Joint leisure travel optimization with user-generated data via perceived utility maximization

K. Gkiotsalitis, A. Stathopoulos

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

Abstract

The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users' willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users' preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals' satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.

Original languageEnglish
Pages (from-to)532-548
Number of pages17
JournalTransportation Research Part C: Emerging Technologies
Volume68
Early online date23 May 2016
DOIs
Publication statusPublished - Jul 2016
Externally publishedYes

Fingerprint

travel
Annealing
Smartphones
social media
Computational complexity
time of day
transportation system
time
flexibility
lack
demand

Keywords

  • Joint travel optimization
  • Pattern recognition
  • Social media
  • Social networks
  • Stochastic search
  • Utility-maximization

Cite this

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title = "Joint leisure travel optimization with user-generated data via perceived utility maximization",
abstract = "The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60{\%} of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users' willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users' preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals' satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.",
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Joint leisure travel optimization with user-generated data via perceived utility maximization. / Gkiotsalitis, K.; Stathopoulos, A.

In: Transportation Research Part C: Emerging Technologies, Vol. 68, 07.2016, p. 532-548.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Gkiotsalitis, K.

AU - Stathopoulos, A.

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AB - The lack of personalized solutions for managing the demand of joint leisure trips in cities in real time hinders the optimization of transportation system operations. Joint leisure activities can account for up to 60% of trips in cities and unlike fixed trips (i.e., trips to work where the arrival time and the trip destination are predefined), leisure activities offer more optimization flexibility since the activity destination and the arrival times of individuals can vary.To address this problem, a perceived utility model derived from non-traditional data such as smartphones/social media for representing users' willingness to travel a certain distance for participating in leisure activities at different times of day is presented. Then, a stochastic annealing search method for addressing the exponential complexity optimization problem is introduced. The stochastic annealing method suggests the preferred location of a joint leisure activity and the arrival times of individuals based on the users' preferences derived from the perceived utility model. Test-case implementations of the approach used 14-month social media data from London and showcased an increase of up to 3 times at individuals' satisfaction while the computational complexity is reduced to almost linear time serving the real-time implementation requirements.

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