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Journal ArticleDOI

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

TLDR
The concept of Queue Rear No-delay Arrival Time is introduced which is related to the non-smoothness of queuing delay patterns and queue length changes and can be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can be constructed.
Abstract
We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation.

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Citations
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Deep learning for short-term traffic flow prediction

TL;DR: A deep learning model is developed that combines a linear model that is fitted using l 1 regularization and a sequence of tanh layers to predict traffic flows and identifies spatio-temporal relations among predictors and other layers model nonlinear relations.
Journal ArticleDOI

Perimeter and boundary flow control in multi-reservoir heterogeneous networks

TL;DR: In this article, the authors macroscopically describe the traffic dynamics in heterogeneous transportation urban networks by utilizing the Macroscopic Fundamental Diagram (MFD), a widely observed relation between networkwide space-mean flow and density of vehicles.
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A Survey of Traffic Control With Vehicular Communications

TL;DR: In this article, the authors present a survey of early-stage traffic control technologies and discuss potential benefits that will be gained by using vehicle-to-vehicle (V2V) communications.
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Urban traffic signal control with connected and automated vehicles: A survey

TL;DR: Six types of CAV-based traffic control methods are summarized and a conceptual mathematical framework is proposed that can be specified to each of six three types of methods by selecting different state variables, control inputs, and environment inputs is proposed.
Journal ArticleDOI

Traffic Signal Control with Connected Vehicles

TL;DR: A decentralized, fully adaptive traffic control algorithm, the predictive microscopic simulation algorithm, which uses a rolling-horizon strategy in which the phasing is chosen to optimize an objective function over a 15-s period in the future is developed.
References
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Traffic signal settings

F V Webster
TL;DR: In this article, the authors present an approach to evaluate the number of delays at a signal-to-interception intersection and propose a formulae to calculate the average delay per vehicle.

Evaluation of Traffic Data Obtained via GPS-Enabled Mobile Phones: the Mobile Century Field Experiment

TL;DR: In this paper, a traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network.
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