Trip destination recognition based on individual memory effect and distance effect

doi:10.3785/j.issn.1008-973X.2024.04.006

Journal of ZheJiang University (Engineering Science)

2024, Vol. 58

Issue (4): 708-717 DOI: 10.3785/j.issn.1008-973X.2024.04.006

Trip destination recognition based on individual memory effect and distance effect

Sijing ZHENG1(

),Yong CHEN2,Yizhang ZHU1,Xiqun CHEN2,*(

)

1. Institute of Intelligent Transportation Systems, Polytechnic Institute, Zhejiang University, Hangzhou 310058, China
2. Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China

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Abstract

A new travel destination recognition model was established through analyzing individual travel track data, mining the characteristics of individual travel history memory and the distance between individual location and potential destination. The model was tested using 62880 trips of 200 anonymous individuals in Hangzhou. The location based service (LBS) data was preprocessed, the segmented travel data fragments were extracted for the purpose of activity, and the meshed individual historical destination set was obtained by GeoHash grid coding method. The training set and test set were constructed by using the random missing individual travel history track data, and the parameters of the model were calibrated by nonlinear least square method. Results show that the proposed model improves the recognition accuracy of travel destination. Comparing the recall rate, discount cumulative return and F1 score of different models, the proposed model was better than the Markov model, decision tree model and random forest model. The robustness of the proposed model was verified by the sensitivity analysis of data missing rate.

Key words： smart mobility individual travel destination recognition memory effect distance effect

Received: 13 February 2023 Published: 27 March 2024

CLC:	F 572
	U 491

Fund: 国家自然科学基金资助项目（72171210）；浙江省自然科学基金资助项目（LZ23E080002）.

Corresponding Authors: Xiqun CHEN E-mail: sijingzheng@zju.edu.cn;chenxiqun@zju.edu.cn

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Cite this article:

Sijing ZHENG,Yong CHEN,Yizhang ZHU,Xiqun CHEN. Trip destination recognition based on individual memory effect and distance effect. Journal of ZheJiang University (Engineering Science), 2024, 58(4): 708-717.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2024.04.006 OR https://www.zjujournals.com/eng/Y2024/V58/I4/708

基于个体记忆效应和距离效应的出行目的地识别

通过分析个体出行轨迹数据，挖掘个体出行历史记忆特征以及个体所处位置与潜在目的地的距离特征，建立新的出行目的地识别模型. 利用杭州市200个匿名个体62880次出行数据测试所建模型的效果. 对基于位置的服务（LBS）数据进行预处理，提取以活动为目的的分段出行数据片段，采用GeoHash网格编码方法得到网格化后的个体历史目的地集合. 利用随机缺失的个体出行历史轨迹数据构建训练集和测试集，采用非线性最小二乘法对模型进行参数标定. 结果表明，所建模型提升了出行目的地识别精度；对比不同模型的召回率、折扣累计收益和F1分数，所建模型优于马尔可夫模型、决策树模型以及随机森林模型；数据缺失率敏感性分析结果验证了所建模型的鲁棒性.

关键词： 智慧出行, 个体出行, 目的地识别, 记忆效应, 距离效应

Fig.1 Illustration of trip destination recognition model based on individual memory effect and distance effect

Fig.2 Flowchart of trip destination recognition model

Fig.3 Descending ranking of number of individual trips

Fig.4 Departure and arrival time distribution of individual trips

Fig.5 Overlay distribution of individual trip destinations

Fig.6 Statistics of all individual trip characteristics

Fig.7 Comparison of real and recognized destination distribution

Tab.1 Performance comparison of different trip destination recognition models

Fig.8 F1-score for different numbers of destinations visited

Tab.2 Ablation experiments of individual memory effect and distance effect models

Fig.9 Comparison of all individual trip characteristics

Tab.3 JS divergence of trip characteristic distribution of different comparison models

Fig.10 Comparison of travel scatter plot for different models

Fig.11 Sensitivity analysis of missing rate for different models

Fig.12 Sensitivity analysis of time for proposed model


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