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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:

Predicting Ambulance Demand
Zhengyi Zhou, David S. Matteson
(2015), pp. 2297-2303
Open Access | Times Cited: 38

Showing 26-50 of 38 citing articles:

An Operations Research Approach for Daily Emergency Management
Niki Matinrad
Linköping studies in science and technology. Thesis (2019)
Open Access | Times Cited: 4
Towards Accurate Predictions and Causal 'What-if' Analyses for Planning and Policy-making: A Case Study in Emergency Medical Services Demand
Kasun Bandara, Christoph Bergmeir, Sam Campbell, et al.
arXiv (Cornell University) (2020)
Open Access | Times Cited: 4
Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok
Ranon Jientrakul, Chumpol Yuangyai, Klongkwan Boonkul, et al.
Sustainability (2022) Vol. 14, Iss. 16, pp. 10247-10247
Open Access | Times Cited: 3
DEAR: Dynamic Electric Ambulance Redeployment
Lukas Rottkamp, Niklas Strauß, Matthias Schubert
(2023) Vol. 13, pp. 11-20
Open Access | Times Cited: 1
A Data Mining Approach for Health Transport Demand
Jorge Blanco-Prieto, M. González, Steven Van Vaerenbergh, et al.
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 1, pp. 78-97
Open Access
A Stacking Ensemble Machine Learning Model for Emergency Call Forecasting
Talotsing Gaelle Patricia Megouo, Samuel Pierre
IEEE Access (2024) Vol. 12, pp. 115820-115837
Open Access
Considerations for Developing Predictive Spatial Models of Crime and New Methods for Measuring Their Accuracy
Chaitanya Joshi, Sophie Curtis‐Ham, Clayton D’Ath, et al.
ISPRS International Journal of Geo-Information (2021) Vol. 10, Iss. 9, pp. 597-597
Open Access | Times Cited: 3
Forecasting Ambulance Demand using Machine Learning: A Case Study from Oslo, Norway
Anna Haugsbo Hermansen, Ole J. Mengshoel
2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2021), pp. 01-10
Closed Access | Times Cited: 3
Ferramenta para análises descritivas e preditivas em dados criminais: Um estudo de caso em Minas Gerais
Yan Andrade, Gabrielle Amarante, Matheus Pimenta, et al.
(2024), pp. 83-86
Open Access
The Application of Market-based Multi-Robot Task Allocation to Ambulance Dispatch.
Eric Schneider, M. Poulton, Archie Drake, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 2
Predicting Melbourne Ambulance Demand using Kernel Warping
Zhengyi Zhou, David S. Matteson
arXiv (Cornell University) (2015)
Open Access
Efficient Estimation of Dynamic Density Functions with Applications in Data Streams
Abdulhakim A. Qahtan, Suojin Wang, Xiangliang Zhang
Studies in big data (2018), pp. 247-278
Closed Access
Predicted Distribution Density Estimation for Streaming Data
Piotr Kulczycki, Tomasz Rybotycki
Lecture notes in computer science (2021), pp. 567-580
Closed Access
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