Daniel H. Stolfi, profile picture
Uploaded byDaniel H. Stolfi
791 views

Predicting Car Park Occupancy Rates in Smart Cities

The document discusses predicting car park occupancy rates in smart cities, focusing on a case study conducted in Birmingham, UK. It highlights the growing difficulty in finding parking due to increased vehicle numbers, leading to wasted time and fuel, as well as environmental concerns. The authors propose various predictive models and approaches, including polynomial fitting and time series analysis, to improve parking management and enhance quality of life in urban areas.

Embed presentation

Downloaded 23 times
PREDICTING CAR PARK OCCUPANCY RATES IN SMART CITIES Daniel H. Stolfi1 dhstolfi@lcc.uma.es Enrique Alba1 eat@lcc.uma.es Xin Yao2 x.yao@cs.bham.ac.uk 1Departamento de Lenguajes y Ciencias de la Computación, University of Malaga, Spain 2CERCIA, School of Computer Science, University of Birmingham, Birmingham, U.K. International Conference on Smart Cities Smart-CT 2017 Málaga, Spain June 14-16 2017
CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
CONTENTS 1 INTRODUCTION 2 OUR PROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City INTRODUCTION Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City SENSORS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 3 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Road Traffic Parking in a Big City SENSORS Sensors reporting car park occupancy Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 3 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed SYSTEM ARCHITECTURE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 4 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. 32 Car Parks32 Car Parks Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Data set: Oct 4th to Dec 19th From 9am to 5pm 18 measures per day 32 car parks 36,285 occupancy measures Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed PREDICTORS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 7 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Fourier Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Fourier Series Time Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Fourier Series Time Series K-Means Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype TRAINING Training Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 10 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD CROSS VALIDATION Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 11 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD CROSS VALIDATION K=10 Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 11 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype MEAN SQUARED ERROR (MSE) MSE = 1 n i(yi − fi)2 Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 12 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Fourier Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Fourier Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means KM-Polynomials Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means KM-Polynomials Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Time Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Time Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Which car park will be best for me tomorrow? Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Which car park will be best for me tomorrow? And the day after tomorrow? Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION EXAMPLES Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION EXAMPLES Working Days Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION EXAMPLES Working Days Weekends Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PARKING IN BIRMINGHAM Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 18 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Training Predicting Prototype PREDICTION STATS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 19 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work CONCLUSIONS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work FUTURE WORK Repeat this study using a larger training data set and other cities Include new predictors in the comparison Develop an application for mobile phones Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work FUTURE WORK Repeat this study using a larger training data set and other cities Include new predictors in the comparison Develop an application for mobile phones Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
Introduction Our Proposal Experiments Conclusions & Future Work Conclusions Future Work FUTURE WORK Repeat this study using a larger training data set and other cities Include new predictors in the comparison Develop an application for mobile phones Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
QUESTIONS Predicting Car Park Occupancy Rates in Smart Cities Prototype: http://mallba3.lcc.uma.es/parking/ Questions? Daniel H. Stolfi dhstolfi@lcc.uma.es Enrique Alba eat@lcc.uma.es Xin Yao http://neo.lcc.uma.es x.yao@cs.bham.ac.uk http://danielstolfi.com Acknowledgements: This research has been partially funded by Spanish MINECO project TIN2014-57341-R (moveON). Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International Campus of Excellence Andalucia TECH.
PARAMETERIZATION Training days: Oct 4th to Dec 12th Testing Week: Dec 13th to Dec 19th Predictor Parameter Training Polynomials: 2o Degree Fold: 1 Fourier Series: 3 Components Fold: 1 K-Means: 3 Clusters Fold: 1 KM-Polynomials: 2o Degree Fold: 1 Shift & Phase : - Fold: 1 Time Series : - Weeks: 8
THREE CLUSTERS Weekdays in each cluster
THREE CLUSTERS Weekdays in each cluster Occupancy values in each cluster
THREE CLUSTERS Weekdays in each cluster Occupancy values in each cluster KM-Polynomials and Shift & Phase fitting

