首页|Findings from Kunming University Broaden Understanding of Machine Learning (The Role of Built Environment In Shaping Reserved Ride-hailing Services: Insights Fr om Interpretable Machine Learning Approach)
Findings from Kunming University Broaden Understanding of Machine Learning (The Role of Built Environment In Shaping Reserved Ride-hailing Services: Insights Fr om Interpretable Machine Learning Approach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting from Kunming, People's Republic of China, by NewsRx journalists, research stated, "Cruising ride-hailing vehicles exacerb ate traffic congestion by generating negative externalities. In contrast, reserv ed ride-hailing services leverage precise information regarding the departure ti mes and originsdestinations of future trips." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Yunnan Fundamental Research Projects, Yunnan Xing Dian Talen ts Plan Young Program (2023). The news correspondents obtained a quote from the research from Kunming Universi ty, "Platforms can use this data to dispatch and route drivers more efficiently, thereby reducing the need for cruising. Although previous research has largely concentrated on real-time ride-hailing services, the impact of the built environ ment on reserved ride-hailing remains unexplored with empirical data. This study integrates multi-source data from Haikou City in China and utilizes the gradien t boosting decision tree model, which is an interpretable machine learning appro ach, to investigate potential relationships between reserved ridehailing trip d emand and the built environment. The rankings of relative importance reveal that factors such as the density of food services, education institutions, accessibi lity to town centers, and proximity to transportation hubs significantly influen ce the demand for reserved ride-hailing. Furthermore, the study demonstrates that the aforementioned factors exhibit non-linear effects on the demand for reserv ed ride-hailing."
KunmingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningKunming University
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