Travel Demand Forecasting: Parameters and Techniques (2012) / Chapter Skim
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Pages 89-99
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From page 89... ...
The evaluation of such demand management strategies using travel demand models places more emphasis on the realistic representation of behavior to accurately reflect traveler responses to management policies. This realization has led to the consideration of the following issues, all of which have the potential to improve upon travel demand forecasts and enable more informed policy making: • Time-space constraints and interactions in the activitytravel decisions of an individual; • The accommodation of interindividual interactions in activity-travel decision making across individuals (such as joint participation in activities and travel, serve passenger trips, and allocation of responsibilities among individuals in a household)
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From page 90... ...
Home HomeWork Shop Home HomeWork HomeShop Figure 6.1. Temporal substitution of trips.
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From page 91... ...
These decisions determine the generation and scheduling of trips. Hence, determining the impact of travel demand management policies on time use behavior is an important precursor step to assessing the impact of such policies on individual travel behavior.
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From page 92... ...
Population synthesis; 2. Long-term choice models; and 3.
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From page 93... ...
Subsequent to the determination of long-term choices, the synthetic population of households and individuals is "processed" through the activity-based travel model system, as discussed in more detail in the following sections. 6.2.2 Generic Structure of Activity-Based Systems Activity-based model systems used in practice typically consist of a series of utility maximization-based discrete choice models (i.e., multinomial logit and nested logit models)
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From page 94... ...
Transportation network data needed in activity models are similar to data used in trip-based models and typically include highway network data, transit network data, and nonmotorized mode data. The transportation system performance data should be of high quality, with time-varying LOS characteristics (in-vehicle, out-of-vehicle, access, egress, and wait times)
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From page 95... ...
individual of a study area. However, it appears that the run times can be significantly reduced by one or more of the following techniques: • Simulation of the activity-travel patterns of a sample of the population without substantially compromising the accuracy of the aggregate-level outputs; • Efficient computing strategies such as data caching and multi-threading; • "Clever" methods of model specification where dummy exogenous variables are used so that a substantial part of the computations in the application context can be undertaken for market segments (defined by combinations of dummy exogenous variables)
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From page 96... ...
Hence, disaggregate population generation procedures are used to create synthetic records of each and every individual and household for activity- travel microsimulation purposes. However, to be able to forecast the individual activity-travel patterns and aggregate transportation demand at a future point in time, activitybased travel demand models require, as inputs, the disaggregate sociodemographics, and the land use and transportation system characteristics of that future point in time.
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From page 97... ...
as well as the spatial path assignment as input, and outputs the spatial-temporal trajectories of vehicles as well as travel times. The time-dependent shortest path routing algorithms and path assignment models take the spatiotemporal vehicle trajectories and travel times as input, and output the spatial path assignment of vehicles.
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From page 98... ...
Person tours generated by the activity-based model that are fully or partially made via transit can have their transit paths simulated individually. This individual simulation requires the specification of all transit vehicle runs and stops and the assigning of passenger trips to these runs and stops, along with their walk and auto access and egress components.
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From page 99... ...
individual of the study area using a microsimulation implementation that provides activity-travel outputs that look similar to survey data and can allow analysis of a wide range of policies on specific sociodemographic segments. Activity-based travel models are increasingly being adopted by the larger MPOs in the country and offer a more comprehensive and potentially more accurate assessment of policies to enhance mobility and reduce emissions.
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