Factors Affecting Transportation Costs Related To Demand Ela

Factors affecting transportation costs related to demand elasticity and model building

Transportation costs are influenced by a multitude of factors, among which demand elasticity plays a significant role, especially in the context of individual travel expenses via air travel. Demand elasticity refers to the responsiveness of consumers to changes in price. In the case of air travel, this responsiveness affects how variations in ticket prices influence a traveler’s decision to purchase and when to travel, ultimately impacting the total transportation costs encountered by the traveler.

For an individual flight traveler, several demand elasticity factors come into play. When prices are highly elastic, a small decrease in fare might result in a significant increase in the number of travelers opting for that flight or an earlier/later travel date, which can influence airlines to adjust prices dynamically. Conversely, inelastic demand, characteristic of travelers with urgent or inflexible schedules, causes less sensitivity to fare changes, leading to less variation in price and potentially higher costs for the traveler during peak times or high-demand periods.

Furthermore, the elasticity impacts ancillary costs such as baggage fees, seat selection, or flexible ticket options, which airlines often adjust based on demand patterns. For instance, during high demand periods, inelastic travelers might pay significantly higher prices, increasing their overall transportation costs. When demand is elastic, travelers may delay or reschedule trips, possibly avoiding high fares altogether, thus reducing costs.

To build a model representing the total travel costs for this user, a comprehensive approach would incorporate fare fluctuations influenced by demand elasticity, additional fees, and timing sensitivity. A possible model could be a function where total cost (C) is expressed as the sum of base fare (F), ancillary fees (A), and flexibility or schedule costs (S), with these elements being functions of demand elasticity (E), time to departure (T), and booking window (BW). For example:

C = F(E, T, BW) + A(E, T, BW) + S(E, T, BW)

This model would analyze how changes in demand elasticity (E) influence each component, possibly by integrating real-time fare data and traveler preferences. Sensitivity analysis could help optimize booking strategies to minimize total transportation costs based on the elasticity forecasts.

In conclusion, understanding demand elasticity's influence on individual transportation costs allows travelers and airlines to better forecast and manage prices. Building a detailed model incorporating elasticity, timing, and ancillary fees can help travelers minimize costs and enable airlines to optimize revenue management practices effectively.

References

• Button, K. J. (2010). Transport Economics (3rd ed.). Edward Elgar Publishing.
• Cohen, A. (2016). The Economics of Airlines and Air Transport. Routledge.
• Hensher, D. A., & Li, M. (2010). Evaluating airline fare elasticities of demand. Transportation Research Part E: Logistics and Transportation Review, 46(5), 683-693.
• Li, J., & Hensher, D. A. (2014). Modelling demand elasticity for air travel: An effective approach. Journal of Transport Geography, 36, 144-156.
• Small, K., & Verhoef, E. (2007). The Economics of Urban Transportation. Routledge.
• Vasigh, B., & Fleming, K. (2014). Introduction to Air Transport Economics: From Theory to Applications. Ashgate Publishing.
• Gillen, D., & Lall, A. (2004). Developing a global airline network: The strategic implications of airline alliances. Journal of Air Transport Management, 10(2), 137-147.
• Oum, T. H., Zhang, A., & Zhang, Y. (1995). Competition, subsidies, and-airline efficiency in Asia. Journal of Transport Economics and Policy, 29(2), 173-189.
• Talluri, K., & Van Ryzin, G. (2004). The Theory and Practice of Revenue Management. Springer Science & Business Media.
• Wang, S., & Huang, Y. (2020). Dynamic pricing and demand elasticity in the airline industry. Transportation Journal, 59(2), 192-208.