Modeling duration choice in space-time multi-state supernetworks for individual activity-travel scheduling

Transportation Research Part C: Emerging Technologies

Volumn 69, Issue , 2016, Pages 16-35

  Liao, Feixiong ^a  

^a EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Activity travel scheduling; Duration choice; Multi state supernetwork; Space time

Indexed keywords

ECONOMIC AND SOCIAL EFFECTS;

ACTIVITY-TRAVEL PATTERNS; ACTIVITY-TRAVEL SCHEDULING; DURATION CHOICE; FORWARD RECURSIVE FORMULATION; RUN TIME COMPLEXITY; SPACE TIME; SUPER-NETWORK; SYSTEMATIC ASSESSMENT;

SCHEDULING;

ACTIVITY PATTERN; MODELING; NETWORK ANALYSIS; OPTIMIZATION; TRAVEL BEHAVIOR;

EID: 84969759664     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2016.05.011     Document Type: Article

References (36)

1. Arentze T.A., Molin E.J.E. Travelers' preferences in multimodal networks: design and results of a comprehensive series of choice experiments. Transp. Res. Part A 2013, 58:15-28.
2. Arentze T.A., Timmermans H.J.P. A multi-state supernetwork approach to modelling multi-activity multi-modal trip chains. Int. J. Geograph. Inf. Sci. 2004, 18(7):631-651.
3. Arentze T.A., Ettema D., Timmermans H.J.P. Incorporating time and income constraints in dynamic agent-based models of activity generation and time use: approach and illustration. Transp. Res. Part C 2010, 18(1):171-183.
4. Auld J., Hope M., Ley H., Sokolov V., Xu B., Zhang K. POLARIS: agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations. Transp. Res. Part C 2016, 61:101-116.
5. Balmer M., Axhausen K., Nagel K. Agent-based demand modelling framework for large scale micro-simulations. Transp. Res. Rec. 2006, 1985:125-134.
6. Bast H., Delling D., Goldberg A., Müller-Hannemann M., Pajor T., Sanders P., Wagner D., Werneck R. Route Planning in Transportation Networks. Technical Report MSR-TR-2014-4 2014, Microsoft Research.
7. Bowman J.L., Ben-Akiva M. Activity-based disaggregate travel demand model system with activity schedules. Transp. Res. Part A 2001, 35(1):1-28.
8. Chabini I. Discrete dynamic shortest path problems in transportation applications: complexity and algorithms with optimal run time. Transp. Res. Rec. 1999, 1645:170-175.
9. Chen Z., Xia J., Irawan B., Caulfied C. Development of location-based services for recommending departure stations to park and ride users. Transp. Res. Part C 2015, 48:256-268.
10. Chow J.Y.J., Djavadian S. Activity-based market equilibrium for capacitated multimodal transport systems. Transp. Res. Part C 2015, 59:2-18.
11. Chow J.Y.J., Nurumbetova A.E. A multi-day activity-based inventory routing model with space-time-needs constraints. Transportmetrica A 2015, 11(3):243-269.
12. Dean B. Algorithms for minimum-cost paths in time-dependent networks with waiting policies. Networks 2004, 44(1):41-46.
13. Dehne F., Omran M.T., Sack J. Shortest paths in time-dependent FIFO networks. Algorithmica 2012, 62:416-435.
14. Ettema D.F., Bastin F., Polak J., Ashiru O. Modelling the joint choice of activity timing and duration. Transp. Res. Part A 2007, 41:827-841.
15. Feil M., Balmer M., Axhausen K.W. New approaches to generating comprehensive all-day activity-travel schedules. Proceedings of 89th Annual Meeting of the Transportation Research Board, Washington, DC 2010.
16. Fu X., Lam W.H.K. A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty. Transportation 2014, 41(1):37-55.
17. George B. Spatio-temporal networks: modeling and algorithms 2008, Ph.D. Dissertation, University of Minnesota, Minneapolis, MN, USA.
18. He F., Yin Y., Zhou J. Deploying public charging stations for electric vehicles on urban road networks. Transp. Res. Part C 2015, 60:227-240.
19. Kang J.E., Chow J.Y.J., Recker W.W. On activity-based network design problems. Transp. Res. Part B 2013, 57:398-418.
20. Kenyon S. The impacts of internet use upon activity participation and travel: results from a longitudinal diary-based panel study. Transp. Res. Part C 2010, 18:21-35.
21. Lam W.H.K., Yin Y. An activity-based time-dependent traffic assignment model. Transp. Res. Part B 2001, 35:549-574.
22. Lathia N., Ahmed S., Capra L. Measuring the impact of opening the London shared bicycle scheme to casual users. Transp. Res. Part C 2012, 22:88-102.
23. Li Z.C., Lam W.H.K., Wong S.C., Sumalee A. An activity-based approach for scheduling multimodal transit services. Transportation 2010, 37(5):751-774.
24. Liao F., Arentze T.A., Timmermans H.J.P. Supernetwork approach for multi-modal and multi-activity travel planning. Transp. Res. Rec.: J. Transp. Res. Board 2010, 2175:38-46.
25. Liao F., Arentze T.A., Timmermans H.J.P. Constructing personalized transportation network in multi-state supernetworks: a heuristic approach. Int. J. Geograph. Inf. Sci. 2011, 25(11):1885-1903.
26. Liao F., Arentze T.A., Timmermans H.J.P. Incorporating space-time constraints and activity-travel time profiles in a multi-state supernetwork approach to individual activity-travel scheduling. Transp. Res. Part B 2013, 55:41-58.
27. Liao F., Arentze T.A., Molin E.J.E., Bothy W., Timmermans H.J.P. Effects of land-use transport scenarios on travel patterns: a multi-state supernetwork application. Transportation 2016, 10.1007/s11116-015-9616-z.
28. Liu P., Liao F., Huang H.J., Timmermans H.J.P. Dynamic activity-travel assignment in multi-state supernetworks. Transp. Res. Part B 2015, 81(3):656-671.
29. Lozano A., Storchi G. Shortest viable hyperpath in multimodal networks. Transp. Res. Part B 2002, 36:853-874.
30. Nagurney A. Supernetworks: paradoxes, challenges, and new opportunities. Transforming Enterprise 2004, 229-254. MIT Press, Cambridge, Massachusetts. W. Dutton, B. Kahin, R. O'Callaghan, A. Wyckoff (Eds.).
31. Pyrga E., Schulz F., Wagner D., Zaroliagis C. Efficient models for timetable information in public transportation systems. ACM J. Experim. Algor. 2008, 12(2):1-39.
32. Ramadurai G., Ukkusuri S. Dynamic user equilibrium model for combined activity-travel choices using activity-travel supernetwork representation. Netw. Spatial Econ. 2010, 10(2):273-292.
33. Recker W.W. The household activity pattern problem: general formulation and solution. Transp. Res. Part B 1995, 29(1):61-77.
34. Sheffi Y. Urban Transportation Networks Equilibrium Analysis with Mathematical Programming Methods 1985, Prentice-Hall, Englewood Cliffs, NJ.
35. Tong L., Zhou X., Miller H.J. Transportation network design for maximizing space-time accessibility. Transp. Res. Part B 2015, 81:555-576.
36. Yuan D. Incorporating individual activity arrival and duration preferences within a time-of-day travel disutility formulation of the household activity pattern problem (HAPP) 2014, Dissertation, University of California, Irvine, USA.