Spatial Interaction Model for Trip-Chaining Behavior Based on Entropy Maximizing Method

Honma, Yudai; Kurita, Osamu; Taguchi, Azuma

doi:10.15807/jorsj.53.235

Cited by 3 publications

(3 citation statements)

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“…From the standpoint stated above, an entropy-maximizing model for trip-chaining behavior was proposed in 2010 [5]. This entropy model can be regarded as a general extension of Wilson's entropy model.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Interaction Model for Trip-Chaining Behavior With a Focus on Calculation Efficiency

Honma

2015

JORSJ

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This paper provides a general framework for a spatial interaction model from the viewpoint of "trip-chain" comprising several trips. To estimate the traffic volume distribution of trip-chains, we proposed a new spatial interaction model based on the entropy maximizing model in 2010. In this paper firstly, an efficient calculation method, based on the spatial interaction model, for the proposed trip-chain is discussed. Furthermore, through these mathematical developments, the mathematical relationships between the entropy model, Markov model, and the discrete choice model, which produce the same traffic volume distribution of trip-chains, are clarified. These discussions not only support the entropy model proposed in a previous paper by human sciences based on expected-utility theory but also cover the shortcomings of the existing Markov model and the discrete choice model. It is often pointed out that the Markov model is a pure stochastic model and there is no support from the individual behavior principle. Moreover, the discrete choice model has the problem that the alternative set becomes huge as a result of dealing with trip-chaining behavior, which has a high degree of freedom. We show, under certain assumptions, the Markov model with individual behavior principle and the discrete choice model without enumerating the alternative set. In addition, we clarify the characteristics between the sequential decision making (Markov model) and the simultaneous decision making (discrete choice model) in terms of trip-chaining behavior.

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Section: Introductionmentioning

confidence: 99%

“…However, the previous model [5] had problems concerning calculation efficiency. Specifically, estimating parameters needed a long time because the alternative set of trip-chaining behavior became vast.…”

Section: Introductionmentioning

confidence: 99%

Spatial Interaction Model for Trip-Chaining Behavior With a Focus on Calculation Efficiency

Honma

2015

JORSJ

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“…Introducing the concept of a trip chain, a series of trip segments made by individuals, we can give a better understanding of the mobility flow. Several works combined the tripchain behavior with the entropy maximization argument in reference to the constrained model [21,22]. Specifically, they focused on the constrained model derived via entropy maximization under the constraint of the departure or arrival strength in each place.…”

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Generalized maximal entropy argument for the gravity law in human mobility

Lee¹,

Kim²,

Lee³

et al. 2020

EPL

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In this work, we elaborate on the entropy argument for the trip-chain distribution and derive the unconstrained gravity model. From the most probable trip-chain configurations under the global constraints of the mean trip-chain length as well as the cost, the resulting flow is shown to follow the gravity law. We apply the model to assess the passenger flow in Seoul public transportation system. With the trip-chain cost taking into account the loss from trip time as well as the gain from visiting, the flow is estimated well from the numbers of departing and arriving passengers. It is thus concluded that the gravity model in general can be rationalized through the single paradigm of maximal entropy.

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Learning Mixtures of Markov Chains from Aggregate Data with Structural Constraints

Luo

Zhen

et al. 2016

IEEE Trans. Knowl. Data Eng.

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Spatial Interaction Model for Trip-Chaining Behavior Based on Entropy Maximizing Method

Abstract: Ih this paper, we propose a new spatial interaction model for trip-chaining

Cited by 3 publications

References 15 publications

Spatial Interaction Model for Trip-Chaining Behavior With a Focus on Calculation Efficiency

Spatial Interaction Model for Trip-Chaining Behavior With a Focus on Calculation Efficiency

Generalized maximal entropy argument for the gravity law in human mobility

Learning Mixtures of Markov Chains from Aggregate Data with Structural Constraints

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