While the aggregate results of rideshare potential at MIT are interesting, the comparison of the modeled results against the observed travel behavior of the MIT community is perhaps more interesting. The matrix shown below compares the modeled and observed travel behavior for the 5,061 commuters considered in the ‘base case’ (five minute deviation) analysis. Along the left side, community members are identified by their modeled rideshare feasibility. Along the top, they are identified by whether they engaged in any form or ridesharing (carpool or vanpool) at least once during the previous workweek. The “All Modes” group of commuters was used rather than the “Primarily SOV” subset because the analysis is attempting to compare modeled rideshare behavior to observed commuter rideshare behavior, regardless of whether or not these commuters are the ones that would be targeted for an MIT community-based rideshare initiative. If the analysis was limited to the “Primarily SOV” subset, it would be attempting to compare modeled and observed rideshare behavior for a subset that was selected specifically because they do not currently rideshare, largely defeating the purpose of the analysis. However, it would be false to state that 3,615 commuters should be targeted in a rideshare initiative. This group includes community members that already use sustainable modes of travel to get to MIT; they walk, cycle or take transit. From a policy standpoint, the 946 frequent SOV drivers should be the primary targets for increased rideshare participation.

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.

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