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Research Article

Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model

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Pages 248-280 | Received 03 Feb 2020, Accepted 19 Apr 2021, Published online: 10 May 2021

References

  • Allahviranloo, Mahdieh, and Will Recker. 2013. “Daily Activity Pattern Recognition by Using Support Vector Machines with Multiple Classes.” Transportation Research Part B: Methodological 58: 16–43. doi:https://doi.org/10.1016/j.trb.2013.09.008.
  • Allahviranloo, Mahdieh, Robert Regue, and Will Recker. 2017. “Modeling the Activity Profiles of a Population.” Transportmetrica B: Transport Dynamics 5 (4): 426–449. doi:https://doi.org/10.1080/21680566.2016.1241960.
  • Arentze, Theo, and Harry Timmermans. 2000. “ALBATROSS: A Learning Based Transportation Oriented Simulation System.” European Institute of Retailing and Services Studies (EIRASS), Technische Universiteit Eindhoven, Netherlands.
  • Auld, Joshua, and Abolfazl Mohammadian. 2009. “Framework for the Development of the Agent-Based Dynamic Activity Planning and Travel Scheduling (ADAPTS) Model.” Transportation Letters 1 (3): 245–255.
  • Auld, Joshua, and Abolfazl Mohammadian. 2010. “Efficient Methodology for Generating Synthetic Populations with Multiple Control Levels.” Transportation Research Record: Journal of the Transportation Research Board 2175 (1): 138–147.
  • Auld, Joshua, Abolfazl Mohammadian, and Sean T. Doherty. 2009. “Modeling Activity Conflict Resolution Strategies Using Scheduling Process Data.” Transportation Research Part A: Policy and Practice 43 (4): 386–400. doi:https://doi.org/10.1016/j.tra.2008.11.006.
  • Auld, Joshua, Taha Hossein Rashidi, Mahmoud Javanmardi, and Abolfazl Mohammadian. 2011. “Dynamic Activity Generation Model Using Competing Hazard Formulation.” Transportation Research Record: Journal of the Transportation Research Board 2254 (1): 28–35. doi:https://doi.org/10.3141/2254-04.
  • Bhat, Chandra R., Jessica Y. Guo, Sivaramakrishnan Srinivasan, and Aruna Sivakumar. 2004. “Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns.” Transportation Research Record: Journal of the Transportation Research Board 1894 (1): 57–66. doi:https://doi.org/10.3141/1894-07.
  • Bhat, Chandra R., and Frank S. Koppelman. 1999. “Activity-Based Modeling of Travel Demand.” Handbook of Transportation Science, edited by Randolph W. Hall, 35–61. Boston, MA: Springer US.
  • Bhat, Chandra R., Aupal Mondal, Katherine E. Asmussen, and Aarti C. Bhat. 2020. “A Multiple Discrete Extreme Value Choice Model with Grouped Consumption Data and Unobserved Budgets.” Transportation Research Part B: Methodological 141: 196–222. doi:https://doi.org/10.1016/j.trb.2020.09.008.
  • Bishop, C. 2007. Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn. 2006. corr. 2nd printing edn. New York: Springer.
  • Bowman, J. L., and M. E. Ben-Akiva. 2001. “Activity-Based Disaggregate Travel Demand Model System with Activity Schedules.” Transportation Research Part A: Policy and Practice 35 (1): 1–28. doi:https://doi.org/10.1016/S0965-8564(99)00043-9.
  • Boyce, D. E., and H. C. W. L. Williams. 2015. “Forecasting Urban Travel: Past, Present and Future.” Edward Elgar Pub. Limited.
  • Chen, Na, and Gulsah Akar. 2016. “Effects of Neighborhood Types and Socio-Demographics on Activity Space.” Journal of Transport Geography 54: 112–121. doi:https://doi.org/10.1016/j.jtrangeo.2016.05.017.
  • Chung, Jin Hyuk, and Konstadinos G. Goulias. 1997. “Travel Demand Forecasting Using Microsimulation: Initial Results from Case Study in Pennsylvania.” Transportation Research Record: Journal of the Transportation Research Board 1607 (1): 24–30. doi:https://doi.org/10.3141/1607-04.
