Speed profiles of freeways in California (I5-S and I210-E)

Speed profiles of freeways in California (I5-S and I210-E). Original data is retrieved from PeMS.

Each YEAR_FREEWAY.csv file contains Timestamp and Speed data.

freeway_meta.csv file contains meta information for each detector: freeway number, direction, detector ID, absolute milepost, and x y coordinates.

# Freeway speed data description

### Data loading example (single freeway: I5-S 2012)


```python
%%time
import pandas as pd

# Date time parser
mydateparser = lambda x: pd.datetime.strptime(x, "%m/%d/%Y %H:%M:%S")

# Freeway data loading (This part should be changed to a proper URL in zenodo.org)
data = pd.read_csv("dataset/2012_I5S.csv", 
                   parse_dates=["Timestamp"],
                   date_parser=mydateparser).pivot(index="Timestamp",columns='Station_ID', values='Speed')


# Meta data loading
meta = pd.read_csv("dataset/freeway_meta.csv").set_index(['Fwy','Dir'])
```

    CPU times: user 50.5 s, sys: 911 ms, total: 51.4 s
    Wall time: 50.9 s


### Speed data and meta data


```python
data.head()
```




<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th>Station_ID</th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
      <th>5</th>
      <th>6</th>
      <th>7</th>
      <th>8</th>
      <th>9</th>
      <th>10</th>
      <th>...</th>
      <th>80</th>
      <th>81</th>
      <th>82</th>
      <th>83</th>
      <th>84</th>
      <th>85</th>
      <th>86</th>
      <th>87</th>
      <th>88</th>
      <th>89</th>
    </tr>
    <tr>
      <th>Timestamp</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>2012-01-01 06:00:00</td>
      <td>70.0</td>
      <td>69.8</td>
      <td>70.1</td>
      <td>69.6</td>
      <td>69.9</td>
      <td>70.8</td>
      <td>70.1</td>
      <td>69.3</td>
      <td>69.2</td>
      <td>68.2</td>
      <td>...</td>
      <td>72.1</td>
      <td>67.6</td>
      <td>71.0</td>
      <td>66.8</td>
      <td>65.9</td>
      <td>58.2</td>
      <td>67.1</td>
      <td>63.8</td>
      <td>67.1</td>
      <td>71.6</td>
    </tr>
    <tr>
      <td>2012-01-01 06:05:00</td>
      <td>69.2</td>
      <td>69.8</td>
      <td>69.8</td>
      <td>69.4</td>
      <td>69.5</td>
      <td>69.5</td>
      <td>68.3</td>
      <td>67.5</td>
      <td>67.4</td>
      <td>67.2</td>
      <td>...</td>
      <td>71.5</td>
      <td>66.1</td>
      <td>69.5</td>
      <td>67.4</td>
      <td>68.3</td>
      <td>59.0</td>
      <td>66.9</td>
      <td>60.8</td>
      <td>66.6</td>
      <td>65.7</td>
    </tr>
    <tr>
      <td>2012-01-01 06:10:00</td>
      <td>69.2</td>
      <td>69.0</td>
      <td>68.6</td>
      <td>68.7</td>
      <td>68.6</td>
      <td>68.9</td>
      <td>61.7</td>
      <td>68.3</td>
      <td>67.4</td>
      <td>67.7</td>
      <td>...</td>
      <td>71.1</td>
      <td>65.2</td>
      <td>71.2</td>
      <td>66.5</td>
      <td>65.4</td>
      <td>59.6</td>
      <td>66.3</td>
      <td>58.4</td>
      <td>68.2</td>
      <td>65.6</td>
    </tr>
    <tr>
      <td>2012-01-01 06:15:00</td>
      <td>69.9</td>
      <td>69.6</td>
      <td>69.7</td>
      <td>69.2</td>
      <td>69.0</td>
      <td>69.1</td>
      <td>65.3</td>
      <td>67.6</td>
      <td>67.1</td>
      <td>66.8</td>
      <td>...</td>
      <td>69.9</td>
      <td>67.1</td>
      <td>69.3</td>
      <td>66.9</td>
      <td>68.2</td>
      <td>60.6</td>
      <td>66.0</td>
      <td>55.5</td>
      <td>67.1</td>
      <td>69.7</td>
    </tr>
    <tr>
      <td>2012-01-01 06:20:00</td>
      <td>68.7</td>
      <td>68.4</td>
      <td>68.2</td>
      <td>67.9</td>
      <td>68.3</td>
      <td>69.3</td>
      <td>67.0</td>
      <td>68.4</td>
      <td>68.2</td>
      <td>68.2</td>
      <td>...</td>
      <td>70.9</td>
      <td>67.2</td>
      <td>69.9</td>
      <td>65.6</td>
      <td>66.7</td>
      <td>62.8</td>
      <td>66.2</td>
      <td>62.6</td>
      <td>67.2</td>
      <td>67.5</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 89 columns</p>
</div>




```python
meta.head()
```




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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th></th>
      <th>ID</th>
      <th>Abs_mp</th>
      <th>Latitude</th>
      <th>Longitude</th>
    </tr>
    <tr>
      <th>Fwy</th>
      <th>Dir</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td rowspan="5" valign="top">5</td>
      <td>S</td>
      <td>1</td>
      <td>0.058</td>
      <td>32.542731</td>
      <td>-117.030501</td>
    </tr>
    <tr>
      <td>S</td>
      <td>2</td>
      <td>0.146</td>
      <td>32.543587</td>
      <td>-117.031769</td>
    </tr>
    <tr>
      <td>S</td>
      <td>3</td>
      <td>1.291</td>
      <td>32.552409</td>
      <td>-117.048120</td>
    </tr>
    <tr>
      <td>S</td>
      <td>4</td>
      <td>2.222</td>
      <td>32.558422</td>
      <td>-117.062360</td>
    </tr>
    <tr>
      <td>S</td>
      <td>5</td>
      <td>2.559</td>
      <td>32.561106</td>
      <td>-117.067228</td>
    </tr>
  </tbody>
</table>
</div>



### Choose a day


```python
# Sampling (2012-01-13)
myday = "2012-01-13"

# Filter the data by the day
myday_speed_data = data.loc[myday]
```

### A speed profile


```python
from matplotlib import pyplot as plt
import matplotlib.dates as mdates

# Axis value setting
mp = meta[meta.ID.isin(data.columns)].Abs_mp
hour = myday_speed_data.index

# Draw the day
fig, ax = plt.subplots()
heatmap = ax.pcolormesh(hour,mp,myday_speed_data.T, cmap=plt.cm.RdYlGn, vmin=0, vmax=80, alpha=1)
plt.colorbar(heatmap, ax=ax)

# Appearance setting
ax.xaxis.set_major_formatter(mdates.DateFormatter("%H"))
plt.title(pd.Timestamp(myday).strftime("%Y-%m-%d [%a]"))
plt.xlabel("hour")
plt.ylabel("milepost")
plt.show()
```


![png](output_9_0.png)