Predicting occupancy trends in Barcelona's bicycle service stations using open data
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The document describes a project to predict occupancy trends in Barcelona's public bicycle sharing stations using open data. The project aims to help users plan trips by showing them which stations will likely have available bikes or empty slots. It uses a random forest model to make 3-day predictions based on past occupancy data, weather forecasts, calendar information such as holidays and weekdays. The model was trained on 90 days of recent observations and evaluated against data from the same months in the previous year.
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Predicting occupancy trends in Barcelona's bicycle service stations using open data
- 1. Gabriel Martins Dias*, Boris Bellalta, Simon Oechsner Universitat Pompeu Fabra, Barcelona, Spain Predicting occupancy trends in Barcelona's bicycle service stations using open data SAI Intelligent Systems Conference 2015 10-11 November 2015 | London UK
- 2. Bicing
- 3. Bicing The public bicycle system of Barcelona is called “Bicing” and it made for local citizens. In order to use it, people have to pay an annual fee that lets them borrow a bike for 30 minutes without any extra cost. If the trip lasts longer than 30 minutes and less than 2 hours, a small fee is applied. Otherwise, if it lasts longer than 2 hours the fee is much more expensive. Therefore, most of the trips (97%) last less than 30 minutes.
- 4. Barcelona
- 5. Barcelona This is the map of Barcelona. At the bottom, we can observe the sea. The terrain is not flat and the highest altitude is over 100 meters above the sea level.
- 6. Bicing stations 352 351 311 353 354 313 355 184 179 404 345 346 347 348 349 185 180 181 314 192 188 183 182 312 193 236 237 94 93 95 420 96 210 186 97 100 384 421 98 99 201 200 194 198 196 195 366 74 102 101 197 367 319 231 107 220 222 223 106 226 108 122 318 277 28 278 21 20 279 164 280 77 241 315 239 253 340 341 317 251 247 246 250 252 249 391 423 240 248 289 238 104 103 127 339 129 128 316 130 131 109 350 88 75 67 87 191 365 89 90 76 385 68 73 72 139 138 137 136 147 157 146 145 156 155 135 134 132 133 141 159 158 160 168 178 162 167 176174 175 144 150 153 154 382 165 166 173 190 225 110 83 80 84 209 81 111 262 140 82 92 374 364 60 61 411 78 79 406 224 27 189 123 29 19 370 22 18 120 121 26 371 218 43 44 42 286 342 142211 143 369 30 24 1 372426 119 16 17 48 393 151 428 161 163 149 409 172 171170 169 49 117 396 397 45 392 47 407 408 46 118 25 362 15 413 23 360 2 414 368 387 363 359 3 4 412 105 66 287 63 62 64 65 395 5 6 418 419 7 8 389 358 390 34 13 69 12 125 116 11 398 41 424 38 39 40 400 32 31 33 124 377 376 375 10 405 914115 361 410 402 126 37 401 55 378 56 57 114 425 36 35 53 380 52379 58 59 51 381 388 415 416 54 187427 50 232 261 70 71 91 112 113 148 386 85 373 86235 234 233 265 394 152
- 7. Bicing stations 352 351 311 353 354 313 355 184 179 404 345 346 347 348 349 185 180 181 314 192 188 183 182 312 193 236 237 94 93 95 420 96 210 186 97 100 384 421 98 99 201 200 194 198 196 195 366 74 102 101 197 367 319 231 107 220 222 223 106 226 108 122 318 277 28 278 21 20 279 164 280 77 241 315 239 253 340 341 317 251 247 246 250 252 249 391 423 240 248 289 238 104 103 127 339 129 128 316 130 131 109 350 88 75 67 87 191 365 89 90 76 385 68 73 72 139 138 137 136 147 157 146 145 156 155 135 134 132 133 141 159 158 160 168 178 162 167 176174 175 144 150 153 154 382 165 166 173 190 225 110 83 80 84 209 81 111 262 140 82 92 374 364 60 61 411 78 79 406 224 27 189 123 29 19 370 22 18 120 121 26 371 218 43 44 42 286 342 142211 143 369 30 24 1 372426 119 16 17 48 393 151 428 161 163 149 409 172 171170 169 49 117 396 397 45 392 47 407 408 46 118 25 362 15 413 23 360 2 414 368 387 363 359 3 4 412 105 66 287 63 62 64 65 395 5 6 418 419 7 8 389 358 390 34 13 69 12 125 116 11 398 41 424 38 39 40 400 32 31 33 124 377 376 375 10 405 914115 361 410 402 126 37 401 55 378 56 57 114 425 36 35 53 380 52379 58 59 51 381 388 415 416 54 187427 50 232 261 70 71 91 112 113 148 386 85 373 86235 234 233 265 394 152 These are the Bicing stations. There are 400 stations and over 6,000 bicycles available.
