transportation network companies (TNCs) as the main mode of personal transportation. At the time of the trips, the subject was not aware that travel data would later be analyzed.
As the study had an extremely small sample size, its authors recommend that it be used primarily as a jumping-off point for larger investigations. It is possible that its conclusions will be reflected in further studies, however. It also represents a constructive application of data—travel reimbursement receipts—that are available to organizations but that are often not analyzed.
The study took TNC trip receipts, hotel reservations, and other information from travel expense reports submitted by the subject. Qualitative factors that were analyzed included the distance of lodging from the trip's primary activities, the costs of car rental fees or TNC trips, and the resulting cost per mile. The authors also interviewed the subject to examine the decision-making processes that influenced behavior. The study's authors found that, despite the use of a single subject, the data collected were remarkably consistent by transportation mode and thus the differences in behavior were determined with statistical significance.
The following lessons were derived from the source report:
- When using TNCs as the primary mode of transportation, the subject booked downtown hotels, as the cost of parking was no longer relevant. This led to such trips having a miles-traveled-per-day rate of less than half that of trips using rental cars.
- The use of TNCs was found to be significantly more economical than the use of rental cars, with less than half the total per-day cost. This was attributed to the lack of parking, gas, and insurance expenses.
- The cost-per-mile of the two modes differed insignificantly, indicating that the price advantage of using TNCs was due to the lower distance traveled. Thus, the comparative advantage of using TNCs would likely be significantly reduced if close lodging is not available, or if longer trips were required.
The use of pre-existing data and the lack of required preprocessing indicates that such analyses may be a low-cost way for organizations to better understand their employees' travel habits, and possibly implement data-based guidelines for travel practices.
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