Pedestrian Flow Prediction and Route Recommendation with Business Events
Due to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surroun...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7478 |
| _version_ | 1797476898807218176 |
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| author | Jiqing Gu Chao Song Zheng Ren Li Lu Wenjun Jiang Ming Liu |
| author_facet | Jiqing Gu Chao Song Zheng Ren Li Lu Wenjun Jiang Ming Liu |
| author_sort | Jiqing Gu |
| collection | DOAJ |
| description | Due to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surrounding pedestrians; we refer to this type of event as a business event. Business events attract pedestrian flows, which means business opportunities for the merchants. Moreover, their placement will affect the distributions of the pedestrian flows. However, deciding which route is chosen for a specified event is difficult. To the best of our knowledge, we are the first to consider business events when predicting pedestrian flow. In this paper, we investigate two problems: one is pedestrian flow prediction with business events, and the other is route recommendation for business events. First, we propose an Attraction-Based Matrix Factorization model (ABMF) to efficiently predict the pedestrian flow with business events, which introduces the attraction index of different categories to pedestrians in matrix factorization. Second, we leverage the Skip-gram mode to learn the latent representations and improve the pair-wise ranking loss to a flow-aware-based method (SG-FWARP), which aims to learn events’ latent representations for route recommendation. Compared with other state-of-the-art methods, the experimental results show ABMF can predict pedestrian flow matrix with a similarity of over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.9</mn></mrow></semantics></math></inline-formula> compared with the ground truth, and SG-FWARP can recommend routes for business events with high accuracy. |
| first_indexed | 2024-03-09T21:10:16Z |
| format | Article |
| id | doaj.art-96669370310f4efdb9a6ff4a026a5498 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:10:16Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-96669370310f4efdb9a6ff4a026a54982023-11-23T21:49:48ZengMDPI AGSensors1424-82202022-10-012219747810.3390/s22197478Pedestrian Flow Prediction and Route Recommendation with Business EventsJiqing Gu0Chao Song1Zheng Ren2Li Lu3Wenjun Jiang4Ming Liu5School of Computer Science and Engineering, University of Electronic Science and Technology of China, Qingshuihe Campus, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Qingshuihe Campus, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Qingshuihe Campus, Chengdu 611731, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Qingshuihe Campus, Chengdu 611731, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaSchool of Computer Science and Engineering, University of Electronic Science and Technology of China, Qingshuihe Campus, Chengdu 611731, ChinaDue to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surrounding pedestrians; we refer to this type of event as a business event. Business events attract pedestrian flows, which means business opportunities for the merchants. Moreover, their placement will affect the distributions of the pedestrian flows. However, deciding which route is chosen for a specified event is difficult. To the best of our knowledge, we are the first to consider business events when predicting pedestrian flow. In this paper, we investigate two problems: one is pedestrian flow prediction with business events, and the other is route recommendation for business events. First, we propose an Attraction-Based Matrix Factorization model (ABMF) to efficiently predict the pedestrian flow with business events, which introduces the attraction index of different categories to pedestrians in matrix factorization. Second, we leverage the Skip-gram mode to learn the latent representations and improve the pair-wise ranking loss to a flow-aware-based method (SG-FWARP), which aims to learn events’ latent representations for route recommendation. Compared with other state-of-the-art methods, the experimental results show ABMF can predict pedestrian flow matrix with a similarity of over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.9</mn></mrow></semantics></math></inline-formula> compared with the ground truth, and SG-FWARP can recommend routes for business events with high accuracy.https://www.mdpi.com/1424-8220/22/19/7478matrix factorizationpedestrian flow predictionroute recommendationembedding learning |
| spellingShingle | Jiqing Gu Chao Song Zheng Ren Li Lu Wenjun Jiang Ming Liu Pedestrian Flow Prediction and Route Recommendation with Business Events Sensors matrix factorization pedestrian flow prediction route recommendation embedding learning |
| title | Pedestrian Flow Prediction and Route Recommendation with Business Events |
| title_full | Pedestrian Flow Prediction and Route Recommendation with Business Events |
| title_fullStr | Pedestrian Flow Prediction and Route Recommendation with Business Events |
| title_full_unstemmed | Pedestrian Flow Prediction and Route Recommendation with Business Events |
| title_short | Pedestrian Flow Prediction and Route Recommendation with Business Events |
| title_sort | pedestrian flow prediction and route recommendation with business events |
| topic | matrix factorization pedestrian flow prediction route recommendation embedding learning |
| url | https://www.mdpi.com/1424-8220/22/19/7478 |
| work_keys_str_mv | AT jiqinggu pedestrianflowpredictionandrouterecommendationwithbusinessevents AT chaosong pedestrianflowpredictionandrouterecommendationwithbusinessevents AT zhengren pedestrianflowpredictionandrouterecommendationwithbusinessevents AT lilu pedestrianflowpredictionandrouterecommendationwithbusinessevents AT wenjunjiang pedestrianflowpredictionandrouterecommendationwithbusinessevents AT mingliu pedestrianflowpredictionandrouterecommendationwithbusinessevents |