- 8. Problem
- 9. Problem In general, the bicycles help people to travel around the city, to go to work, to the school, and so on. However, there are two problems which users face very often: 1. Not finding a bicycle when they want to go somewhere; 2. Not finding a free slot in the station when they arrived to their destination.
- 10. Idea 💡
- 11. Idea 💡 Plan trips in advance
- 12. Idea 💡 Plan trips in advance What if we could plan this before?
- 13. Idea
- 14. Idea The idea involves creating an application where anybody can inform where they want to go and when.
- 15. Idea
- 16. Idea
- 17. Idea Based on the information provided by the user, it shows a suggestion about which stations they should use.
- 18. Levels Full Almost full Bikes and slots available Almost empty Empty
- 19. Levels Full Almost full Bikes and slots available Almost empty Empty We observed that for a person that is looking for a bike, it does not matter whether there are 5, 10 or 50 bicycles available in a station. However, they want to avoid stations that may be nearly empty. On the other hand, a person that is looking for a free slot will avoid stations that are nearly or completely full. Therefore, we defined such statuses as the critical ones.
- 20. Levels Full Almost full Bikes and slots available Almost empty Empty
- 21. Levels Full Almost full Bikes and slots available Almost empty Empty Lookingforabike
- 22. Levels Full Almost full Bikes and slots available Almost empty Empty ✅ ✅ ✅ ⚠ 🚫 Lookingforabike
- 23. Levels Full Almost full Bikes and slots available Almost empty Empty ✅ ✅ ✅ ⚠ 🚫 Lookingforabike Lookingforafree slot
- 24. Levels Full Almost full Bikes and slots available Almost empty Empty ✅ ✅ ✅ ⚠ 🚫 🚫 ⚠ ✅ ✅ ✅ Lookingforabike Lookingforafree slot
- 25. Levels Full Almost full Bikes and slots available Almost empty Empty
- 26. Levels Full Almost full Bikes and slots available Almost empty Empty
- 27. Levels Full Almost full Bikes and slots available Almost empty Empty
- 28. Levels Full Almost full Bikes and slots available Almost empty Empty
- 29. Levels Full Almost full Bikes and slots available Almost empty Empty Almost Full / Full Bikes and slots available Almost empty / Empty
- 30. Barcelona - Stations 352 351 307 308 309 311 353 302 310 354 313 355 184 179 404 345 346 347 348 349 185 180 181 314 192 188 183 182 312 199 193 306 303 236 237 94 93 95 420 96 210 186 97 100 384 421 98 99 304 305 204 337 338 334 331 329 332 330 336 335 333 325 212 327 326 328 322 214 215 323 324 213 203 284 206 205 202 207 208 201 200 194 198 196 195 366 74 102 101 197 367 216 319 219 217 321 320 230 221 228 229 357 227 356 231 107 220 222 223 106 226 108 122 276 318 277 28 278 21 20 164 28 129 109 350 88 75 67 87 191 365 89 90 76 385 68 73 72 225 110 83 80 84 209 81 111 262 140 82 92 374 364 60 61 411 78 79 406 224 27 189 123 29 19 370 22 18 120 121 26 371 218 43 44 28 14211 369 30 24 1 372426 119 16 17 48 428 161 163 149 409 172 171170 169 49 117 396 397 45 392 47 407 408 46 118 25 362 15 413 23 360 2 414 368 387 363 359 3 4 412 105 66 287 63 62 64 65 395 5 6 418 419 7 8 389 358 390 34 13 69 12 125 116 11 398 41 424 38 39 40 400 32 31 33 124 377 376 375 10 405 914115 361 410 402 126 37 401 55 378 56 57 114 425 36 35 53 380 52379 58 59 51 381 388 415 416 54 187427 50 232 261 70 71 91 112 113 148 386 85 373 86235 234 233 265 394
- 31. Barcelona - Stations 352 351 307 308 309 311 353 302 310 354 313 355 184 179 404 345 346 347 348 349 185 180 181 314 192 188 183 182 312 199 193 306 303 236 237 94 93 95 420 96 210 186 97 100 384 421 98 99 304 305 204 337 338 334 331 329 332 330 336 335 333 325 212 327 326 328 322 214 215 323 324 213 203 284 206 205 202 207 208 201 200 194 198 196 195 366 74 102 101 197 367 216 319 219 217 321 320 230 221 228 229 357 227 356 231 107 220 222 223 106 226 108 122 276 318 277 28 278 21 20 164 28 129 109 350 88 75 67 87 191 365 89 90 76 385 68 73 72 225 110 83 80 84 209 81 111 262 140 82 92 374 364 60 61 411 78 79 406 224 27 189 123 29 19 370 22 18 120 121 26 371 218 43 44 28 14211 369 30 24 1 372426 119 16 17 48 428 161 163 149 409 172 171170 169 49 117 396 397 45 392 47 407 408 46 118 25 362 15 413 23 360 2 414 368 387 363 359 3 4 412 105 66 287 63 62 64 65 395 5 6 418 419 7 8 389 358 390 34 13 69 12 125 116 11 398 41 424 38 39 40 400 32 31 33 124 377 376 375 10 405 914115 361 410 402 126 37 401 55 378 56 57 114 425 36 35 53 380 52379 58 59 51 381 388 415 416 54 187427 50 232 261 70 71 91 112 113 148 386 85 373 86235 234 233 265 394 Recall the map with the Bicing stations.
