首页|Exploring determinants of feeder mode choice behavior using Artificial Neural Network: Evidences from Delhi metro

Exploring determinants of feeder mode choice behavior using Artificial Neural Network: Evidences from Delhi metro

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© 2022 Elsevier B.V.First and last mile connectivity are the most crucial elements of transit system. However, inadequate attention is given to such issues in developing countries like India. The present study aims to analyze feeder mode choice behavior of people accessing Delhi metro. Multinomial logit model and Artificial Neural Network are deployed to analyze the travel behavior. Findings suggest that ANNs are highly efficient in learning and recognizing connections between parameters for best prediction of an outcome. Since, utility of ANNs has been critically limited due to its ‘Black Box’ nature, the study involves the use of Garson's algorithm and Partial Dependence Plots for model interpretation. Findings of the study can be useful for policy makers and transport planners for improving service quality of existing feeder services and, establishing efficient feeder system that promote the use of transit.

Feeder mode choiceGarson's algorithmNeural networksPartial Dependence PlotsTransit accessibilityTransport Policy

Saiyad G.、Rathwa D.、Srivastava M.

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Civil Engineering Department The Maharaja Sayajirao University of Baroda

Transport Planning Division CSIR-Central Road Research Institute

2022

10.1016/j.physa.2022.127363

Physica

Physica

ISSN：0378-4371
年,卷(期)：2022.598
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