Multimodal Location-aware Taxi Demand Prediction
A Short video of the project is present on the below link
https://youtu.be/zE3hiu3pPFc
You can share this video with other students and the report is on this github and is named as Multimodal_Taxi_Demand_Prediction.pdf
wget==3.2
pandas==0.23.4+0.g0409521.dirty
nltk==3.3
Keras==2.2.4
tensorflow==1.12.0
numpy==1.15.4
tabulate==0.8.2
matplotlib==2.2.3
beautifulsoup4==4.6.3
selenium==3.141.0
scikit_learn==0.20.1
Location-aware LSTM (lags + event info)
|
mae |
rmse |
| 97 |
11.645 |
14.6072 |
| 25 |
10.0594 |
12.5433 |
| 181 |
11.8526 |
15.0872 |
| 189 |
1.67727 |
2.26616 |
Location-aware MLP (lags + event info)
|
mae |
rmse |
| 97 |
11.7706 |
14.7235 |
| 25 |
10.1951 |
12.6717 |
| 181 |
11.9744 |
15.1028 |
| 189 |
1.69314 |
2.27517 |
LSTM Location-aware (only lags)
|
mae |
rmse |
| 97 |
12.014 |
15.0118 |
| 25 |
10.2896 |
12.936 |
| 181 |
12.1111 |
15.4005 |
| 189 |
1.75808 |
2.30187 |
|
mae |
rmse |
| 97 |
12.0416 |
15.0563 |
| 25 |
10.3543 |
12.9647 |
| 181 |
12.2302 |
15.541 |
| 189 |
1.75734 |
2.32166 |
Location-aware MLP model only lags
|
mae |
rmse |
| 97 |
12.0378 |
15.025 |
| 25 |
10.4146 |
13.0308 |
| 181 |
12.1352 |
15.3548 |
| 189 |
1.82246 |
2.47262 |
|
mae |
rmse |
| 97 |
13.8542 |
17.2312 |
| 25 |
10.8611 |
13.5539 |
| 181 |
13.1142 |
16.5431 |
| 189 |
1.8749 |
2.46243 |
|
mae |
rmse |
| 97 |
13.0931 |
16.2276 |
| 25 |
10.3735 |
12.7546 |
| 181 |
14.158 |
18.1432 |
| 189 |
2.39364 |
3.30199 |
Data summary for pickup zone: 97
|
year |
month |
day |
hour |
minute |
weekday |
pickup_no |
| count |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
| mean |
2017.33 |
5.52564 |
15.6813 |
11.5 |
0.5 |
4 |
19.3771 |
| std |
0.470762 |
3.30677 |
8.77644 |
6.92232 |
0.50001 |
2.00004 |
15.3033 |
| min |
2017 |
1 |
1 |
0 |
0 |
1 |
0 |
| 25% |
2017 |
3 |
8 |
5.75 |
0 |
2 |
6 |
| 50% |
2017 |
5 |
16 |
11.5 |
0.5 |
4 |
17 |
| 75% |
2018 |
8 |
23 |
17.25 |
1 |
6 |
29 |
| max |
2018 |
12 |
31 |
23 |
1 |
7 |
105 |
Data summary for pickup zone: 25
|
year |
month |
day |
hour |
minute |
weekday |
pickup_no |
| count |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
| mean |
2017.33 |
5.52564 |
15.6813 |
11.5 |
0.5 |
4 |
16.0918 |
| std |
0.470762 |
3.30677 |
8.77644 |
6.92232 |
0.50001 |
2.00004 |
12.1896 |
| min |
2017 |
1 |
1 |
0 |
0 |
1 |
0 |
| 25% |
2017 |
3 |
8 |
5.75 |
0 |
2 |
6 |
| 50% |
2017 |
5 |
16 |
11.5 |
0.5 |
4 |
14 |
| 75% |
2018 |
8 |
23 |
17.25 |
1 |
6 |
24 |
| max |
2018 |
12 |
31 |
23 |
1 |
7 |
87 |
Data summary for pickup zone: 181
|
year |
month |
day |
hour |
minute |
weekday |
pickup_no |
| count |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
| mean |
2017.33 |
5.52564 |
15.6813 |
11.5 |
0.5 |
4 |
20.2954 |
| std |
0.470762 |
3.30677 |
8.77644 |
6.92232 |
0.50001 |
2.00004 |
16.9635 |
| min |
2017 |
1 |
1 |
0 |
0 |
1 |
0 |
| 25% |
2017 |
3 |
8 |
5.75 |
0 |
2 |
7 |
| 50% |
2017 |
5 |
16 |
11.5 |
0.5 |
4 |
17 |
| 75% |
2018 |
8 |
23 |
17.25 |
1 |
6 |
29 |
| max |
2018 |
12 |
31 |
23 |
1 |
7 |
154 |
Data summary for pickup zone: 189
|
year |
month |
day |
hour |
minute |
weekday |
pickup_no |
| count |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
26208 |
| mean |
2017.33 |
5.52564 |
15.6813 |
11.5 |
0.5 |
4 |
2.73077 |
| std |
0.470762 |
3.30677 |
8.77644 |
6.92232 |
0.50001 |
2.00004 |
3.03585 |
| min |
2017 |
1 |
1 |
0 |
0 |
1 |
0 |
| 25% |
2017 |
3 |
8 |
5.75 |
0 |
2 |
1 |
| 50% |
2017 |
5 |
16 |
11.5 |
0.5 |
4 |
2 |
| 75% |
2018 |
8 |
23 |
17.25 |
1 |
6 |
4 |
| max |
2018 |
12 |
31 |
23 |
1 |
7 |
40 |