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Understanding children's cycling route selection through spatial trajectory data mining
Journal article   Open access   Peer reviewed

Understanding children's cycling route selection through spatial trajectory data mining

Han Bao, Xun Zhou, Cara Hamann and Steven Spears
Transportation research interdisciplinary perspectives, Vol.20, 100855
07/2023
DOI: 10.1016/j.trip.2023.100855
url
https://doi.org/10.1016/j.trip.2023.100855View
Published (Version of record) Open Access

Abstract

•We utilize naturalistic cycling behavior data collected from children.•We match road related features and children cycling trajectory data through an efficient algorithm.•We study children route selection in cycling activities based on the data collected.•Propose a data mining framework for cycling route selection analysis under different contexts.•Interpret children cycling route selection using a set of road related features. A sustainable alternative transportation mode to address growing transportation and environmental stress is cycling, which is eco-friendly and healthy for humans. Improving the quality of the bicycling experience is crucial for increasing bicycle use. Good bicycling experience is more critical for child bicyclists because they are less experienced and need more space for error. Therefore, a scientific assessment of child bicyclist perception of selected route safety, comfort, and environment is of great interest. Finding an effective way to learn child bicyclist behavior and help them reduce cycling risk is necessary. In response to this need, we utilize a data mining model to develop a methodology for measuring children's bicycling route safety conditions by evaluating multiple road safety-related features. The proposed method uses a set of route features representing the situation of street environments extracted from state data and first-hand children’s bicycle trajectory data collected using Global Positioning Systems (GPS) from volunteer children bicyclists. A random forest (RF), a well-known classifier, is adopted to predict child bicyclists' behavior. We extract the different route segments between children’s selected routes and the shortest path to learn the child bicyclists' behavior and use the selected best features to interpret their changing cycling behavior. The result shows that children bicyclists' behavior could be analyzed by giving trajectory and nearby road safety situation data. Our model achieves a promising accuracy with an average rate of 92% over multiple scenarios, demonstrating the proposed method's feasibility and the effectiveness of selected features. In addition, we compare our feature effectiveness with the state's generated road safety score data to evaluate the feature robustness. Our features outperform the safety score feature with an average of 10% improvement in prediction accuracy. Furthermore, our method proposes a model framework that can be applied to different study regions and adult bicycling behavior learning.
 Safety Children cycling route selection GPS data Spatial trajectory data mining

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