Congestion prediction for smart sustainable cities using IoT and machine learning approaches
Journal article
Majumdar, Sharmila, Subhani, Moeez M., Roullier, Benjamin, Anjum, Ashiq and Zhu, Rongbo 2020. Congestion prediction for smart sustainable cities using IoT and machine learning approaches. Sustainable Cities and Society. 64, p. 102500. https://doi.org/10.1016/j.scs.2020.102500
| Authors | Majumdar, Sharmila, Subhani, Moeez M., Roullier, Benjamin, Anjum, Ashiq and Zhu, Rongbo |
|---|---|
| Abstract | Congestion on road networks has a negative impact on sustainability in many cities through the exacerbation of air pollution. Smart congestion management allows road users to avoid congested areas, decreasing pollutant concentration. Accurately predicting congestion propagation is difficult however, due to the dynamic non-linear behavior of traffic flow. With the rise of Internet of Things devices, there are now data sets available that can be used to provide smart, sustainable transport solutions within cities. In this work, we introduce long short-term memory networks for the prediction of congestion propagation across a road network. Based on vehicle speed data from traffic sensors at two sites, our model predicts the propagation of congestion across a 5-min period within a busy town. Analysis of both univariate and multivariate predictive models show an accuracy of 84–95% depending on the road layout. This accuracy shows that long short-term memory networks are suitable for predicting congestion propagation on road networks and may form a key component of future traffic modelling approaches for smart and sustainable cities around the world. |
| Keywords | Renewable Energy, Sustainability and the Environment; Geography, Planning and Development; Civil and Structural Engineering; Transportation |
| Year | 2020 |
| Journal | Sustainable Cities and Society |
| Journal citation | 64, p. 102500 |
| Publisher | Elsevier BV |
| ISSN | 2210-6707 |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.scs.2020.102500 |
| Web address (URL) | http://hdl.handle.net/10545/625387 |
| https://www.elsevier.com/tdm/userlicense/1.0/ | |
| hdl:10545/625387 | |
| Publication dates | 25 Sep 2020 |
| Publication process dates | |
| Deposited | 17 Nov 2020, 09:17 |
| Accepted | 16 Sep 2020 |
| Rights | © 2020 Elsevier Ltd. All rights reserved. |
| Contributors | University of Derby, University of Leicester and South Central University for Nationalities, Wuhan, China |
| File | File Access Level Open |
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