Joel Freedman

Development of Microsimulation Activity-Based Model for San Francisco: Destination and Mode Choice Models

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.

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.

Ohio Long-Distance Travel Model

Credible forecasts of long-distance travel are an important tool for evaluating proposed intercity transportation improvements, including intercity highway and transit projects. Although researchers have studied the topic and have developed frameworks for modeling long-distance travel behavior, these research models have not been integrated into comprehensive model systems used for a wide range of applications. This paper presents a long-distance travel model that bridges the gap between research and practice. It is based on a rigorous behavioral framework that models the unique aspects of long-distance travel, such as a less regular frequency of trips and a different set of modal alternatives. The model structure includes the choice of whether to travel, the selection of the days on which to travel, scheduling to a specific time of day, destination choice, and mode choice. The model is sensitive to important descriptive variables, including the demographic characteristics of travelers, the attractiveness of possible destinations, and the levels of service of air, transit, and highway networks. It has been successfully implemented as part of the Ohio statewide model, which also features an advanced tour-based model of short-distance travel. Through this integration, it allows for behavioral consistency within the entire model system and competition among all travelers for transportation capacity. Lessons are learned about the data needs and research needs to further improve long-distance travel models.

MODELING THE CHOICE TO USE TOLL AND HIGH-OCCUPANCY VEHICLE FACILITIES

Traditionally, mode choice models distinguish between drive-alone and shared ride modes, leaving the network assignment models to predict the assignment of vehicles to toll and high-occupancy vehicle (HOV) facilities. If the shortest generalized cost path in the user equilibrium assignment is a toll or HOV path, the trip becomes a toll or HOV trip. Mode choice models that include the use of general-purpose highways, toll roads, and HOV lanes simultaneously with the choice of the drive-alone and shared ride modes are developed. Multinomial logit and nested logit models are estimated for this full set of alternatives. The models are estimated from a sample of data enriched by special surveys of toll, HOV, and transit users in the Houston, Texas, region. These data provide an empirical basis for studying the behaviors of toll and HOV facility users that is not normally available. The results indicate that the time saved by using these facilities has a higher utility weight than the time differences between other modes. Furthermore, for each mile traveled on a toll or HOV facility, there is an additional benefit that is partially offset by any excess in total travel distance necessary to use the toll or HOV facility. The additional preference for toll and HOV facilities can be explained by a perception of lower travel time, less driving stress, and higher reliability on these facilities. These results suggest that selection of a least-cost path in trip assignment is not sufficient for modeling the use of toll and HOV facilities.

Analysis of New Starts Project by Using Tour-Based Model of San Francisco, California

Activity-based models are increasingly attractive as alternatives to traditional trip-based travel demand forecasting models because of growing dissatisfaction with the internal consistency, aggregation bias, and lack of detail of trip-based approaches. New policy analysis requirements demand that forecasting models represent travel choices and the contexts in which these travel choices are made with ever-increasing geographic, temporal, and behavioral detail. Activity-based models can incorporate this detail and can provide decision makers with more precise insights into potential outcomes of transportation and land use investment and development strategies. The model of San Francisco, California, is a tour-based microsimulation model that forecasts daily activity patterns for individual San Francisco residents and has been used in transportation planning practice since 2000. The San Francisco model uses the daily activity pattern approach, first introduced by Bowman and Ben-Akiva, within a disaggregate microsimulation framework. This paper describes an application of the San Francisco model to the proposed new Central Subway project in downtown San Francisco. This is the first application of an activity-based travel demand model in the United States to a major infrastructure project in support of a submission to FTA for project funding through the New Starts program. To enable the submittal of a New Starts request, software was developed to collapse the microsimulation output of the tour and trip mode choice models into a format compatible with the FTA SUMMIT program. SUMMIT was then successfully used to summarize and analyze user benefits accruing to the project and to prepare an acceptable New Starts submittal.

Pricing and Reliability Enhancements in the San Diego, California, Activity-Based Travel Model

The estimation of demand for priced highway lanes is becoming increasingly important to agencies seeking to improve mobility and find alternative revenue sources for the provision of transportation infrastructure. However, many modeling tools fall short of what is required for robust estimates of demand with respect to toll and managed lanes in two key areas: the value of time is often aggregate and not consistently defined throughout the model system, and the reliability of transport infrastructure is rarely considered. This paper describes an effort that implemented recommendations of the Strategic Highway Research Program on pricing and reliability within a regional activity-based modeling system for the San Diego, California, region. The implemented recommendations included distributed travel time sensitivities across the synthetic population and special travel markets, continuous cost sensitivity on the basis of income, and multiple value of time bins in highway skimming and assignment. The work also included innovative research related to the analysis of travel time variability on the basis of a temporally disaggregate (1-min interval) data set of automobile travel speeds for most automobile links in the San Diego network for the month of October 2012. Regression equations that related the travel time reliability to link characteristics, incorporated reliability in automobile travel skims, incorporated those skims in the travel demand model system, and calculated toll elasticity on toll roads in San Diego County were estimated. The enhanced model matched observed toll demand better than the original model. Resulting elasticity values were generally found to be in the ranges reported in the literature.