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 origindestination (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 Article history:

Willingness to share trips and surge pricing schemes remain unexplored areas in ridesourcing research. This study first conducts an explorative spatiotemporal analysis on both features’ interdependencies, that is, willingness to share and surge pricing. The willingness-to-share behavior is analyzed with respect to its econometric and psychological aspects. Mile-price and hour-of-day are used as proxies for the elasticity and psychological perception of security to share rides with strangers, respectively. Surge pricing is discussed within the classic economic theory on supply and demand dynamics, as well as other factors such as traffic conditions and trip length. The willingness-to-share pattern mined in this analysis is further analyzed in a behavioral market segmentation context to identify the type of underlying existing spatiotemporal trends. The proposed methodological protocol builds on mining spatial heterogeneity and temporal trends to provide a comprehensive understanding of the willingness-to-share behavior. The study uses the large ridesourcing dataset collected in the City of Chicago to showcase the implementation and provides a critical and contextual discussion on the behavioral segmentation pattern and the underlying urban socioeconomic fabric. Two types of oscillating and sporadic trends were captured in statistically significant hot and cold willingness-to-share spots that relate to more and less socially disadvantaged community areas, respectively. Two regression models were developed to identify the determinants of the observed trends. The non-white population percentage, percentage of low-income households, and a younger population exhibit significant positive relationships with more willingness-to-share ridesourcing trips.

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