The Effects of Weather on Passenger Flow of Urban Rail Transit

Wang, Xiaoyuan; Guo, Yongqing; Chen, Bai; Liu, Shanliang; Liu, Shijie; Han, Junyan

doi:10.28991/cej-2020-03091449

Cited by 14 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meanwhile, cold weather and heavily polluted air impede passengers' occasional travels [68]. In addition, both IO flow and OD flow show different spatial and temporal patterns for holidays, weekends, and working days [6], [80]. Therefore, we consider the four external factors -weather conditions, air quality, holidays, and days of the week -in predicting OD flows.…”

Section: ) Io Flowmentioning

confidence: 99%

Adaptive Feature Fusion Networks for Origin-Destination Passenger Flow Prediction in Metro Systems

Lyu

Xiong

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Accurately predicting Origin-Destination (OD) passenger flow can help metro service quality and efficiency. Existing works have focused on predicting incoming and outgoing flows for individual stations, while little attention was paid to OD prediction in metro systems. The challenges are that OD flows 1) have high temporal dynamics and complex spatial correlations, 2) are affected by external factors, and 3) have sparse and incomplete data slices. In this paper, we propose an Adaptive Feature Fusion Network (AFFN) to a) adaptively fuse spatial dependencies from multiple knowledge-based graphs and even hidden correlations between stations and b) accurately capture the periodic patterns of passenger flows based on the auto-learned impact from external factors. To deal with the incompleteness and sparsity of OD matrices, we extend AFFN to multi-task AFFN to predict the inflow and outflow of each station as a side-task to further improve OD prediction accuracy. We conducted extensive experiments on two real-world metro trip datasets collected in Nanjing and Xi'an, China. Evaluation results show that our AFFN and multi-task AFFN outperform the state-of-theart baseline techniques and AFFN variants in various accuracy metrics, demonstrating the effectiveness of AFFN and each of its key components in OD prediction.

show abstract

Section: ) Io Flowmentioning

confidence: 99%

Adaptive Feature Fusion Networks for Origin-Destination Passenger Flow Prediction in Metro Systems

Lyu

Xiong

et al. 2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Cutting down extensive surveys or the database's density improvement can be one of the solutions [39]. Some proposals on the propagation model-based database enhancement have a drawback in the high cost of the channel measurement device and measurement campaigns [40][41][42]. The other approaches are by using the image method in which the grayscale image of the image processing method is applied to convert the RSSI values in the area of interest [38].…”

Section: Figure 5 Challenges and Prospective Solutions Of The Fingerp...mentioning

confidence: 99%

Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

et al. 2022

View full text Add to dashboard Cite

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

show abstract

“…Fingerprint recognition is a contact check, which has problems of insanity and susceptibility to interference. Iris recognition is difficult to operate and slow to recognize [45][46][47][48][49].…”

Section: Advantages Of Face Recognition Over Other Biometric Technolo...mentioning

confidence: 99%

Design and Study of Urban Rail Transit Security System Based on Face Recognition Technology

Guo

Xiao

Alroobaea

et al. 2022

IJCAI

View full text Add to dashboard Cite

In the modern world, it is difficult to prevent terrorism due to the relatively closed environment, dense personnel, large passenger flow, long line and wide coverage of urban rail transit. Identity recognition is a core element of security. The design and study of an urban rail transit security system based on face recognition technology are proposed in this paper. Through the study on the face recognition algorithm of intelligent security systems in urban rail transit, the related introduction of face recognition technology is done. The analysis of the main mode of face recognition is carried out utilizing the practical application design ideas. The results by experimental analysis show that if FAR is set to a very low range (such as 0.1% or even 0.01%) meanwhile FRR can reach a very low level (such as less than 1%). Such a system has practical value and otherwise, it may face a large number of passenger affairs and complaints to be handled. When FAR is set to 0.1% and N is 1.6 million, FRR can reach 2.1%. However, according to the test, when the picture quality deteriorates (during image captured by a webcam), the FRR will increase by 2 to 3 times. If a Webcam is used for recognition in Mugshot, the lowest FRR of the three top algorithms is only 5.21%.Povzetek: Tehnologija prepoznavanja obrazov je uporabljena za nadzor osumljencev -teroristov na vlakih.

show abstract

The Effects of Weather on Passenger Flow of Urban Rail Transit

Cited by 14 publications

References 20 publications

Adaptive Feature Fusion Networks for Origin-Destination Passenger Flow Prediction in Metro Systems

Adaptive Feature Fusion Networks for Origin-Destination Passenger Flow Prediction in Metro Systems

Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Design and Study of Urban Rail Transit Security System Based on Face Recognition Technology

Contact Info

Product

Resources

About