# Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning

arXiv: https://arxiv.org/abs/2209.07980 · HTML: https://mlsanity.com/paper/2209.07980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 82 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [A Review of Location Encoding for GeoAI: Methods and Applications](https://mlsanity.com/paper/2111.04006/) (2021)
- [Model-agnostic Feature Importance and Effects with Dependent Features -- A Conditional Subgroup Approach](https://mlsanity.com/paper/2006.04628/) (2020)
- [Real-Time Forecasting of Dockless Scooter-Sharing Demand: A Spatio-Temporal Multi-Graph Transformer Approach](https://mlsanity.com/paper/2111.01355/) (2021)
- [K-Prototype Segmentation Analysis on Large-scale Ridesourcing Trip Data](https://mlsanity.com/paper/2006.13924/) (2020)
- [Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach](https://mlsanity.com/paper/1902.02904/) (2019)
- [Interpretable Data-Driven Demand Modelling for On-Demand Transit Services](https://mlsanity.com/paper/2010.15673/) (2020)

## Built on by (most cited first, 2 of 2)

- [Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires](https://mlsanity.com/paper/2304.06233/) (2023)
- [Machine Learning For An Explainable Cost Prediction of Medical Insurance](https://mlsanity.com/paper/2311.14139/) (2023)

## Abstract (arXiv)

The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the nonlinear effects and spatial heterogeneity of ridesourcing demand determinants. This study applies an explainable-machine-learning-based analytical framework to identify the key factors that shape ridesourcing demand and to explore their nonlinear associations across various spatial contexts (airport, downtown, and neighborhood). We use the ridesourcing-trip data in Chicago for empirical analysis. The results reveal that the importance of built environment varies across spatial contexts, and it collectively contributes the largest importance in predicting ridesourcing demand for airport trips. Additionally, the nonlinear effects of built environment on ridesourcing demand show strong spatial variations. Ridesourcing demand is usually most responsive to the built environment changes for downtown trips, followed by neighborhood trips and airport trips. These findings offer transportation professionals nuanced insights for managing ridesourcing services.