Interrelationships between urban travel demand and electricity consumption: a deep learning approach
Abstract The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel de...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-33133-y |
| _version_ | 1797841103567716352 |
|---|---|
| author | Ali Movahedi Amir Bahador Parsa Anton Rozhkov Dongwoo Lee Abolfazl Kouros Mohammadian Sybil Derrible |
| author_facet | Ali Movahedi Amir Bahador Parsa Anton Rozhkov Dongwoo Lee Abolfazl Kouros Mohammadian Sybil Derrible |
| author_sort | Ali Movahedi |
| collection | DOAJ |
| description | Abstract The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships. We use Long Short Term Memory (LSTM) networks to model electricity consumption patterns of zip codes based on the traffic volume of the same zip code and nearby zip codes. For this, we merge two datasets for November 2017 in Chicago: (1) aggregated electricity use data in 30-min intervals within the city of Chicago and (2) traffic volume data captured on the Chicago expressway network. Four analyses are conducted to identify interrelationships: (a) correlation between two time series, (b) temporal relationships, (c) spatial relationships, and (d) prediction of electricity consumption based on the total traffic volume. Overall, from over 250 models, we identify and discuss complex interrelationships between travel demand and electricity consumption. We also analyze and discuss how and why model performance varies across Chicago. |
| first_indexed | 2024-04-09T16:25:36Z |
| format | Article |
| id | doaj.art-c8f334999d7c45439949328344addd6c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T16:25:36Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c8f334999d7c45439949328344addd6c2023-04-23T11:15:31ZengNature PortfolioScientific Reports2045-23222023-04-0113111310.1038/s41598-023-33133-yInterrelationships between urban travel demand and electricity consumption: a deep learning approachAli Movahedi0Amir Bahador Parsa1Anton Rozhkov2Dongwoo Lee3Abolfazl Kouros Mohammadian4Sybil Derrible5Department of Civil, Materials, and Environmental Engineering, University of Illinois at ChicagoDepartment of Civil, Materials, and Environmental Engineering, University of Illinois at ChicagoDepartment of Urban Planning and Policy, University of Illinois at ChicagoDepartment of Policy and Administration, Incheon National UniversityDepartment of Civil, Materials, and Environmental Engineering, University of Illinois at ChicagoDepartment of Civil, Materials, and Environmental Engineering, University of Illinois at ChicagoAbstract The analysis of infrastructure use data in relation to other components of the infrastructure can help better understand the interrelationships between infrastructures to eventually enhance their sustainability and resilience. In this study, we focus on electricity consumption and travel demand. In short, the premise is that when people are in buildings consuming electricity, they are not generating traffic on roads, and vice versa, hence the presence of interrelationships. We use Long Short Term Memory (LSTM) networks to model electricity consumption patterns of zip codes based on the traffic volume of the same zip code and nearby zip codes. For this, we merge two datasets for November 2017 in Chicago: (1) aggregated electricity use data in 30-min intervals within the city of Chicago and (2) traffic volume data captured on the Chicago expressway network. Four analyses are conducted to identify interrelationships: (a) correlation between two time series, (b) temporal relationships, (c) spatial relationships, and (d) prediction of electricity consumption based on the total traffic volume. Overall, from over 250 models, we identify and discuss complex interrelationships between travel demand and electricity consumption. We also analyze and discuss how and why model performance varies across Chicago.https://doi.org/10.1038/s41598-023-33133-y |
| spellingShingle | Ali Movahedi Amir Bahador Parsa Anton Rozhkov Dongwoo Lee Abolfazl Kouros Mohammadian Sybil Derrible Interrelationships between urban travel demand and electricity consumption: a deep learning approach Scientific Reports |
| title | Interrelationships between urban travel demand and electricity consumption: a deep learning approach |
| title_full | Interrelationships between urban travel demand and electricity consumption: a deep learning approach |
| title_fullStr | Interrelationships between urban travel demand and electricity consumption: a deep learning approach |
| title_full_unstemmed | Interrelationships between urban travel demand and electricity consumption: a deep learning approach |
| title_short | Interrelationships between urban travel demand and electricity consumption: a deep learning approach |
| title_sort | interrelationships between urban travel demand and electricity consumption a deep learning approach |
| url | https://doi.org/10.1038/s41598-023-33133-y |
| work_keys_str_mv | AT alimovahedi interrelationshipsbetweenurbantraveldemandandelectricityconsumptionadeeplearningapproach AT amirbahadorparsa interrelationshipsbetweenurbantraveldemandandelectricityconsumptionadeeplearningapproach AT antonrozhkov interrelationshipsbetweenurbantraveldemandandelectricityconsumptionadeeplearningapproach AT dongwoolee interrelationshipsbetweenurbantraveldemandandelectricityconsumptionadeeplearningapproach AT abolfazlkourosmohammadian interrelationshipsbetweenurbantraveldemandandelectricityconsumptionadeeplearningapproach AT sybilderrible interrelationshipsbetweenurbantraveldemandandelectricityconsumptionadeeplearningapproach |