MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA
Roundabouts, as an unsignalized intersection, have an effective preventative measure designed to control straight-line crashes. Efficient traffic flow in cities depends upon appropriate capacity estimation of roundabouts. This study attempts to develop models for roundabout entry capacity by applyin...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Silesian University of Technology
2024-06-01
|
| Series: | Scientific Journal of Silesian University of Technology. Series Transport |
| Subjects: | |
| Online Access: | https://sjsutst.polsl.pl/archives/2024/vol123/209_SJSUTST123_2024_Munshi_Patnaik.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841561521385111552 |
|---|---|
| author | Aarohi Kumar MUNSHI Ashish Kumar PATNAIK |
| author_facet | Aarohi Kumar MUNSHI Ashish Kumar PATNAIK |
| author_sort | Aarohi Kumar MUNSHI |
| collection | DOAJ |
| description | Roundabouts, as an unsignalized intersection, have an effective preventative measure designed to control straight-line crashes. Efficient traffic flow in cities depends upon appropriate capacity estimation of roundabouts. This study attempts to develop models for roundabout entry capacity by applying Artificial Neural Network (ANN) analysis for mixed traffic flow conditions. Data was gathered from 27 roundabouts spread across India. The influence area for gap acceptance (INAGA) concept was used as a graphical method to identify critical gap (Tc) of entry flow at roundabouts. This study indicated that the Bayesian Regularisation Neural Network (BRNN) based model has the best R2 and RMSE of 0.97 and 167.8. The connection weight approach and Garson algorithm evaluate the significance of each explanatory variable and identify follow-up time (Tf) as a critical parameter with values of 11.10 and 21.15%, respectively. |
| format | Article |
| id | doaj-art-d9cf9f1a81dd435ea64c8d32d4ad57c6 |
| institution | Kabale University |
| issn | 0209-3324 2450-1549 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Silesian University of Technology |
| record_format | Article |
| series | Scientific Journal of Silesian University of Technology. Series Transport |
| spelling | doaj-art-d9cf9f1a81dd435ea64c8d32d4ad57c62025-01-03T01:34:39ZengSilesian University of TechnologyScientific Journal of Silesian University of Technology. Series Transport0209-33242450-15492024-06-0112320922610.20858/sjsutst.2024.123.10MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIAAarohi Kumar MUNSHIAshish Kumar PATNAIKRoundabouts, as an unsignalized intersection, have an effective preventative measure designed to control straight-line crashes. Efficient traffic flow in cities depends upon appropriate capacity estimation of roundabouts. This study attempts to develop models for roundabout entry capacity by applying Artificial Neural Network (ANN) analysis for mixed traffic flow conditions. Data was gathered from 27 roundabouts spread across India. The influence area for gap acceptance (INAGA) concept was used as a graphical method to identify critical gap (Tc) of entry flow at roundabouts. This study indicated that the Bayesian Regularisation Neural Network (BRNN) based model has the best R2 and RMSE of 0.97 and 167.8. The connection weight approach and Garson algorithm evaluate the significance of each explanatory variable and identify follow-up time (Tf) as a critical parameter with values of 11.10 and 21.15%, respectively.https://sjsutst.polsl.pl/archives/2024/vol123/209_SJSUTST123_2024_Munshi_Patnaik.pdfinagaann entry capacitygarson algorithm |
| spellingShingle | Aarohi Kumar MUNSHI Ashish Kumar PATNAIK MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA Scientific Journal of Silesian University of Technology. Series Transport inaga ann entry capacity garson algorithm |
| title | MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA |
| title_full | MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA |
| title_fullStr | MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA |
| title_full_unstemmed | MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA |
| title_short | MODELLING ROUNDABOUT ENTRY CAPACITY FOR MIXED TRAFFIC FLOW USING ANN: A CASE STUDY IN INDIA |
| title_sort | modelling roundabout entry capacity for mixed traffic flow using ann a case study in india |
| topic | inaga ann entry capacity garson algorithm |
| url | https://sjsutst.polsl.pl/archives/2024/vol123/209_SJSUTST123_2024_Munshi_Patnaik.pdf |
| work_keys_str_mv | AT aarohikumarmunshi modellingroundaboutentrycapacityformixedtrafficflowusingannacasestudyinindia AT ashishkumarpatnaik modellingroundaboutentrycapacityformixedtrafficflowusingannacasestudyinindia |