- 33. Barcelona - Stations We randomly selected 4 stations to make the predictions.
- 34. Open data
- 35. Open data
- 36. Open data
- 37. Open data Besides considering the number of bicycles, we have also observed the calendar: the season of the year, the holidays, the weekday, etc..
- 38. Open data
- 39. Open data
- 40. Open data Moreover, the weather forecast was also checked.
- 41. Predictors
- 42. Predictors
- 44. Predictors Temperature Relative humidity☂ Is it holiday? ✈
- 45. Predictors Temperature Relative humidity☂ Is it holiday? ✈ Week of the year Weekday⌚︎
- 46. Predictors Temperature Relative humidity☂ Is it holiday? ✈ Week of the year Weekday⌚︎ This is the information considered for the predictions.
- 47. 3 days of predictions Using Random Forest
- 48. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations
- 49. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations
- 50. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations
- 51. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations
- 52. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations 0 10 20 30 30 days of observations - 1 year before
- 53. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations 0 10 20 30 30 days of observations - 1 year before
- 54. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations 0 10 20 30 30 days of observations - 1 year before +
- 55. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations 0 10 20 30 30 days of observations - 1 year before 0 10 20 30 Next 72 hours +
- 56. 3 days of predictions Using Random Forest 0 10 20 30 90 days of observations 0 10 20 30 30 days of observations - 1 year before 0 10 20 30 Next 72 hours + We considered the last 90 days and the 30 days observed 1 year before to predict the statuses in the next 72 hours.
- 57. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data
- 58. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data
- 59. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data
- 60. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open dataLargeim provem ent
- 61. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open dataLargeim provem ent In station #124, the use of open data increased 15% of the accuracy.
- 62. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open dataLargeim provem ent
- 63. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data
- 64. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data No im provem ent
- 65. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data No im provem ent In station #50, there was no improvement in the accuracy due to the use of open data.
- 66. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data No im provem ent
- 67. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data
- 68. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data
- 69. Accuracy On different stations 0 % 25 % 50 % 75 % 100 % Station #50 Station #124 Station #92 Station #305 Without open data Using open data In three stations, we could observe improvements.
- 70. Accuracy According to the age of the predictions 0 % 50 % 100 % 0 days old 1 day 2 days Without open data Using open data
- 71. Accuracy According to the age of the predictions 0 % 50 % 100 % 0 days old 1 day 2 days Without open data Using open data
- 72. Accuracy According to the age of the predictions 0 % 50 % 100 % 0 days old 1 day 2 days Without open data Using open data We observed that the average accuracy was improved when we used open data in the predictions.
- 73. Sensitivity Using open data According to the age of the predictions 0 % 25 % 50 % 75 % 100 % 0 days old 1 day 2 days
- 74. Sensitivity Using open data According to the age of the predictions 0 % 25 % 50 % 75 % 100 % 0 days old 1 day 2 days
- 75. Sensitivity Using open data According to the age of the predictions 0 % 25 % 50 % 75 % 100 % 0 days old 1 day 2 days & Specificity
- 76. Sensitivity Using open data According to the age of the predictions 0 % 25 % 50 % 75 % 100 % 0 days old 1 day 2 days & Specificity
- 77. Sensitivity Using open data According to the age of the predictions 0 % 25 % 50 % 75 % 100 % 0 days old 1 day 2 days & Specificity The sensitivity is the percentage of critical statuses that were correctly predicted. It was over 75%.
- 78. Conclusion
- 80. Conclusion Impact of external factors The external factors have an impact in the use of the bicycles and should not be ignored in the predictions.
- 83. Conclusion Impact of external factors Critical statuses can be predicted Use of open data
- 84. Conclusion Impact of external factors Critical statuses can be predicted Use of open dataThe critical statuses can be predicted with the use of open data.
- 86. Future work Scalability Other data sources
- 87. Future work Scalability Other data sources Our future work involve making the predictions for all 400 stations, considering other sources of open data.
- 88. Future work Scalability Other data sources
- 89. Future work Scalability Other data sources Smartphone application
- 90. Future work Scalability Other data sources Smartphone application Other applications
- 91. Future work Scalability Other data sources Smartphone application Other applications We expect that the city council might use the predictions to improve their schedule to collect the bikes from the full stations.
- 92. Gabriel Martins Dias gabriel.martins@upf.edu Boris Bellalta, Simon Oechsner Universitat Pompeu Fabra Barcelona, Spain Predicting occupancy trends in Barcelona's bicycle service stations using open data Impact of external factors Critical statuses can be predicted Use of open data