More Related Content

PDF
Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms
byDaniel H. Stolfi
 
PDF
Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel...
byDaniel H. Stolfi
 
PDF
Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14)
byDaniel H. Stolfi
 
PDF
Red Swarm: Smart Mobility in Cities with EAs (GECCO'13)
byDaniel H. Stolfi
 
PDF
Un Algoritmo Evolutivo para la Reducción de Tiempos de Viaje y Emisiones Util...
byDaniel H. Stolfi
 
PDF
Reducing Gas Emissions in Smart Cities by Using the Red Swarm Architecture (C...
byDaniel H. Stolfi
 
PDF
An Evolutionary Algorithm to Generate Real Urban Traffic Flows
byDaniel H. Stolfi
 
PPTX
EDF2014: Talk of Vassileios Tsetsos, Chief Technical Officer, Mobics Ltd: Pre...
byEuropean Data Forum
 
Computing New Optimized Routes for GPS Navigators Using Evolutionary Algorithms
byDaniel H. Stolfi
 
Smart Mobility Policies with Evolutionary Algorithms: The Adapting Info Panel...
byDaniel H. Stolfi
 
Eco-friendly Reduction of Travel Times in European Smart Cities (GECCO'14)
byDaniel H. Stolfi
 
Red Swarm: Smart Mobility in Cities with EAs (GECCO'13)
byDaniel H. Stolfi
 
Un Algoritmo Evolutivo para la Reducción de Tiempos de Viaje y Emisiones Util...
byDaniel H. Stolfi
 
Reducing Gas Emissions in Smart Cities by Using the Red Swarm Architecture (C...
byDaniel H. Stolfi
 
An Evolutionary Algorithm to Generate Real Urban Traffic Flows
byDaniel H. Stolfi
 
EDF2014: Talk of Vassileios Tsetsos, Chief Technical Officer, Mobics Ltd: Pre...
byEuropean Data Forum
 

Similar to Predicting Car Park Occupancy Rates in Smart Cities

PDF
Different Modules for Car Parking System Demonstrated Using Hough Transform f...
byJANAK TRIVEDI
 
PPTX
Facilitating Mobility: Parking, Public and Alternative Transportation
byChristian McCarrick
 
PPTX
Android based application for smart parking
byGhanendra Yadav
 
PPTX
Synergy Sample project competition template.pptx
byakshada2515
 
PPTX
Easy deployable-parking-percom-v6.1
bycwyou
 
PPTX
smart parking1 (1) a view of ppt presentation of parking technology
by1RN20IS063JNithish
 
PDF
Smart parking system
byEditorIJAERD
 
PPTX
Final PPT Smart Parking System (4).pptx
byAmitJha978827
 
PDF
Analysis of Different Parking Space and its Comparison
byijsrd.com
 
PDF
Urban Parking Poster
byJake Garrison
 
PPTX
Enhanced Car Parking System dipex competition sample project presentation tem...
byakshada2515
 
PDF
SMART CAR PARKING SYSTEM USING IR SENSOR
byIRJET Journal
 
PDF
IRJET- Smart Parking System using Internet of Things Technology
byIRJET Journal
 
PPT
PPT for ParkNet Dfive-by Sensing of Road-Side Parking Statistics
byShivamGupta603083
 
PDF
IRJET- I-Parker-A New Smart Car Parking System
byIRJET Journal
 
PDF
User Personality and the New User Problem in a Context-Aware Point of Interes...
byUniversity of Bergen
 
PPTX
major project phse 1.pptx smart city civil
bydemoemail0026
 
PDF
IRJET- Smart Parking : Parking Occupancy Monitoring and Visualisation Syst...
byIRJET Journal
 
