Exploring and visualizing spatial effects and patterns in ride-sourcing trip demand and characteristics
The complex demand pattern of ride-sourcing remains to be a challenge to transportation modeling practitioners due to the infancy and the inherently dynamic nature of the ride-sourcing system. Spatial effects exploration and analysis protocols can provide informative insights on the underlying structure of demand and trip characteristics. Those protocols can be thought of as an opportunistic strategy to alleviate the complexity and help specifying the appropriate econometric models for the system. Spatial effects exploration is comparable to point pattern analysis, in which, signals from spatial entities, like census tracts, can be analyzed statistically to reveal whether a specific phenomenon respective signal distribution is a completely random process or if it follows some regular pattern. The results of such analysis help to explore the investigated phenomenon and conceptualize its causal forces. In this paper, we apply spatial pattern analysis edge methods integrated into a visual analytics framework to: (1) test the null hypothesis of system demand complete randomness; (2) further analyze and explain this demand in terms of the origin-destination (OD) flow and trips characteristics, i.e., length and duration; and (3) develop a pattern profile of the demand and trip characteristics to provide potential directions to modeling and predictive analytics approaches. This framework helps explain the ride-sourcing system demand and trip characteristics in space and time to fill the gap in integrating the system in multimodal transportation frameworks. We use the ride-sourcing trip dataset released from the City of Chicago, USA, for the year 2019 to showcase the proposed methods and their novelty in capturing such effects as well as explaining the underlying complexities in a streamlined workflow. The ride-sourcing demand hotspots were explored and identified in the city’s central business district. A novel method to capture and analyze the origin-destination flowlines was developed and implemented. Finally, a complementary trip characteristics pattern analysis was conducted to fully comprehend the system and validate the findings from the system demand points and OD-flowlines.
This work is licensed under a Creative Commons Attribution 4.0 International License.