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Enhancing Underutilized Bus Routes with Advance Reservations and Semiflexible Routing
- Md Hishamur Rahman
  Md Hishamur Rahman
  [email protected]
  https://orcid.org/0000-0001-7555-5703
  Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112
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  ,
- Ye Chen
  Ye Chen
  [email protected]
  https://orcid.org/0000-0002-2906-4126
  Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284
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  ,
- Shijie Chen
  Shijie Chen
  [email protected]
  https://orcid.org/0000-0003-4396-1357
  Department of Industrial and Manufacturing Engineering, Florida A&M University–Florida State University College of Engineering, Florida State University, Tallahassee, Florida 32310
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  ,
- Yanshuo Sun
  Yanshuo Sun
  [email protected]
  https://orcid.org/0000-0003-2943-4323
  Department of Industrial and Manufacturing Engineering, Florida A&M University–Florida State University College of Engineering, Florida State University, Tallahassee, Florida 32310
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  ,
- Muhammad Imran Younus Siddiqui
  Muhammad Imran Younus Siddiqui
  [email protected]
  IT Curves, Gaithersburg, Maryland 20879
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  ,
- Matthew Mohebbi
  Matthew Mohebbi
  [email protected]
  IT Curves, Gaithersburg, Maryland 20879
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  ,
- Nikola Marković
  Corresponding Author
  Nikola Marković
  [email protected]
  https://orcid.org/0000-0003-0883-2701
  Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112
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Published Online:May 19, 2025
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Abstract
This paper seeks to improve an underutilized conventional bus route by converting it into a semiflexible transit system where passengers provide advance notice of their intended stops, allowing buses to skip downstream stops without demand by taking ...
A Comparison of Two Models for Rolling Stock Scheduling
- Boris Grimm
  Boris Grimm
  [email protected]
  https://orcid.org/0009-0005-8080-9663
  Zuse Institute Berlin, 14195 Berlin, Germany; and Freie Universität Berlin, 14195 Berlin, Germany
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  ,
- Rowan Hoogervorst
  Corresponding Author
  Rowan Hoogervorst
  [email protected]
  https://orcid.org/0000-0003-0358-9503
  Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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- Ralf Borndörfer
  Ralf Borndörfer
  [email protected]
  https://orcid.org/0000-0001-7223-9174
  Zuse Institute Berlin, 14195 Berlin, Germany; and Freie Universität Berlin, 14195 Berlin, Germany
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Published Online:May 16, 2025
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Abstract
A major step in the planning process of passenger railway operators is the assignment of rolling stock, that is, train units, to the trips of the timetable. A wide variety of mathematical optimization models have been proposed to support this task, which ...
Using Multiple Biased Data Sets to Recover Missing Trips with a Behaviorally Informed Model
- Xiangyang Guan
  Xiangyang Guan
  [email protected]
  Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195
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  ,
- Shuai Huang
  Shuai Huang
  [email protected]
  Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 91195
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  ,
- Cynthia Chen
  Corresponding Author
  Cynthia Chen
  [email protected]
  https://orcid.org/0000-0002-1110-8610
  Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195
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Published Online:May 16, 2025
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Abstract
Trip generation, a critical first step in travel demand forecasting, requires not only estimating trips from the observed sample data, but also calculating the total number of trips in the population, including both the observed trips and the trips missed ...
Learning State-Dependent Policy Parametrizations for Dynamic Technician Routing with Rework
- Jonas Stein
  Corresponding Author
  Jonas Stein
  [email protected]
  https://orcid.org/0009-0001-9844-9310
  Faculty of Economics and Management, Management Science, Otto-von-Guericke-Universität Magdeburg, 39106 Magdeburg, Germany
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- Florentin D. Hildebrandt
  Florentin D. Hildebrandt
  [email protected]
  https://orcid.org/0000-0003-0298-275X
  Faculty of Economics and Management, Management Science, Otto-von-Guericke-Universität Magdeburg, 39106 Magdeburg, Germany
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  ,
- Marlin W. Ulmer
  Marlin W. Ulmer
  [email protected]
  https://orcid.org/0000-0003-2499-6570
  Faculty of Economics and Management, Management Science, Otto-von-Guericke-Universität Magdeburg, 39106 Magdeburg, Germany
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  ,
- Barrett W. Thomas
  Barrett W. Thomas
  [email protected]
  https://orcid.org/0000-0002-6080-6191
  Department of Business Analytics, Tippie College of Business, University of Iowa, Iowa City, Iowa 52242
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Published Online:May 15, 2025
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Abstract
Home repair and installation services require technicians to visit customers and resolve tasks of different complexities. Technicians often have heterogeneous skills. The geographical spread of customers makes achieving only “ideal” matches between ...
Simultaneous Multimodal Demand Imputation and Forecasting via Graph-Guided Generative and Adversarial Network
- Can Li
  Can Li
  [email protected]
  The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200070, China
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  ,
- Wei Liu
  Corresponding Author
  Wei Liu
  [email protected]
  https://orcid.org/0000-0001-8638-3695
  Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
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  ,
- Wanjing Ma
  Wanjing Ma
  [email protected]
  The Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 200070, China
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  ,
- Hai Yang
  Hai Yang
  [email protected]
  https://orcid.org/0000-0001-5210-8468
  Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong, China
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Published Online:May 13, 2025
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Abstract
Accurate prediction of multimodal transport demand is essential for effective transport planning and management and enables service optimization based on historical and (predicted) future demand. However, dealing with missing data remains a common ...

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