Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations
This paper introduces an innovative approach to predicting bus ridership andanalysing transportation scenarios through a fusion of machine learning (ML) techniques and multi-agent simulations. Utilising a comprehensive dataset from an urban bus system, we employ ML models to accurately forecast pass...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Ediciones Universidad de Salamanca
2024-12-01
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| Series: | Advances in Distributed Computing and Artificial Intelligence Journal |
| Subjects: | |
| Online Access: | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31866 |
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| _version_ | 1832590623352291328 |
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| author | Pasqual Martí Alejandro Ibáñez Vicente Julian Paulo Novais Jaume Jordán |
| author_facet | Pasqual Martí Alejandro Ibáñez Vicente Julian Paulo Novais Jaume Jordán |
| author_sort | Pasqual Martí |
| collection | DOAJ |
| description | This paper introduces an innovative approach to predicting bus ridership andanalysing transportation scenarios through a fusion of machine learning (ML) techniques and multi-agent simulations. Utilising a comprehensive dataset from an urban bus system, we employ ML models to accurately forecast passenger flows, factoring in diverse variables such as weather conditions. The novelty of our method lies in the application of these predictions to generate detailed simulation scenarios, which are meticulously executed to evaluate the efficacy of public transportation services. Our research uniquely demonstrates the synergy between ML predictions and agent-based simulations, offering a robust tool for optimising urban mobility. The results reveal critical insights into resource allocation, service efficiency, and potential improvements in public transport systems. This study significantly advances the field by providing a practical framework for transportation providers to optimise services and address long-term challenges in urban mobility |
| format | Article |
| id | doaj-art-a4f0040983af4332870d34db1d5165bf |
| institution | Kabale University |
| issn | 2255-2863 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ediciones Universidad de Salamanca |
| record_format | Article |
| series | Advances in Distributed Computing and Artificial Intelligence Journal |
| spelling | doaj-art-a4f0040983af4332870d34db1d5165bf2025-01-23T11:25:18ZengEdiciones Universidad de SalamancaAdvances in Distributed Computing and Artificial Intelligence Journal2255-28632024-12-0113e31866e3186610.14201/adcaij.3186637347Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent SimulationsPasqual Martí0Alejandro Ibáñez1Vicente Julian2Paulo Novais3Jaume Jordán4Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Valencia, SpainValencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Valencia, SpainValencian Graduate School and Research Network of Artificial Intelligence, Universitat Politècnica de València, Valencia, SpainALGORITMI Centre, Universidade do Minho, Braga, PortugalValencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de València, Valencia, SpainThis paper introduces an innovative approach to predicting bus ridership andanalysing transportation scenarios through a fusion of machine learning (ML) techniques and multi-agent simulations. Utilising a comprehensive dataset from an urban bus system, we employ ML models to accurately forecast passenger flows, factoring in diverse variables such as weather conditions. The novelty of our method lies in the application of these predictions to generate detailed simulation scenarios, which are meticulously executed to evaluate the efficacy of public transportation services. Our research uniquely demonstrates the synergy between ML predictions and agent-based simulations, offering a robust tool for optimising urban mobility. The results reveal critical insights into resource allocation, service efficiency, and potential improvements in public transport systems. This study significantly advances the field by providing a practical framework for transportation providers to optimise services and address long-term challenges in urban mobilityhttps://revistas.usal.es/cinco/index.php/2255-2863/article/view/31866passenger predictionurban mobilityagent simulation |
| spellingShingle | Pasqual Martí Alejandro Ibáñez Vicente Julian Paulo Novais Jaume Jordán Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations Advances in Distributed Computing and Artificial Intelligence Journal passenger prediction urban mobility agent simulation |
| title | Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations |
| title_full | Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations |
| title_fullStr | Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations |
| title_full_unstemmed | Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations |
| title_short | Bus Ridership Prediction and Scenario Analysis through ML and Multi-Agent Simulations |
| title_sort | bus ridership prediction and scenario analysis through ml and multi agent simulations |
| topic | passenger prediction urban mobility agent simulation |
| url | https://revistas.usal.es/cinco/index.php/2255-2863/article/view/31866 |
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