[1]LI Bohan,GUO Maozu,ZHAO Lingling.Passenger flow prediction in urban areas based on residual networks with split attention mechanism[J].CAAI Transactions on Intelligent Systems,2022,17(4):839-848.[doi:10.11992/tis.202202014]
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Passenger flow prediction in urban areas based on residual networks with split attention mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 4 Page number: 839-848 Column: 吴文俊人工智能科学技术奖论坛 Public date: 2022-07-05
Title:
Passenger flow prediction in urban areas based on residual networks with split attention mechanism
Author(s):
LI Bohan1; 2;  GUO Maozu1; 2;  ZHAO Lingling3
1. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
2. Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing University of Civil Engineering and Architecture, Beijing 100044, China;
3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Keywords:
passenger flow prediction; spatio-temporal data; deep learning; split-attention residual network; urban functional area; feature extraction; intelligent city; intelligent transportation
CLC:
TP391
DOI:
10.11992/tis.202202014
Abstract:
Passenger flow prediction is an important basis for intelligent management of urban transportation resources and public safety. In order to comprehensively consider the existing periodicity, trend and suddenness of urban passenger crowd flow itself, as well as the coupling relationship with urban physical and social space, based on the spatio-temporal residual network, this paper proposes an urban fine-grained passenger flow prediction model based on the residual network of split-attention mechanism with deep spatio-temporal data. Firstly, based on the regional passenger flow history data of different spatio-temporal intervals, the segmented attention mechanism module is introduced to assign different weights to the data of each modality to dynamically capture the abstract data features of higher relevance; on the basis of spatio-temporal data, the city functional area attributes are introduced as joint features, which are combined with external features such as holidays and climate to form a deep&wide network structure to effectively remember the contribution of important features to passenger flow changes. The regional passenger flow comparison experiments based on Beijing cab data show that compared with the traditional deep spatio-temporal residual network and other classical machine learning models, [] the prediction model introducing segmented attention mechanism and urban functional area features can better extract the features of data diversity, and the prediction accuracy is significantly better than other methods of the same category.
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