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Paper Number

2584

Paper Type

Complete

Description

In the era of the experience economy, “customized tours” and “self-guided tours” have become mainstream. This paper proposes an end-to-end framework for solving the tourist trip design problems (TTDP) using deep reinforcement learning (DRL) and data analysis. The proposed approach considers heterogeneous tourist preferences, customized requirements, and stochastic traffic times in real applications. With various heuristics methods, our approach is scalable without retraining for every new problem instance, which can automatically adapt the solution when the problem constraint changes slightly. We aim to provide websites or users with software tools that make it easier to solve TTDP, promoting the development of smart tourism and customized tourism.

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Dec 12th, 12:00 AM

An Intelligent Customization Framework for Tourist Trip Design Problems

In the era of the experience economy, “customized tours” and “self-guided tours” have become mainstream. This paper proposes an end-to-end framework for solving the tourist trip design problems (TTDP) using deep reinforcement learning (DRL) and data analysis. The proposed approach considers heterogeneous tourist preferences, customized requirements, and stochastic traffic times in real applications. With various heuristics methods, our approach is scalable without retraining for every new problem instance, which can automatically adapt the solution when the problem constraint changes slightly. We aim to provide websites or users with software tools that make it easier to solve TTDP, promoting the development of smart tourism and customized tourism.

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