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Urban high-quality navigation path planning that integrates human emotion perception learning |
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| Authors: | Shaojun Liu Yi Long Ling Zhang Yi Huang |
| Institution: | 1. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China;2. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing, China;3. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, China;4. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing, China |
| Abstract: | Geographic information system navigation services are now incorporating psychological well-being as a factor when devising navigation routes. However, challenges such as limited data, method generalizability, and subjective human perception remain unresolved. Therefore, a general humanized path-navigation method that effectively quantifies human emotional perception demands is required. In this study, we designed a deep learning model using a large, crowdsourced dataset to predict emotional responses to street-view images. Our method enhances urban path planning, thus providing comprehensive emotional benefits. Our approach and several goal-oriented methods were applied in Nanjing, and the findings were compared via comparative analyses and questionnaire surveys. The results confirmed that our proposed method outperforms utilitarian goal-driven path planning methods in terms of subjective perception. This study provides a widely available technique for high-quality navigation planning that meets psychological perception needs and offers a valuable guidance for the research on humanized geographic information services. |
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