Dynamic Control of Traffic Lights

2017 ◽  
pp. 371-386
Author(s):  
Rene Haijema ◽  
Eligius M. T. Hendrix ◽  
Jan van der Wal
Keyword(s):  
Dynamic Control ◽  
Traffic Lights

AN MDP DECOMPOSITION APPROACH FOR TRAFFIC CONTROL AT ISOLATED SIGNALIZED INTERSECTIONS

2008 ◽  
Vol 22 (4) ◽  
pp. 587-602 ◽  
Author(s):  
René Haijema ◽  
Jan van der Wal
Keyword(s):  
Traffic Control ◽  
Dynamic Control ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Value Iteration ◽  
Decomposition Approach ◽  
Traffic Lights ◽  
Novel Approach ◽  
Markovian Decision Process ◽  
One Step

This article presents a novel approach for the dynamic control of a signalized intersection. At the intersection, there is a number of arrival flows of cars, each having a single queue (lane). The set of all flows is partitioned into disjoint combinations of nonconflicting flows that will receive green together. The dynamic control of the traffic lights is based on the numbers of cars waiting in the queues. The problem concerning when to switch (and which combination to serve next) is modeled as a Markovian decision process in discrete time. For large intersections (i.e., intersections with a large number of flows), the number of states becomes tremendously large, prohibiting straightforward optimization using value iteration or policy iteration. Starting from an optimal (or nearly optimal) fixed-cycle strategy, a one-step policy improvement is proposed that is easy to compute and is shown to give a close to optimal strategy for the dynamic problem.

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions

Modeling Interstation Travel Speed of Hybrid Bus Rapid Transit within A Bayesian Framework

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Ji-hua Hu ◽  
Jia-xian Liang
Keyword(s):  
Road Traffic ◽  
Travel Speed ◽  
Bayesian Framework ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Important Indicator ◽  
Main Road ◽  
Traffic Lights ◽  
Speed Distribution ◽  
Hybrid Bus

Interstation travel speed is an important indicator of the running state of hybrid Bus Rapid Transit and passenger experience. Due to the influence of road traffic, traffic lights and other factors, the interstation travel speeds are often some kind of multi-peak and it is difficult to use a single distribution to model them. In this paper, a Gaussian mixture model charactizing the interstation travel speed of hybrid BRT under a Bayesian framework is established. The parameters of the model are inferred using the Reversible-Jump Markov Chain Monte Carlo approach (RJMCMC), including the number of model components and the weight, mean and variance of each component. Then the model is applied to Guangzhou BRT, a kind of hybrid BRT. From the results, it can be observed that the model can very effectively describe the heterogeneous speed data among different inter-stations, and provide richer information usually not available from the traditional models, and the model also produces an excellent fit to each multimodal speed distribution curve of the inter-stations. The causes of different speed distribution can be identified through investigating the Internet map of GBRT, they are big road traffic and long traffic lights respectively, which always contribute to a main road crossing. So, the BRT lane should be elevated through the main road to decrease the complexity of the running state.

Dynamic control of photonic crystal nanocavities for photon manipulation

2014 ◽  
Vol 1 ◽  
pp. 356-359
Author(s):  
Yoshinori Tanaka ◽  
Takashi Asano ◽  
Susumu Noda
Keyword(s):  
Photonic Crystal ◽  
Dynamic Control

Computational Fluid Dynamic Control

1989 ◽  
Author(s):  
Tom T. Hartley ◽  
Alex DeAbreu-Garcia
Keyword(s):  
Computational Fluid Dynamic ◽  
Fluid Dynamic ◽  
Dynamic Control

Lightweight Fault Localization using Weighted Dynamic Control Flow Subgraph

2020 ◽  
Vol 16 (2) ◽  
pp. 214
Author(s):  
Wang Yong ◽  
Liu SanMing ◽  
Li Jun ◽  
Cheng Xiangyu ◽  
Zhou Wan
Keyword(s):  
Dynamic Control ◽  
Fault Localization ◽  
Control Flow
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