A new approach to Interoperability within the Smart City based on time series-embedded adaptive Traffic Prediction Modelling
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2024
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| _version_ | 1866901051262631936 |
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| author | Fernandez Pallarés, Victor Pérez, Virgilio |
| author_facet | Fernandez Pallarés, Victor Pérez, Virgilio |
| contents | <p>The rapid urbanization observed over recent decades has led to important challenges<br>in urban mobility, notably traffic congestion, pollution, and inefficient energy<br>consumption. Concurrently, the rise in electric vehicles (EVs) offers a promising<br>shift towards sustainable urban transport yet introduces complexities such as the<br>need for extensive charging infrastructure and effective energy demand management.<br>This study addresses these challenges by proposing a predictive model for<br>real-time and future traffic volume estimation, leveraging historical data, real-time<br>information, scheduled city events, and the availability of EV charging infrastructure.<br>The methodology proposed employs actuarial techniques to create a comprehensive<br>framework that predicts traffic patterns and optimizes energy resources<br>within smart cities. By integrating variables such as historical traffic patterns and<br>real-time data, our model provides accurate traffic forecasts essential for urban planning<br>and energy distribution. We utilize a time-series based algorithm to predict<br>traffic, validated through real data from pilot projects in Ljubljana, Slovenia. The<br>study’s findings underscore the model’s potential to enhance urban mobility and<br>energy efficiency, providing a robust tool for city planners and policymakers</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_1007_s11067-024-09662-y |
| institution | Zenodo |
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| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A new approach to Interoperability within the Smart City based on time series-embedded adaptive Traffic Prediction Modelling Fernandez Pallarés, Victor Pérez, Virgilio Urban mobility · Electric vehicles · Traffic prediction · Smart cities · Energy management · Predictive modelling <p>The rapid urbanization observed over recent decades has led to important challenges<br>in urban mobility, notably traffic congestion, pollution, and inefficient energy<br>consumption. Concurrently, the rise in electric vehicles (EVs) offers a promising<br>shift towards sustainable urban transport yet introduces complexities such as the<br>need for extensive charging infrastructure and effective energy demand management.<br>This study addresses these challenges by proposing a predictive model for<br>real-time and future traffic volume estimation, leveraging historical data, real-time<br>information, scheduled city events, and the availability of EV charging infrastructure.<br>The methodology proposed employs actuarial techniques to create a comprehensive<br>framework that predicts traffic patterns and optimizes energy resources<br>within smart cities. By integrating variables such as historical traffic patterns and<br>real-time data, our model provides accurate traffic forecasts essential for urban planning<br>and energy distribution. We utilize a time-series based algorithm to predict<br>traffic, validated through real data from pilot projects in Ljubljana, Slovenia. The<br>study’s findings underscore the model’s potential to enhance urban mobility and<br>energy efficiency, providing a robust tool for city planners and policymakers</p> |
| title | A new approach to Interoperability within the Smart City based on time series-embedded adaptive Traffic Prediction Modelling |
| topic | Urban mobility · Electric vehicles · Traffic prediction · Smart cities · Energy management · Predictive modelling |
| url | https://doi.org/10.1007/s11067-024-09662-y |