| Autor: |
Youness Riouali, Laila Benhlima, Slimane Bah |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Journal of Sensor and Actuator Networks, Vol 8, Iss 3, p 49 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2224-2708 |
| DOI: |
10.3390/jsan8030049 |
| Popis: |
The tremendous increase in the urban population highlights the need for more efficient transport systems and techniques to alleviate the increasing number of the resulting traffic-associated problems. Modeling and predicting road traffic flow are a critical part of intelligent transport systems (ITSs). Therefore, their accuracy and efficiency have a direct impact on the overall functioning. In this scope, a new approach for predicting the road traffic flow is proposed that combines the Petri nets model with a dynamic estimation of intersection turning movement counts to ensure a more accurate assessment of its performance. Thus, this manuscript extends our work by introducing a new feature, namely turning movement counts, to attain a better prediction of road traffic flow. A simulation study is conducted to get a better understanding of how predictive models perform in the context of estimating turning movements. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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