Modeling traffic congestion in developing countries using google maps data

Keywords

CCDS Authors

Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

• Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Artificial Intelligence, Machine Learning

M Ashraful Amin, PhD

M Ashraful Amin, PhD

• Founder Director, CCDS
• Director, AI/ML, HCI & DS Wings

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

AKM Mahbubur Rahman, PhD

AKM Mahbubur Rahman, PhD

• Director, DS & IPT Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

References

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Cited By

1. 1.Shilpa R. Nair, B.K. Bhavathrathan. (2021). Hybrid segmentation approach to identify crash susceptible locations in large road networks. Safety Science, 145, 105515[10.1016/j.ssci.2021.105515]
2. 2.Md. Moshiur Rahman, Naushin Nower. (2023). Attention based Deep Hybrid Networks for Traffic Flow Prediction using Google Maps Data. , 74–81[10.1145/3589883.3589894]
3. 3.Deepika, Gitanjali Pandove. (2025). Prediction of traffic time using XGBoost model with hyperparameter optimization. Multimedia Tools and Applications, 84(30), 37045–37090[10.1007/s11042-025-20646-z]
4. 4.Andrius Barauskas, Agnė Brilingaitė, Linas Bukauskas, Vaida Čeikutė, Alminas Čivilis, Simonas Šaltenis. (2023). Test-data generation and integration for long-distance e-vehicle routing. GeoInformatica, 27(4), 737–758[10.1007/s10707-022-00485-y]
5. 5.Hongjun Cui, Xiushi Dong, Yue Su, Minqing Zhu, Xinwei Ma. (2024). Exploring Spatiotemporal Patterns of Frequently Congested Urban Road Segments Based on Multi-Source Data: A Case Study of China’s Super-Large Cities. Transportation Research Record Journal of the Transportation Research Board, 2678(12), 2077–2093[10.1177/03611981241254111]
6. 6.Nishat Ahmed Samrin, Md. Mahmudul Hasan Suzan, Md. Selim Hossain, Mohammad Sarwar Hossain Mollah, Md. Dulal Haque. (2022). Analysis of COVID-19 Trends in Bangladesh: A Machine Learning Analysis. Lecture notes on data engineering and communications technologies, 611–625[10.1007/978-3-030-90618-4_31]
7. 7.Ruslan Shevchuk, Mikolaj Karpinski, Tetiana Yurchyshyn, Ihor Lishchynskyy, Maria Lyzun, Andriy Melnyk. (2024). A Context-Aware Approach and Software for Notifications about Personal Safety. , 20, 665–669[10.1109/acit62333.2024.10712568]
8. 8.Andrius Barauskas, Agnė Brilingaitė, Linas Bukauskas, Vaida Čeikutė, Alminas Čivilis, Simonas Šaltenis. (2021). Semi-synthetic Data and Testbed for Long-Distance E-Vehicle Routing. Communications in computer and information science, 61–71[10.1007/978-3-030-85082-1_6]
9. 9.(2022). Feature-driven label generation for congestion detection in smart cities under big data. International Journal of Advanced Technology and Engineering Exploration, 9(86)[10.19101/ijatee.2021.874739]
10. 10.Hassan Shuaibu Abdulrahman, Gabriella Amarachi Ekuma. (2024). Extraction of Traffic Flow Models Using Typical Traffic Information from Google. [10.52460/issc.2024.018]
11. 11.Surapon Riyana, Nattapon Harnsamut. (2025). An Efficient and Effective Model for Preserving Privacy Data in Location-Based Graphs. Symmetry, 17(10), 1772[10.3390/sym17101772]