Lessons from the Small Business Health Options Program: The SHOP Experience in California and Colorado

The Small Business Health Options Program (SHOP) got off to a slow start, with lower-than-expected enrollment and a public perception problem. This report examines California and Colorado's small-business marketplaces, which opened on schedule in October 2013. For business owners, employee choice was the most important reason cited for considering SHOP, with ease of administration a distant second. Several owners see SHOP as a viable alternative to the private exchanges now taking root among large and midsize employers. Interviews also revealed that business owners consider insurance brokers to be an important source of enrollment assistance. Those in the insurance and policy communities perceived small-business owners to be poorly informed about available tax credits; business owners disagreed, saying the credits were simply not key to their decision to elect SHOP. Potential growth areas for SHOP include developing alternative benefit designs, contracting with Medicaid plans, and offering ancillary products, such as wellness programs

Strict and Deep Comparison of Revealed Transit Trip Structure between Computer-Assisted Telephone Interview Household Travel Survey and Smart Cards

Large sample household travel surveys (HTSs) are an essential tool for the planning of urban transit systems. The progressive adoption by transit agencies of fare collection systems based on smart cards (SCs) has, for the first time, provided opportunities to compare the survey data with detailed, population-level data collected independently. These comparisons have produced some surprising results. Although the underreporting of non-home-based and off-peak trips was to be expected, the significant overestimation of transit use during peak periods was not anticipated. Using the Greater Montreal Area as a case study, this paper performs a strict and deep comparison of computer-assisted telephone interview (CATI) HTS data and SC data across several dimensions: transit agency usage, departure time from home, number of trips per traveler, and activity durations. The analysis reveals that the HTS constitutes a simplified portrayal of transit usage patterns. Non-home-based trips and trips made for activities of short duration are underrepresented in the survey data, leading to an underestimation of off-peak travel by transit. In addition, the systematic overestimation of peak period transit use appears to be because of the corrective weighting of the 20–29 demographic which is notoriously difficult to reach in a telephone-based household survey

Application of Machine Learning to Two Large-Sample Household Travel Surveys: A Characterization of Travel Modes

Even in a context of rapidly evolving transportation and information technologies, household travel surveys remain an essential source of information for transportation planning. Moreover, as planning authorities become increasingly concerned with reducing the use of the private car, travelers’ mode choice patterns should be reexamined. In this study, a machine learning algorithm (Random Forest) was employed to characterize the use of eight different travel modes observed in two consecutive household travel surveys undertaken in Montreal, Canada. The analysis incorporated roughly 160,000 observed trips. The Random Forest algorithm was trained on the 2008 survey data and applied to the 2013 survey. The usefulness of the algorithm was evaluated using two numerical representations: the confusion matrix and the importance matrix. The results of this evaluation showed that the Random Forest algorithm could generate a detailed and precise characterization of travel submarkets for four of the most commonly observed modes of travel (auto-drive, public transit, school bus, and walk) using 11 attributes of households, persons, and trips. However, the auto-passenger mode was difficult to characterize because of its dependence on unobserved intra-household interactions. The algorithm also had difficulty identifying users of rarely observed modes (park-and-ride, kiss-and-ride, bicycle), but performed better in this regard than a traditional mode choice model. Finally, traveler’s age and the spatial orientation of origin–destination pairs were found to be decisive factors in the use of the auto-drive mode. This finding, combined with the stability of mode choice patterns observed over 5 years, highlights the difficulty of significantly reducing automobile use