Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

Shi, Zhuangbin; Pan, Wenqin; He, Mingwei; Liu, Yang

doi:10.1007/s11116-023-10432-x

Transportation

2023

DOI: 10.1007/s11116-023-10432-x

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Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

Zhuangbin Shi,

Wenqin Pan,

Mingwei He

et al.

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2024

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Cited by 2 publications

References 58 publications

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Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia

Kendra,

Blažeková,

Vojteková

2024

Transportation

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The demand for public transport by tourists increases significantly in tourist-attractive destinations. This is in addition to regular passengers commuting to school and work. The level of irregular demand is influenced by several factors related to the characteristics of the day of the week, the period of the year, and the current weather. The main goal of the paper is to verify which factors most influence the irregular demand for transport in a tourist-attractive area to ensure operational planning of public passenger transport. Thanks to this, it is possible to ensure sufficient capacity and, at the same time, the efficiency of the operation of public passenger transport. The paper analyzes the main determinants of the irregular demand for regional public rail passenger transport in the High Tatras region of Slovakia. Multiple linear regressions were used to model the number of irregular passengers. The variables representing the day of the week, the attractiveness of the period, and the holiday were found to be the most significant. The variables describing the weather such as maximum daily temperature, precipitation, clouds, and wind had less influence. The obtained mathematical models for forecasting the irregular demand for public passenger transport can help optimize the timetable’s operational setting and the train sets’ size.

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Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia

Kendra,

Blažeková,

Vojteková

2024

Transportation

View full text Add to dashboard Cite

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Incorporating mobile phone data-based travel mobility analysis of metro ridership in aboveground and underground layers

Xing,

Jiang,

Yuan

et al. 2024

era

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<p>Metro transit is the core of urban transportation, and the mobility analysis of metro ridership can contribute to enhance the overall service level of the metro transit. Researchers studying metro ridership are focused on the spatiotemporal distribution characteristics of the ridership in the underground system of metro station by metro smart card data. However, limited by lack of travel mobility chain of ridership integrity, their activity patterns cannot be used to identify the heterogeneity of metro ridership's origin and transfer travel mode. In our research, we applied full spatiotemporal coverage of mobile phone data to identify the complete travel mobility of metro ridership in the perspective of ground and underground transit. First, the mobility of the boarding and alighting stations was extracted and the order of the transfer station was then extracted. Second, relying on the ridership flow identification method, the aboveground origin and destination of the ridership outside the metro system were extracted, and their transferred traffic mode was identified. The empirical results have shown that our proposed framework can accurately analyze the mobility patterns of metro ridership in an aboveground area and underground station.</p>

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Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

Cited by 2 publications

References 58 publications

Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia

Determinants of irregular demand for regional rail passenger services – case study of High Tatras in Slovakia

Incorporating mobile phone data-based travel mobility analysis of metro ridership in aboveground and underground layers

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