Taxi services are an integral part of urban transport and are a major
contributor to air pollution and traffic congestion, which adversely affect
human life and health. Sharing taxi rides is one way to reduce the unfavorable
effects of cab services on cities. However, this comes at the expense of
passenger discomfort, quantified in terms of longer travel times. Taxi
ridesharing is a sophisticated mode of urban transport that combines individual
trip requests with similar spatiotemporal characteristics into a shared ride.
We propose a one-to-one sharing strategy that pairs trips with similar starting
and ending points. We examine the method using an open dataset with trip
information on over 165 million taxi rides. We show that the cumulative journey
time can be reduced by 48 percent while maintaining a relatively low level of
passenger inconvenience, with a total average delay compared to an individual
mobility case of 6 minutes and 42 seconds. This advantage is accompanied by
decreases in emissions of 20.129 tons on an ordinary day and a potential fare
reduction of 49 percent, which could point to a widespread passenger acceptance
of shared taxi services. Overall, a matching rate of 13 percent is reached
while a 27 percent matching rate is attained for high-demand areas. Compared to
many-to-many sharing dynamic routing methodologies, our scheme is easier to
implement and operate, making fewer assumptions about data availability and
customer acceptance