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Predicting Car Park Occupancy Rates in Smart Cities

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In this article we address the study of parking occupancy data published by the Birmingham city council with the aim of testing several prediction strategies (polynomial fitting, Fourier series, k-means clustering, and time series) and analyzing their results. We have used cross validation to train the predictors and then tested them on unseen occupancy data. Additionally, we present a web page prototype to visualize the current and historical parking data on a map, allowing users to consult the occupancy rate forecast to satisfy their parking needs up to one day in advance. We think that the combination of accurate intelligent techniques plus final user services for citizens is the direction to follow for knowledge-based real smart cities.

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Predicting Car Park Occupancy Rates in Smart Cities

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 76. 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.
  77. 77. 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
  78. 78. THREE CLUSTERS Weekdays in each cluster
  79. 79. THREE CLUSTERS Weekdays in each cluster Occupancy values in each cluster
  80. 80. THREE CLUSTERS Weekdays in each cluster Occupancy values in each cluster KM-Polynomials and Shift & Phase fitting

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