Validation of TASHA: A 24-h activity scheduling microsimulation model

Roorda, Matthew J.; Miller, Eric J.; Habib, Khandker Nurul

doi:10.1016/j.tra.2007.10.004

Cited by 118 publications

(74 citation statements)

References 13 publications

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“…the average travel distance and travel duration, derived from both the predicted sequences and the observed ones, are examined (e.g. Cherchi & Cirillo, 2010;Roorda et al, 2008). The sequence alignment method (SAM), which compares two sequences based on the composition and temporal ordering of the daily activities (Abbott & Forrest, 1986;Wilson, 1998), is also employed to assess the similarities between each of the observed sequences and its predicted counterpart (e.g.…”

Section: Problem Statementmentioning

confidence: 99%

Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

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Activity-based micro-simulation transportation models typically predict 24-hour activitytravel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure. Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models. By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.

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Section: Problem Statementmentioning

confidence: 99%

Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…According to Lee (29), any random variable can be expressed as a standard normal distribution. Transforming the previous error terms in (1) and (4) to corresponding standard Normal distributed values, we get:…”

Section: Methodsmentioning

confidence: 99%

“…Activity-based approaches to model travel demand are increasingly moving from theoretical to operational models (1). In this context, agent-based microsimulation models are a promising approach since they explicitly conceive travel as an emergent phenomenon from peoples' activity characteristics, and more explicitly, from their activity scheduling processes.…”

Section: Introductionmentioning

confidence: 99%

Social Context of Activity Scheduling

Habib

Carrasco

Miller

2008

Transportation Research Record: Journal of the Transportation R

Self Cite

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Activity-based approaches to travel demand modeling are increasingly moving from theoretical to operational models. Agent-based microsimulation models are a promising approach as they explicitly conceive travel as an emergent phenomenon from people's activity characteristics and, more explicitly, from their activity-scheduling processes. Activity-scheduling processes are influenced by individuals’ characteristics as well as by the people with whom they interact. Thus, the activity-scheduling process has an intrinsic social context. With social activities used as a case study, the objective of this paper is to investigate empirically the relationship between social context (measured by with whom respondents interacted) and two key aspects of activity scheduling: start time and duration. Econometric models of the combined decisions of with whom to participate and when to start or how much time to spend are estimated to investigate the correlations between “with whom” and start time and duration decisions. Data collected by a 7-day activity diary survey were used for model development. Findings suggest that social context has a relevant role in activity-scheduling processes. For example, with whom people socialize influences social activity-scheduling processes more than do travel time or distances to social travel. In addition to theoretical understanding of the questions investigated here, the models serve as an empirical support for agent-based microsimulation models that could incorporate the role of social networks in activity-scheduling attributes.

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“…Models like CEMDAP (Bhat et al 2004), Albatross (Arentze and Timmermans 2004), Famos (Pendyala et al 2005) and TASHA (Roorda et al 2008) are fully operational and provide planning support for policy makers. Nevertheless, existing activity-based models still have their limitations.…”

Section: Introductionmentioning

confidence: 99%

Incorporating planned activities and events in a dynamic multi-day activity agenda generator

2012

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Daily agenda formation is influenced by formal commitments, satisfaction of needs surpassing some threshold and the desire to conduct particular activities in anticipation of socially and religiously driven events such as birthdays, Christmas, etc. As part of a research program to develop a dynamic activity-based model of transport demand, this paper proposes a model to represent dynamic agenda formation, including these different underlying processes. Bayesian estimation of the model is based on data collected through a Web-based survey for a sample of approximately 300 respondents. The survey uses an extension of a 1-day activity diary where respondents are asked to recall activities in retrospect and to identify planned activities in prospect. Estimation results suggest that planned activities influence agenda formation in general, but that their significance and size depends on activity type, socio-demographics and dwelling characteristics.

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Validation of TASHA: A 24-h activity scheduling microsimulation model

Cited by 118 publications

References 13 publications

Building a validation measure for activity-based transportation models based on mobile phone data

Building a validation measure for activity-based transportation models based on mobile phone data

Social Context of Activity Scheduling

Incorporating planned activities and events in a dynamic multi-day activity agenda generator

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