Work

Research on Personalized O2O Instant Logistics Time Prediction Model based on Time Series Analysis and Spatial Continuity

Year: 2025

Type: conference-paper

Abstract: In order to make better use of spatio-temporal correlation in the model, this paper proposes a spatio-temporal transportation vehicle driving state matrix model VDSM. At the same time, a personalized O2O instant logistics time prediction model based on time series analysis and spatial continuity was proposed. Finally, in the experimental part, 10 transportation lines of trunk and non-trunk sections are selected, and the logistics in-transit time prediction model based on VDSM is used to predict the logistics in-transit time on the historical data sets of these lines. The final experimental results show that the strategy adopted by the personalized O2O instant logistics time prediction model based on time series analysis and spatial continuity can not only effectively reduce the overall prediction error of the model, but also increase the stability of the model prediction.

Source: International Conference on Computer Information and Big Data Applications (CIBDA)

DOI: 10.1145/3746709.3746780

Authors: Xiaoyan Chen, Hui Li, Zhengxiang Shen, Bo Zhang, Yudi Zhang, Chenghan Wang

Institutions: Wuhan Business University, Wuhan City Polytechnic, Wuhan University

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4413812445

FWCI:

Cites: 5

Cited by:

Related to: 10