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1.	Jonsson, Daniel, et al. (author) Reconciling user benefit and time-geography-based individual accessibility measures 2014 In: Environment and Planning, B. - : Sage Publications. - 0265-8135 .- 1472-3417. ; 41:6, s. 1031-1043 Journal article (peer-reviewed)abstract This paper presents a dynamic discrete choice model of activity scheduling that features classic time-geography properties within a microeconomic framework. We present results that show how the model can produce accessibilities that form space-time prisms, while retaining the properties of traditional measures based on consumer surplus in the form of logsums. The main features of the model are that it handles time-space constraints, travel time uncertainty, and endogenous trip chaining in one consistent framework. The resulting accessibility respects the individual's time budget and fixed activities. The dynamic discrete choice framework makes possible estimation of behavioural parameters using well-known methods. Some of the remaining computational challenges are discussed. The final section provides some examples of the policy analysis possibilities provided by a model of this kind.

Jonsson, Daniel, et al. (author)
Reconciling user benefit and time-geography-based individual accessibility measures
2014
In: Environment and Planning, B. - : Sage Publications. - 0265-8135 .- 1472-3417. ; 41:6, s. 1031-1043
Journal article (peer-reviewed)abstract
- This paper presents a dynamic discrete choice model of activity scheduling that features classic time-geography properties within a microeconomic framework. We present results that show how the model can produce accessibilities that form space-time prisms, while retaining the properties of traditional measures based on consumer surplus in the form of logsums. The main features of the model are that it handles time-space constraints, travel time uncertainty, and endogenous trip chaining in one consistent framework. The resulting accessibility respects the individual's time budget and fixed activities. The dynamic discrete choice framework makes possible estimation of behavioural parameters using well-known methods. Some of the remaining computational challenges are discussed. The final section provides some examples of the policy analysis possibilities provided by a model of this kind.

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