DeepAI AI Chat
Log In Sign Up

User-centric interdependent urban systems: using time-of-day electricity usage data to predict morning roadway congestion

by   Pinchao Zhang, et al.
Carnegie Mellon University

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.


page 10

page 13

page 19

page 20

page 21


From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data

The effectiveness of traditional traffic prediction methods is often ext...

PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Prediction

Traffic problems have seriously affected people's life quality and urban...

A method for evaluating options for motif detection in electricity meter data

Investigation of household electricity usage patterns, and matching the ...

Distributed Classification of Urban Congestion Using VANET

Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congest...

Inferring Microclimate Zones from Energy Consumption Data

Climate zones are an established part of urban energy management. Califo...

Bayesian Learning of Consumer Preferences for Residential Demand Response

In coming years residential consumers will face real-time electricity ta...

Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems

Solving multiagent problems can be an uphill task due to uncertainty in ...