PDF
IJEEE - QR CODE BASED SMART PARKINGSYSTEM.pdf
byspub1985
 
PDF
Business Analytics and Optimization Introduction (part 2)
byRaul Chong
 
Different Modules for Car Parking System Demonstrated Using Hough Transform f...
byJANAK TRIVEDI
 
Facilitating Mobility: Parking, Public and Alternative Transportation
byChristian McCarrick
 
Android based application for smart parking
byGhanendra Yadav
 
Synergy Sample project competition template.pptx
byakshada2515
 
Easy deployable-parking-percom-v6.1
bycwyou
 
smart parking1 (1) a view of ppt presentation of parking technology
by1RN20IS063JNithish
 
Smart parking system
byEditorIJAERD
 
Final PPT Smart Parking System (4).pptx
byAmitJha978827
 
Analysis of Different Parking Space and its Comparison
byijsrd.com
 
Urban Parking Poster
byJake Garrison
 
Enhanced Car Parking System dipex competition sample project presentation tem...
byakshada2515
 
SMART CAR PARKING SYSTEM USING IR SENSOR
byIRJET Journal
 
IRJET- Smart Parking System using Internet of Things Technology
byIRJET Journal
 
PPT for ParkNet Dfive-by Sensing of Road-Side Parking Statistics
byShivamGupta603083
 
IRJET- I-Parker-A New Smart Car Parking System
byIRJET Journal
 
User Personality and the New User Problem in a Context-Aware Point of Interes...
byUniversity of Bergen
 
major project phse 1.pptx smart city civil
bydemoemail0026
 
IRJET- Smart Parking : Parking Occupancy Monitoring and Visualisation Syst...
byIRJET Journal
 
IJEEE - QR CODE BASED SMART PARKINGSYSTEM.pdf
byspub1985
 
Business Analytics and Optimization Introduction (part 2)
byRaul Chong
 

More from Daniel H. Stolfi

PDF
Improving Pheromone Communication for UAV Swarm Mobility Management
byDaniel H. Stolfi
 
PDF
Optimising Autonomous Robot Swarm Parameters for Stable Formation Design
byDaniel H. Stolfi
 
PDF
Evaluating Surrogate Models for Robot Swarm Simulations
byDaniel H. Stolfi
 
PDF
Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates
byDaniel H. Stolfi
 
PDF
A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UA...
byDaniel H. Stolfi
 
PDF
Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
byDaniel H. Stolfi
 
PDF
Ocupación de Aparcamientos y Predicción
byDaniel H. Stolfi
 
PDF
Bio-inspired Computing and Smart Mobility
byDaniel H. Stolfi
 
PDF
Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case S...
byDaniel H. Stolfi
 
Improving Pheromone Communication for UAV Swarm Mobility Management
byDaniel H. Stolfi
 
Optimising Autonomous Robot Swarm Parameters for Stable Formation Design
byDaniel H. Stolfi
 
Evaluating Surrogate Models for Robot Swarm Simulations
byDaniel H. Stolfi
 
Competitive Evolution of a UAV Swarm for Improving Intruder Detection Rates
byDaniel H. Stolfi
 
A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UA...
byDaniel H. Stolfi
 
Optimizing the Performance of an Unpredictable UAV Swarm for Intruder Detection
byDaniel H. Stolfi
 
Ocupación de Aparcamientos y Predicción
byDaniel H. Stolfi
 
Bio-inspired Computing and Smart Mobility
byDaniel H. Stolfi
 
Fine Tuning of Traffic in Our Cities with Smart Panels: The Quito City Case S...
byDaniel H. Stolfi
 

Recently uploaded

PPTX
ANIMAL_CELL_,_TISSUE_AND_ORGAN_CULTURE[1].pptx
byAdrijaKrishna
 
PDF
s - Block Handwritten Notes PDF - Irfanullah Mehar - JJ Sir Chemistry - World...
byWorld of Wisdom
 
PPT
ch7_Acids&Bases.ppt. acid and bases , reactions of acids and bases, equilibri...
bykhadijaHijazi1
 
