Via computing the total cost of ownership (TCO), using literature-derived and simulated data, I analyze the per-mile monetary cost to travel across pooled ridehailing, solo ridehailing and a personal. I “expand” this TCO by adding the travel time cost (TTC) for traveling as a passenger (defined as “generalized cost”). I also include the effect of “road fees” aimed at mitigating congestion and discouraging low-occupancy travel. The key findings show that although pooled ridehailing is cheaper than its solo counterpart, the cost of the driver causes this service to remain three times higher than driving one’s own car. I found that as battery costs continue to drop over the next decade, electric vehicles (EV) will be cost-competitive with traditional gasoline especially in high-mileage ridehailing applications. However, the good news should not lead authorities to conclude that, as EVs will reduce tailpipe emissions, electrification alone cannot solve all problems, as other negative transportation attributes such as high VMT and road congestion will persist; there is need for more regulations to limit low-occupancy travel. Indeed, this research shows that current road fees do not increase the relative costs of traveling alone (with private cars and solo ridehailing) significantly, with the exception of congestion area fees (such as London’s) that can substantially discourage solo travelling in favor of pooling. Results show that TTCs are sometimes greater in value than the monetary costs. I find that the longer travel time typical of a pooled ride – mainly caused by the detours and walking time to/from the pick-up/drop-off place – may make pooled ridehailing “costlier” than the faster counterpart. In the future, automated vehicles (AV) and the lack of driver cost may dramatically reduce the monetary costs of ridehailing, and, at a small price difference, the travelers may find the TTC of an AV-base solo ridehail less expensive than a pooled AV ride.

I investigate other psychological and situational non-monetary factors that influence the choice of pooled ridehailing vs. its solo counterpart, through the analysis of data collected with a rich travel survey in 2019 in California. The results of a mixed logit model confirm that: lower-income people, non-white minorities, women, younger adults, as well as frequent ridehailing users are more likely to pool. Trips that originate in high-density areas are also more likely to be pooled. Whereas, not having to pay for a trip (e.g., a work trip paid by an employer) reduces the likelihood of pooling. Further, I find a positive relationship between the frequent use of public transit and active modes and the likelihood of pooling, which highlights the risk of competition, but in turn may highlight an openness for multi-modal travel.