Commodity-based and trip-based approaches of freight demand modelling using trip length distributions
By: Chandra, Aitichya
.
Contributor(s): Pani, Agnivesh.
Publisher: USA Springer 2023Edition: Vol.104(2).Description: 417-434p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series ASummary: The freight demand forecasting process is broadly categorized into trip-based modelling and commodity-based modelling due to the multidimensional units of analysis (i.e., trips, tonnage, volume, etc.). Freight activities can be represented in an improved manner when at least two of these measures can be jointly modelled. The joint modelling concept requires an approximate function to convert tonnage trip length distribution (TLD) to truck (trip) TLD. This research investigates the possibility of such a function for several ISIC commodity classes. Class-specific trip and tonnage lengths are modelled, and, subsequently, approximation functions are developed. The results reveal that approximated trip length distributions match closely with the observed distributions for each class. The approximation functions will be valuable in integrating commodity-based- and trip-based modelling approaches. The inclusion of this function at the trip distribution modelling stage would assist not only in planning for the logistics requirements but also cost-savings in data collection exercises.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-1744 |
The freight demand forecasting process is broadly categorized into trip-based modelling and commodity-based modelling due to the multidimensional units of analysis (i.e., trips, tonnage, volume, etc.). Freight activities can be represented in an improved manner when at least two of these measures can be jointly modelled. The joint modelling concept requires an approximate function to convert tonnage trip length distribution (TLD) to truck (trip) TLD. This research investigates the possibility of such a function for several ISIC commodity classes. Class-specific trip and tonnage lengths are modelled, and, subsequently, approximation functions are developed. The results reveal that approximated trip length distributions match closely with the observed distributions for each class. The approximation functions will be valuable in integrating commodity-based- and trip-based modelling approaches. The inclusion of this function at the trip distribution modelling stage would assist not only in planning for the logistics requirements but also cost-savings in data collection exercises.
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