Hubris or humility? Accuracy issues for the next 50 years of travel demand modeling

Hartgen, D T

doi:10.1007/s11116-013-9497-y

Cited by 27 publications

(11 citation statements)

References 24 publications

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“…This is a problem that seriously hampers further model development and model application (e.g. Hartgen, 2013). Having useful and reliable benchmark and evaluation criteria for activity-based micro-simulation models has thus been a major concern.…”

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

“…Hartgen, 2013;Janssens, Giannotti, Nanni, Pedreschi, & Rinzivillo, 2012). To overcome this problem, SAM has been employed to assess activity-based models by pairwise comparing the observed sequences extracted from the diaries and their predicted counterpart (e.g.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Characterizing activity sequences using profile Hidden Markov Models

Liu

Janssens

Cui

et al. 2015

Expert Systems with Applications

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“…Furthermore, if problems are found, it is also challengeable to trace these errors back to the model-building process. Nevertheless, despite the limitations, both internal and external validations are commonly adopted, as no methods have been developed for more accurately validating the model results [3].…”

Section: Introductionmentioning

confidence: 99%

Validating Activity-Based Travel Demand Models Using Mobile Phone Data

Li¹,

Gao²,

Jia³

et al. 2018

Mobile Computing - Technology and Applications

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Activity-based travel demand models predict travel sequences on a day for each individual in a study region. These sequences serve as important input for travel demand estimate and forecast in the area. However, a reliable method to evaluate the generated sequences has been lacking, hampering further development and application of the models. In this chapter, we use travel behavioral information inferred from mobile phone data for such validation purposes. Our method is composed of three major steps. First, locations where a user made calls on a day are extracted from his/her mobile phone records, and these locations form a location trajectory. All the trajectories from the user across multiple days are then transformed into actual travel sequences. The sequences derived from all phone users are further classified into typical patterns which, along with their relative frequencies, define travel profiles. These profiles characterize current travel behavior in the study region and can thus be utilized for assessing sequences generated from activity-based models. By comparing the obtained profiles with statistics drawn from conventional travel surveys, the validation potential of the proposed method is demonstrated.

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Hubris or humility? Accuracy issues for the next 50 years of travel demand modeling

Cited by 27 publications

References 24 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

Characterizing activity sequences using profile Hidden Markov Models

Validating Activity-Based Travel Demand Models Using Mobile Phone Data

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