PPTX
FRACTURE NECK OF FEMUR fracture with treatment
byHarry271308
 
PPTX
Second Law of Thermodynamics - General Chemistry 2
byjohndaenielverzosa2
 
PPTX
VIRULENCE FACTOR OF BACTERIA: STRUCTURAL ELEMENT, ENZYMES, TOXINS...
byBIOSPHERE OF KNOWLEDGE
 
PDF
Control and Coordination - Short Notes (Prashant Kirad) (1).pdf
byAnu kumari
 
PDF
Probiotic bacteria and their importance in aquaculture.pdf
byChinaSamy1
 
PPTX
Chimeric Antigen Receptors T-cell (CAR-T) & Bispecific T- cell engager (BiTEs)
byrhysudi12
 
PDF
이영욱 교수님의 물리학회 발표자료 전채_20251120.pdf
bysciencepeople
 
PPTX
TOPIC - DROUGHT PRIMING AND STRESS MEMORY
byAbhishekkumawat67
 
PPTX
10. US Soil Taxonomy, suface, sub-surface, diagnostic horizons.pptx
bybhaktipragynabehura
 
PPTX
Mass selection is one of the oldest methods of plant breeding Commonly use_20...
bysashanklakineni
 
PPTX
SCIENCE 9 Q4 Module2 MOMENTUM AND IMPULSE
byMarkjohnBello4
 
PPTX
HUBUNGAN EKOLOGI DENGAN KONSERVASI .pptx
byaprilliaroslina4
 
PPTX
Warming and cooling the Earth and the Atmosphere
byJohnLoydCorros
 
PDF
Factors affecting Plant Health – Edaphic factors and Biotic Factors
byNistarini College, Purulia (W.B) India
 
PDF
Peran Ginjal sebagai Organ Utama dalam Pembersihan Darah dan Sistem Ekskresi
byasyahara23
 
PDF
Bio - Molecules ( जैव अणु ) - PDF Notes - Irfanullah Mehar - JJ Sir Chemistry...
byWorld of Wisdom
 
PDF
Detection of an NH3 Absorption Band at 2.2μm on Europa
bySérgio Sacani
 
ANIMAL_CELL_,_TISSUE_AND_ORGAN_CULTURE[1].pptx
byAdrijaKrishna
 
s - Block Handwritten Notes PDF - Irfanullah Mehar - JJ Sir Chemistry - World...
byWorld of Wisdom
 
ch7_Acids&Bases.ppt. acid and bases , reactions of acids and bases, equilibri...
bykhadijaHijazi1
 
FRACTURE NECK OF FEMUR fracture with treatment
byHarry271308
 
Second Law of Thermodynamics - General Chemistry 2
byjohndaenielverzosa2
 
VIRULENCE FACTOR OF BACTERIA: STRUCTURAL ELEMENT, ENZYMES, TOXINS...
byBIOSPHERE OF KNOWLEDGE
 
Control and Coordination - Short Notes (Prashant Kirad) (1).pdf
byAnu kumari
 
Probiotic bacteria and their importance in aquaculture.pdf
byChinaSamy1
 
Chimeric Antigen Receptors T-cell (CAR-T) & Bispecific T- cell engager (BiTEs)
byrhysudi12
 
이영욱 교수님의 물리학회 발표자료 전채_20251120.pdf
bysciencepeople
 
TOPIC - DROUGHT PRIMING AND STRESS MEMORY
byAbhishekkumawat67
 
10. US Soil Taxonomy, suface, sub-surface, diagnostic horizons.pptx
bybhaktipragynabehura
 
Mass selection is one of the oldest methods of plant breeding Commonly use_20...
bysashanklakineni
 
