TraCI4Matlab: Enabling the Integration of the SUMO Road Traffic Simulator and Matlab® Through a Software Re-engineering Process

Acosta, Andres; Espinosa, Jorge E.; Espinosa, Jairo

doi:10.1007/978-3-319-15024-6_9

Cited by 55 publications

(22 citation statements)

References 5 publications

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“…In order to evaluate the performance of the control system, two key performance indicators (KPIs) were defined as the mean of the absolute value of vehicle queues at all intersections and the global pollution during the simulation time (2 h). In the case of vehicle queues, the average value for all simulations was 13.48 for open-loop and 12.04 for closed-loop, representing an improvement of 10.70%. Regarding global pollution, the average value was 2.39 × 10 −4 for open-loop and 2.38 × 10 −4 for closed-loop, with a difference of 0.37%.…”

Section: Resultsmentioning

confidence: 94%

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Consensus-Based Cooperative Control Approach Applied to Urban Traffic Networks

Artuñedo

Toro

Haber

2016

Proceedings of the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Availabl

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Nowadays many studies are conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is applied in order to provide solutions for mobility related problems in cities. The interconnected traffic lights controllers (TLC) network adapts traffic lights cycles, based on traffic and air pollution information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease of the pollution level. Then the problem becomes more complex. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach has been followed as design method and a distributed consensus-based control algorithm has been applied. The applied control law contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Furthermore, Discrete Event Systems Specification (DEVS) formalism is applied for modelling and simulation purpose. The proposed solution has been tested and validated in a simulated environment corroborating that the proposed solution is a powerful technique to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.

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Section: Resultsmentioning

confidence: 94%

“…Figure 2 shows the simulated behaviour of the urban traffic network by using SUMO simulation tool [12] and TraCI4Matlab [13] as a library of functions for interaction with SUMO from Matlab command line. Furthermore, MatlabDEVS Toolbox [14] was used for DEVS models and simulation purposes.…”

Section: Scenario Simulationmentioning

confidence: 99%

Consensus-Based Cooperative Control Approach Applied to Urban Traffic Networks

Artuñedo

Toro

Haber

2016

Proceedings of the 3rd International Electronic Conference on Sensors and Applications, 15–30 November 2016; Availabl

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“…The integrated lane change model considers the dependencies of longitudinal and lateral movements. While running the simulation, an online interaction can be established with the interface developed by [12]. The interface is used to gather vehicle data and to set commands in order to influence the vehicle's behavior.…”

Section: Traffic Simulationmentioning

confidence: 99%

An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

Kruber

Wurst

Botsch

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper. The procedure yields an unsupervised machine learning method. The algorithm generates a proximity matrix which contains a similarity measure. This matrix is then reordered with hierarchical clustering to achieve a graphically interpretable representation. It is shown how the resulting proximity matrix can be visually interpreted and how the variation of the methods' metaparameter reveals different insights into the data. The proposed method is able to cluster data from any data source. To demonstrate the methods' potential, multiple features derived from a traffic simulation are used in this paper.The knowledge of traffic scenario clusters is crucial to accelerate the validation process. The clue of the method is that scenario templates can be generated automatically from actual traffic situations. These templates can be employed in all stages of the development process. The results prove that the procedure is well suited for an automatic categorization of traffic scenarios. Diverse other applications can benefit from this work.

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“…The fleet size is assumed to be fixed and ride-sharing is allowed. A fleet controller implemented in Matlab is used to control the route choice of the MOD vehicles using the TraCI4Matlab package [64]. The simulation framework is summarized in Fig.…”

Section: Traffic Simulatormentioning

confidence: 99%

Efficient Mobility-on-Demand System with Ride-Sharing

Huang

Peng

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Shared Mobility-on-Demand using automated vehicles can reduce energy consumption and cost for future mobility. However, its full potential in energy saving has not been fully explored. An algorithm to minimize fleet fuel consumption while satisfying customers' travel time constraints is developed in this paper. Numerical simulations with realistic travel demand and route choice are performed, showing that if fuel consumption is not considered, the MOD service can increase fleet fuel consumption due to increased empty vehicle mileage. With fuel consumption as part of the cost function, we can reduce total fuel consumption by 12% while maintaining a high level of mobility service.

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TraCI4Matlab: Enabling the Integration of the SUMO Road Traffic Simulator and Matlab® Through a Software Re-engineering Process

Cited by 55 publications

References 5 publications

Consensus-Based Cooperative Control Approach Applied to Urban Traffic Networks

Consensus-Based Cooperative Control Approach Applied to Urban Traffic Networks

An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

Efficient Mobility-on-Demand System with Ride-Sharing

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