Data-Driven Modeling of Demand-Responsive Transit: Evaluating Sustainability Across Urban, Rural, and Intercity Scenarios

Abstract

Demand-responsive transit (DRT) is an innovative public transportation model that dynamically adjusts routes based on passengers’ specific demands. While existing studies offer insights into routing, scheduling, and network design, they remain fragmented, with limited integration of user behavior, policy relevance, and sustainability. To address these gaps, this paper develops a scenario-based evaluation framework that synthesizes bibliometric evidence, operational conditions, modeling approaches, and evaluated outcomes. Using CiteSpace, we conducted keyword co-occurrence and clustering analysis. Thematic clusters such as “routing and scheduling,” “network design,” “stated preference,” “public transport,” and “demand-responsive transit” were mapped to a three-tier analytical structure. Scenarios integrate economic, environmental, and social dimensions, enabling comparative insights across urban, rural, and intercity scenarios. The scenario-based approach offers two key advantages: (1) it captures heterogeneity across operational environments, ensuring that evaluation frameworks are not overly generalized. Research shows that urban scenarios emphasize scheduling precision, rural pilots face cost-efficiency but enhance resilience, and intercity services depend on multimodal synchronization. (2) It facilitates synthesis by linking technical models with real-world outcomes, enhancing policy relevance. This study contributes to sustainable transport research by providing a coherent, empirically validated, and conceptually integrated framework for evaluating DRT systems.

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