Development of freight travel demand model with characteristics of vehicle tour activities

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dc.contributor.authorVenkadavarahan M.
dc.contributor.authorRaj, Celestin Thivya
dc.contributor.authorMarisamynathan, Sankaran
dc.date.accessioned2026-09-10T07:24:04Z
dc.date.available2026-09-10T07:24:04Z
dc.date.issued2020-11-01
dc.description.abstractThe actual movements of freight vehicles demonstrate comprehensive tour activities instead of a single trip. Freight vehicle tours activity represent trip chaining relationship between shippers, receivers, and carriers as a journey of tours. In India, identifying and modeling tour activity is complex because disaggregated freight tour activity data are generally not available to access. Hence, there is a need to identify the most suitable and sufficient modeling framework, which explains the non-linear behavior and complexity of tour-based freight travel demand. Thus, the objectives of this study are to identify the driver, vehicle, and journey characteristics of freight vehicle movement and their tour activities using the conventional non-linear and soft computing methods to estimate the number of tours and to identify the most suitable and effective modeling method. The questionnaire form designed, and study locations were selected in Tiruchirappalli, India. The selected survey locations were identified based on the availability of fright drivers who were making tours. An extensive face to face data collection process was performed, and a total of 600 drivers' data were collected from survey. A detailed description of the collected data was obtained through descriptive analysis. Next, significant relationship between characteristics of freight vehicle drivers and their tour activities was identified based on various correlation tests. Further, a non-linear regression method was adopted for explaining non-linear behavior and complexity of tour-based freight travel demand. Followed by soft computing models using Support-vector machines (SVM), k-Nearest Neighbors (k-NN), and Artificial Neuron Network (ANN) algorithms were developed to predict tour-based freight travel demand, which performed better than conventional non-linear model. Among all, ANN model outperformed and explained the variations with better accuracy. Finally, the outcome of this study will be helpful to transport planners and researchers for understanding the characteristics of freight vehicle tour activity and estimating tour-based freight travel demand.
dc.identifier.issn25901982
dc.identifier.urihttp://dx.doi.org/10.1016/j.trip.2020.100241 http://nitt.ndl.gov.in/handle/123456789/253
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofseriesTransportation Research Interdisciplinary Perspectives; Vol. 8/ Art. 100241
dc.titleDevelopment of freight travel demand model with characteristics of vehicle tour activities
dc.typeArticle
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