Embed Size (px)
Citation preview
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
Bicing
BicingThe 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.
Barcelona
BarcelonaThis 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.
Bicing stations
352
351
311
353
354
313355
184
179
404
345
346
347348
349
185
180 181
314 192
188
183
182
312193
236
237
9493
95420
96
210 18697
100384
421
9899
201200
194
198
196
195
366
74
102101
197367
319231
107220
222
223
106
226
108
122
318
277
28
278
21
20
279
164 280
77
241
315239
253
340
341
317
251
247246 250
252249
391423
240
248
289238
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
156155
135
134
132
133 141
159158
160168
178
162
167
176174
175
144
150
153 154 382
165
166
173
190
225
11083 80
84 209
81 111
262140
82
92 374
364
6061
411
78 79 406
224
27
189123
29
19
370
22
18
120 121
26
371
218
43
44 42
286
342
142211 143
369
3024
1
372426
119
1617
48
393151
428
161
163
149
409
172 171170 169
49
117396
397
45392
47
407408
46
118
25362
15413
23
360
2
414
368
387
363
35934
412 105
66
287
6362
64 65
395
5 6
418 419
7 8
389
358390
34
1369
12125
116
11398
41
424
3839
40
400
32
31 33 124
377
376 375
10
405
914115
361410
402 126
37401
55
378
56
57
114
425
36
35
53
380
52379
5859 51
381388
415
416
54187427
50
232
261
70 71
91
112
113
148
386
85
373
86235
234
233
265
394
152
Bicing stations
352
351
311
353
354
313355
184
179
404
345
346
347348
349
185
180 181
314 192
188
183
182
312193
236
237
9493
95420
96
210 18697
100384
421
9899
201200
194
198
196
195
366
74
102101
197367
319231
107220
222
223
106
226
108
122
318
277
28
278
21
20
279
164 280
77
241
315239
253
340
341
317
251
247246 250
252249
391423
240
248
289238
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
156155
135
134
132
133 141
159158
160168
178
162
167
176174
175
144
150
153 154 382
165
166
173
190
225
11083 80
84 209
81 111
262140
82
92 374
364
6061
411
78 79 406
224
27
189123
29
19
370
22
18
120 121
26
371
218
43
44 42
286
342
142211 143
369
3024
1
372426
119
1617
48
393151
428
161
163
149
409
172 171170 169
49
117396
397
45392
47
407408
46
118
25362
15413
23
360
2
414
368
387
363
35934
412 105
66
287
6362
64 65
395
5 6
418 419
7 8
389
358390
34
1369
12125
116
11398
41
424
3839
40
400
32
31 33 124
377
376 375
10
405
914115
361410
402 126
37401
55
378
56
57
114
425
36
35
53
380
52379
5859 51
381388
415
416
54187427
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.
Problem
ProblemIn 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.
Idea 💡
Idea 💡Plan trips in advance
Idea 💡Plan trips in advance
What if we could plan this before?
Idea
Idea
The idea involves creating an application where anybody can inform
where they want to go and when.
Idea
Idea
Idea
Based on the information provided by the user, it shows a suggestion
about which stations they should use.
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
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.
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
Look
ing fo
r a bi
ke
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
✅
✅
✅
⚠
🚫
Look
ing fo
r a bi
ke
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
✅
✅
✅
⚠
🚫
Look
ing fo
r a bi
ke
Look
ing fo
r a fr
ee sl
ot
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
✅
✅
✅
⚠
🚫
🚫
⚠
✅
✅
✅
Look
ing fo
r a bi
ke
Look
ing fo
r a fr
ee sl
ot
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
Levels
Full
Almost full
Bikes and slots available
Almost empty
Empty
Almost Full / Full
Bikes and slots available
Almost empty / Empty
Barcelona - Stations
352
351
307
308
309
311
353
302
310
354
313355
184
179
404
345
346
347348
349
185
180 181
314 192
188
183
182
312
199
193
306
303
236
237
9493
95420
96
210 18697
100384
421
9899
304
305204
337338
334
331
329
332
330
336
335333
325
212
327 326
328
322
214
215
323
324
213203
284 206205
202 207208
201200
194
198
196
195
366
74
102101
197367
216
319219
217
321
320
230
221
228
229
357
227
356
231
107220
222
223
106
226
108
122
276
318
277
28
278
21
20 164 280
77
129
109 350 88
75
67
87
191
365
89 90
76
385
68
73 72
225
11083 80
84 209
81 111
262140
82
92 374
364
6061
411
78 79 406
224
27
189123
29
19
370
22
18
120 121
26
371
218
43
44 42
286
142211
369
3024
1
372426
119
1617
48
428
161
163
149
409
172 171170 169
49
117396
397
45392
47
407408
46
118
25362
15413
23
360
2
414
368
387
363
35934
412 105
66
287
6362
64 65
395
5 6
418 419
7 8
389
358390
34
1369
12125
116
11398
41
424
3839
40
400
32
31 33 124
377
376 375
10
405
914115
361410
402 126
37401
55
378
56
57
114
425
36
35
53
380
52379
5859 51
381388
415
416
54187427
50
232
261
70 71
91
112
113
148
386
85
373
86235
234
233
265
394
Barcelona - Stations
352
351
307
308
309
311
353
302
310
354
313355
184
179
404
345
346
347348
349
185
180 181
314 192
188
183
182
312
199
193
306
303
236
237
9493
95420
96
210 18697
100384
421
9899
304
305204
337338
334
331
329
332
330
336
335333
325
212
327 326
328
322
214
215
323
324
213203
284 206205
202 207208
201200
194
198
196
195
366
74
102101
197367
216
319219
217
321
320
230
221
228
229
357
227
356
231
107220
222
223
106
226
108
122
276
318
277
28
278
21
20 164 280
77
129
109 350 88
75
67
87
191
365
89 90
76
385
68
73 72
225
11083 80
84 209
81 111
262140
82
92 374
364
6061
411
78 79 406
224
27
189123
29
19
370
22
18
120 121
26
371
218
43
44 42
286
142211
369
3024
1
372426
119
1617
48
428
161
163
149
409
172 171170 169
49
117396
397
45392
47
407408
46
118
25362
15413
23
360
2
414
368
387
363
35934
412 105
66
287
6362
64 65
395
5 6
418 419
7 8
389
358390
34
1369
12125
116
11398
41
424
3839
40
400
32
31 33 124
377
376 375
10
405
914115
361410
402 126
37401
55
378
56
57
114
425
36
35
53
380
52379
5859 51
381388
415
416
54187427
50
232
261
70 71
91
112
113
148
386
85
373
86235
234
233
265
394
Recall the map with the Bicing stations.
