Share:


Strategic modelling of passenger transport in waterways: the case of the Magdalena River

    Laura Berrio Affiliation
    ; Víctor Cantillo Affiliation
    ; Julian Arellana Affiliation

Abstract

In some Colombian regions, inland waterways play a relevant role in passenger mobility. However, many characteristics of their operation, required for planning purposes, are unknown. Existing data and studies are few and undetailed. In this context, collecting data and developing supply and demand models will make it possible to not only improving the knowledge of inland waterway transport in the country, but also the planning of the system. In this investigation, a survey instrument was designed and employed to collect data about passenger flows in seven ports on the Magdalena River, the most important river in Colombia. The collected information was used to specify and estimate strategic supply and demand models. Models based on the classic four-step model and alternative synthetic models were estimated and compared. The proposed models contribute to better understanding of the behaviour of inland waterway transport passengers. They were used to evaluate policies aimed at improving the users’ level of service and to encourage the utilisation of this mode of transport. Results show that accessibility variables and variables related to zone size define trip generation and distribution. In addition, it was found that inland waterway users in Colombia are highly cost sensitive.

Keyword : inland waterways, passenger transport, Magdalena River, strategic modelling, intermodal network, accessibility

How to Cite
Berrio, L., Cantillo, V., & Arellana, J. (2019). Strategic modelling of passenger transport in waterways: the case of the Magdalena River. Transport, 34(2), 215-224. https://doi.org/10.3846/transport.2019.8943
Published in Issue
Mar 18, 2019
Abstract Views
1107
PDF Downloads
642
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ben-Akiva, M.; Morikawa, T. 1990. Estimation of travel demand models from multiple data sources, in Transportation and Traffic Theory: Proceedings of the Eleventh International Symposium, 18–20 July, 1990, Yokohama, Japan, 461–476.

Caliper Corporation. 2016. Travel demand modeling with TransCAD: Transportation Planning Software. 24 p. Available from Internet: https://www.caliper.com/pdfs/TravelDemand-ModelingBrochure.pdf

Camay, S.; Zielinski, E.; Zaranko, A. 2012. New York City’s east river ferry: expanding passenger ferry service and stimulating economic development in the New York City Region, Transportation Research Record: Journal of the Transportation Research Board 2274: 192–200. https://doi.org/10.3141/2274-21

Cantillo, V.; Holguín-Veras, J.; Jaller, M. 2014. The Colombian strategic freight transport model based on product analysis, Promet – Traffic & Transportation 26(6): 487–496. https://doi.org/10.7307/ptt.v26i6.1460

Cascetta, E.; Pagliara, F.; Papola, A. 2007. Alternative approaches to trip distribution modelling: a retrospective review and suggestions for combining different approaches, Regional Science 86(4): 597–620. https://doi.org/10.1111/j.1435-5957.2007.00135.x

Dobbins, J. P.; Langsdon, L. C. 2013. Use of data from automatic identification systems to generate inland waterway trip information, Transportation Research Record: Journal of the Transportation Research Board 2330: 73–79. https://doi.org/10.3141/2330-10

Domencich, T.; McFadden, D. L. 1975. Urban Travel Demand: a Behavioral Analysis. North-Holland Publishing Co. 215 p.

Dong, X.; Ben-Akiva, M.; Bowman, J. L.; Walker, J. L. 2006. Moving from trip-based to activity-based measures of accessibility, Transportation Research Part A: Policy and Practice 40(2): 163–180. https://doi.org/10.1016/j.tra.2005.05.002

Hyman, G. M. 1969. The calibration of trip distribution models, Environment and Planning A: Economy and Space 1(1): 105–112. https://doi.org/10.1068/a010105

Ivančić, P.; Kasum, J.; Pavić, I. 2013. Proposed guidelines on developing the optimisation model for passage planning in inland waterways navigation, Pomorstvo – Scientific Journal of Maritime Research 27(2): 343–350.

Kim, J. H.; Bae, Y. K.; Chung, J.-H. 2012. Effects of personal proenvironmental attitudes on mode choice behavior: new ecofriendly water transit system in Seoul, South Korea, Transportation Research Record: Journal of the Transportation Research Board 2274: 175–183. https://doi.org/10.3141/2274-19

Márquez, L.; Cantillo, V.; Arellana, J. 2014. How are comfort and safety perceived by inland waterway transport passengers?, Transport Policy 36: 46–52. https://doi.org/10.1016/j.tranpol.2014.07.006

Márquez, L.; Cantillo, V. 2013. Evaluating strategic freight transport corridors including external costs, Transportation Planning and Technology 36(6): 529–546. https://doi.org/10.1080/03081060.2013.830892

Mihic, S.; Golusin, M.; Mihajlovic, M. 2011. Policy and promotion of sustainable inland waterway transport in Europe – Danube River, Renewable and Sustainable Energy Reviews 15(4): 1801–1809. https://doi.org/10.1016/j.rser.2010.11.033

Nam, K.; Win, E. 2014. Competitiveness between road and inland water transport: the case of Myanmar, Transport Problems – Problemy Transportu 9(4): 49–61.

Ortúzar, J. de D.; Willumsen, L. G. 2011. Modelling Transport. John Wiley & Sons, Ltd. 607 p. https://doi.org/10.1002/9781119993308

Sheffi, Y. 1985. Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice Hall. 399 p.

Tournaye, C.; Pauli, G.; Saha, D. M.; Van der Werf, H. 2010. Current issues of inland water transport in Europe, Proceedings of the Institution of Civil Engineers – Civil Engineering 163(5): 19–28. https://doi.org/10.1680/cien.2010.163.5.19

Vega, L.; Cantillo, V.; Arellana, J. 2019. Assessing the impact of major infrastructure projects on port choice decision: the Colombian case, Transportation Research Part A: Policy and Practice 120: 132–148. https://doi.org/10.1016/j.tra.2018.12.021

Wardman, M.; Lythgoe, W.; Whelan, G. 2007. Rail passenger demand forecasting: cross-sectional models revisited, Research in Transportation Economics 20: 119–152. https://doi.org/10.1016/S0739-8859(07)20005-8