  • Cipriani, E., U. Crisalli, A. Gemma, and L. Mannini. 2020. “Integration Between Activity-Based Demand Models and Multimodal Assignment: Some Empirical Evidences.” Case Studies on Transport Policy 8 (3): 1019–1029. doi:https://doi.org/10.1016/j.cstp.2020.04.004.
  • Clarke, M. I. 1986. “Activity Modeling: A Research Tool or a Practical Planning Technique.” Behavioral Research for Transport Policy, 3–15. Ultrecht: VNU Science Press.
  • Daisy, Naznin Sultana. 2018. “Microsimulation of Activity Participation, Tour Complexity, and Mode Choice within an Activity-Based Travel Demand Model System.” (Ph.D. dissertation). Department of Civil and Resource Engineering. Dalhousie University.
  • Daisy, Naznin Sultana, Lei Liu, and Hugh Millward. 2018. “Trip Chaining Propensity and Tour Mode Choice of Out-of-Home Workers: Evidence from a Mid-Sized Canadian City.” Transportation 47: 763–792. doi:https://doi.org/10.1007/s11116-018-9915-2.
  • Daisy, Naznin Sultana, Hugh Millward, and Lei Liu. 2018a. “Individuals’ Activity-Travel Behavior in Travel Demand Models: A Review of Recent Progress.” 18th COTA International Conference of Transportation Professionals, 78–90. American Society of Civil Engineers (ASCE). doi:https://doi.org/10.1061/9780784481523.260.
  • Daisy, Naznin Sultana, Hugh Millward, and Lei Liu. 2018b. “Trip Chaining and Tour Mode Choice of Non-Workers Grouped by Daily Activity Patterns.” Journal of Transport Geography 69: 150–162. doi:https://doi.org/10.1016/j.jtrangeo.2018.04.016.
  • Daisy, Naznin Sultana, Hugh Millward, and Lei Liu. 2020. “Modeling Activity-Travel Behavior of Non-Workers Grouped by Their Daily Activity Patterns.” Mapping the Travel Behavior Genome, edited by Konstadinos G. Goulias and Adam W. Davis, 339–370. Santa Barbara, CA: Elsevier. doi:https://doi.org/10.1016/B978-0-12-817340-4.00018-8.
  • Daisy, Naznin Sultana, Hafezi Mohammad Hesam, Liu Lei, and Millward Hugh. 2018. “Understanding and Modeling the Activity-Travel Behavior of University Commuters at a Large Canadian University.” Journal of Urban Planning and Development 144 (2). doi:https://doi.org/10.1061/(ASCE)UP.1943-5444.0000442.
  • de Lima, Viegas, Mazen Danaf Isabel, Arun Akkinepally, Carlos Lima De Azevedo, and Moshe Ben Akiva. 2018. “Modeling Framework and Implementation of Activity and Agent-Based Simulation: An Application to the Greater Boston Area.” Transportation Research Record: Journal of the Transportation Research Board 2672 (49): 146–157. doi:https://doi.org/10.1177/0361198118798970.
  • Drchal, Jan, Michal Certicky, and Michal Jakob. 2019. “Data-Driven Activity Scheduler for Agent-Based Mobility Models.” Transportation Research Part C: Emerging Technologies 98: 370–390. doi:https://doi.org/10.1016/j.trc.2018.12.002.
  • Ellegard, Kajsa. 1999. “A Time-Geographical Approach to the Study of Everyday Life of Individuals: A Challenge of Complexity.” GeoJournal 48 (3): 167–175. doi:https://doi.org/10.1023/a:1007071407502.
  • Ettema, Dick, Aloys Borgers, and Harry Timmermans. 1993. “Simulation Model of Activity Scheduling Behavior.” Transportation Research Record: Journal of the Transportation Research Board 1413: 1–11.
  • Ettema, Dick, Aloys Borgers, and Harry Timmermans. 1996. “SMASH (Simulation Model of Activity Scheduling Heuristics): Some Simulations.” Transportation Research Record: Journal of the Transportation Research Board 1551: 88–94.
