Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

Gao, Kai; Han, Farong; Dong, Pingping; Xiong, Naixue; Du, Ruxu

doi:10.3390/s19092059

Cited by 99 publications

(72 citation statements)

References 52 publications

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“…In our previous work, we proposed an algorithm for queue length estimation assuming that the length of vehicles is identical by converting according to the vehicle conversion coefficient [43]. Nowadays, connected automated vehicles are not very popular.…”

Section: Basic Conditionsmentioning

confidence: 99%

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An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

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Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency.Abstract: Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

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Section: Basic Conditionsmentioning

confidence: 99%

“…It exposes that the accuracy of estimation is associated with the penetration rate of connected vehicles. Moreover, a comprehensive review gives a summary of road traffic density estimation methods based on vehicular ad hoc networks [43]. The infrastructure-based mechanisms mentioned in the paper belong to an IoT-based solution.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

et al. 2020

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“…In the perspective of technical and economic characteristics. Nayeem et al [17][18][19] has established a model based on population growth that still satisfied the maximum passengers, the minimum transfers, and the minimum travel time.…”

Section: Competition and Cooperation In Public Transport Networkmentioning

confidence: 99%

Optimizing Bus Line Based on Metro-Bus Integration

Wei

Long

et al. 2020

Sustainability

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Metros are usually built and added on the basis of a completed bus network in Chinese cities. After the metro construction, it is faced with the problem of how to adjust and optimize the original bus lines based on the new metro system. This research mainly proposes a bus line optimization method based on bus and metro integration. In the consideration of the geographical space, the cooperation and competition relationship between bus and metro lines is qualitatively introduced according to the geographical location and service range of metro (800 m radius) and bus (500 m radius) stations. The competition and cooperation indexe sare applied to define the co-opetition relationship between bus and metro lines. The bus line optimization model is constructed based on the co-opetition coefficient and Changsha Metro Line Number 2 is chosen as a case study to verify the optimization model. The results show that the positive competition, efficient cooperation, and travel efficiency between metro and bus has been significantly enhanced after optimization. Moreover, this paper provides a reasonable reference for public transport network planning and resource allocation.

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“…With the global energy crisis and environmental pollution problems intensifying, electric vehicles have been rapidly promoted by countries around the world for their advantages of high efficiency, energy saving, low noise and zero emissions [1,2]. Due to the high energy density, high cell voltage, low self-discharge rate and long life, lithium-ion batteries are widely used in power systems of electric vehicles [3,4].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction

et al. 2020

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Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address this challenge, this paper proposes a new data-driven method using feature enhancement and adaptive optimization. First, the features of battery aging are extracted online. Then, the feature enhancement technologies, including the box-cox transformation and the time window processing, are used to fully exploit the potential of features. The box-cox transformation can improve the correlation between the features and the aging status of the battery, and the time window processing can effectively exploit the time information hidden in the historical features sequence. Based on this, gradient boosting decision trees are used to establish the RUL prediction model, and the particle swarm optimization is used to adaptively optimize the model parameters. This method was applied on actual lithium-ion battery degradation data, and the experimental results show that the proposed model is superior to traditional prediction methods in terms of accuracy.

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Connected Vehicle as a Mobile Sensor for Real Time Queue Length at Signalized Intersections

Cited by 99 publications

References 52 publications

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

Optimizing Bus Line Based on Metro-Bus Integration

A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction

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