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

作者：Zhang Pinchao; Qian Zhen^*

来源：Transportation Research Part C: Emerging Technologies , 2018, 92: 392-411.

DOI：10.1016/j.trc.2018.05.008

• Data mining
• Electricity use
• Travel time prediction
• Urban system interdependency

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 2 am. This predictor significantly outperforms a time-series predictor that uses only real-time travel time data up to 6 am. 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.

• 出版日期2018-7