Adaptive traffic light prediction via Kalman filtering

Protschky, Valentin; Wiesner, Kevin; Feit, Stefan

doi:10.1109/ivs.2014.6856394

Cited by 20 publications

(6 citation statements)

References 8 publications

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“…In [31], Khamis et al use Bayesian probability interpretation and the Intelligent Driver Model (IDM) to reduce the average trip waiting time. Protschky et al use the Kalman filter and a generic statistical prediction model to perform traffic light control [32].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Traffic Light Control With Quality-of-Service Provisioning for Connected and Automated Vehicles at Isolated Intersections

Miao

Leitner

2021

IEEE Access

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Today's traffic intersections primarily use fixed-cycle signaling, which is highly suboptimal and aggravates congestion and waste of fuel in urban areas. In addition, it offers minimal quality-of-service guarantee and makes travel time prediction hard. In this paper, we study how traffic signal scheduling can be done in order to not only reduce average wait time but also provide certain worst-case wait time guarantee to Connected and Automated Vehicles (CAVs). First, we derive the time a CAV spends at an intersection in the best-case and the worst-case, respectively. Using the worst-case wait time result, we propose two novel controllers which assign deadlines to CAVs based on the system state and then perform traffic signal control to meet these deadlines. We use the Simulation of Urban MObility (SUMO) simulator to implement and verify the performance of the proposed controllers. The simulation results show that they not only can guarantee the deadlines, but also outperform other methods such as the default and the delay-based controllers in terms of average delay in all scenarios.

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Section: Related Workmentioning

confidence: 99%

Adaptive Traffic Light Control With Quality-of-Service Provisioning for Connected and Automated Vehicles at Isolated Intersections

Miao

Leitner

2021

IEEE Access

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“…In statistical learning, Auto-Regressive Integrated Moving [3] and Kalman Filters [10] can capture the property of time series, but Demonstration CIKM '20, October 19-23, 2020, Virtual Event, Ireland they rely strongly on proper theoretical assumptions. Traditional machine learning methods such as K-nearest Neighbours [1] and Support Vector Regression [11] have also been applied for traffic forecasting.…”

Section: Related Work 51 Statistical and Machine Learning Approachesmentioning

confidence: 99%

InterNet

Xin

Miao

Zhu

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Traffic forecasting on the entire road network is challenging due to the non-linear temporal dynamics and complex spatial correlations. Multi-step traffic forecasting further increases the difficulty because of the accumulated prediction errors. Existing forecasting models attempt to extract both spatial and temporal features of all locations on the road network for prediction, but often overlook the interaction between the two types of features, which has led to sub-optimal performance. In this work, we tackle this problem by proposing InterNet, which applies the multi-head attention mechanism on the extracted spatio-temporal features and enables the interaction of the spatial (temporal) features of one location with the temporal (spatial) features of all locations. Moreover, we extract the features of all locations using a graph convolutional layer and a bidirectional LSTM layer, before feeding them into the multihead attention layer. The three layers are seamlessly integrated and thereby enable end-to-end learning. Experimental results show that the InterNet model outperforms the state-of-the-art models in terms of the prediction accuracy, which demonstrates the potential of such interactions.

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“…When a phase change is permitted, each light conducts a decentralized, weighted, micro-auction to determine the next phase. Other studies deal with the prediction of traffic signals enabling innovative functionalities such as Green Light Optimal Speed Advisory (GLOSA) or efficient start-stop control [23].…”

Section: Related Workmentioning

confidence: 99%

Information Dynamics in Transportation Systems with Traffic Lights Control

Litescu

Viswanathan

Aydt³

et al. 2016

Procedia Computer Science

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Due to recent advanced communication possibilities between traffic infrastructure, vehicles and drivers, the optimization of traffic lights control can be approached in novel ways. At the same time, this may introduce new unexpected dynamics in transportation systems. Our research aims to determine how drivers and traffic lights systems interact and influence each other when they are informed one about another's behaviour. In order to study this, we developed an agent based model to simulate transportation systems with static and dynamic traffic lights and drivers using information about the traffic lights behaviour. Experiments reveal that the system's performance improves when a bigger share of drivers receive information for both static and dynamic traffic lights systems. This performance improvement is due to drivers managing to avoid stopping at red light rather them adapting their speed to different distances to the traffic lights systems. Additionally, it is demonstrated that the duration of the fixed phases also influences the performance when drivers use speed recommendations. Moreover, the results show that dynamic traffic lights can produce positive effects for roads with high speed limits and high traffic intensity, while in the rest of the cases static control is better. Our findings can be used for building more efficient traffic lights systems.

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Adaptive traffic light prediction via Kalman filtering

Cited by 20 publications

References 8 publications

Adaptive Traffic Light Control With Quality-of-Service Provisioning for Connected and Automated Vehicles at Isolated Intersections

Adaptive Traffic Light Control With Quality-of-Service Provisioning for Connected and Automated Vehicles at Isolated Intersections

InterNet

Information Dynamics in Transportation Systems with Traffic Lights Control

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