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Transport Volume 29, 2014 - Issue 3: Special Issue on Travel Demand Management
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Original papers

Evaluating staggered working hours using a multi-agent-based Q-learning model

Min YangSchool of Transportation, Southeast University, China
,
Dounan TangSchool of Transportation, Southeast University, China
,
Haoyang DingSchool of Transportation, Southeast University, ChinaCorrespondence[email protected]
,
Wei WangSchool of Transportation, Southeast University, China
,
Tianming LuoSchool of Transportation, Southeast University, China
&
Sida LuoSchool of Transportation, Southeast University, China
Pages 296-306 | Received 14 Oct 2013, Accepted 06 Apr 2014, Published online: 22 Sep 2014

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Read on this site (4)

Yudong Guo, Fei Yang, Siyuan Xie & Zhenxing Yao. (2023) Activity-based model based on long short-term memory network and mobile phone signalling data. Transportmetrica A: Transport Science 0:0, pages 1-21.
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Mohammad Hesam Hafezi, Naznin Sultana Daisy, Hugh Millward & Lei Liu. (2022) Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model. Transportmetrica A: Transport Science 18:2, pages 248-280.
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Anil NP Koushik, M. Manoj & N. Nezamuddin. (2020) Machine learning applications in activity-travel behaviour research: a review. Transport Reviews 40:3, pages 288-311.
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Wilfredo F. Yushimito, José Holguín-Veras & Tomás Gellona. (2018) Firm’s efficiency and the feasibility of incentives for flextime adoption: a preliminary analysis of Chilean employer’s response. Transportation Letters 10:4, pages 202-214.
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Articles from other publishers (1)

Wei Zhang, Andrea Valencia & Ni-Bin Chang. (2023) Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review. IEEE Transactions on Neural Networks and Learning Systems 34:5, pages 2170-2190.
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