可解释性
可预测性
计算机科学
背景(考古学)
目的地
旅游行为
公共交通
数据挖掘
机器学习
人工智能
旅游
运输工程
工程类
古生物学
物理
法学
生物
量子力学
政治学
作者
Juanjuan Zhao,Jiexia Ye,Minxian Xu,Cheng‐Zhong Xu
摘要
Abstract Real‐time individuals' destination prediction is of great significance for real‐time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this paper, taking the public transportation metro systems as example, we design a practical method called practical model with strong interpretability and predictability to predict each passenger's destination. Our main novelties are two aspects: (1) We propose to predict individuals' destination by combining personal and crowd behavior under certain context. (2) An explanatory model combining discrete choice model and neural network model is proposed to predict individuals' stochastic trip's destination, which can be applied to other transportation analysis scenarios about individuals' choice behavior such as travel mode choice or route choice. We validate our method based on extensive experiments, using smart card data collected by automatic fare collection system and weather data in Shenzhen, China. The experimental results demonstrate that our approach can achieve better performance than other baselines in terms of prediction accuracy.
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