Hensher, D.A., Button, K.J.: Handbook of Transport Modelling, 2nd edn. (eds.) Metaheuristic Applications in Structures and Infrastructures. In: Yang, X.-S., Talatahari, S., Alavi, A.H. Gopalakrishnan, K.: Particle swarm optimization in civil infrastructure systems: state-of-the-art review. Google: General transit feed specification reference. 1(1), 29–46 (2012)įung, S., Tong, C., Wong, S.: Validation of a conventional metro network model using real data. įlorian, M., Constantin, I.: A note on logit choices in strategy transit assignment. #SINGLE CLASS TRAFFIC ASSIGNMENT TRANSCAD MANUAL#Wiley, New York (2006)įederal Highway Administration (FHWA): Travel Model Validation and Reasonability Checking Manual Second Edition. 6(1), 97–101 (2006)Įngelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Springer, Singapore (2019)ĭong, C., Liu, Z., Liu, X.: Chaos-particle swarm optimization algorithm and its application to urban traffic control. Advances in Intelligent Systems and Computing, vol. (eds.) Harmony Search and Nature Inspired Optimization Algorithms. In: Yadav, N., Yadav, A., Bansal, J., Deep, K., Kim, J. #SINGLE CLASS TRAFFIC ASSIGNMENT TRANSCAD WINDOWS#205, 67–85 (1967)ĭixit, A., Mishra, A., Shukla, A.: Vehicle routing problem with time windows using meta-heuristic algorithms: a survey. Elsevier, Amsterdam (2007)ĭial, R.B.: Transit pathfinder algorithm. (eds.) Handbooks in Operations Research and Management Science. CRC Press, Boca Raton (2016)Ĭepeda, M., Cominetti, R., Florian, M.: A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria. Caliper Corporation, Newton (2015)Ĭeder, A.: Public Transit Planning and Operation: Modeling, Practice and Behavior, 2nd edn. #SINGLE CLASS TRAFFIC ASSIGNMENT TRANSCAD SOFTWARE#512, 27–38 (2014)Ĭaliper: Transcad Transportation Planning Software User’s Guide Version 7. In: Proceedings of the 36th Australasian Transport Research Forum (ATRF), Brisbane, Australia (2013)īandara, R., Walker, J.P., Rüdiger, C.: Towards soil property retrieval from space: proof of concept using in situ observations. Board 2535, 88–96 (2015)īagherian, M., Massah, S., Kermanshahi, S.: A swarm based method for solving transit network design problem. Part C 87, 123–137 (2018)Īlsger, A.A., Mesbah, M., Ferreira, L., Safi, H.: Use of smart card fare data to estimate public transport origin–destination matrix. 143(4), 04017003 (2017)Īlsger, A., Tavassoli, A., Mesbah, M., Ferreira, L., Hickman, M.: Public transport trip purpose inference using smart card fare data. Overall, the analysis indicates that the AFC data is a valuable and rich source in calibrating and validating a transit assignment model.Īlsger, A., Tavassoli, A., Mesbah, M., Ferreira, L.: Evaluation of effects from sample-size origin–destination estimation using smart card fare data. Furthermore, a comparison is made between the strategies used by passengers and the generated strategies by the model between each origin and destination to get more insights about the detailed behaviour of the model. Higher dispersions are seen for the bus mode, in contrast to rail and ferry modes. The results indicate that the proposed procedure can successfully develop a multi-modal transit assignment model at a large scale. The AFC system in SEQ has voluminous and high quality data on passenger boardings and alightings across bus, rail and ferry modes. The South-East Queensland (SEQ) network in Australia is used as a case study. This study combines data from three sources: the general transit feed specification, AFC, and a strategic transport model from a large-scale multimodal public transport network. The proposed methodology uses automatic fare collection (AFC) data to estimate the origin–destination matrix. Lastly, the model is validated using another weekday data. This study is based on the frequency-based assignment model using the concept of optimal strategy while any transit assignment model can be used in the proposed methodological framework. This error term which is based on the percentage of root mean square error and the mean absolute percent error encompasses deviation of model outputs from observations considering both segment level as well as the mode level and can be applied to a large scale network. An optimization method based on particle swarm algorithm is adopted to minimize a defined error term. This paper describes a practical automated procedure to calibrate and validate a transit assignment model.
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