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A tour-based microsimulation approach to modeling destination choice and mode choice of San Francisco residents is presented. These models were developed as part of an overall tour-based travel demand forecasting model (SF model) for the San Francisco County Transportation Authority to provide detailed forecasts of travel demand for various planning applications. The models described represent two of the nine primary components of the SF model. Both model components consist of multiple logit choice models and include both tour-level models (which refer to the primary activity of the tour) and trip-level models (other activities on the tour). A separate model was estimated for each tour purpose, including work, school, other, and work-based. The destination choice models combine the trip attraction and trip distribution components of the traditional four-step process and use a multinomial logit specification. The mode choice models utilize a nested logit formulation to capture the similarities among sets of similar modes. The two models are linked by incorporating the mode choice utility logsum in the destination choice models; the result is equivalent to a nested structure with a mode choice nest under destination choice. It is demonstrated that the microsimulation approach easily allows the inclusion of a number of key variables in destination and mode choice models that have a significant explanatory power compared with those in traditional models. It is also shown that this approach allows estimation of the effects of tour characteristics on the choice of destination and mode using widely available data and estimation procedures.

This paper documents the results of a pilot test done for the Oregon Household Travel Survey. The pilot was designed to enable the Oregon Department of Transportation to determine the role of a Global Positioning System (GPS) in the upcoming survey effort. Specifically, a three-pronged approach was employed. Households were randomly selected for inclusion in the study and then assigned to one of three groups: ( a) the traditional survey approach, ( b) the traditional approach with GPS, and ( c) GPS only. A total of 299 households from the city of Portland, Oregon, were recruited into the pilot, with 235 completing all required activities. A comprehensive evaluation of the similarities and differences in results across the three groups showed differences in respondent burden, completeness of travel details obtained, and costs. Results from this experiment also showed differences in nonresponse bias. The traditional survey had an expected nonresponse for the large households, low-income households, and young adults. Minority participation was on par with census figures. The GPS groups showed higher participation rates for young adults and nonminorities. These data confirmed the general thought that GPS was an effective tool for mitigating nonresponse among young adults. However, the minority nonresponse bias increased significantly with technology, suggesting that other methods would be more appropriate. With regard to completeness of data, geocoding rates are higher for the GPS groups, and there are significant differences in trip departure times, which could affect peak hour and time-of-day modeling. As expected, the costs were higher for the GPS groups, but the expectation is that these costs will fall as processes are standardized across studies and new technologies are introduced.

A key difference between stochastic microsimulation models and more traditional forms of travel demand forecasting models is that micro-simulation-based forecasts change each time the sequence of random numbers used to simulate choices is varied. To address practitioners’ concerns about this variation, a common approach is to run the microsimulation model several times and average the results. The question then becomes: What is the minimum number of runs required to reach a true average state for a given set of model results? This issue was investigated by means of a systematic experiment with the San Francisco model, a microsimulation model system used in actual planning applications since 2000. The system contains models of vehicle availability, day pattern choice, tour time-of-day choice, destination choice, and mode choice. To investigate the variability of the forecasts of this system due to random simulation error, the model system was run 100 times, each time changing only the sequence of random numbers used to simulate individual choices from the logit model probabilities. The extent of random variability in the model results is reported as a function of two factors: ( a) the type of model (vehicle availability, tour generation, destination choice, or mode choice); and ( b) the level of geographic detail—transit at the analysis zone level, neighborhood level, or countywide level. For each combination of these factors, it is shown graphically how quickly the mean values of key output variables converge toward a stable value as the number of simulation runs increases.