Vehicle Utilization Rates Optimization in the Transport Network Company (TNC) Model Open Access
This praxis develops a new logistics optimized approach to improve utilization rates in the TNC model and provides an alternative to the predominant existing TNC model used by both Uber & Lyft, which has been found to be inefficient in providing modern transportation mobility services. Utilization rates are looked at in terms of driver/vehicle loaded miles, unloaded miles, trip time (with rider onboard) and unloaded time (wait time between trips). Four primary arguments are made regarding utilization rates: a) Through validated results of the implemented new optimized TNC model, this new model is more efficient than the current TNC model predominantly employed by the two largest transportation mobility services companies (Uber & Lyft). b) The newly optimized TNC model, because of the efficiency gains it achieves, leads to higher hourly compensation rates for drivers, without underlying increases in per mile and/-or per minute rates to drivers. c) Through a survey carried out the following were revealed: i. Drivers, on average, would prefer to work for longer hours at higher compensation levels, than they are currently being paid. ii. Higher compensation paid to drivers above a certain threshold does not proportionately lead to a preference by drivers to drive longer hours. iii. Drivers prefer to drive longer hours as full-time drivers, dispelling the notion that drivers, on average, prefer to work part-time over a full-time arrangement. d) The efficiency gains realized from the new TNC model may be used as a template to re-factor the driver pool levels employed by existing TNCs, such as Uber and Lyft. The praxis ends with a summary, making the case that a re-factoring from using a higher driver pool level to a lower more sustainable driver pool level will lead to reduced driver turnover rates among TNCs and provide a better return on investment operational footprint for a more profitable deployment of autonomous vehicles on TNCs networks. The final chapter of this praxis ends with recommendation on future research that the author recommends as a follow-up to this work.
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