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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, etc. Predicting such flow dynamics is an important task for urban traffic signal control and planning. Current methods use microscopic simulation for studying the impact of a large number of signal timing plans at each of the intersections. A major drawback of microscopic simulation is that they are they are based on source destination traffic generation models and cannot incorporate the high resolution loop detector data such as that are provided by automated traffic signal performance measures (ATSPM) based systems. The arrival (or departure) information of each vehicle on a detector can be thought of as a time series waveform. Given the high granularity of ATSPM data, this waveform can be used to several interesting analyses. The waveforms can be used to derive information on platoon dispersion as vehicles progress across the corridor. Also, these waveforms can be modelled to understand how the vehicles progress across the corridor for a variety of signal timing plans. In this paper, we show that deep neural networks based machine learning systems can be used to effectively leverage the waveforms collected at multiple sensors (stopbar and advanced) on the intersection to model the traffic dynamics both at an intersection and across intersections. We show that modelling of these waveforms can be useful to understand traffic flow dynamics under different signal timing plans and can be potentially integrated into signal timing optimization software. Further, these methods are three to four orders of magnitudes faster than using microscopic simulations.

In this paper, we present a novel and effective approach to truck and trailer classification, which integrates deep learning models and conventional image processing and computer vision techniques. The developed method groups trucks into subcategories by carefully examining the truck classes and identifying key geometric features for discriminating truck and trailer types. We also present three discriminating features that involve shape, texture, and semantic information to identify trailer types. Experimental results demonstrate that the developed hybrid approach can achieve high accuracy with limited training data, where the vanilla deep learning approaches show moderate performance due to over-fitting and poor generalization. Additionally, the models generated are human-understandable.