User-centric interdependent urban systems: using time-of-day electricity usage data to predict morning roadway congestion
Pinchao Zhang, Zhen (Sean) Qian

TL;DR
This study demonstrates that early morning electricity usage patterns from households can reliably predict morning highway congestion times, revealing interdependencies among urban systems.
Contribution
It introduces a framework leveraging anonymous electricity data to predict congestion, highlighting interdependence between energy use and transportation systems.
Findings
Electricity usage patterns can predict congestion starting times as early as 2am.
The model outperforms traditional real-time travel time predictors.
Most electricity use patterns significantly influence morning congestion in Austin.
Abstract
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…
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Taxonomy
TopicsTransportation Planning and Optimization · Traffic Prediction and Management Techniques · Human Mobility and Location-Based Analysis
