Exploring and visualizing spatial effects and patterns in ride-sourcing trip demand and characteristics

Abstract

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:

Spatiotemporal Analysis of the Relationships Between Pricing Strategies and Willingness to Share for Ridesourcing Modes

“…This absence of intensifying or persistent patterns is unexpected for cold spots, that is, conservative ride-sharers, since the vast majority of trips made within those urban pockets are short in length, as concluded by Kelleny and Ishak ( 12 ), and shorter trips are typically less likely to be shared. However, in the same work by Kelleny and Ishak ( 12 ), traces of longer trips between the CBD and the outskirts of the city were revealed. Therefore, one way to explain this is that even though we are analyzing WTS behavior at the pick-up locations, those locations are not necessarily the starting point of people’s itineraries.…”

“…Those are the census tract populations that show more propensity toward sharing their ridesourcing trips and were identified earlier as captive ride-sharers. The white space in Figure 15b shows census tracts with no pattern detected, and they are typical medium dense neighborhoods with mild demand for ridesourcing, as evident in Kelleny and Ishak (12). To add more context to these results, the spatial bivariate distribution of median age and per capita income is shown in Figure 16 for the year 2019.…”

“…This negative impact of a younger population on the WTS is evident, too, from the bivariate Pearson correlation analysis shown in Figure 19. The concentration of younger professionals in the hot demand areas, that is, the CBD and its surroundings, generates shorter trips ( 12 ). Individuals usually seek reliability in such mandatory weekday shorter trips, and that can explain the lower WTS-associated behavior, but further evidence is needed from a more disaggregated analysis.…”

“…Therefore, Dean and Kockelman ( 24 ) adopted a spatial regressive modeling in in their exploration of WTS behavior causal forces using the same data we use from the City of Chicago. Kelleny and Ishak ( 12 ) asserted the presence of spatial dependence in the trip ends, pick-ups, and drop-offs at the census tracts level, and Soria and Stathopoulos ( 25 ) confirmed the “presence of spatial effects” at the community level in WTS behavior in the City of Chicago TNC trip dataset. They also highlighted the positive correlation of socioeconomic disadvantage with more authorized-to-share (ATS) trips using a “social disadvantage index” ( 25 ).…”

“…One can attribute these inconsistent results to the difference in the granularity level of the analysis, that is, community level as opposed to census tract level. However, given that spatial effects were already asserted to be present in ridesourcing demand ( 12 ), and the suggestion from previous work to account for WTS varying disutility, that is, heterogeneity ( 13 ), there is motivation for the incorporation of spatial and temporal effects in analyzing and introspecting WTS behavioral patterns. Therefore, a behavioral market segmentation analysis will be conducted to better understand the causal forces of the behavior in a spatiotemporal context, and control for any uncaptured factor in the previous analysis.…”