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Datasets:
jojonocode
/
traffic-prediction-dataset-engineered

Tasks:
Time Series Forecasting
Modalities:
Tabular
Text
Formats:
csv
Languages:
French
Size:
10K - 100K
Tags:
smart-city
transport
traffic-flow
deep-learning
lstm
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Dataset Viewer
Auto-converted to Parquet Duplicate
DateTime
string
Junction
int64
Vehicles
int64
ID
int64
hour
int64
dayofweek
int64
month
int64
is_weekend
int64
hour_sin
float64
hour_cos
float64
veh_lag_1
float64
veh_lag_2
float64
veh_lag_3
float64
veh_lag_24
float64
2015-11-02 00:00:00
1
14
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2015-11-02 01:00:00
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2015-11-02 02:00:00
1
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20,151,102,021
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2015-11-02 03:00:00
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20,151,102,031
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2015-11-02 04:00:00
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20,151,102,041
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2015-11-02 05:00:00
1
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20,151,102,051
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0
11
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2015-11-02 06:00:00
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20,151,102,061
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0
11
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1
0
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12
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2015-11-02 07:00:00
1
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20,151,102,071
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0
11
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0.965926
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12
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2015-11-02 08:00:00
1
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20,151,102,081
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0
11
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0.866025
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11
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2015-11-02 09:00:00
1
22
20,151,102,091
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0
11
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0.707107
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2015-11-02 10:00:00
1
32
20,151,102,101
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0
11
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0.5
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14
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2015-11-02 11:00:00
1
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20,151,102,111
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0
11
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0.258819
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32
22
12
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2015-11-02 12:00:00
1
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20,151,102,121
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0
11
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-1
31
32
22
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2015-11-02 13:00:00
1
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20,151,102,131
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0
11
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-0.258819
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2015-11-02 14:00:00
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-0.5
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2015-11-02 15:00:00
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2015-11-02 16:00:00
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2015-11-02 17:00:00
1
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20,151,102,171
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0
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2015-11-02 18:00:00
1
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0
11
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-1
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27
30
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2015-11-02 19:00:00
1
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20,151,102,191
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0
11
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-0.965926
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2015-11-02 20:00:00
1
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20,151,102,201
20
0
11
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2015-11-02 21:00:00
1
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20,151,102,211
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0
11
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2015-11-02 22:00:00
1
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20,151,102,221
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0
11
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2015-11-02 23:00:00
1
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0
11
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30
32
29
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2015-11-03 00:00:00
1
21
20,151,103,001
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1
11
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0
1
27
30
32
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2015-11-03 01:00:00
1
18
20,151,103,011
1
1
11
0
0.258819
0.965926
21
27
30
12
2015-11-03 02:00:00
1
19
20,151,103,021
2
1
11
0
0.5
0.866025
18
21
27
14
2015-11-03 03:00:00
1
13
20,151,103,031
3
1
11
0
0.707107
0.707107
19
18
21
12
2015-11-03 04:00:00
1
11
20,151,103,041
4
1
11
0
0.866025
0.5
13
19
18
12
2015-11-03 05:00:00
1
11
20,151,103,051
5
1
11
0
0.965926
0.258819
11
13
19
11
2015-11-03 06:00:00
1
11
20,151,103,061
6
1
11
0
1
0
11
11
13
13
2015-11-03 07:00:00
1
14
20,151,103,071
7
1
11
0
0.965926
-0.258819
11
11
11
14
2015-11-03 08:00:00
1
15
20,151,103,081
8
1
11
0
0.866025
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14
11
11
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2015-11-03 09:00:00
1
29
20,151,103,091
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1
11
0
0.707107
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15
14
11
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2015-11-03 10:00:00
