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Ridership estimation of a new LRT system: Direct demand model approach

Author

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  • Kepaptsoglou, Konstantinos
  • Stathopoulos, Antony
  • Karlaftis, Matthew G.

Abstract

The successful introduction of LRT systems is inevitably related to the realistic estimation of their ridership; this is particularly true for cases of no prior experience in the use of such modes in the part of the traveling public. This paper presents a practical approach for developing a direct demand model, for the case of a planned LRT system in Cyprus connecting the three major cities of Nicosia, Larnaca and Limassol. The proposed approach is based on existing traffic demand data and limited roadside surveys. Results indicate that the introduction of the proposed LRT would attract a moderate number of 23,000 passengers daily and shift a small percentage of 3.5% of traffic to the system. It was also found that approximately 33% of these trips correspond to the urban section of the network, while about 62% of the estimated ridership will use the part of the system connecting Nicosia and Larnaca.

Suggested Citation

  • Kepaptsoglou, Konstantinos & Stathopoulos, Antony & Karlaftis, Matthew G., 2017. "Ridership estimation of a new LRT system: Direct demand model approach," Journal of Transport Geography, Elsevier, vol. 58(C), pages 146-156.
    • Handle: RePEc:eee:jotrge:v:58:y:2017:i:c:p:146-156
    DOI: 10.1016/j.jtrangeo.2016.12.004
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    References listed on IDEAS

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    Cited by:

    1. Ren, Peng & Liu, Shuang & Qin, Beining & Chen, Yue & Xu, Qi & He, Peng, 2025. "A novel multimodal deep learning-based direct ridership model for planning-oriented demand prediction in urban rail transit networks," Journal of Transport Geography, Elsevier, vol. 129(C).
    2. Yan, Xiang & Liu, Xinyu & Zhao, Xilei, 2020. "Using machine learning for direct demand modeling of ridesourcing services in Chicago," Journal of Transport Geography, Elsevier, vol. 83(C).
    3. Chayan, Md Mahmudul Huque & Cirillo, Cinzia, 2024. "Predicting transit ridership using an agent-based modeling approach," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    4. Iseki, Hiroyuki & Liu, Chao & Knaap, Gerrit, 2018. "The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 635-649.
    5. Jurkowski Wojciech & Smolarski Mateusz, 2021. "The influence of transport offer on passenger traffic in the railway transport system in a post-socialist country: case study of Poland," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 53(53), pages 33-42, September.
    6. Tu, Wei & Cao, Rui & Yue, Yang & Zhou, Baoding & Li, Qiuping & Li, Qingquan, 2018. "Spatial variations in urban public ridership derived from GPS trajectories and smart card data," Journal of Transport Geography, Elsevier, vol. 69(C), pages 45-57.
    7. Ding, Chuan & Cao, Xinyu & Liu, Chao, 2019. "How does the station-area built environment influence Metrorail ridership? Using gradient boosting decision trees to identify non-linear thresholds," Journal of Transport Geography, Elsevier, vol. 77(C), pages 70-78.
    8. Yadi Zhu & Feng Chen & Zijia Wang & Jin Deng, 2019. "Spatio-temporal analysis of rail station ridership determinants in the built environment," Transportation, Springer, vol. 46(6), pages 2269-2289, December.

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