3. Urban Datasets

UCTB is designed for urban computing in various scenarios. Currently, It releases a public dataset repository including bike sharing, ride sharing, traffic speed, and pedestrian counting applications. If you are interested in this project, making a contribution to the dataset is strongly welcomed :)

3.1. Datasets Overview

Currently, UCTB offers the following datasets in 7 scenarios, with detailed information provided in the table below. We are constantly working to release more datasets in the future.

Application City Time Range Temporal Granularity Dataset Link
Bike-sharing NYC 2013.07.01-2017.09.30 5 minutes 66.0M
Bike-sharing Chicago 2013.07.01-2017.09.30 5 minutes 30.2M
Bike-sharing DC 2013.07.01-2017.09.30 5 minutes 31.0M
Pedestrian Count Melbourne 2021.01.01-2022.11.01 60 minutes 1.18M
Vehicle Speed LA 2012.03.01-2012.06.28 5 minutes 11.8M
Vehicle Speed BAY 2017.01.01-2017.07.01 5 minutes 27.9M
Taxi Demand Chicago 2013.01.01-2018.01.01 15 minutes 6.1M
Bus NYC 2022.02.01-2024.01.13 60 mins 4.89M
Metro NYC 2022.02.01-2023.12.21 60 mins 11.3M
Traffic Flow Luzern 2015.01.01-2016.01.01 3 mins 21M
Ride-sharing Chicago (community) 2013.01.01-2018.01.01 15 mins 6.06
Ride-sharing Chicago (census tract) 2013.01.01-2018.01.01 15 mins 10M
Ride-sharing NYC 2009.01.01-2023.06.01 5 mins 36.9M

3.2. Load UCTB Dataset

The pickle module is an external library that comes built-in with Python and provides functionality for converting Python objects into a byte stream (serialization) and restoring them back to their original state (deserialization). We use it to help data format instances to transform between memory and disk.

3.2.1. Dataset format

We’ve collected some public datasets and processing them into UCTB dataset format. UCTB dataset is a python build-in dictionary object that could be loaded by pickle package. Here is the example of UCTB dataset.

# Let's say ``my_dataset`` is your dataset.
my_dataset = {
    "TimeRange": ['YYYY-MM-DD', 'YYYY-MM-DD'],
    "TimeFitness": 60, # Minutes
    
    "Node": {
        "TrafficNode": np.array, # With shape [time, num-of-node]
        "TrafficMonthlyInteraction": np.array, # With shape [month, num-of-node. num-of-node]
        "StationInfo": list # elements in it should be [id, build-time, lat, lng, name]
        "POI": []
    },

    "Grid": {
        "TrafficGrid": [],
        "GridLatLng": [],
        "POI": []
    },

    "ExternalFeature": {
         "Weather": [time, weather-feature-dim]
    }
}

3.2.2. Use datasets from Urban Datasets

In this section, we will introduce how to get the dataset from Urban_Dataset and read the dataset using python.

You are proposed to download the zip file from the link and unzip the file. Let’s say the following scripts are placed at the same directory with the dataset.

import pickle as pkl
import numpy as np
data_path = 'Pedestrian_Melbourne.pkl'
with open(data_path,'rb') as fp:
    data = pkl.load(fp)

Take a look at the necessary information about the dataset:

# Traffic data 
print('Data time range', data['TimeRange'])
print('Traffic data shape:', np.shape(data['Node']['TrafficNode']))
# The first dimension of data['Node']['TrafficNode'] is the length of time-sequence.
# The second dimension is the number of stations.
print('Time fitness:', data['TimeFitness'], 'minutes')
print('Time sequence length:', data['Node']['TrafficNode'].shape[0])
print('Number of stations:', data['Node']['TrafficNode'].shape[1])

Data time range ['2021-01-01', '2022-11-01']
Traffic data shape: (16056, 55)
Time fitness: 60 minutes
Time sequence length: 16056
Number of stations: 55

Visualize the distribution of the traffic data:

import matplotlib.pyplot as plt
plt.plot(data['Node']['TrafficNode'][:, 0])
plt.show()

png

3.3. How to get the datasets at other granularities?

We could merge the fine-grained data to obtain the datasets at other granularities (e.g., by summing the 12 flows from the 5-minutes datasets to obtain 60-minutes datasets). UCTB provides the API to merge data. You could specify MergeIndex and MergeWay in the NodeTrafficLoader and GridTrafficLoader. Here is an example:

from UCTB.dataset import NodeTrafficLoader

# loading 5-minutes datasets

data_loader = NodeTrafficLoader(dataset="Bike", city="NYC") 
print(data_loader.dataset.node_traffic.shape) # with shape (446976, 820)

data_loader = NodeTrafficLoader(dataset="Bike", city="NYC", MergeIndex=12, MergeWay="sum")
print(data_loader.dataset.node_traffic.shape) # with shape (37248, 820)

