There is relatively little in the recent literature that has attempted to quantify the benefits of ridesharing, and even fewer resources that have proposed a comprehensive methodology of doing so. Given the rather substantial amount of personal information required to determine rideshare viability, it is conceivable that institutions or organizations have conducted these types of analyses but have kept the results private.

Research and consulting reports have been one source for quantified rideshare potential. One early attempt was a 1994 report summarizing the effectiveness of transportation control measures (TCMs) from various state-level trip reduction programs (Apogee Research, 1994). The report found that the provision of rideshare benefits at a regional level could eliminate up to 2% of VMT and 1% of trips. More recently, a report titled Moving Cooler estimated the GHG reduction potential from a wide range of transportation strategies, implemented individually and as bundles (Cambridge Systematics, 2009). For the strategy labeled “Employer-Based Commute Strategies” (of which ridesharing is a component), a logit mode choice model (named COMMUTER) was used to estimate mode shifts and the resulting change in emissions. The COMMUTER model uses aggregate mode choice data for different ‘classes’ of metropolitan area. Emission reductions from baseline were estimated at 0.4 – 2.0% depending on the level of effort employed. The Growing Cooler results require some cautious interpretation; as one might expect, ‘employer-based commute strategies’ includes far more than ridesharing. In fact, this strategy includes provisions for ridesharing, a transit subsidy, modifications in parking policies, a compressed workweek provision and telecommuting. If ridesharing alone is isolated from this bundle, emission reductions from baseline are towards the lower end of the scale (approximately 0.4%).

Academia has also attempted to measure the potential market for ridesharing. A study by Tsao & Lin (1999) is one of the more comprehensive attempts to measure the potential of ridesharing based on spatial and temporal factors. Unfortunately, the study made several simplifying assumptions that greatly underestimate the potential of ridesharing, and likely led the authors to conclude that the benefits were too small to quantify. The study presented a hypothetical metropolitan area with a uniform density of jobs and workers across the entire area. This assumption, while simplifying the author’s model specification, conflicts substantially with observed metropolitan spatial distribution of jobs and housing. In reality, metropolitan areas have substantially varied commercial and residential densities. Higher densities of either commercial or residential activity, and more specifically, the variability of high densities across a geographic area is a major determinant of commuting patterns and increases the likelihood of finding a rideshare match. The authors also assumed that participants would only consider sharing a ride if they lived in the same two-mile by two-mile square area. While some recent research (Buliung, 2010) suggests that rideshare matching at the residential end of a trip is a strong determinant of rideshare potential, Tsao and Lin’s assumption effectively eliminates the ability to match riders and passengers based on the route they travel, thereby underestimating the number of potential riders. While the methodology was meant to look at rideshare potential in a hypothetical metropolitan area, it is important to note that both of the author’s simplifying assumptions lead to an underestimation of rideshare potential.

An analysis conducted by students at the University of Toronto (Sarraino et al., 2008) attempted to measure the number of staff, faculty and students that could rideshare to the St. George campus (downtown Toronto), based on data provided by the university administration. The study used a GIS approach to identify common clusters of commuters that were traveling to campus. It was assumed that shared rides would only occur between drivers and passengers living within a 3 km radius of one and other. This residential proximity assumption is similar to the one used by Tsao and Lin, and could limit some mid-trip pairings. Commuters were only considered as matches if they were leaving their residence within the same 30-minute period. Unfortunately, due to data limitations, only AM residential departure times were available, making any assessment of return trip (or roundtrip) rideshare viability impossible. The analysis found that during morning commute hours (7:00 – 10:30am), 1,461 of 3,030 drive trips (48%) were suitable for ridesharing based on residential proximity and similar residential departure times. Had roundtrip matching been possible, the expected match rate would be lower.

Comments are closed.