Search for: All records

Abstract Energy efficiency improvement is often hindered by the energy efficiency gap. This paper examines the effect of short-run temperature fluctuations on the Energy Star air conditioner purchases in the United States from 2006 to 2019 using transaction-level data. Results show that the probability of purchasing an Energy Star air conditioner increases as the weekly temperature before the transaction deviates from 20–22 °C. A larger response is related to fewer cooling degree days in the previous years, higher electricity prices/income/educational levels/age/rate of owners, more common use of electricity, and stronger concern about climate change. 1 °C increase and decrease from 21 °C would lead to a reduction of total energy expenditure by 35.46 and 17.73 million dollars nationwide (0.13% and 0.06% of the annual total energy expenditure on air conditioning), respectively. Our findings have important policy implications for demand-end interventions to incorporate the potential impact of the ambient physical environment. 

Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method. 

Camera-based systems are increasingly used for collecting information on intersections and arterials. Unlike loop controllers that can generally be only used for detection and movement of vehicles, cameras can provide rich information about the traffic behavior. Vision-based frameworks for multiple-object detection, object tracking, and near-miss detection have been developed to derive this information. However, much of this work currently addresses processing videos offline. In this article, we propose an integrated two-stream convolutional networks architecture that performs real-time detection, tracking, and near-accident detection of road users in traffic video data. The two-stream model consists of a spatial stream network for object detection and a temporal stream network to leverage motion features for multiple-object tracking. We detect near-accidents by incorporating appearance features and motion features from these two networks. Further, we demonstrate that our approaches can be executed in real-time and at a frame rate that is higher than the video frame rate on a variety of videos collected from fisheye and overhead cameras.