![]() Shao, et al., Mobile crowd sensing for traffic prediction in internet of vehicles. Chen, et al., Road traffic speed prediction: a probabilistic model fusing multi-source data. Morimura, et al., City-wide traffic flow estimation from a limited number of low-quality cameras. Wang, et al., A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. Simon, Survey on traffic prediction in smart cities. Paper presented at Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 13–17 August 2016Ī.M. Demiryurek, et al., Latent space model for road networks to predict time-varying traffic. Yi, et al., Citywide traffic volume estimation using trajectory data. Ott, et al., The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. Jeon, et al., Image-to-image learning to predict traffic speeds by considering area-wide spatio-temporal dependencies. ![]() Paper presented at Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 24–28 October 2016ĭ. Pei, et al., Urban traffic prediction through the second use of inexpensive big data from buildings. Paper presented at Proceedings of the 5th Conference on Systems for Built Environments, 7–8 November 2018 Singh, Rimor: towards identifying anomalous appliances in buildings. Wang, et al., Buildings affect mobile patterns: developing a new urban mobility model. Banerjee, IoT-based sensor data fusion for occupancy sensing using Dempster–Shafer evidence theory for smart buildings. Friday, Beyond data in the smart city: repurposing existing campus IoT. Stergiou, et al., Efficient IoT-based sensor BIG Data collection–processing and analysis in smart buildings. We believe that this work can further open new applications of reusing building sensing data for urban traffic sensing, therefore building up connections between smart buildings and intelligent transportation.Ī.P. Several studies have shown the superiority of BuildSenSys, with the progress in accuracy up to 65.3% in the prediction of nearby traffic volume. Then, we put forward a novel Recurrent Neural Network (RNN) to predict traffic volume, leveraging cross-domain learning with two-attention mechanisms. First, we performed a comprehensive building-traffic analysis based on a multi-source dataset to reveal the relationship between building sensing data and nearby traffic volume, as well as its cause. This chapter has two parts, namely correlation analysis and cross-domain learning. It is the first system to predict nearby traffic volumes by reusing building sensing data. To deal with these two challenges, we create BuildSenSys and put it into use. Moreover, the task of accurately predicting traffic volumes using dynamic cross-domain, non-linear and time-varying building-traffic correlations is more difficult than before. ![]() First, there have been some advances in the relationship between building sensing data and traffic data, and the complexity of studying this situation is due to the spatiotemporal complexity. We should also notice that it’s quite incredible to predict with the use of such cross-domain data when there’s two problems. The difference between this work and previous research is that reusing building sensing data is highly affordable and trustworthy. Serving as a research in the fundamental stage, this work looks into the way of reusing building sensing data for the prediction of traffic volume on nearby roads in this chapter. There is no doubt that building sensing data shines a bright future to enrich a series of data-demanding and expensive urban mobile applications. As smart cities and smart buildings develop rapidly, they generate a large number of building sensing data by the equipped sensors. ![]()
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