1
33
20,151,103,101
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1
11
0
0.5
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29
15
14
32
2015-11-03 11:00:00
1
32
20,151,103,111
11
1
11
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0.258819
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33
29
15
31
2015-11-03 12:00:00
1
32
20,151,103,121
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1
11
0
0
-1
32
33
29
35
2015-11-03 13:00:00
1
29
20,151,103,131
13
1
11
0
-0.258819
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32
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33
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2015-11-03 14:00:00
1
27
20,151,103,141
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1
11
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-0.5
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29
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32
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2015-11-03 15:00:00
1
26
20,151,103,151
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1
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27
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32
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2015-11-03 16:00:00
1
28
20,151,103,161
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1
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2015-11-03 17:00:00
1
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20,151,103,171
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1
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-0.965926
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28
26
27
27
2015-11-03 18:00:00
1
25
20,151,103,181
18
1
11
0
-1
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26
28
26
24
2015-11-03 19:00:00
1
29
20,151,103,191
19
1
11
0
-0.965926
0.258819
25
26
28
26
2015-11-03 20:00:00
1
26
20,151,103,201
20
1
11
0
-0.866025
0.5
29
25
26
29
2015-11-03 21:00:00
1
24
20,151,103,211
21
1
11
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-0.707107
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29
25
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2015-11-03 22:00:00
1
25
20,151,103,221
22
1
11
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-0.5
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24
26
29
30
2015-11-03 23:00:00
1
20
20,151,103,231
23
1
11
0
-0.258819
0.965926
25
24
26
27
2015-11-04 00:00:00
1
18
20,151,104,001
0
2
11
0
0
1
20
25
24
21
2015-11-04 01:00:00
1
18
20,151,104,011
1
2
11
0
0.258819
0.965926
18
20
25
18
2015-11-04 02:00:00
1
13
20,151,104,021
2
2
11
0
0.5
0.866025
18
18
20
19
2015-11-04 03:00:00
1
13
20,151,104,031
3
2
11
0
0.707107
0.707107
13
18
18
13
2015-11-04 04:00:00
1
10
20,151,104,041
4
2
11
0
0.866025
0.5
13
13
18
11
2015-11-04 05:00:00
1
12
20,151,104,051
5
2
11
0
0.965926
0.258819
10
13
13
11
2015-11-04 06:00:00
1
13
20,151,104,061
6
2
11
0
1
0
12
10
13
11
2015-11-04 07:00:00
1
11
20,151,104,071
7
2
11
0
0.965926
-0.258819
13
12
10
14
2015-11-04 08:00:00
1
13
20,151,104,081
8
2
11
0
0.866025
-0.5
11
13
12
15
2015-11-04 09:00:00
1
22
20,151,104,091
9
2
11
0
0.707107
-0.707107
13
11
13
29
2015-11-04 10:00:00
1
26
20,151,104,101
10
2
11
0
0.5
-0.866025
22
13
11
33
2015-11-04 11:00:00
1
27
20,151,104,111
11
2
11
0
0.258819
-0.965926
26
22
13
32
2015-11-04 12:00:00
1
31
20,151,104,121
12
2
11
0
0
-1
27
26
22
32
2015-11-04 13:00:00
1
24
20,151,104,131
13
2
11
0
-0.258819
-0.965926
31
27
26
29
2015-11-04 14:00:00
1
23
20,151,104,141
14
2
11
0
-0.5
-0.866025
24
31
27
27
2015-11-04 15:00:00
1
26
20,151,104,151
15
2
11
0
-0.707107
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23
24
31
26
2015-11-04 16:00:00
1
26
20,151,104,161
16
2
11
0
-0.866025
-0.5
26
23
24
28
2015-11-04 17:00:00
1
24
20,151,104,171
17
2
11
0
-0.965926
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26
26
23
26
2015-11-04 18:00:00
1
23
20,151,104,181
18
2
11
0
-1
-0
24
26
26
25
2015-11-04 19:00:00
1
25
20,151,104,191
19
2
11
0
-0.965926
0.258819
23
24
26
29
2015-11-04 20:00:00
1
26
20,151,104,201
20
2
11
0
-0.866025
0.5
25
23
24
26
2015-11-04 21:00:00
1
24
20,151,104,211
21
2
11
0
-0.707107
0.707107
26
25
23
24
2015-11-04 22:00:00
1
26
20,151,104,221
22
2
11
0
-0.5
0.866025
24
26
25
25
2015-11-04 23:00:00
1
24
20,151,104,231
23
2
11
0
-0.258819
0.965926
26
24
26
20
2015-11-05 00:00:00
1
19
20,151,105,001
0
3
11
0
0
1
24
26
24
18
2015-11-05 01:00:00
1
20
20,151,105,011
1
3
11
0
0.258819
0.965926
19
24
26
18
2015-11-05 02:00:00
1
18
20,151,105,021
2
3
11
0
0.5
0.866025
20
19
24
13
2015-11-05 03:00:00
1
13
20,151,105,031
3
3
11
0
0.707107
0.707107
18
20
19
13
2015-11-05 04:00:00
1
13
20,151,105,041
4
3
11
0
0.866025
0.5
13
18
20
10
2015-11-05 05:00:00
1
9
20,151,105,051
5
3
11
0
0.965926
0.258819
13
13
18
12
2015-11-05 06:00:00
1
12
20,151,105,061
6
3
11
0
1
0
9
13
13
13
2015-11-05 07:00:00
1
12
20,151,105,071
7
3
11
0
0.965926
-0.258819
12
9
13
11
2015-11-05 08:00:00
1
15
20,151,105,081
8
3
11
0
0.866025
-0.5
12
12
9
13
2015-11-05 09:00:00
1
16
20,151,105,091
9
3
11
0
0.707107
-0.707107
15
12
12
22
2015-11-05 10:00:00
1
23
20,151,105,101
10
3
11
0
0.5
-0.866025
16
15
12
26
2015-11-05 11:00:00
1
24
20,151,105,111
11
3
11
0
0.258819
-0.965926
23
16
15
27
2015-11-05 12:00:00
1
25
20,151,105,121
12
3
11
0
0
-1
24
23
16
31
2015-11-05 13:00:00
1
24
20,151,105,131
13
3
11
0
-0.258819
-0.965926
25
24
23
24
2015-11-05 14:00:00
1
26
20,151,105,141
14
3
11
0
-0.5
-0.866025
24
25
24
23
2015-11-05 15:00:00
1
22
20,151,105,151
15
3
11
0
-0.707107
-0.707107
26
24
25
26
2015-11-05 16:00:00
1
20
20,151,105,161
16
3
11
0
-0.866025
-0.5
22
26
24
26
2015-11-05 17:00:00
1
20
20,151,105,171
17
3
11
0
-0.965926
-0.258819
20
22
26
24
2015-11-05 18:00:00
1
22
20,151,105,181
18
3
11
0
-1
-0
20
20
22
23
2015-11-05 19:00:00
1
26
20,151,105,191
19
3
11
0
-0.965926
0.258819
22
20
20
25
2015-11-05 20:00:00
1
22
20,151,105,201
20
3
11
0
-0.866025
0.5
26
22
20
26
2015-11-05 21:00:00
1
21
20,151,105,211
21
3
11
0
-0.707107
0.707107
22
26
22
24
2015-11-05 22:00:00
1
21
20,151,105,221
22
3
11
0
-0.5
0.866025
21
22
26
26
2015-11-05 23:00:00
1
21
20,151,105,231
23
3
11
0
-0.258819
0.965926
21
21
22
24
2015-11-06 00:00:00
1
16
20,151,106,001
0
4
11
0
0
1
21
21
21
19
2015-11-06 01:00:00
1
18
20,151,106,011
1
4
11
0
0.258819
0.965926
16
21
21
20
2015-11-06 02:00:00
1
19
20,151,106,021
2
4
11
0
0.5
0.866025
18
16
21
18
2015-11-06 03:00:00
1
14
20,151,106,031
3
4
11
0
0.707107
0.707107
19
18
16
13
End of preview. Expand in Data Studio

