Exploring the influencing factors on demand-responsive customized bus ridership using multi-source data

Huo, Yamin; Qiu, Zhongquan; Yi, Xin; Dai, Shifeng; Pan, Renbin; Yu, Wen

doi:10.1109/ictis54573.2021.9798636

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…As discussed in [21], achieving high efficiency may necessitate a sizable fleet, potentially leading to a heavily subsidised taxi-like service and, in extreme cases, may even exacerbate road congestion. Therefore, conducting thorough analyses of regions using multi-source data [40] becomes imperative, considering factors such as peak and off-peak hours, socioeconomic conditions [41], and the availability of public transportation. Subsequently, these services could be used to complement and enhance public transportation rather than entirely replace it or to introduce new on-demand services by identifying regions that could benefit from it [42].…”

Section: Discussionmentioning

confidence: 99%

Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Melo,

Gomes,

Abbasi

et al. 2024

Sustainability

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Transport-on-demand services, such as demand-responsive transport (DRT), involve a flexible transportation service that offers convenient and personalised mobility choices for public transport users. Integrating DRT with mobile data and data analytics enhances understanding of travel patterns and allows the development of improved algorithms to support design-optimised services. This study introduces a replicable framework for DRT that employs an on-demand transport simulator and routing algorithm. This framework is supported by a mobile data set, enabling a more accurate service design grounded on actual demand data. Decision-makers can use this framework to understand traffic patterns better and test a DRT solution before implementing it in the actual world. A case study was conducted in Porto, Portugal, to demonstrate its practicality and proof of concept. Results show that the DRT solution required 135% fewer stops and travelled 81% fewer kilometres than the existing fixed-line service. Findings highlight the potential of this data-driven framework for urban public transportation systems to improve key performance metrics in required buses, energy consumption, travelled distance, and stop frequency, all while maintaining the number of served passengers. Under specific circumstances, embracing this approach can offer a more efficient, user-centric, and environmentally sustainable urban transportation service.

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