Interrelationships between urban travel demand and electricity consumption: a deep learning approach.

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.