地理空间分析
背景(考古学)
计算机科学
全球导航卫星系统应用
数据科学
模式(计算机接口)
空间语境意识
土地覆盖
数据挖掘
运输工程
地理
全球定位系统
遥感
土地利用
人工智能
工程类
人机交互
电信
土木工程
考古
作者
Ye Hong,Emanuel Stüdeli,Martin Raubal
标识
DOI:10.1016/j.jtrangeo.2023.103736
摘要
Detecting travel modes from global navigation satellite system (GNSS) trajectories is essential for understanding individual travel behavior and a prerequisite for achieving sustainable transport systems. While studies have acknowledged the benefits of incorporating geospatial context information into travel mode detection models, few have summarized context modeling approaches and analyzed the significance of these context features, hindering the development of an efficient model. Here, we identify context representations from related work and propose an analytical pipeline to assess the contribution of geospatial context information for travel mode detection based on a random forest model and the SHapley Additive exPlanation (SHAP) method. Through experiments on a large-scale GNSS tracking dataset, we report that features describing relationships with infrastructure networks, such as the distance to the railway or road network, significantly contribute to the model's prediction. Moreover, features related to the geospatial point entities help identify public transport travel, but most land-use and land-cover features barely contribute to the task. We finally reveal that geospatial contexts have distinct contributions in identifying different travel modes, providing insights into selecting appropriate context information and modeling approaches. The results from this study enhance our understanding of the relationship between movement and geospatial context and guide the implementation of effective and efficient transport mode detection models.
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