Mapping one-day travel pattern in Chicago
Over the past few years, a new travel option has begun to emerge. Transportation Network Companies like Uber, Lyft, and Sidecar are changing the transportation landscape. As a more flexible and cost-effective travel option, they hit the market and quickly began popular among people. Until now, this ride-share system has been an unignorable power for people’s traveling.
As more people use this service, the data of these trips can tell us more about how people move across the city and how the travel pattern changes over time. In this passage, I focus on the data of ride-share trips in Chicago. By using one-day trip data on Sep 30 in 2020, my research questions are how people use this service for the whole day and how the travel pattern changes in one-day.
How do people use this service for the whole day?
On Sep 30, 2020, the number of total trips is 100,073. The datasets of trips contain 21 columns, including the information of start and end location, price, and trip miles, etc. After dropping the datasets without detailed location information, I only take 82,191 trips into further analysis. Besides, by using 330feet X 330feet grids, there are 794 locations of ride-sharing across the city, which means these 82,191 trips happened between these near 800 locations. The connections between these locations are shown in figure one. We can see that the center of trip distribution is around the River North Neighborhood and this area has more connection with the north area than the south area.
From the values of incoming and outgoing degrees of each location, we can find that the distribution trends of the top10 pick-up and drop-off locations are the same. These two maps all show that the top10 locations concentrate more in the neighborhood of River North, Streeterville, and Loop, over half of them. Also, one of the top10 locations located in the airport area, which indicates that people living in Chicago use the airplane frequently. Meanwhile, the slight difference between these two maps is that the top10 pick-up locations are more concentrated in the center of the city around the River North Neighborhood than another map, which shows people’s start points are more concentrated in the center area of the city.
To further map the location centrality, I use the degree centrality and eigenvector centrality, which represents total connections between each location with other locations under the consideration of neighborhood influence or not. From these two maps, we can see the top10 popular locations whatever under the neighborhood effect or not, are all located around the Chicago River. Besides, after considering the surrounding’s effect, the cluster of top10 popular locations is clearer and around the neighborhood of River North, Streeterville, and Loop, which indicates the locations with higher trips cluster more in these three neighborhoods.
How does the travel pattern change in one-day?
To capture the travel pattern in one-day on Sep 30, 2020, I collect data every six hours as one time period. The outcome shows that most trips happened between 0 pm to 6 pm, a total of 38,700 trips. The period between 0 am to 6 am has the least travel, only 7,220 trips. Given overall OD trends, the difference of trip distributions between the north and south areas of the Chicago River reach the largest in the period from 6 pm to 12 pm.
Besides, by highlighting the top 10 popular locations using degree centrality, we can see more clearly people’s travel pattern change. The biggest change is the comparison between the first period and the last period of one day. From the map of the top 10 popular locations from 0 am to 6 am, the distribution of these locations is scattered all over Chicago away from the city center. The map from 6 pm to 12 pm shows the complete opposite picture and all the top 10 popular locations are concentrated in the city center around the Chicago River, which may indicate the distribution difference between the residential area and workplace.
Conclusion
By using the OD analysis based on TNP trip data, we can draw people’s travel patterns in Chicago. The outcome shows that people’s traveling center is around the Chicago River and this area has a stronger connection with the north area than the south. Meanwhile, the strong connection between downtown and the airport is clearly shown in the travel pattern. By tracing the different periods in one day, we can see people’s travel patterns change. From 0 am to 6 am, people’s trips happened more in suburb area. Conversely, from 6 pm to 12 pm, people’s trips happened more in the downtown area. This change reflects the people’s travel demand change and indicates the distribution difference between residential areas and workplaces.
Data Resource:
Chicago Transportation Network Providers (TNP) trips, 2020, City of Chicago, URL: https://data.cityofchicago.org/Transportation/Transportation-Network-Providers-Trips/m6dm-c72p
Neighborhood boundaries in Chicago, 2020, City of Chicago, URL: https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Neighborhoods/bbvz-uum9
