|Title||Understanding City Traffic Dynamics Utilizing Sensor and Textual Observations|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Pramod Anantharam, Krishnaprasad Thirunarayan, Surendra Marupudi, Amit Sheth, Tanvi Banerjee|
|Conference Name||30th AAAI Conference on Artificial Intelligence (AAAI-16)|
|Conference Location||Phoenix, Arizona|
|Keywords||city traffic events, linear dynamical system, log likelihood, restricted switching linear dynamical system, sensor data, textual data, time series, traffic dynamics, twitter traffic event|
Understanding speed and travel-time dynamics in response to various city related events is an important and challenging problem. Sensor data (numerical) containing average speed of vehicles passing through a road segment can be interpreted in terms of near real-time report of traffic related incidents from city authorities and social media data (textual), providing a complementary understanding of traffic dynamics. State-of-the-art research is focused on either analyzing sensor observations or citizen observations; we seek to exploit both in a synergistic manner. We demonstrate the role of domain knowledge in capturing the non-linearity of speed and travel-time dynamics by segmenting speed and travel-time observations into simpler components amenable to description using linear models such as Linear Dynamical System (LDS). Specifically, we propose Restricted Switching Linear Dynamical System (RSLDS) to model normal speed and travel time dynamics and thereby characterize anomalous dynamics. We utilize the city traffic events extracted from text to explain anomalous dynamics. We present a large scale evaluation of the proposed approach on a real-world traffic and twitter dataset collected over a year with promising results.