Journal Article10.1007/S11116-017-9809-8
Activity-based trip chaining behavior analysis in the network under the parking fee scheme
Ge Gao,Huijun Sun,Jianjun Wu +2 more
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TL;DR: In this article, an integrated model which combines Beckman-type congestion link terms and entropy-type logit demand terms is proposed to describe the traveler behavior, and a bi-level model is designed to maximize the social welfare by appropriate parking fee.
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Abstract: In this paper, we incorporate activity-based trip chaining behavior into the network equilibrium analysis. An integrated model which combines Beckman-type congestion link terms and entropy-type logit demand terms is proposed to describe the traveler behavior. The convexity and equivalency conditions of the model are discussed. Based on the integrated model, a bi-level model is designed to maximize the social welfare by appropriate parking fee. Also, an expanded network is developed to eliminate the non-additivity of the utilities of activities and travelling in the original network. Then, the Simulated Annealing (SA) method is used to solve the proposed bi-level model. Numerical examples are presented to examine the model’s availability and effects of the parking fee scheme on traveler behavior and social welfare. Results show that the model is effective in describing the trip chaining behavior in the network.
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Citations
Global optimization and simulated annealing
AF Anton Dekkers,Emile H. L. Aarts,Emile H. L. Aarts +2 more
- 01 Jan 1988
TL;DR: The mathematical formulation of the simulated annealing algorithm is extended to continuous optimization problems, and it is proved asymptotic convergence to the set of global optima.
382
Predicting the travel mode choice with interpretable machine learning techniques: A comparative study
Mohammad Tamim Kashifi,Arshad Jamal,Mohammad Samim Kashefi,Meshal Almoshaogeh,Syed Masiur Rahman +4 more
TL;DR: In this paper , the authors proposed a machine learning framework for travel mode choice prediction in the Dutch National Travel Survey (NHTS) data, which is based on Logistic Regression, Random Forests, Decision Tree, Multilayer Perceptron, Light Gradient Boosting Decision Tree and LightGBDT.
77
Travel mode choice: a data fusion model using machine learning methods and evidence from travel diary survey data
TL;DR: The data fusion model based on stacking strategy and a hybrid model of the unsupervised Denoising Autoencoder (DAE) combining with the supervised Random Forest (RF) are proposed, which is particularly useful and powerful in the choice behavior analysis and outperforms other widely used classifiers.
51
Empirical analysis of urban road traffic network: A case study in Hangzhou city, China
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35
Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data
TL;DR: Wang et al. as discussed by the authors explored the correlation between taxi demand and socioeconomic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City.
References
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