Barcelona - Stations
Barcelona - Stations
We randomly selected 4 stations to make the predictions.
Open data
Open data
Open data
Open data
Besides considering the number of bicycles, we have also observed
the calendar: the season of the year, the holidays, the weekday, etc..
Open data
Open data
Open data
Moreover, the weather forecast was also checked.
Predictors
Predictors
Predictors
TemperatureRelative humidity☂
Predictors
TemperatureRelative humidity☂
Is it holiday? ✈
Predictors
TemperatureRelative humidity☂
Is it holiday? ✈ Week of the year Weekday⌚︎
Predictors
TemperatureRelative humidity☂
Is it holiday? ✈ Week of the year Weekday⌚︎
This is the information considered for the predictions.
3 days of predictions
Using Random Forest
3 days of predictions
Using Random Forest
0
10
20
30
90 days of observations
3 days of predictions
Using Random Forest
0
10
20
30
90 days of observations
3 days of predictions
Using Random Forest
0
10
20
30
90 days of observations
3 days of predictions
Using Random Forest
0
10
20
30
90 days of observations
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
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
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+
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
+
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.
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Large
improvem
ent
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Large
improvem
ent
In station #124, the use of open data increased 15% of the accuracy.
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Large
improvem
ent
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
No impro
vement
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
No impro
vement
In station #50, there was no improvement in the accuracy due to the
use of open data.
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
No impro
vement
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
Accuracy
On different stations
0 %
25 %
50 %
75 %
100 %
Station #50 Station #124 Station #92 Station #305
Without open data Using open data
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.
Accuracy
According to the age of the predictions
0 %
50 %
100 %
0 days old 1 day 2 days
Without open data Using open data
Accuracy
According to the age of the predictions
0 %
50 %
100 %
0 days old 1 day 2 days
Without open data Using open data
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.
Sensitivity
Using open dataAccording to the age of the predictions
0 %
25 %
50 %
75 %
100 %
0 days old 1 day 2 days
Sensitivity
Using open dataAccording to the age of the predictions
0 %
25 %
50 %
75 %
100 %
0 days old 1 day 2 days
Sensitivity
Using open dataAccording to the age of the predictions
0 %
25 %
50 %
75 %
100 %
0 days old 1 day 2 days
& Specificity
Sensitivity
Using open dataAccording to the age of the predictions
0 %
25 %
50 %
75 %
100 %
0 days old 1 day 2 days
& Specificity
Sensitivity
Using open dataAccording 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%.
Conclusion
Conclusion
Impact ofexternalfactors
Conclusion
Impact ofexternalfactors
The external factors have an impact in the use of the bicycles and
should not be ignored in the predictions.
Conclusion
Impact ofexternalfactors
Conclusion
Impact ofexternalfactorsCritical
statuses canbe predicted
Conclusion
Impact ofexternalfactorsCritical
statuses canbe predicted
Use of open data
Conclusion
Impact ofexternalfactorsCritical
statuses canbe predicted
Use of open dataThe critical statuses can be predicted with the use of open data.
Future work
Scalability
Future work
Scalability
Other data sources
Future work
Scalability
Other data sources
Our future work involve making the predictions for all 400 stations,
considering other sources of open data.
Future work
Scalability
Other data sources
Future work
Scalability
Other data sources
Smartphone application
Future work
Scalability
Other data sources
Smartphone application
Other applications
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.
Gabriel Martins Dias [email protected]
Boris Bellalta, Simon Oechsner
Universitat Pompeu Fabra Barcelona, Spain
Predicting occupancy trends in Barcelona's bicycle service stations using open data
Impact ofexternalfactors
Critical statuses canbe predicted
Use of open data
