Assessment of spatial transferability of an activity-based model, TASHA
Spatial transferability has been recognized as a useful validation test for travel demand models. To date, however, transferability of activity-based models has not been frequently assessed. This paper assesses the spatial transferability of an activity-based model, TASHA (Travel Activity Scheduler for Household Agents), which has been developed for the Greater Toronto Area (GTA), Canada. TASHA has been transferred to the context of the Island of Montreal, Canada using the 2003 Origin–Destination (O–D) travel survey and the 2001 Canadian Census. It generates daily schedules of activities (individual and joint) for each individual in this region. The modelled activity attributes (frequency, start time, duration and distance) from TASHA and observed attributes from the 2003 O–D travel survey are compared for five different activities (i.e. work, school, shopping, other, and return to home). At the aggregate level, TASHA provides quite reasonable outcomes (in some cases – better results than for the Toronto Area) for all four attributes for work, school and return to home activities with few exceptions (for instance, school start time). The model outcomes are also promising for shopping frequency and start times; however, TASHA provides larger differences for average shopping durations and distances. Only the forecasts for all four attributes for the ‘other’ activity type differ greatly with the observed attributes for the Montreal Island. These large differences most likely indicate the differences in behaviour between the Montreal Island and the Toronto Area. In general, we conclude that re-estimation of model parameters and the use of local activity attribute distributions (frequency, start time and duration) is a desirable step in the transfer of the TASHA model from one context to another.
Systematic Investigation of Variability due to Random Simulation Error in an Activity-Based Microsimulation Forecasting Model
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.
Spatial Transferability of Person-Level Daily Activity Generation and Time Use Models
An empirical assessment is made of the spatial transferability of person-level daily activity generation and time use models in regions in Florida and between Florida and California. The empirical models are for unemployed adults and are based on the multiple discrete-continuous extreme value structure. The prediction properties of the model are examined first. The results shed new light on the prediction properties of the multiple discrete-continuous extreme value model that have implications for transferability and that provide insight into how the model structure could be improved. Two approaches to transferring models are evaluated—naïve transfer and updating model constants—with measures such as log likelihood–based metrics, aggregate predictive ability, and model sensitivity to changes in demographic characteristics. Results suggest that accurate prediction of aggregate observed patterns is not an adequate yardstick with which to assess transferability; emphasis should be placed on model sensitivity to changes in explanatory variables. Updating of constants improves a transferred model's aggregate prediction ability, but not necessarily its policy sensitivity. The extent of transferability between regions within a state is greater than that across states. Within Florida, there is greater transferability between urban regions (especially between Southeast Florida and Central Florida) than between urban and rural regions.