Traffic Congestion Prediction Using Machine Learning Techniques
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912335353872384 |
|---|---|
| author | Yasir, Rafed Muhammad Asad, Moumita Nower, Naushin Shoyaib, Mohammad |
| author_facet | Yasir, Rafed Muhammad Asad, Moumita Nower, Naushin Shoyaib, Mohammad |
| contents | The prediction of traffic congestion can serve a crucial role in making future decisions. Although many studies have been conducted regarding congestion, most of these could not cover all the important factors (e.g., weather conditions). We proposed a prediction model for traffic congestion that can predict congestion based on day, time and several weather data (e.g., temperature, humidity). To evaluate our model, it has been tested against the traffic data of New Delhi. With this model, congestion of a road can be predicted one week ahead with an average RMSE of 1.12. Therefore, this model can be used to take preventive measure beforehand. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_10983 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Traffic Congestion Prediction Using Machine Learning Techniques Yasir, Rafed Muhammad Asad, Moumita Nower, Naushin Shoyaib, Mohammad Machine Learning Signal Processing The prediction of traffic congestion can serve a crucial role in making future decisions. Although many studies have been conducted regarding congestion, most of these could not cover all the important factors (e.g., weather conditions). We proposed a prediction model for traffic congestion that can predict congestion based on day, time and several weather data (e.g., temperature, humidity). To evaluate our model, it has been tested against the traffic data of New Delhi. With this model, congestion of a road can be predicted one week ahead with an average RMSE of 1.12. Therefore, this model can be used to take preventive measure beforehand. |
| title | Traffic Congestion Prediction Using Machine Learning Techniques |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2206.10983 |