| Title |
Towards A Green Supply Chain Based On Smart Urban Traffic Using Deep Learning Approach |
| ID_Doc |
57844 |
| Authors |
Terrada L.; El Khaili M.; Daaif A.; Ouajji H. |
| Year |
2022 |
| Published |
Statistics, Optimization and Information Computing, 10, 1 |
| DOI |
http://dx.doi.org/10.19139/soic-2310-5070-1203 |
| Abstract |
Green Supply Chain Management (GrSCM) has become one of the most crucial innovation in the Supply Chain Management (SCM). This approach involves environmental concerns and issues into the SCM, thus, companies and authorities tend to exploit the GrSCM through logistics process in order to improve their performance. In this paper, we will give a demonstration of the added value of the Urban Traffic Management (UTM) and its integration in the concept of GrSCM, we also aim to study its impact on the performance improvement in Transport Management with a focus on Air quality improvement. This study proposes a new approach and model based on Deep learning for Urban Traffic Control Management to solve the traffic flow problem in order to reduce the congestion, improve the air quality and enhance the urban supply chain. Our proposed framework for Data collection and processing is mainly based on Internet of Thing (IoT) technologies for an efficient Smart City © 2022 International Academic Press |
| Author Keywords |
Deep learning; Environmental management; Green supply chain management; Iot; Smart city; Urban traffic management |