  • Fu, Xiao, William H. K. Lam, and Yiliang Xiong. 2016. “Modelling Intra-Household Interactions in Household’s Activity-Travel Scheduling Behaviour.” Transportmetrica A: Transport Science 12 (7): 612–628. doi:https://doi.org/10.1080/23249935.2016.1189710.
  • Garling, Tommy, Mei Po Kwan, and Reginald G. Golledge. 1994. “Computational-Process Modeling of Household Activity Scheduling.” Transportation Research Part B: Methodological 28 (5): 355–364. doi:https://doi.org/10.1016/0191-2615(94)90034-5.
  • Golshani, Nima, Ramin Shabanpour, Joshua Auld, and Abolfazl Mohammadian. 2018. “Activity Start Time and Duration: Incorporating Regret Theory Into Joint Discrete–Continuous Models.” Transportmetrica A: Transport Science 14 (9): 809–827. doi:https://doi.org/10.1080/23249935.2018.1440261.
  • Golshani, Nima, Ramin Shabanpour, Seyed Mehdi Mahmoudifard, Sybil Derrible, and Abolfazl Mohammadian. 2018. “Modeling Travel Mode and Timing Decisions: Comparison of Artificial Neural Networks and Copula-Based Joint Model.” Travel Behaviour and Society 10: 21–32. doi:https://doi.org/10.1016/j.tbs.2017.09.003.
  • Goran, J. 2001. “Activity Based Travel Demand Modeling: A Literature Study.” Technical report, Danmarks Transport-Forskning.
  • Goulias, Konstadinos G. 1999. “Longitudinal Analysis of Activity and Travel Pattern Dynamics Using Generalized Mixed Markov Latent Class Models.” Transportation Research Part B: Methodological 33 (8): 535–558. doi:https://doi.org/10.1016/S0191-2615(99)00005-3.
  • Goulias, Konstadinos G, and Tae Gyu Kim. 2005. “An Analysis of Activity Type Classification and Issues Related to the with Whom and for Whom Questions of an Activity Diary.” Progress in Activity-Based Analysis, 309–334.
  • Goulias, Konstadinos G., and Ryuichi Kitamura. 1997. “A Dynamic Microsimulation Model System for Regional Travel Demand Forecasting.” In Panels for Transportation Planning: Methods and Applications, edited by Thomas F. Golob, Ryuichi Kitamura, and Lyn Long, 321–348. Boston, MA: Springer US.
  • Habib, Nurul Khandker. 2018. “A Comprehensive Utility-Based Systemp of Activity-Travel Scheduling Options Modelling (CUSTOM) for Worker's Daily Activity Scheduling Processes.” Transportmetrica A: Transport Science 14 (4): 292–315. doi:https://doi.org/10.1080/23249935.2017.1385656.
  • Hafezi, Mohammad Hesam. 2018. “Modeling Activity Selection and Scheduling Behavior of Population Cohorts within an Activity-Based Travel Demand Model System.” (Ph.D. dissertation). Department of Civil and Resource Engineering. Dalhousie University.
  • Hafezi, Mohammad Hesam, Naznin Sultana Daisy, Lei Liu, and Hugh Millward. 2019a. “Modelling Transport-Related Pollution Emissions for the Synthetic Baseline Population of a Large Canadian University.” International Journal of Urban Sciences 23 (4): 1–15. doi:https://doi.org/10.1080/12265934.2019.1571432.
  • Hafezi, Mohammad Hesam, Naznin Sultana Daisy, Hugh Millward, and Lei Liu. 2021. “Ensemble Learning Activity Scheduler for Activity Based Travel Demand Models.” Transportation Research Part C: Emerging Technologies 123: 102972. doi:https://doi.org/10.1016/j.trc.2021.102972.
  • Hafezi, Mohammad Hesam, Lei Liu, and Hugh Millward. 2017a. “Identification of Representative Patterns of Time Use Activity Through Fuzzy C-Means Clustering.” Transportation Research Record: Journal of the Transportation Research Board 2668: 38–50. doi:https://doi.org/10.3141/2668-05.
  • Hafezi, Mohammad Hesam, Lei Liu, and Hugh Millward. 2017b. “A Time-Use Activity-Pattern Recognition Model for Aactivity-Based Travel Demand Modeling.” Transportation 46: 1–26. doi:https://doi.org/10.1007/s11116-017-9840-9.
