Real time queue length estimation for signalized intersections using travel times from mobile sensors

By Xuegang Ban, Peng Hao, Zhanbo Sun. 2011

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Deep learning for short-term traffic flow prediction (2016, methodology): “Ban et al. [2011] proposes a method for estimating queue lengths at controlled intersections, based on the travel time data measured by GPS probes. The method relies on detecting discontinuities and changes of slopes in travel time data. Ramezani and Geroliminis [2015] combines the traffic flow shoc”
Traffic Signal Control with Connected Vehicles (2013, methodology): “Application Minimum EV Penetration Rate Traffic signal control [23,27,28] 20-30% Freeway incident detection [31] 20% Lane-level speed estimation [32,33] 20% Arterial performance measurement [34] 10-50% Queue length estimation [35] 30% Table 1.”
Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection (2013, background): “As it is difficult to measure the residual queue directly, it can be estimated by loop detector [11] or mobile sensors [12] information using shockwave theory.”