact of urban built environment on online car-hailing travel in spatio-temporal dimension: an exploratory analysis in Chengdu, China
Sustainability, 2019, 11, 1336.
Tian Li, Peng Jing, Linchao Li, Dazhi Sun, Wenbo Yan
[1]Revealing the varying impact of urban built environment on online car-hailing travel in spatio-temporal dimension: an exploratory analysis in Chengdu, China
Sustainability, 2019, 11, 1336.
Tian Li, Peng Jing, Linchao Li, Dazhi Sun, Wenbo Yan
📅 $\color{red} {2018} $
[8]Travel time prediction for highway network based on the ensemble empirical mode decomposition and random vector functional link network
Applied Soft Computing, 2018, 73, 921-932.
Linchao Li, Xu Qu, Jian Zhang, Hanchu Li, Bin Ran
[7]Analyzing passenger travel demand related to the transportation hub inside a city area using mobile phone data
Transportation research record, 2018, 2672(50), 23-34.
Gang Zhong, Jian Zhang, Linchao Li, Xiaoxuan Chen, Fan Yang, Bin Ran
[6]Missing value imputation for traffic-related time series data based on a multi-view learning method
IEEE Transactions on Intelligent Transportation Systems, 2018, 20(8), 2933-2943.
Linchao Li, Jian Zhang, Yonggang Wang, Bin Ran
[5]Short-to-medium term passenger flow forecasting for metro stations using a hybrid model
KSCE Journal of Civil Engineering, 2018, 22(5), 1937-1945.
Linchao Li, Yonggang Wang, Gang Zhong, Jian Zhang, Bin Ran
[4]Robust and flexible strategy for missing data imputation in intelligent transportation system
IET Intelligent Transport Systems, 2018, 12(2), 151-157.
Linchao Li, Jian Zhang, Fan Yang, Bin Ran
[3]Missing value imputation method for heterogeneous traffic flow data based on feature fusion(基于特征级融合的高速公路异质交通流数据修复方法)
Journal of Southeast University (Natural Science Edition)(东南大学学报(自然科学版)), 2018, 48(5), 972-978.
Linchao Li, Xu Qu, Jian Zhang, Yonggang Wang, Hannchu Li, Bin Ran (李林超,曲栩,张健,王永岗,李汉初,冉斌)
[2]An improved single-lane cellular automaton model considering driver’s radical feature
Journal of Advanced Transportation, 2018.
Xu Qu, Mofeng Yang, Fan Yang, Bin Ran, Linchao Li
[1]An improved single-lane cellular automaton model considering driver’s radical feature
Journal of Advanced Transportation, 2018.
Xu Qu, Mofeng Yang, Fan Yang, Bin Ran, Linchao Li
📅 $\color{red} {2017} $
[3]Traffic incident detection based on extreme machine learning
Journal of Applied Science and Engineering, 2017, 20(4), 409-416.
Linchao Li, Xu Qu, Jian Zhang, Bin Ran
[2]Traffic speed prediction for highway operations based on a symbolic regression algorithm
Promet-Traffic&Transportation, 2017, 29(4), 433-441.
Linchao Li, Tomislav Fratrović, Zhang Jian, Ran Bin
[1]Traffic volume prediction based on support vector regression with switch kernel functions(基于核函数切换和支持向量回归的交通量短时预测模型)
Journal of Southeast University (Natural Science Edition)(东南大学学报(自然科学版)), 2017, 47(5), 1032-1036.
Linchao Li, Jian Zhang, Fan Yang, Bin Ran (李林超,张健,杨帆,冉斌)
📅 $\color{red} {2016} $
[3]Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information
Journal of Advanced Transportation, 2016, 50(8), 2029-2040.
Linchao Li, Shanglu He, Jian Zhang, Bin Ran
[2]Analysis of factors influencing the vehicle damage level in fatal truck-related accidents and differences in rural and urban areas
Promet-Traffic&Transportation, 2016, 28(4), 331-340.
Linchao Li, Tomislav Fratrović
[1]Online Short-term Traffic Flow Prediction Considering the Impact of Temporal-spatial Features(时空因素影响下在线短时交通量预测)
Journal of Transportation System Engineering and Information Technology (交通运输系统工程与信息), 2016, 16(5), 165-171.
Linchao Li, Shanglu He, Jian Zhang (李林超,何尚璐,张健)
📅 $\color{red} {2015} $
[1]Professional drivers’ views on risky driving behaviors and accident liability: a questionnaire survey in Xining, China
Transportation letters, 2015, 6(3), 126-135.
Yonggang Wang, Linchao Li, Lei Feng, Hui Peng
Conference Publications
📅 $\color{red} {2023} $
[3]Risks evaluation of utility tunnel for gas leakage, fire and explosion disasters
2023 2nd International Conference on Artificial Intelligence and Computer Information Technology.
Zhou, Haijun, Liu, Zhunqiao, Li, Linchao, Fan, Chong and Sun, Bin
[2]Real-time fire detection for urban tunnels based on multi-source data and transfer learning
2023 4th International Symposium on Computer Engineering and Intelligent Communications.
Li, Linchao and Yi, Junnan
[1]Nonlinear Relationship between Built Environments and Metro Ridership at Station-to-Station Level on Machine Learning Methods: A Comparison of Commuters and Non-Commuters
CICTP 2023, 2573-2583.
Zhong, Liangjian, Gan, Zuoxian and Li, Linchao