SCIENCE 9 Q4 Module2 MOMENTUM AND IMPULSE
byMarkjohnBello4
 
HUBUNGAN EKOLOGI DENGAN KONSERVASI .pptx
byaprilliaroslina4
 
Warming and cooling the Earth and the Atmosphere
byJohnLoydCorros
 
Factors affecting Plant Health – Edaphic factors and Biotic Factors
byNistarini College, Purulia (W.B) India
 
Peran Ginjal sebagai Organ Utama dalam Pembersihan Darah dan Sistem Ekskresi
byasyahara23
 
Bio - Molecules ( जैव अणु ) - PDF Notes - Irfanullah Mehar - JJ Sir Chemistry...
byWorld of Wisdom
 
Detection of an NH3 Absorption Band at 2.2μm on Europa
bySérgio Sacani
 

Predicting Car Park Occupancy Rates in Smart Cities

  • 1.
    PREDICTING CAR PARKOCCUPANCY RATES IN SMART CITIES Daniel H. Stolfi1 dhstolfi@lcc.uma.es Enrique Alba1 eat@lcc.uma.es Xin Yao2 x.yao@cs.bham.ac.uk 1Departamento de Lenguajes y Ciencias de la Computación, University of Malaga, Spain 2CERCIA, School of Computer Science, University of Birmingham, Birmingham, U.K. International Conference on Smart Cities Smart-CT 2017 Málaga, Spain June 14-16 2017
  • 2.
    CONTENTS 1 INTRODUCTION 2 OURPROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
  • 3.
    CONTENTS 1 INTRODUCTION 2 OURPROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
  • 4.
    CONTENTS 1 INTRODUCTION 2 OURPROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
  • 5.
    CONTENTS 1 INTRODUCTION 2 OURPROPOSAL 3 EXPERIMENTS 4 CONCLUSIONS & FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 1 / 21
  • 6.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City INTRODUCTION Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
  • 7.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
  • 8.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
  • 9.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
  • 10.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
  • 11.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
  • 12.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City INTRODUCTION Nowadays in our cities. . . There is a larger number of vehicles in the streets Finding an available parking space is hard Time and Fuel are wasted in finding a free space Tons of greenhouse gases are emitted to the atmosphere The citizens’ quality of life is decreasing Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 2 / 21
  • 13.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City SENSORS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 3 / 21
  • 14.
    Introduction Our Proposal Experiments Conclusions &Future Work Road Traffic Parking in a Big City SENSORS Sensors reporting car park occupancy Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 3 / 21
  • 15.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed SYSTEM ARCHITECTURE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 4 / 21
  • 16.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
  • 17.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
  • 18.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
  • 19.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
  • 20.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed BIRMINGHAM, U.K. 32 Car Parks32 Car Parks Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 5 / 21
  • 21.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
  • 22.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
  • 23.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
  • 24.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed DATA SOURCE Data set: Oct 4th to Dec 19th From 9am to 5pm 18 measures per day 32 car parks 36,285 occupancy measures Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 6 / 21
  • 25.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed PREDICTORS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 7 / 21
  • 26.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
  • 27.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
  • 28.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Fourier Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
  • 29.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Fourier Series Time Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
  • 30.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed POLYNOMIALS, FOURIER, TIME SERIES & K-MEANS Polynomial Fitting Fourier Series Time Series K-Means Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 8 / 21
  • 31.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
  • 32.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
  • 33.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
  • 34.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
  • 35.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
  • 36.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
  • 37.
    Introduction Our Proposal Experiments Conclusions &Future Work Predicting Car Park Occupancy Rates Case Study: Birmingham U.K. Predictors Analyzed KM-POLYNOMIALS AND SHIFT & PHASE F(x) = a0 + a1 · x + a2 · x2 + . . . + an · xn F(x) =(a0 + δ) + a1 · (x + φ) + a2 · (x + φ)2 + . . . + an · (x + φ)n δ = Shift, φ = Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 9 / 21
  • 38.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype TRAINING Training Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 10 / 21
  • 39.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD CROSS VALIDATION Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 11 / 21
  • 40.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD CROSS VALIDATION K=10 Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 11 / 21
  • 41.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype MEAN SQUARED ERROR (MSE) MSE = 1 n i(yi − fi)2 Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 12 / 21
