Neural Network-Based Combined Synthetic Model of Trip Generation and Distribution

Kim, Daehyon; Chang, Yohan; Yang, Kyong-Uk

doi:10.1080/12265934.2008.9693624

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Kim et al proposed a neural network-based combined model for trip generation, distribution, and modal spilt. The experimental results showed that the proposed direct demand model using the neural network model is an attractive proposition, particularly in areas where we have to deal with a large number of zones to determine future trips [8]. Rasouli and Nikraz estimated the distribution of the journey to work trips in the city of Mandurah in WA using generalized regression neural network (GRNN) and gravity model models.…”

Section: Introductionmentioning

confidence: 99%

Trip Distribution Modeling Using Neural Network and Direct Demand Model

Maniat¹,

Abdoli²,

Raufi³

et al. 2022

CRPASE

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The transportation system as one of the main elements of providing services in cities is of particular importance in urban management, and therefore high budgets are spent annually in this sector. In this regard, the management of this system takes various measures to apply the costs optimally and improve the transportation system's performance. Since these costs must be applied fairly in the sectors with the highest demand, an accurate estimate of travel demand should be available. In this study, using direct demand and neural network models, a travel demand forecasting model between the provinces of Iran has been compared and investigated. In other words, the second stage of the four basic stages of the travel demand, i.e., the trip distribution in the direct model, obtained a regression coefficient of 0.41, and for the neural network model, this value was 0.70, which shows that the neural network model is a far better model for trip distribution.

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Section: Introductionmentioning

confidence: 99%