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Modelling of Active and Latent Attributes Based on Traveler Perspectives: Case of Port City of Douala

DOI: 10.4236/wjet.2023.111012, PP. 164-198

Keywords: Multinomial logit Model, Latent Attributes, Mode Choice, Individual Behavior, Active Attributes

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Abstract:

A growing stream of study stresses the relevance of subjective elements in understanding the hierarchy of preferences that underpin individual travel behavior. The purpose of this study is to evaluate the impact of various factors on mode choice. To achieve this, a multinomial logit model (MNL) was used?to analyze the relationships between mode choice and three classes of attributes; Combined Active and Latent, Active only and Latent only attributes. The data used are derived from surveys in the port city of Douala, Cameroon as a case study. Results stipulated that, the combined attributes model performed better than both active only attributes and latent only attributes models. Likewise, latent only attributes model performed better than active only attributes model. The advantage of modelling all three groups is for better selection of the most relevant attributes, and this is very relevant in understanding travel behavior of individuals and mode choice decisions.

References

[1]  Seyedabrishami, S. and Shafahi, Y. (2013) A Joint Model of Destination and Mode Choice for Urban Trips: A Disaggregate Approach. Transportation Planning and Technology, 36, 703-721.
https://doi.org/10.1080/03081060.2013.851507
[2]  Amoroso, F., et al. (2010) A Demand-Based Methodology for Planning the Bus Network of a Small or Medium Town. European Transport, 44, 41-56.
[3]  Teravaninthorn, S. and Raballand, G. (2008) Transport Prices and Costs in Africa: A Review of the Main International Corridors. World Bank, Washington DC.
[4]  Ben-Akiva, M. and Boccara, B. (1995) Discrete Choice Models with Latent Choice Sets. International Journal of Research in Marketing, 12, 9-24.
https://doi.org/10.1016/0167-8116(95)00002-J
[5]  Khan, O. (2007) Modelling Passenger Mode Choice Behavior Using Computer Aided Stated Preference Data. PhD Thesis, Queensland University of Technology, Brisbane.
[6]  Torok, B., et al. (2020) Representing Autonomated Vehicles in a Macroscopic Transportation Model. Periodica Polytechnica Transportation Engineering, 48, 269-275.
https://doi.org/10.3311/PPtr.13989
[7]  Bergantino, S., et al. (2013) Taste Heterogeneity and Latent Preferences in the Choice Behavior of Freight Transport Operators. Transport Policy, 30, 77-91.
https://doi.org/10.1016/j.tranpol.2013.08.002
[8]  Yanez, D., et al. (2010) Inclusion of Latent Variables in Mixed Logit Models: Modelling and Forecasting. Transportation Research Part A, 44, 744-753.
https://doi.org/10.1016/j.tra.2010.07.007
[9]  Bergantino, A.S. and Catalano, M. (2016) Individual’s Psychological Traits and Urban Travel Behaviour. International Journal of Transport Economics, 43, 341-359.
[10]  Wang, F. and Ross, C. (2018) Machine Learning Travel Mode Choices: Comparing the Performance of an Extreme Gradient Boosting Model with a Multinomial Logit Model. Journal of the Transportation Research Board, 2672, 35-45.
https://doi.org/10.1177/0361198118773556
[11]  Claudia, N.B., Uwe, D., Yishen, L. and Harris, S. (2017) Transport Policies and Development. The Journal of Development Studies, 53, 465-480.
https://doi.org/10.1080/00220388.2016.1199857
[12]  T. E. o. E. Britannica (1999, September 1) Britanica.
https://www.britannica.com/place/Douala
[13]  SITRASS (2004) Poverty and Urban Mobilty. African Region World Bank, Douala.
[14]  Minimum-Wage.org (2022).
https://www.minimum-wage.org/international/cameroon
[15]  Cao, J. and Cao, X.S. (2017) Comparing Importance-Performance Analysis and Three-Factor Theory in Assessing Rider Satisfaction with Transit. Journal of Transport and Land Use, 10, 837-854.
https://doi.org/10.5198/jtlu.2017.907
[16]  Maria, V.J., Tobias, H. and Per, J. (2004) Latent Variables in a Travel Mode Choice Model; Attitudinal and Behavioural Indicator Variables. Swedish National Road and Transport Research Institute, Linkoping.
[17]  Frank, S.K. and Chandra, B. (2006) A Self Instructing Course in Mode Choice Modeling: Multinomial and Nested Logit Models. Georgia Institute of Technology, Georgia.
[18]  Bhat, C. (1995) A Heteroscedastic Extreme Value Model of Intercity Mode Choice. Transportation Research Part B: Methodological, 29, 471-483.
https://doi.org/10.1016/0191-2615(95)00015-6

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