Built Environment’s Nonlinear Effects on Mode Shares Around BRT and Rail Stations

Seyed Sajjad Abdollahpour,Ralph Buehler,Huyen T. K. Le,Arefeh Nasri,Steve Hankey

TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT（2024）

Virginia Polytech Inst & State Univ | Ohio State Univ | Transportat Res Board

Cited 8|Views7

Abstract

This study investigates nonlinear associations between built environment (BE) attributes and commuting mode share within rail and bus rapid transit (BRT) catchment areas. Data from approximately 2,790 fixed guideway transit station areas across 34 metropolitan statistical areas in the United States were analyzed using a random forest approach. Results show nonlinear associations of the BE with mode share in transit catchment areas with substantial differences between rail and BRT catchment areas. Rail catchment areas exhibit greater sensitivity to the BE to reduce car dependency as compared to BRT stations. Moreover, polycentricity and population density at regional level are effective at reducing car dependency. We suggest that policymakers consider BE threshold effects and station types when adjusting land-use policies, e.g., in transit-oriented development. Additionally, BRT systems could be a useful alternative to rail in sprawling areas that do not have the compactness needed to support rail.

Translated text

Key words

Land use,Travel behavior,Local and regional planning,Machine learning,National level study

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper