A Study on the Method for Mining Intercity Truckers'Travel Preferences from the Perspective of Trip Chains
This study designs a trajectory data-based framework for constructing and classifying trip chains to mine truckers'travel preferences.First,we identify the trip ends and base depots of truckers to construct intercity trip chains.Second,we characterize these trip chains and employ the Gaussian Mixture Model to classify them into six patterns based on numerical features.Third,our study utilizes the Longest Common Subsequence algorithm to extract route features for truckers with different trip chain pattern preferences.The results indicate that the majority of truckers exhibit a dominant preference for a specific trip chain pattern,and these preferences vary among truckers of different truck types.Additionally,there are differences in route decision-making and geographical activity ranges among truckers with different trip chain pattern preferences.Through analyzing truckers'travel behavior and characteristics in intercity freight,this study contributes to a deeper understanding of their travel preferences and facilitates efficient management of intercity freight.
freight big dataintercity freighttrip chainsroute preference
万方数据
维普
