Prototype Model of Household Activity-Travel Scheduling

Miller, Eric J.; Roorda, Matthew J.

doi:10.3141/1831-13

Cited by 291 publications

(129 citation statements)

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“…Large-scale activity based travel demand and land use evolution models that take into account the individual agent decisions and interactions, are actively been developed in research and practice (Waddell, 2002;Miller and Roorda, 2003;Arentze and Timmermans, 2004;Balmer et al, 2006;Miller et al, 2011). These behavioural and market oriented models are an active tool for detailed impact forecasting of transportation, land use, environmental, and energy related policies.…”

Section: Introductionmentioning

confidence: 99%

Simulation based population synthesis

Farooq

Bierlaire

Hurtubia

et al. 2013

Transportation Research Part B: Methodological

145

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1Microsimulation of urban systems evolution requires synthetic population as a key input. Currently, the focus is on treating synthesis as a tting problem and thus various techniques have been developed, including Iterative Proportional Fitting (IPF) and Combinatorial Optimization based techniques. The key shortcomings of these procedures include: a) tting of one contingency table, while there may be other solutions matching the available data b) due to cloning rather than true synthesis of the population, losing the heterogeneity that may not have been captured in the microdata c) over reliance on the accuracy of the data to determine the cloning weights d) poor scalability with respect to the increase in number of attributes of the synthesized agents. In order to overcome these shortcomings, we propose a Markov Chain Monte Carlo (MCMC) simulation based approach. Partial views of the joint distribution of agent's attributes that are available from various data sources can be used to simulate draws from the original distribution. The real population from Swiss census is used to compare the performance of simulation based synthesis with the standard IPF. The standard root mean square error statistics indicated that even the worst case simulation based synthesis (SRMSE = 0.35) outperformed the best case IPF synthesis (SRMSE=0.64). We also used this methodology to generate the synthetic population for Brussels, Belgium where the data

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Section: Introductionmentioning

confidence: 99%

Simulation based population synthesis

Farooq

Bierlaire

Hurtubia

et al. 2013

Transportation Research Part B: Methodological

145

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“…The Integrated Land Use, Transportation and Environment (ILUTE) model (Salvini and Miller, 2003) model is designed to simulate the evolution of people and their activity patterns, transportation networks, houses, commercial buildings, the economy, and the job market over time. Within this vision, Miller and Roorda (2003), developed the Toronto Area Scheduling model for Household Agents (TASHA) that uses projects to organize activity episodes into schedules of persons. Schedules for members in a household are simultaneously generated to allow for joint activities.…”

Section: Daily Activity Patternmentioning

confidence: 99%

Travel Behavior and Demand Analysis and Prediction

Goulias

2009

Encyclopedia of Complexity and Systems Science

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Glossary Activity-based approachA modeling method that accounts for the interdependent relationships among activities and persons to derive travel demand equations. Dynamic planningThe incorporation of trends, cycles, and feedback mechanisms into a process of actively shaping our future. Desired futures are first defined in terms of performance measures and a combination of forecasting and backcasting methods are used to identify the right paths to follow in achieving these futures. MicrosimulationA method to represent the movement in space and time of the most elementary units of a phenomenon. When applied in traffic engineering the units are vehicles. When applied in travel behavior the units are persons and households. Multi-agent microsimulation allows to also represent human interaction with each person modeled as an agent. Travel demandThe amount of travel within a time interval such as number of trips in a day, total amount of distance and total amount of travel time, the locations (destinations) visited, the means used to reach these locations, departure time and arrival time of trips, routes followed in reaching these locations, the sequencing and assembly of trips in groups, and the purpose or activity engaged in at the end of each trip. I. DefinitionsTransportation modeling and simulation aims at the design of an efficient infrastructure and service to meet our needs for accessibility and mobility. At its heart is good understanding of human behavior that includes the identification of the determinants of behavior and the change in human behavior when circumstances change either due to control (e.g., policy actions), trends (e.g., demographic change), or unexpectedly (e.g., disasters). This is the key ingredient that drives most decisions in transportation planning and traffic operations. Since transportation systems are the backbone connecting the vital parts of a city (a region, a state or an entire country), in-depth understanding of transportation-related human behavior is essential to the planning, design, and operational analysis of all the systems that make a city function.Understanding human nature requires us to analyze and develop synthetic models of human agency in its most important dimensions and the most elemental constituent parts. This includes, and it is not limited to, understanding of individual evolution along a life cycle path (from birth to entry in the labor force to retirement to death) and the complex interaction between an individual and the anthropogenic environment, natural environment, and the social environment. Travel behavior research is one aspect of analyzing human nature and aims at understanding how traveler values, norms, attitudes, and constraints lead to observed behavior. Traveler values and attitudes refer to motivational, cognitive, situational, and disposition factors determining human behavior.Travel behavior refers primarily to the modeling and analysis of travel demand, based on theories and analytical methods from a variety of scientific fields. These inc...

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“…Some of them are SCHEDULER (Gärling et al, 1994), AMOS (Pendyala et al, 1995(Pendyala et al, , 1998, SMASH (Ettema et al, 1994(Ettema et al, , 2000, PCATS (Kitamura et al, 1996), GISICAS (Kwan, 1997), ALBATROSS (Arentze and Timmermans, 2000), AURORA Joh et al, 2003Joh et al, , 2004, TASHA (Miller and Roorda, 2003), FAMOS (Pendyala et al, 2005) and FEATHERS (Arentze et al, 2006). Recent advances in household activity models considering the scheduling process can be found in Timmermans and Zhang (2009).…”

Section: Introductionmentioning

confidence: 99%

Assessing planning decisions by activity type during the scheduling process

Ruíz

Roorda

2011

Transportmetrica

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Existing activity-based models still make assumptions about scheduling decision processes that are not well-informed by empirical evidence. In this paper, a step forward is taken to better understand the activity-scheduling process and to improve activity-based models. In particular, different planning decision mechanisms depending on several activity type classifications are explored.First, models describing the planning of several aggregate activity types are considered. For these activities, three planning decisions are studied: location, planning time horizon and rescheduling. The "with whom" planning decision is also studied when sub-types of recreational/entertainment activities are investigated in depth.Significant differences are found in modeling results for each activity type and sub-type and each planning decision. These results confirm the existence of different mechanisms underlying the activity-travel decision process when activity types and sub-types are considered. Important conclusions related to the improvement of microsimulation models are highlighted.

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Prototype Model of Household Activity-Travel Scheduling

Cited by 291 publications

References 3 publications

Simulation based population synthesis

Simulation based population synthesis

Travel Behavior and Demand Analysis and Prediction

Assessing planning decisions by activity type during the scheduling process

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