  Urban systems are interdependent as individuals' daily activities engage
using those urban systems at certain time of day and locations. There may exist
clear spatial and temporal correlations among usage patterns across all urban
systems. This paper explores such a correlation among energy usage and roadway
congestion. We propose a general framework to predict congestion starting time
and congestion duration in the morning using the time-of-day electricity use
data from anonymous households with no personally identifiable information. We
show that using time-of-day electricity data from midnight to early morning
from 322 households in the City of Austin, can make reliable prediction of
congestion starting time of several highway segments, at the time as early as
2am. This predictor significantly outperforms a time-series predictor that uses
only real-time travel time data up to 6am. We found that 8 out of the 10
typical electricity use patterns have statistically significant affects on
morning congestion on highways in Austin. Some patterns have negative effects,
represented by an early spike of electricity use followed by a drastic drop
that could imply early departure from home. Others have positive effects,
represented by a late night spike of electricity use possible implying late
night activities that can lead to late morning departure from home.
