Algorithmic Planning, Simulation and Validation of Smart ...
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Institute of Architecture of Application Systems Research Algorithmic Planning, Simulation and Validation of Smart, Shared Parking Services using Last Mile Hardware Muralikrishna Thulasi Raman [email protected]
Transcript of Algorithmic Planning, Simulation and Validation of Smart ...
Institute of Architecture of Application Systems
Research
Algorithmic Planning, Simulation and Validation of Smart, Shared Parking Services using Last Mile
Hardware
Muralikrishna Thulasi [email protected]
Introduction
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hImportant Background Terminologies
▪ Smart gate and Smart parking▪ VANET
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hContribution
▪ Setting up simulation-based testbed▪ System design by extending existing mathematical models▪ Analyse parameters related to various stakeholders
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Simulation Scenario
System Design changes
Scenario input setup
Output logs and data
Further processing and Inference
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hLimitations of previous work
▪ Surveys to gather data▪ Mathematical models - System design
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System Design and Architecture
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hMicroscopic area or Simulation base map network
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OSM SUMO Network
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hApproach - Shared Private Parking Model in Web Service
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hDesign of the system
▪ Mathematical model - I (Focus on preferred walking distance)
▪ Mathematical model - II (Focus on overtime revenue)
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hData flow between components
Parking Request Resume
Results
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hMetrics
▪ Vehicular Emission, Reroutes and Distance travelled▪ Utilization rate of parking lots ▪ Revenue generated by parking lots▪ Correlation heatmap
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hSimulation-based testbed setup
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hEmploying a communication strategy to search for a parking spot
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hBenefits of shared private parking model to vehicle or drivers
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hBenefits of shared private parking model to parking lot owners - Utilisation Rate
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hBenefits of shared private parking model to parking lot owners - Revenue
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hEffects of change in number of shared private parking lots
Conclusion
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hConclusion
▪ Proving effectiveness of the shared private parking model▪ Improvement to traditional model by lesser emissions▪ Comparison analysis to prove effectiveness▪ Correlation analysis to conclude reaction of vehicles and model.
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hOut of scope
▪ Ensure better scalability▪ Better route planning ▪ Needs parking and overtime parking behaviour model▪ To improve standardisation of V2X messages
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hFuture Works
▪ Larger city-based scenario▪ HIL Simulation or Digital testbed▪ Bike travel as Intermodal travel option
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Questions?
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Appendix
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hSurvey
▪ Among 34 employees from itemis▪ From different German locations▪ To gather opinions on shared private parking model
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hDemand data survey points
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hRoute planning procedure
▪ Sources and destinations are chosen as geographic coordinates▪ Fed to custom script▪ Integrated database is used for navigation
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hShared Private Parking Lots model database schema
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hApplications deployed in vehicle and RSU
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hDesign - Cellular Automata - Allocation module
▪ Cellular Automata used in allocation module▪ Ensures even distribution▪ Rule type - Rule 51▪ Expression: t - t
lr,m > t
th
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hAllocation Module stages
Use P2
Special Case
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hApproach - Technical stack
▪ Eclipse MOSAIC - V2X Framework with vehicles, RSUs▪ Eclipse SUMO - Simulates traffic
▪ Python based web service - Hosts the shared private parking model▪ Python scripts for processing and analysis
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hSimulation-based testbed setup
▪ Need for a setup▪ Facilitates to extract logs▪ Emission logs and Database records
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hCorrelation analysis between vehicular emissions and distance travelled
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hUtilisation of individual parking lots
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