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Activity Based Model


SANDAG is in the early stages of transitioning from an enhanced four-step transportation model to an activity-based model (ABM). An ABM simulates individual and household transportation decisions that compose their daily travel itinerary.

Next –Generation Transportation Modeling: The Activity Based Model

SANDAG is in the early stages of transitioning from an enhanced four-step transportation model to an activity-based model (ABM). An ABM simulates individual and household transportation decisions that compose their daily travel itinerary. People travel outside their home for activities such as work, school, shopping, healthcare, and recreation (Figure 1), and the ABM attempts to predict whether, where, when, and how this travel occurs.

The SANDAG ABM strives to be as behaviorally realistic as possible and is based on empirical data collected by SANDAG, Caltrans, and the federal government. The model development has been regularly peer-reviewed by the ABM Advisory Committee, a panel of national experts in the travel demand forecasting field. The SANDAG ABM is innovative, and it builds off of a proven model design that has been tested in practice in the San Francisco, Atlanta, and New York regions. This document briefly describes how the SANDAG ABM works and highlights some of the model’s key assumptions.

Figure 1: Travel is often a necessary part of engaging in desired activities

How Will an ABM Enhance SANDAG's Planning Analysis?

Activity-based models are at the forefront of travel demand modeling technology. These models allow for a more nuanced analysis of complex policies and projects. The powerful analytic capabilities of an ABM are particularly helpful in evaluating Transportation Demand Management (TDM) policies, social equity, carpooling, transit access, parking conditions, tolling and pricing. Because an ABM tracks the characteristics of each person, the model can be used to analyze the travel patterns of a wide range of socioeconomic groups. For example, a household with many members may be more likely to carpool, own multiple vehicles, and share shopping responsibilities.

Figure 2: A very wide range of policies can be analyzed with an ABM

A Methodologically-Advanced Approach to Forecasting Travel Demand

The SANDAG ABM includes a number of methodological strengths. It predicts the travel decisions of San Diego residents at a detailed level, taking into account the way people schedule their day, their behavioral patterns, and the need to cooperate with other household members. When simulating a person’s travel patterns, the ABM takes into consideration a multitude of personal and household attributes like age, income, and gender. The model’s fine temporal and spatial resolution ensures that it is able to capture subtle aspects of travel behavior.

The Steps in the SANDAG ABM

Because the personal travel decisions of over 3 million San Diego residents are complex, the ABM is comprised of numerous, interacting components (“steps”). The first step is to build a representative population that looks like the real San Diego. Once a representative population is created, the model predicts long-term and medium-term decisions such as a choice of work location and a household’s choice of number of cars to own. Next, each person’s day is scheduled, taking into account the priority of various activities. Once all journeys to and from home have been scheduled, the model predicts specific travel details such as mode and the number of stops to make. The final step of the ABM is traffic assignment.

Model Integration

Model integration is an important aspect of the SANDAG system of regional models. The ABM needs to be integrated with PECAS and various travel models. The entire model system will be run iteratively.

Validation and Calibration

If necessary, adjustments to the model estimates will be made so that output better fits with observed data (this process is called calibration). The ABM will be calibrated to match various survey data. To gauge the quality of ABM results, model output will be compared with independent sources of data (this process is called validation). For example, predicted automobile travel can be compared with traffic counts, and travel by workers can be compared with worker flow data from the Census. The population synthesis step of the ABM is validated by comparing characteristics of the synthetic population with Census data.

Software

Running an ABM requires not only extensive computing power but also a software architecture that allows for model improvement and growth. The SANDAG ABM is implemented in Java and takes full advantage of object-oriented programming principles. Adherence to these principles ensures that the model’s software components will be flexible, interchangeable, and easy to maintain and enhance. The ABM software implementation supports model evolution and facilitates cooperation in model development.

Data Sources

The SANDAG ABM utilizes a variety of data as inputs. The most important data source is household travel survey data. The latest household travel survey conducted for SANDAG was the 2006 Household Travel Behavior Survey (TBS06). TBS06 surveyed 3,651 households in San Diego County. The survey asked all household members to record all trips for a specified 24-hour weekday period using a specially designed travel log.