2014 · 61 citations · 12 references
ComputingComputer ScienceMachine LearningMobile ComputingPredictive AnalyticsIntelligent Transportation SystemsMobile Human-computer InteractionMobile SensingParking Demand AnalysisParking ManagementActivity RecognitionCrowdsensingCrowdsourcingMobile CrowdsensingParking Availability PredictionParking Data AnalyticsParking User BehaviorUser Activity Recognition
Searching for parking spots generates frustration and pollution. To address these parking problems, we present PocketParker, a crowdsourcing system using smartphones to predict parking lot availability. PocketParker is an example of a subset of crowdsourcing we call pocketsourcing. Pocketsourcing applications require no explicit user input or additional infrastructure, running effectively without the phone leaving the user's pocket. PocketParker detects arrivals and departures by leveraging existing activity recognition algorithms. Detected events are used to maintain per-lot availability models and respond to queries. By estimating the number of drivers not using PocketParker, a small fraction of drivers can generate accurate predictions. Our evaluation shows that PocketParker quickly and correctly detects parking events and is robust to the presence of hidden drivers. Camera monitoring of several parking lots as 105 PocketParker users generated 10;827 events over 45 days shows that PocketParker was able to correctly predict lot availability 94% of the time.
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Using mobile phones to determine transportation modes
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SPARK: A New VANET-Based Smart Parking Scheme for Large Parking Lots
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Computing, Automotive Engineering, Automotive Electronics +15