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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Heterogeneity assessment in incident duration modelling: Implications for development of practical strategies for small & large scale incidents
Behram Wali, Asad J. Khattak, Jun Liu
Journal of Intelligent Transportation Systems (2021) Vol. 26, Iss. 5, pp. 586-601
Closed Access | Times Cited: 12

Showing 12 citing articles:

Traffic accident duration prediction using multi-mode data and ensemble deep learning
Jiaona Chen, Weijun Tao, Jing Zhang, et al.
Heliyon (2024) Vol. 10, Iss. 4, pp. e25957-e25957
Open Access | Times Cited: 10
Machine learning based real-time prediction of freeway crash risk using crowdsourced probe vehicle data
Zihe Zhang, Qifan Nie, Jun Liu, et al.
Journal of Intelligent Transportation Systems (2022) Vol. 28, Iss. 1, pp. 84-102
Closed Access | Times Cited: 24
Contributing factors on the level of delay caused by crashes: a hybrid method of latent class analysis and XGBoost based SHAP algorithm
Zehao Wang, Pengpeng Jiao, Jianyu Wang, et al.
Journal of Transportation Safety & Security (2023) Vol. 16, Iss. 2, pp. 97-129
Closed Access | Times Cited: 12
Role of walkability, bike infrastructure, and greenspace in combatting chronic diseases: A heterogeneous ecological analysis in the United States
Behram Wali, Lawrence D. Frank, Jim Chapman, et al.
Sustainable Cities and Society (2024) Vol. 113, pp. 105550-105550
Closed Access | Times Cited: 3
Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data
Qiang Shang, Tian Xie, Yani Yu
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 17, pp. 10903-10903
Open Access | Times Cited: 12
Traffic accident duration prediction using text mining and ensemble learning on expressways
Jiaona Chen, Weijun Tao
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 9
A Novel Accident Duration Prediction Method Based on a Conditional Table Generative Adversarial Network and Transformer
Yongdong Wang, Haonan Zhai, Xianghong Cao, et al.
Sustainability (2024) Vol. 16, Iss. 16, pp. 6821-6821
Open Access | Times Cited: 1
Prediction of the traffic incident duration using statistical and machine-learning methods: A systematic literature review
Hüseyin Korkmaz, Mehmet Ali Ertürk
Technological Forecasting and Social Change (2024) Vol. 207, pp. 123621-123621
Closed Access | Times Cited: 1
Deep survival analysis model for incident clearance time prediction
Eui-Jin Kim, Min-Ji Kang, Shin Hyoung Park
Journal of Intelligent Transportation Systems (2024), pp. 1-14
Closed Access
Analysis of Highway Vehicle Lane Change Duration Based on Survival Model
Sheng Zhao, Shengwen Huang, Huiying Wen, et al.
Big Data and Cognitive Computing (2024) Vol. 8, Iss. 9, pp. 114-114
Open Access
Incident Duration Prediction Fusing Chinese Text: An End-to-End Deep Learning Framework
Peng Gao, Erlong Tan, Linwei Li, et al.
(2024)
Closed Access
Incident Duration Reliability Assessment Using Monte-Carlo Simulation and Kernel Density Estimation of Machine Learning-Based Models
Lubna Obaid, Khaled Hamad, Samer Barakat
International Journal of Transportation Science and Technology (2024)
Open Access
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