| Abstract: |
Predicting intersection turning movements is an important task for urban traffic analysis, planning, and signal control. However, traffic flow dynamics in the vicinity of urban arterial intersections is a complex and nonlinear phenomenon influenced by factors such as signal timing plan, road geometry, driver behaviors, queuing, etc. Most current methods focus on predicting turning movement counts using data at coarser aggregations in the order of minutes or above. Important details such as platoon movements may be lost at such coarse resolutions. In this work, we propose machine learning approaches to imputing turning movement counts at intersections using data at subcycle resolutions, from 5 seconds to 375 seconds. In particular, we show that deep neural networks are capable of directly learning an abstract representation of intersection traffic dynamics using detector actuation waveforms and signal state information. We generate a large dataset of 30 million cycles by approximately replicating real-world traffic arrival patterns from archived loop detector data in a microscopic traffic simulator. We extensively evaluate our models and show that our models predict turning movement counts with greater accuracy when higher resolution data are provided. |