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 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.
Highlights
The transportation market is no exception to the evolving trend of sharing economy, as the relatively newly introduced family of transportation services known as shared mobility has witnessed a wide acceptance and adoption among users, especially in highly urbanized areas and metro cities (Roukouni & Homem de Almeida Correia, 2020)
The workflow of the analysis conducted in this paper adopted two spatial statistical methods to reveal the underlying effects in ride-sourcing trip demand, OD-flow, and trip characteristics; (1) GetisOrd Gi*; and (2) Local Anselin Moran’s I, in their respective domain of implementation
The key findings from the Getis-Ord Gi* implementation on trip ends and OD-flow hotspot analysis can be summarized as follows: (1) Transportation Network Companies (TNCs) Trip Ends, i.e., pick-ups and drop-offs, both show spatial heterogeneity of statistically significant high rates concentrated within and around the CBD area; (2) TNC OD-flow analysis shows a strong exponentially decaying trend between the OD flow and the Euclidean distance (OD length); and (3) TNC OD-pairs show spatial heterogeneity, statistically significant too, with a larger footprint extending from the CBD toward the northwest census tracts
Summary
The transportation market is no exception to the evolving trend of sharing economy, as the relatively newly introduced family of transportation services known as shared mobility has witnessed a wide acceptance and adoption among users, especially in highly urbanized areas and metro cities (Roukouni & Homem de Almeida Correia, 2020). Despite the relief TNCs offered to some accessibility and mobility chronic issues (e.g., impaired driving, first and last-mile connections to public transit, and late-night services), recently released reports and studies suggest that these newly introduced services accounted for approximately 50% of the increase in congestion in San Francisco between 2010 and 2016 (Castiglione et al, 2018) These figures on VMT and transit ridership can even get more significant when considering zero-occupied vehicles in an unmanned-autonomous-vehicles (UAV) ecosystem. Capturing those patterns and their spatial effects significance is pivotal to integrating the system into regional multimodal transportation modeling frameworks, and to providing a coherent understanding of the way the system’s trip demand and characteristics are spatially being shaped and growing Developing such spatial effects exploration protocols, perceived as a research gap in ridesourcing, is essentially data-driven and would be meaningless if conducted without resorting to realworld data. The conclusions section will set the future direction to advance this research
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