A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

Cao, Zhiguang; Jiang, Siwei; Zhang, Jie; Guo, Hongliang

doi:10.1109/tits.2016.2613997

Cited by 166 publications

(85 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…After finishing, we will verify the algorithm validity. In future, we will also further study the action control and path planning for multiple cooperative robot fishes, by investigating and exploring the cooperative routing solutions from the transportation filed [36][37][38].…”

Section: Discussionmentioning

confidence: 99%

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

et al. 2019

View full text Add to dashboard Cite

To achieve fast and accurate adjustment of robotic fish, this paper proposes state prediction model based on the extreme learning machine optimized by particle swarm algorithm. The proposed model can select desirable actions for robotic fish according to precisely predicted states, "adjusting position" or "pushing ball" defined herein. Specifically, the extreme learning machine (ELM) is leveraged to predict the state of robotic fish, from the observations of current surrounding environment. As the outputs in ELM are varying with the randomly initialized parameters, particle swarm optimization (PSO) algorithm further improves the accuracy and robustness of the ELM by optimizing initial parameters. The empirical results on URWPGSim2D simulation platform indicate that the robotic fish tends to carry out appropriate actions using the state prediction model so that we can complete the game efficiently. It proves that the proposed model can make best use of the real-time information of robotic fish and water polo and derive fulfilling action strategy in various scenarios, which meet the requirements of motion control for robotic fish.

show abstract

Section: Discussionmentioning

confidence: 99%

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The open source model can be found at https://www.colorado.edu/lab/sbs/scc-library. We propose a novel multi-level, multi-layer, multi-agent approach to enable flexible modeling of the three interconnected systems Transportation NA [36] In-house codes Power flow and travel cost optimization Unknown NA [37] In-house codes Transportation-energy nexus assessment Unknown NA [38] OpenDSS of energy, transportation, and communication. The proposed framework is then implemented on Modelica-based modeling platforms [48]- [50], which provides important features like objected-oriented, acausal modeling and equation-based schemes.…”

Section: B Contributionsmentioning

confidence: 99%

An Open Source Modeling Framework for Interdependent Energy-Transportation-Communication Infrastructure in Smart and Connected Communities

Hinkelman

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Infrastructure in future smart and connected communities is envisioned as an aggregate of public services, including the energy, transportation and communication systems, all intertwined with each other. The intrinsic interdependency among these systems may exert underlying influence on both design and operation of the heterogeneous infrastructures. However, few prior studies have tapped into the interdependency among the three systems in order to quantify their potential impacts during standard operation. In response to this, this paper proposes an open source, flexible, integrated modeling framework suitable for designing coupled energy, transportation, and communication systems and for assessing the impact of their interdependencies. First, a novel multi-level, multi-layer, multi-agent approach is proposed to enable flexible modeling of the interconnected energy, transportation, and communication systems. Then, for the framework's proof-of-concept, preliminary component and system-level models for different systems are designed and implemented using Modelica, an equation-based object-oriented modeling language. Finally, three case studies of gradually increasing complexity are presented (energy, energy + transportation, energy + transportation + communication) to evaluate the interdependencies among the three systems. Quantitative analyses show that the deviation of the average velocity on the road can be 10.5% and the deviation of the power draw from the grid can be 7% with or without considering the transportation and communication system at the peak commute time, indicating the presence of notable interdependencies. The proposed modeling framework also has the potential to be further extended for various modeling purposes and use cases, such as dynamic modeling and optimization, resilience analysis, and integrated decision making in future connected communities.

show abstract

“…Numerous route planning software packages have been applied in practice in recent years. [36][37][38] However, route planning of a moving EV in the future IoE should consider both road traffic and battery states together, and the route planning and charging planning for a moving EV thereby become coupled problems. In this work, we assume a moving EV needs to examine whether the battery has enough power to reach the destination after it starts moving.…”

Section: Architecture Of the Real-time Power Scheduling Systemmentioning

confidence: 99%

An emission‐aware real‐time power scheduling system for the Internet of Energy

Huang

Liu

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In the recent literature, the architecture of the Internet of Energy (IoE) has been proposed to replace the current smart grid in the future. However, the large volume of energy produced, the copious amounts of accompanying consumption data, and the uncertainties germane to intermittent energy sources will result in the real‐time energy management of the IoE in the future being much more difficult to deal with than that of traditional centralized‐controlled power generation systems. In this work, a real‐time power scheduling system based on the architecture of the IoE is proposed to tackle the complicated real‐time energy management problems. We divide the whole power network into different geographical regional grids under a hierarchical framework, and the scheduling process is activated at the regional grid if a power shortage is estimated to happen during a fixed time period ahead. After electricity consumption data and renewable generation data are collected at the prosumer level, the governing regional grid schedules the usage of electricity for the prosumers by considering the efficiency of the use of distributed renewables and the peak shaving of the electricity load. Excess electricity generated in a regional grid is allowed to be allocated to others facing power supply shortages, whereby a reduction of emissions during peak load periods and the maximal utilization of distributed renewables can be achieved. Notably, the charging management of moving electric vehicles is also included in the proposed power scheduling mechanism. The simulation results reveal that the proposed real‐time power scheduling system for the IoE can efficiently utilize renewable generation, lessen the demand of traditional power, and balance peak and off‐peak period loads in an electricity market in real time.

show abstract

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

Cited by 166 publications

References 29 publications

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

The Action Control Model for Robotic Fish Using Improved Extreme Learning Machine

An Open Source Modeling Framework for Interdependent Energy-Transportation-Communication Infrastructure in Smart and Connected Communities

An emission‐aware real‐time power scheduling system for the Internet of Energy

Contact Info

Product

Resources

About