  • 42.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
  • 43.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
  • 44.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
  • 45.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Fourier Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
  • 46.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (I) Polynomial Fitting Fourier Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 13 / 21
  • 47.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
  • 48.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
  • 49.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means KM-Polynomials Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
  • 50.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype K-FOLD TRAINING RESULTS (II) K-Means KM-Polynomials Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 14 / 21
  • 51.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
  • 52.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
  • 53.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Time Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
  • 54.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype SHIFT & PHASE AND TIME SERIES Shift & Phase Time Series Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 15 / 21
  • 55.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Which car park will be best for me tomorrow? Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
  • 56.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Which car park will be best for me tomorrow? And the day after tomorrow? Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
  • 57.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
  • 58.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
  • 59.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION RESULTS: UNSEEN WEEK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 16 / 21
  • 60.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION EXAMPLES Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
  • 61.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION EXAMPLES Working Days Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
  • 62.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION EXAMPLES Working Days Weekends Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 17 / 21
  • 63.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PARKING IN BIRMINGHAM Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 18 / 21
  • 64.
    Introduction Our Proposal Experiments Conclusions &Future Work Training Predicting Prototype PREDICTION STATS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 19 / 21
  • 65.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work CONCLUSIONS Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
  • 66.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
  • 67.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
  • 68.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
  • 69.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
  • 70.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
  • 71.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work CONCLUSIONS Six predictor for forecasting car park occupancy rates Trained by using real parking data Tested with one week of unseen parking data Time Series shows the best results during working days Shift & Phase has good results during weekends We have presented a web based prototype Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 20 / 21
  • 72.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work FUTURE WORK Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
  • 73.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work FUTURE WORK Repeat this study using a larger training data set and other cities Include new predictors in the comparison Develop an application for mobile phones Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
  • 74.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work FUTURE WORK Repeat this study using a larger training data set and other cities Include new predictors in the comparison Develop an application for mobile phones Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
  • 75.
    Introduction Our Proposal Experiments Conclusions &Future Work Conclusions Future Work FUTURE WORK Repeat this study using a larger training data set and other cities Include new predictors in the comparison Develop an application for mobile phones Daniel H. Stolfi, Enrique Alba & Xin Yao Predicting Car Park Occupancy Rates in SC 21 / 21
  • 76.
    QUESTIONS Predicting Car ParkOccupancy Rates in Smart Cities Prototype: http://mallba3.lcc.uma.es/parking/ Questions? Daniel H. Stolfi dhstolfi@lcc.uma.es Enrique Alba eat@lcc.uma.es Xin Yao http://neo.lcc.uma.es x.yao@cs.bham.ac.uk http://danielstolfi.com Acknowledgements: This research has been partially funded by Spanish MINECO project TIN2014-57341-R (moveON). Daniel H. Stolfi is supported by a FPU grant (FPU13/00954) from the Spanish Ministry of Education, Culture and Sports. University of Malaga. International Campus of Excellence Andalucia TECH.
  • 78.
    PARAMETERIZATION Training days: Oct4th to Dec 12th Testing Week: Dec 13th to Dec 19th Predictor Parameter Training Polynomials: 2o Degree Fold: 1 Fourier Series: 3 Components Fold: 1 K-Means: 3 Clusters Fold: 1 KM-Polynomials: 2o Degree Fold: 1 Shift & Phase : - Fold: 1 Time Series : - Weeks: 8
  • 79.
  • 80.
    THREE CLUSTERS Weekdays ineach cluster Occupancy values in each cluster
  • 81.
    THREE CLUSTERS Weekdays ineach cluster Occupancy values in each cluster KM-Polynomials and Shift & Phase fitting