IDEAS home Printed from https://ideas.repec.org/r/kap/transp/v32y2005i4p369-397.html
   My bibliography  Save this item

Generating complete all-day activity plans with genetic algorithms

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Francesco Ciari & Milos Balac & Michael Balmer, 2015. "Modelling the effect of different pricing schemes on free-floating carsharing travel demand: a test case for Zurich, Switzerland," Transportation, Springer, vol. 42(3), pages 413-433, May.
  2. Thibaut Dubernet & Kay Axhausen, 2015. "Implementing a household joint activity-travel multi- agent simulation tool: first results," Transportation, Springer, vol. 42(5), pages 753-769, September.
  3. Ihab Kaddoura & Benjamin Kickhöfer & Andreas Neumann & Alejandro Tirachini, 2015. "Agent-based optimisation of public transport supply and pricing: impacts of activity scheduling decisions and simulation randomness," Transportation, Springer, vol. 42(6), pages 1039-1061, November.
  4. Witsarut Achariyaviriya & Yoshitsugu Hayashi & Hiroyuki Takeshita & Masanobu Kii & Varameth Vichiensan & Thanaruk Theeramunkong, 2021. "Can Space–Time Shifting of Activities and Travels Mitigate Hyper-Congestion in an Emerging Megacity, Bangkok? Effects on Quality of Life and CO 2 Emission," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
  5. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 2021. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 48(3), pages 1481-1502, June.
  6. Zannat, Khatun E. & Laudan, Janek & Choudhury, Charisma F. & Hess, Stephane, 2024. "Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh," Transport Policy, Elsevier, vol. 148(C), pages 92-106.
  7. Kaddoura, Ihab & Nagel, Kai, 2019. "Congestion pricing in a real-world oriented agent-based simulation context," Research in Transportation Economics, Elsevier, vol. 74(C), pages 40-51.
  8. Gunnar Flötteröd & Yu Chen & Kai Nagel, 2012. "Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation," Networks and Spatial Economics, Springer, vol. 12(4), pages 481-502, December.
  9. Badiola, Nicolás & Raveau, Sebastián & Galilea, Patricia, 2019. "Modelling preferences towards activities and their effect on departure time choices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 39-51.
  10. Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2020. "A Method for the Optimization of Daily Activity Chains Including Electric Vehicles," Energies, MDPI, vol. 13(4), pages 1-21, February.
  11. Xu, Zhiheng & Kang, Jee Eun & Chen, Roger, 2018. "A random utility based estimation framework for the household activity pattern problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PB), pages 321-337.
  12. Huang, Arthur & Levinson, David, 2017. "A model of two-destination choice in trip chains with GPS data," Journal of choice modelling, Elsevier, vol. 24(C), pages 51-62.
  13. Konrad Meister & Martin Frick & Kay Axhausen, 2005. "A GA-based household scheduler," Transportation, Springer, vol. 32(5), pages 473-494, September.
  14. Ihab Kaddoura & Kai Nagel, 2018. "Simultaneous internalization of traffic congestion and noise exposure costs," Transportation, Springer, vol. 45(5), pages 1579-1600, September.
  15. Pougala, Janody & Hillel, Tim & Bierlaire, Michel, 2022. "Capturing trade-offs between daily scheduling choices," Journal of choice modelling, Elsevier, vol. 43(C).
  16. Leng, Nuannuan & Corman, Francesco, 2020. "The role of information availability to passengers in public transport disruptions: An agent-based simulation approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 214-236.
  17. Amin Mazloumian & Nikolas Geroliminis & Dirk Helbing, "undated". "The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity," Working Papers CCSS-09-009, ETH Zurich, Chair of Systems Design.
  18. La Paix Puello, Lissy & Chowdhury, Saidul & Geurs, Karst, 2019. "Using panel data for modelling duration dynamics of outdoor leisure activities," Journal of choice modelling, Elsevier, vol. 31(C), pages 141-155.
  19. Schwarz, Gregor & Bichler, Martin, 2022. "How to trade thirty thousand products: A wholesale market design for road capacity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 167-185.
  20. Domokos Esztergár-Kiss, 2020. "Trip Chaining Model with Classification and Optimization Parameters," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
  21. Theo Arentze & Pauline van den Berg & Harry Timmermans, 2012. "Modeling Social Networks in Geographic Space: Approach and Empirical Application," Environment and Planning A, , vol. 44(5), pages 1101-1120, May.
  22. Alejandro Rojano-Padrón & Marc Olivier Metais & Francisco J. Ramos-Real & Yannick Perez, 2023. "Tenerife’s Infrastructure Plan for Electromobility: A MATSim Evaluation," Energies, MDPI, vol. 16(3), pages 1-24, January.
  23. Jamil Hamadneh & Domokos Esztergár-Kiss, 2021. "The Influence of Introducing Autonomous Vehicles on Conventional Transport Modes and Travel Time," Energies, MDPI, vol. 14(14), pages 1-28, July.
  24. Gunnar Flötteröd & Michel Bierlaire & Kai Nagel, 2011. "Bayesian Demand Calibration for Dynamic Traffic Simulations," Transportation Science, INFORMS, vol. 45(4), pages 541-561, November.
  25. Nagel Kai & Grether Dominik & Beuck Ulrike & Chen Yu & Rieser Marcel & Axhausen Kay W., 2008. "Multi-Agent Transport Simulations and Economic Evaluation," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 228(2-3), pages 173-194, April.
  26. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Cools, Mario, 2018. "Investigating the impact of river floods on travel demand based on an agent-based modeling approach: The case of Liège, Belgium," Transport Policy, Elsevier, vol. 67(C), pages 102-110.
  27. Amit Agarwal & Benjamin Kickhöfer, 2018. "The correlation of externalities in marginal cost pricing: lessons learned from a real-world case study," Transportation, Springer, vol. 45(3), pages 849-873, May.
  28. Shanjiang Zhu & David Levinson & Lei Zhang, 2007. "An Agent-based Route Choice Model," Working Papers 000089, University of Minnesota: Nexus Research Group.
  29. Reza Vosooghi & Joseph Kamel & Jakob Puchinger & Vincent Leblond & Marija Jankovic, 2019. "Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use," Transportation, Springer, vol. 46(6), pages 1997-2015, December.
  30. Lucas javaudin & André de Palma, 2024. "METROPOLIS2: Bridging Theory and Simulation in Agent-Based Transport Modeling," THEMA Working Papers 2024-03, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  31. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2021. "Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas," Research in Transportation Economics, Elsevier, vol. 90(C).
  32. Usman Ahmed & Ana Tsui Moreno & Rolf Moeckel, 0. "Microscopic activity sequence generation: a multiple correspondence analysis to explain travel behavior based on socio-demographic person attributes," Transportation, Springer, vol. 0, pages 1-22.
  33. Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2023. "Heuristic time-dependent personal scheduling problem with electric vehicles," Transportation, Springer, vol. 50(5), pages 2009-2048, October.
  34. Sylvie Occelli & Luca Staricco, 2009. "Learning about Urban Mobility: Experiences with a Multiagent-System Model," Environment and Planning B, , vol. 36(5), pages 772-786, October.
  35. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.
  36. Zahra Navidi & Nicole Ronald & Stephan Winter, 2018. "Comparison between ad-hoc demand responsive and conventional transit: a simulation study," Public Transport, Springer, vol. 10(1), pages 147-167, May.
  37. Hackney, Jeremy & Marchal, Fabrice, 2011. "A coupled multi-agent microsimulation of social interactions and transportation behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 296-309, May.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.