Crowd sensing of traffic anomalies based on human mobility and social media

By Bei Pan, Yu Zheng, David Wilkie, Cyrus Shahabi. 2013

389 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wenxiu Ding
2. Yu Zheng
3. Philip S. Yu
4. Zheng Yan
5. Laurence Tianruo Yang
6. Xuyang Jing
7. Senzhang Wang
8. Emiliano De Cristofaro

In Epidemiology

1. Carmela Troncoso
2. Yu Zheng

In Transportation

1. Philip S. Yu
2. Senzhang Wang
3. Leon Stenneth
4. Zhoujun Li
5. Lifang He
6. Huichu Zhang
7. Lei Chen
8. Yexin Li

Citations in the corpus

• A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion (2019, methodology): “Probability-based data fusion methods include Bayesian inference [32, 33], state-space models, Markov models, belief propagation, maximum likelihood methods, possibility theory, evidential reasoning and, more specifically, evidence theory, and least square-based estimation methods, e.”
• Methodologies for Cross-Domain Data Fusion: An Overview (2015, background): “Future [89], Causality Inference, Object Profiling [73], [74], [76], and Anomaly Detection [15], [49], [90], etc.”
• Traffic prediction in a bike-sharing system (2015, methodology): “Pan et al. [15] proposed methods to detect traffic anomalies based on GPS trajectories and social media.”
• Trajectory Data Mining (2015, background): “Later, given the weighted graph, we can find the most-likely route (traveled by people) between a few query points [134], identify the most popular route between a source and a destination [64], detect traffic anomalies [76], and update maps automatically.”
• What Does The Crowd Say About You? Evaluating Aggregation-based Location Privacy (2017, background): “, applications geared to improve urban planning [4], study the effect of “shocks” on transport [44], predict events [22], detect traffic anomalies [32], generate real-time traffic statistics [1], etc.”
• Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, background): “Identifying the evolving patterns may help us better plan the road construction and detect anomaly [Pan et al. 2013].”