  • Hafezi, Mohammad Hesam, Lei Liu, and Hugh Millward. 2018a. "Learning Daily Activity Sequences of Population Groups Using Random Forest Theory.” Transportation Research Record: Journal of the Transportation Research Board 2672 (47): 194–207. doi:10.1177%2F0361198118773197.
  • Hafezi, Mohammad Hesam, Lei Liu, and Hugh Millward. 2018b. “Modeling Activity Scheduling Behavior of Travelers for Activity-Based Travel Demand Model.” Presented at the 97th Annual Meeting of the Transportation Research Board, Washington, DC.
  • Hafezi, Mohammad Hesam, Hugh Millward, and Lei Liu. 2018. “Activity-Based Travel Demand Modeling: Progress and Possibilities.” International Conference on Transportation and Development 2018: Planning, Sustainability, and Infrastructure Systems, 138–47. American Society of Civil Engineers (ASCE). doi:https://doi.org/10.1061/9780784481561.014.
  • Hafezi, Mohammad Hesam, Daisy Naznin Sultana, Millward Hugh, and Liu Lei. 2019b. “Machine Learning and Daily Activity Patterns .” Presented at the 99th Annual Meeting of the Transportation Research Board. Washington, DC.
  • Hafezi, Mohammad Hesam, Daisy Naznin Sultana, Liu Lei, and Millward Hugh. 2018. “Daily Activity and Travel Sequences of Students, Faculty and Staff at a Large Canadian university.” Transportation Planning and Technology 41 (5): 536–556. doi:https://doi.org/10.1080/03081060.2018.1469286.
  • Hagerstrand, Torsten. 1970. “What About People in Regional Science?” Papers in Regional Science 24 (1): 7–24. doi:https://doi.org/10.1111/j.1435-5597.1989.tb01166.x.
  • Hilgert, Tim, Michael Heilig, Martin Kagerbauer, and Peter Vortisch. 2017. “Modeling Week Activity Schedules for Travel Demand Models.” Transportation Research Record: Journal of the Transportation Research Board 2666 (1): 69–77. doi:https://doi.org/10.3141/2666-08.
  • Jang, Yunemi, Yi Chang Chiu, and Hong Zheng. 2013. “Modeling Within-Day Activity Rescheduling Decisions Under Time-Varying Network Conditions.” In Advances in Dynamic Network Modeling in Complex Transportation Systems, edited by V. Satish Ukkusuri, and Kaan Ozbay, 225–244. New York, NY: Springer New York.
  • Jovicic, Goran. 2001. “Activity Based Travel Demand Modeling: A Literature Study.” Danmarks TransportForskning.
  • Kim, Seheon, Soora Rasouli, Harry Timmermans, and Dujuan Yang. 2018. “Estimating Panel Effects in Probabilistic Representations of Dynamic Decision Trees Using Bayesian Generalized Linear Mixture Models.” Transportation Research Part B: Methodological 111: 168–184. doi:https://doi.org/10.1016/j.trb.2018.03.010.
  • Kitamura, Ryuichi. 1988. “An Evaluation of Activity-Based Travel Analysis.” Transportation 15 (1): 9–34. doi:https://doi.org/10.1007/bf00167973.
  • Kitamura, Ryuichi, Cynthia Chen, and Ram Pendyala. 1997. “Generation of Synthetic Daily Activity-Travel Patterns.” Transportation Research Record: Journal of the Transportation Research Board 1607: 154–163. https://doi.org/10.3141/1607-21.
  • Kitamura, Ryuichi, Eric I. Pas, Clarisse V. Lula, T. Keith Lawton, and Paul E. Benson. 1996. “The Sequenced Activity Mobility Simulator (SAMS): An Integrated Approach to Modeling Transportation, Land Use and Air Quality.” Transportation 23 (3): 267–291. doi:https://doi.org/10.1007/bf00165705.
  • Langerudi, Mehran Fasihozaman, Mahmoud Javanmardi, Ramin Shabanpour, Taha Hossein Rashidi, and Abolfazl Mohammadian. 2017. “Incorporating In-Home Activities in ADAPTS Activity-Based Framework: A Sequential Conditional Probability Approach.” Journal of Transport Geography 61: 48–60. doi:https://doi.org/10.1016/j.jtrangeo.2017.04.010.
