Filters

2019 - 201912020 - 20211

More filters

2021 - 20222
Reset filters

Settings

Author: Zhou, Dong ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Identifying the most influential roads based on traffic correlation networks
    (Berlin ; Heidelberg [u.a.] : Springer Open, 2019) Guo, Shengmin; Zhou, Dong; Fan, Jingfang; Tong, Qingfeng; Zhu, Tongyu; Lv, Weifeng; Li, Daqing; Havlin, Shlomo
    Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s).
  • Thumbnail Image
    Item
    Improved earthquake aftershocks forecasting model based on long-term memory
    ([London] : IOP, 2021) Zhang, Yongwen; Zhou, Dong; Fan, Jingfang; Marzocchi, Warner; Ashkenazy, Yosef; Havlin, Shlomo
    A prominent feature of earthquakes is their empirical laws, including memory (clustering) in time and space. Several earthquake forecasting models, such as the epidemic-type aftershock sequence (ETAS) model, were developed based on these empirical laws. Yet, a recent study [1] showed that the ETAS model fails to reproduce the significant long-term memory characteristics found in real earthquake catalogs. Here we modify and generalize the ETAS model to include short- and long-term triggering mechanisms, to account for the short- and long-time memory (exponents) discovered in the data. Our generalized ETAS model accurately reproduces the short- and long-term/distance memory observed in the Italian and Southern Californian earthquake catalogs. The revised ETAS model is also found to improve earthquake forecasting after large shocks.