Citywide traffic congestion estimation with social media

By Senzhang Wang, Lifang He, Leon Stenneth, Philip S. Yu, Zhoujun Li. 2015

72 citations recorded by the source corpus.

Conventional traffic congestion estimation approaches require the deployment of traffic sensors or large-scale probe vehicles. The high cost of deploying and maintaining these equipments largely limits their spatial-temporal coverage. This paper proposes an alternative solution with lower cost and wider spatial coverage by exploring traffic related information from Twitter. By regarding each Twitter user as a traffic monitoring sensor, various real-time traffic information can be collected freely from each corner of the city. However, there are two major challenges for this problem. Firstly, the congestion related information extracted directly from real-time tweets are very sparse due both to the low resolution of geographic location mentioned in the tweets and the inherent sparsity nature of Twitter data. Secondly, the traffic event information coming from Twitter can be multi-typed including congestion, accident, road construction, etc. It is non-trivial to model the potential impacts of diverse traffic events on traffic congestion. We propose to enrich the sparse real-time tweets from two directions: 1) mining the spatial and temporal correlations of the road segments in congestion from historical data, and 2) applying auxiliary information including social events and road features for help. We finally propose a coupled matrix and tensor factorization model to effectively integrate rich information for Citywide Traffic Congestion Eestimation (CTCE). Extensive evaluations on Twitter data and 500 million public passenger buses GPS data on nearly 700 mile roads of Chicago demonstrate the efficiency and effectiveness of the proposed approach.

Subfields: Building and Construction, Transportation, Signal Processing

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Citations in the corpus

Improving stock market prediction via heterogeneous information fusion (2017, methodology): “Thus, we use an element-wise optimization algorithm to iteratively update each entry in the matrices and tensor independently by gradient descent [50, 51].”
Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, methodology): “2012], or how to monitor traffic congestions with real-time traffic event tweets [Chen et al. 2014; Wang et al. 2015].”
Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020, methodology): “Generally, traditional traffic passenger flows prediction methods can be categorized into classic statistical models, including Auto-Regressive Integrated Moving Average (ARIMA) based methods [25, 26] and parametric learning methods, including K-nearest neighbor (KNN) nonparametric regression method”