  • Li, Siyu, and Der Horng Lee. 2017. “Learning Daily Activity Patterns with Probabilistic Grammars.” Transportation 44 (1): 49–68. doi:https://doi.org/10.1007/s11116-015-9622-1.
  • Li, Qing, Feixiong Liao, Harry J. P. Timmermans, and Jing Zhou. 2016. “A User Equilibrium Model for Combined Activity–Travel Choice Under Prospect Theoretical Mechanisms of Decision-Making Under Uncertainty.” Transportmetrica A: Transport Science 12 (7): 629–649. doi:https://doi.org/10.1080/23249935.2016.1189718.
  • Liao, Feixiong. 2016. “Modeling Duration Choice in Space–Time Multi-State Supernetworks for Individual Activity-Travel Scheduling.” Transportation Research Part C: Emerging Technologies 69: 16–35. doi:https://doi.org/10.1016/j.trc.2016.05.011.
  • Liao, Feixiong, Theo Arentze, and Harry Timmermans. 2013a. “Incorporating Space–Time Constraints and Activity-Travel Time Profiles in a Multi-State Supernetwork Approach to Individual Activity-Travel Scheduling.” Transportation Research Part B: Methodological 55 (Supplement C): 41–58. doi:https://doi.org/10.1016/j.trb.2013.05.002.
  • Liu, Peng, Feixiong Liao, Hai Jun Huang, and Harry Timmermans. 2016. “Dynamic Activity-Travel Assignment in Multi-State Supernetworks Under Transport and Location Capacity Constraints.” Transportmetrica A: Transport Science 12 (7): 572–590. doi:https://doi.org/10.1080/23249935.2016.1189739.
  • Ma, Lu. 2011. “Generating Disaggregate Population Characteristics for Input to Travel-Demand Models.” (Ph.D. dissertation). Department of Civil and Coastal Engineering. University of Florida.
  • Marcotte, P., and S. Nguyen. 2013. “Equilibrium and Advanced Transportation Modelling.” Springer US.
  • Miller, Eric, and Matthew Roorda. 2003. “Prototype Model of Household Activity-Travel Scheduling.” Transportation Research Record: Journal of the Transportation Research Board 1831: 114–121. doi:https://doi.org/10.3141/1831-13.
  • Millward, Hugh, Hafezi Mohammad Hesam, and Daisy Naznin Sultana. 2019. “Activity Travel of Population Segments Grouped by Daily Time-Use: GPS Tracking in Halifax, Canada.” Travel Behaviour and Society 16: 161–170. doi:https://doi.org/10.1016/j.tbs.2019.05.005.
  • Ngo, Long Thanh, and Binh Huy Pham. 2012. “A Type-2 Fuzzy Subtractive Clustering Algorithm.” Mechanical engineering and technology: Selected and revised results of the 2011 international conference on mechanical engineering and technology, London, UK, November 24-25, 2011, edited by Tianbiao Zhang, 395-402. Berlin, Heidelberg: Springer Berlin Heidelberg.
  • Ozonder, Gozde, and Eric J. Miller. 2020. “Longitudinal Analysis of Activity Generation in the Greater Toronto and Hamilton Area.” Transportation, doi:https://doi.org/10.1007/s11116-020-10089-w.
  • Pendyala, Ram M, and Xin Ye. 2005. “Contributions to Understanding Joint Relationships Among Activity and Travel Variables.” Progress in Activity-Based Analysis, edited by H. Timmermans. doi:https://doi.org/10.1016/b978-008044581-6/50004-1.
  • Pitombo, C. S., E. Kawamoto, and A. J. Sousa. 2011. “An Exploratory Analysis of Relationships Between Socioeconomic, Land Use, Activity Participation Variables and Travel Patterns.” Transport Policy 18 (2): 347–357. doi:https://doi.org/10.1016/j.tranpol.2010.10.010.
