The Impact of the Modifiable Area Unit Problem on Online Ride-hailing Travel Behavior Analysis
Pages 120 - 126
Abstract
As the built environment gradually deepens the analysis of online ride-hailing travel behavior, it is necessary to further explore the impact of modifiable areal unit problem (MAUP) on behavior analysis modeling. Therefore, this article selects Didi travel data and multi-source spatial data in Haikou City to construct built environment and socioeconomic attribute indicators, and analyzes the impact of the built environment on morning and evening peak passenger travel by constructing a geographically weighted regression (GWR) model. On this basis, the impact of MAUP on behavioral analysis is explored by comparing different modeling results. The results show that Moran's I show a fluctuating trend as the scale increases; the interpretability and goodness of fit of the model gradually improve as the scale becomes larger; from the comparative analysis of the regression coefficients of GWR, it can be concluded that variables such as Transport Facilities, Accommodation Hotel, and Healthcare are greatly affected by spatial scale, and the impact on these variables should be considered in modeling analysis. This study can provide a theoretical basis for the analysis of online car-hailing travel behavior and better explore the impact of the built environment on online car-hailing travel.
References
[1]
Gao Jiong, Ma Shoufeng, Li Lulu, Zuo Jian and Du Huibin. 2022. Does travel closer to TOD have lower CO2 emissions? Evidence from ride-hailing in Chengdu, China. Journal of Environmental Management, 308, 114636.
[2]
Reid Ewing, and Robert Cervero. 2010. Travel and the built environment: A meta-analysis. Journal of the American planning association, 76(3), 265-294.
[3]
Huang Guan, Qiao Si and Yeh Anthony Gar-On. 2021. Spatiotemporally heterogeneous willingness to ridesplitting and its relationship with the built environment: A case study in Chengdu, China. Transportation Research Part C: Emerging Technologies, 133, 103425.
[4]
Zheng Zhicheng, Zhang Jingfei, Zhang Lijun, Li Mengdi, Rong Peijun and Qin Yaochen. 2022. Understanding the impact of the built environment on ride-hailing from a spatio-temporal perspective: A fine-scale empirical study from China. Cities, 126, 103706.
[5]
Du Mingyang, Cheng Lin, Li Xuefeng, Liu Qiyang and Yang Jingzong. 2022. Spatial variation of ridesplitting adoption rate in Chicago. Transportation research part a: policy and practice, 164, 13-37.
[6]
Tian Li, Peng Jing, Linchao Li, Dazhi Sun and Wenbo Yan. 2019. Revealing the varying impact of urban built environment on online car-hailing travel in spatio-temporal dimension: An exploratory analysis in Chengdu, China. Sustainability, 11(5), 1336.
[7]
Openshaw, Stan. 1983. The Modifiable Areal Unit Problem Concepts and Techniques in Modern Geography, 38: 1-41.
[8]
He Mingwei, He Chengfeng, Shi Zhuangbin and He Min. 2022. Spatiotemporal heterogeneous effects of socio-demographic and built environment on private car usage: An empirical study of Kunming, China. Journal of Transport Geography, 101, 103353.
[9]
Chen Chao, Feng Tao, Ding Chuan, Yu Bin and Yao Baozhen. 2021. Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model. Journal of Transport Geography, 96, 103172.
[10]
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2015. China 100m Population. Alpha version 2010, 2015 and 2020 estimates of numbers of people per pixel (ppp) and people per hectare (pph), with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.
[11]
Shannon, C. E. 1948. A mathematical theory of communication. The Bell system technical journal, 27(3), 379-423.
[12]
Chen, Yuehong, Wu, Guohao, Ge, Yong, and Xu, Zekun. 2022. Mapping gridded gross domestic product distribution of China using deep learning with multiple geospatial big data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 1791-1802.
[13]
Moran, P. A. 1950. Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23.
[14]
Brunsdon, Chris, Fotheringham, A. Stewart, and Charlton, Martin E. 1996. Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298.
[15]
Fotheringham, A. Stewart, Charlton, Martin E., & Brunsdon, Chris. 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and planning A, 30(11), 1905-1927.
[16]
Zhao Guanwei, Li Zhitao, Shang Yuzhen, and Yang Muzhuang.2022. How Does the Urban Built Environment Affect Online Car-Hailing Ridership Intensity among Different Scales? International Journal of Environmental Research and Public Health.19(9):5325.
Index Terms
- The Impact of the Modifiable Area Unit Problem on Online Ride-hailing Travel Behavior Analysis
Recommendations
Multiscale analysis of the relationship between topography and aboveground biomass in the tropical rainforests of Sulawesi, Indonesia
Spatial EcologyThis article aims to explore spatial and altitudinal non-stationarity in the relationship between aboveground biomass (AGB) of tropical rainforest in Sulawesi (Indonesia) and topography. An autoregressive model through a geographically weighted ...
Scale, population, and spatial analysis: a methodological investigation
GIS '07: Proceedings of the 15th annual ACM international symposium on Advances in geographic information systemsThe analysis of aggregated data introduces challenges in spatial analysis which has been described as the modifiable areal unit problem (MAUP). MAUP consists of two distinct but related research problems: scale effects and zoning effects. Various ...
Study on the Spatial Distribution and Driving Factors of National Forest Villages in the Yangtze River Delta Region Based on Spatial Analysis Algorithms and GeoDetector, GWR Models
EBIMCS '23: Proceedings of the 2023 6th International Conference on E-Business, Information Management and Computer SciencePromoting the construction of national forest villages is of great significance to promoting rural revitalization and high-quality regional development. This paper takes Yangtze River Delta as study area, spatial analysis methods such as nearest neighbor ...
Comments
Information & Contributors
Information
Published In
September 2024
140 pages
ISBN:9798400717512
DOI:10.1145/3698300
Copyright © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 December 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Shandong Provincial Natural Science Foundation
Conference
ICBDT 2024
ICBDT 2024: 2024 7th International Conference on Big Data Technologies
September 20 - 22, 2024
Hubei, Hangzhou, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 7Total Downloads
- Downloads (Last 12 months)7
- Downloads (Last 6 weeks)7
Reflects downloads up to 09 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in