🚦 Prédiction du Trafic Urbain : Dataset Card

Bienvenue ! Ce dataset n'est pas qu'une simple suite de chiffres ; c'est une vue numérique du pouls de notre ville. Il a été conçu pour entraîner des modèles capables de comprendre le rythme des déplacements urbains sur 4 jonctions clés.

💡 Pourquoi ce Dataset est unique ?

Passer des données brutes à l'intelligence nécessite du soin. Nous avons transformé les colonnes classiques en features intelligentes pour aider les réseaux de neurones (LSTM) à mieux "voir" le temps.

🧠 Le Feature Engineering : Le secret de la précision

1. La perception du temps cyclique (Sin/Cos)

Les machines voient souvent l'heure 23 et l'heure 0 comme les deux points les plus éloignés. Pour un humain, c'est presque le même moment (minuit). Notre solution : Nous projetons l'heure sur un cercle trigonométrique.

# Donner au modèle la notion de cycle
df['hour_sin'] = np.sin(2 * np.pi * df['hour']/24.0)
df['hour_cos'] = np.cos(2 * np.pi * df['hour']/24.0)

2. L'effet miroir du passé (Lags)

  • veh_lag_1, 2, 3 : Capturent la tendance immédiate.
  • veh_lag_24 : Capture la routine quotidienne.

📂 Contenu du coffret

Fichier Utilité
traffic_original.csv La source brute.
traffic_engineered_full.csv La version prête pour l'IA.
splits/train.csv 80% des données pour l'apprentissage.
splits/test.csv 20% des données pour la validation.
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Number of rows:
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Total file size:
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