  • Rasouli, Soora, and Harry Timmermans. 2014. “Activity-Based Models of Travel Demand: Promises, Progress and Prospects.” International Journal of Urban Sciences 18 (1): 31–60. doi:https://doi.org/10.1080/12265934.2013.835118.
  • Ravulaparthy, Srinath K., Karthik C. Konduri, and Konstadinos G. Goulias. 2017. “Exploratory Analysis of the Activity Time-Use Frontier and Its Effect on Episodic Well-Being: Data from the Disability and Use of Time Survey.” Transportation Research Record: Journal of the Transportation Research Board 2669 (1): 80–90. doi:https://doi.org/10.3141/2669-09.
  • Recker, Wilfred W, Michael G McNally, and Gregory S Root. 1986. “A Model of Complex Travel Behavior: Part I: Theoretical Development.” Transportation Research Part A: General 20 (4): 307–318.
  • Roorda, Matthew J., Eric J. Miller, and Khandker M. N. Habib. 2008. “Validation of TASHA: A 24-h Activity Scheduling Microsimulation Model.” Transportation Research Part A: Policy and Practice 42 (2): 360–375. doi:https://doi.org/10.1016/j.tra.2007.10.004.
  • Semanjski, Ivana, Angel Lopez, and Sidharta Gautama. 2016. “Forecasting Transport Mode Use with Support Vector Machines Based Approach.” Transactions on Maritime Science 5 (02): 111–120.
  • Shabanpour, Ramin, Nima Golshani, Joshua Auld, and Abolfazl Mohammadian. 2017. “Dynamics of Activity Time-of-Day Choice.” Transportation Research Record: Journal of the Transportation Research Board 2665 (1): 51–59. doi:https://doi.org/10.3141/2665-06.
  • Stopher, Peter R., David T. Hartgen, and Yuanjun Li. 1996. “SMART: Simulation Model for Activities, Resources and Travel.” Transportation 23 (3): 293–312. doi:https://doi.org/10.1007/bf00165706.
  • TURP. 2008. “TURP (Time Use Research Program).” Halifax Regional Space Time Activity Research (STAR) Survey: A GPS-assisted household time-use survey, survey methods. Halifax: Saint Mary’s University.
  • Vastberg, Oskar Blom, Anders Karlström, Daniel Jonsson, and Marcus Sundberg. 2020. “A Dynamic Discrete Choice Activity-Based Travel Demand Model.” Transportation Science 54 (1): 21–41. doi:https://doi.org/10.1287/trsc.2019.0898.
  • Vovsha, Peter, Eric Petersen, and Robert Donnelly. 2002. “Microsimulation in Travel Demand Modeling: Lessons Learned from the New York Best Practice Model.” Transportation Research Record: Journal of the Transportation Research Board 1805: 68–77. doi:https://doi.org/10.3141/1805-09.
  • Weiner, Edward. 1999. “Urban Transportation Planning in the United States: An Historical Overview.” Praeger.
  • Yang, Min, Dounan Tang, Haoyang Ding, Wei Wang, Tianming Luo, and Sida Luo. 2014. “Evaluating Staggered Working Hours Using a Multi-Agent-Based Q-Learning Model.” Transport 29 (3): 296–306. doi:https://doi.org/10.3846/16484142.2014.953997.
  • Yang, Fei, Zhenxing Yao, Yang Cheng, Bin Ran, and Da Yang. 2016. “Multimode Trip Information Detection Using Personal Trajectory Data.” Journal of Intelligent Transportation Systems 20 (5): 449–460. doi:https://doi.org/10.1080/15472450.2016.1151791.
  • Zhang, Anpeng, Jee Eun Kang, Kay Axhausen, and Changhyun Kwon. 2018. “Multi-Day Activity-Travel Pattern Sampling Based on Single-Day Data.” Transportation Research Part C: Emerging Technologies 89: 96–112. doi:https://doi.org/10.1016/j.trc.2018.01.024.
  • Zhu, Zheng, Xiqun Chen, Chenfeng Xiong, and Lei Zhang. 2018. “A Mixed Bayesian Network for Two-Dimensional Decision Modeling of Departure Time and Mode Choice.” Transportation 45 (5): 1499–1522. doi:https://doi.org/10.1007/s11116-017-9770-6.

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