3.4. Build your own datasets

If you want to apply uctb dataloaders to your dataset, make your dataset compatible with the template as section 3.2.1 shown. And then save it with package pickle to a local path pkl_file_name.

import pickle
pkl_file_name = './my_dataset.pkl'  
with open(pkl_file_name, 'wb') as handle:
    pickle.dump(my_dataset, handle, protocol=pickle.HIGHEST_PROTOCOL)

Finally, you can make uses of your dataset by UCTB’s loader APIs:

data_loader = NodeTrafficLoader(dataset=pkl_file_name)

Also, we provide interface to help build your own dataset, through which we clarify whether a field is necessary or optional when building a UCTB dataset.

To build a UCTB dataset, it is necessary to provide variables listed as below.

Variable_name Description
time_fitness The length of the interval between adjacent slots
time_range The time interval at the beginning and end of the data
traffic_node The spatio-temporal information
node_satation_info The basic information of each data collecting node
dataset_name Name of the dataset
city A variable used to integrate holiday and weather information to traffic data

Then, use the specified path to save the dataset, otherwise it will be saved in the current run-time path.

Although it’s diffcult to form an integrated function to include all situation you may meet during the transforming process, there are some procedures you might obey to simplify the data preprocessing.

  • Data preprocessing

    1. Zero values

    2. Missing values(NA)

    3. Unknown values

    4. Abnormal values

    5. duplicates

    6. Statistics(station number and time slots)

  • Dictionary building

    • Basic information(time range and time fitness)

    • Traffic node building

      • Spatio-temporal raster data building

        1. Initialization

        2. iterate raw data table and fill the matrix

      • Station information

    • Traffic grid building

    • External feature

Now, we assume that you have already finished variable preparation. UCTB provide API to assist you with dataset building.

build_uctb_dataset(traffic_node=traffic_node, time_fitness=time_fitness, 
                node_station_info=node_station_info, time_range=time_range, 
                output_dir='tmp_dir', dataset_name='dataset', city = 'Chicago')

Also, if you want to check what fields are in your datasets, set the argument print_dataset to True.

build_uctb_dataset(traffic_node=traffic_node, time_fitness=time_fitness, 
                node_station_info=node_station_info, time_range=time_range, 
                output_dir='tmp_dir', dataset_name='dataset', city = 'Chicago', print_dataset=True)

Output:

dataset[TimeRange]:<class 'list'>  (len=2)
dataset[TimeFitness]:<class 'int'>
dataset[Node]:<class 'dict'>{
    dataset[Node][TrafficNode]:<class 'numpy.ndarray'>  (shape=(37248, 532))
    dataset[Node][StationInfo]:<class 'list'>  (len=(532, 5))
    dataset[Node][TrafficMonthlyInteraction]:<class 'NoneType'>
}
dataset[Grid]:<class 'dict'>{
    dataset[Grid][TrafficGrid]:<class 'NoneType'>
    dataset[Grid][GridLatLng]:<class 'NoneType'>
}
dataset[ExternalFeature]:<class 'dict'>{
    dataset[ExternalFeature][Weather]:<class 'list'>  (len=0)
}
dataset[LenTimeSlots]:<class 'int'>

What’s more, if you want to integrate additional information of the dataset, just specify the optional argument as bellow.

Variable_name Description
traffic_monthly_interaction The interactive information among data collecting nodes.
node_poi Point of interests conformed with node format
grid_poi Point of interests conformed with grid format
traffic_grid The spatio-temporal information in grid format.
gird_lat_lng The basic information of each data collecting grid.
external_feature_weather The weather information of each day.

for example, specify the argument external_feature_weather with numpy.array object.

build_uctb_dataset(traffic_node=traffic_node, time_fitness=time_fitness, 
                node_station_info=node_station_info, time_range=time_range, 
                output_dir='tmp_dir', dataset_name='dataset', city = 'Chicago', 
                print_dataset=True, external_feature_weather=np.zeros([37248,26]))

The code above use zero matrix to specify the argument external_feature_weather. While in practical application scenario, you should substitute it with real feather matrix. The first dimension of the matrx is the number of time slots, and the second dimension corresponds to the dimension of weather features.