Attention based Deep Hybrid Networks for Traffic Flow Prediction using Google Maps Data-Reference-Cited by-同舟云学术

Attention based Deep Hybrid Networks for Traffic Flow Prediction using Google Maps Data

Published:2023-03-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 8th International Conference on Machine Learning Technologies
language:
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Author:

Rahman Md. Moshiur¹^ORCID,Nower Naushin¹^ORCID

Affiliation:

1. Institute of Information Technology, University of Dhaka, Bangladesh

Funder

Information and Communication Technology Division, Ministry of Telecommunications and Information Technology, Government of Bangladesh

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589883.3589894

Reference27 articles.

1. Sonjoy Chakraborty . 2010. Traffic congestion in Dhaka city and its economic impact. Traffic congestion in Dhaka city: Its impact on business and some remedial measures ( 2010 ). Sonjoy Chakraborty. 2010. Traffic congestion in Dhaka city and its economic impact. Traffic congestion in Dhaka city: Its impact on business and some remedial measures (2010).

2. Improved Deep Hybrid Networks for Urban Traffic Flow Prediction Using Trajectory Data

3. Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction

4. gadget 360. 2022. How Google Maps Gets Its Remarkably Accurate Real-Time Traffic Data. https://www.gadgets360.com/apps/features/how-google-maps-gets-its-remarkably-accurate-real-time-traffic-data-1665385 gadget 360. 2022. How Google Maps Gets Its Remarkably Accurate Real-Time Traffic Data. https://www.gadgets360.com/apps/features/how-google-maps-gets-its-remarkably-accurate-real-time-traffic-data-1665385

5. Abu Afsarul Haider . 2018. Traffic jam: The ugly side of Dhaka’s development. The Daily Star 13 ( 2018 ). Abu Afsarul Haider. 2018. Traffic jam: The ugly side of Dhaka’s development. The Daily Star 13 (2018).

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