Recent surveys of the slugging population in the Washington DC area and the casual carpool population in the San Francisco Bay area suggest that drivers and passengers choose to share rides for very different reasons. For drivers, the largest benefit from picking up passengers is the travel time savings from the use of the HOV lanes. For passengers, it appears that the motivations to share rides are more diverse, with cost savings and travel time savings remaining the most important factors, but flexibility and environmental benefits ranking strongly as well. This finding is important as groups seek to recruit additional drivers and passengers in rideshare arrangements.

While HOT lanes have garnered much interest recently, the importance of non-tolled infrastructure that encourages high occupancy travel should not be ignored. These types of facility provide users with an additional travel behavior choice and may provide important equity benefits for those groups that cannot afford access to tolled infrastructure. Ideally, future infrastructure will encourage higher occupancy travel through both toll and non-toll travel choices.

The high level of rideshare potential within the MIT community suggests that policy makers may want to target large organizations for increased rideshare participation. Large organizations have some key characteristics that make them amenable to rideshare promotion including a large ‘social network’ of employees that are likely to know one and other (thereby reducing safety concerns), a common destination (making the matching process simpler & increasing match rates), the ability to offer benefits deemed valuable to employees (such as reduced parking costs and flextime), and the legitimacy to gather large amounts of personal travel information from employees.

Large organizations that have detailed travel information also have the ability to engage employees in customized travel planning. Providing highly tailored travel information, such as the variety of travel modes available to a specific employee, and/or the number of fellow employees that they could potentially rideshare with, allows firms to provide an unconventional benefit while simultaneously encouraging changes in travel behavior.

At first glance, the 201 community members that shared a ride when the model suggests they should not have (top-left quadrant) is concerning as it suggests deficiencies or missing variables in the model. Two possible explanations for this include (a) filters that were too restrictive, and/or (b) the influence of unobserved human preferences, particularly the incidence of ridesharing with family members not affiliated with MIT. It is possible that the filters applied were too restrictive in identifying those commuters most likely to rideshare. A more likely explanation is that some of the rideshare trips undertaken were with family members where at least one member of the rideshare was not affiliated with MIT, and therefore did not complete the survey. Previous research has found that between 25% and 80% of ridesharing trips are intra-household, or between family members, so it seems possible that at least some of these shared rides are family based. Unfortunately, the MIT Commuter Survey does not ask respondents to indicate with whom they shared a ride.

There are a number of important insights that follow from this analysis. To begin, the percentage of the MIT community that can feasibly share rides is very high. Depending on the driver deviation assumptions, between 70% and 88% of the surveyed MIT community has the option of engaging in ridesharing. For those whose primary mode of commuting is SOV travel, approximately 49% to 92% could rideshare if they chose to do so, again depending on the driver deviation assumptions. For the “All Modes” group of commuters, 83% of drivers would have to deviate less than two (2) miles.

On a daily basis, approximately 50% to 77% of the MIT Community could rideshare depending on the model assumptions. This is substantially higher than the current share of the community that chooses to rideshare (8.2%). In terms of VMT reduction potential, the model suggests that 9% to 27% of daily, commuter-based VMT could be reduced through choosing to rideshare, with a ‘base-case’ estimate of a 19% daily reduction in VMT.

Finally, from a methodological standpoint, it is interesting to consider the difference between the CPLEX optimization and simple heuristic approaches to identifying the feasible rideshare pairs on a single day. In terms of the maximization of pairings, one can clearly see that larger datasets favor the optimization approach. For smaller datasets, the difference between the two approaches is less pronounced. For the determination of VMT savings, the two approaches yield remarkably similar results.