Bus passenger flow statistics algorithm based on deep learning

Zhang, Yong; Tu, Wentao; Chen, Kairui; Wu, Chun-Ho; Li, Li; Ip, W. H.; Chan, Ching Yuen

doi:10.1007/s11042-020-09487-0

Cited by 6 publications

(10 citation statements)

References 14 publications

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“…93%, jointly for both directions) reported in the scenario of [31], and counting 107 from 109 passengers (i.e., approx. 98%, jointly for both directions) reported in the scenario of [32]. Note that the authors of [32] report an accuracy of 100% for the boarding direction; however, their test data consists of only 28 boarding passengers (ours: ranging 4555-5841 boarding passengers, i.e., a roughly 200 times bigger test set).…”

Section: Discussionmentioning

confidence: 93%

“…98%, jointly for both directions) reported in the scenario of [32]. Note that the authors of [32] report an accuracy of 100% for the boarding direction; however, their test data consists of only 28 boarding passengers (ours: ranging 4555-5841 boarding passengers, i.e., a roughly 200 times bigger test set). Looking at the performance measure of [33], NAPC obtained on average an absolute relative error of approx.…”

Section: Discussionmentioning

confidence: 93%

“…This APC counted 108 of 116 passengers in the bus transportation scene [31]. In [32], a two-class SSD and a Kalman filter are used for detecting passengers and tracking their movements. They conducted experiments using a 7-segment bus monitoring video where the segments have different characteristics, such as dark/strong outside light, crowded while getting on/off, including passengers carrying children, including passengers with babies and children.…”

Section: Introductionmentioning

confidence: 99%

“…They propose a boarding and alighting passenger counting method combining a two-step (generating and refining head proposal) head detection with a tracking algorithm for the generated depth video samples [33]. Different from [31], and [32], in [33] the APC was tested on a large data set with 2000 videos with four different sub-categories, which were defined based on the noise level (strong/mild sunlight) and crowdedness (crowded/uncrowded) of the scene. The performance for the different sub-categories ranged from 72.3% to 85.4% for boarding and from 91.3% to 93.7% for alighting passengers.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

Seidel

Jahn

Seo

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers' privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx. 13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx. 96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

Seidel

Jahn

Seo

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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“…Passenger counting used to be done using Radio-Frequency Identification (RFID) technology [ 15 , 28 ]. Surveillance picture capture techniques are also employed [ 34 ]. There are several challenges in forecasting bus passenger arrival times and dispatch times, as well as when the bus is overloaded, all of which contribute to a problem in predicting passengers [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Passenger flow prediction in bus transportation system using deep learning

Nagaraj¹,

Gururaj²,

Swathi³

et al. 2022

Multimed Tools Appl

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The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

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A lightweight bus passenger detection model based on YOLOv5

Li,

Wu,

et al. 2023

IET Image Processing

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The bus passenger detection algorithm is a key component of a public transportation bus management system. The detection techniques based on the convolutional neural network have been widely used in bus passenger detection. However, they require high memory and computational requirements, which hinder the deployment of bus passenger detectors in the bus system. In this paper, a lightweight bus passenger detection model based on YOLOv5 is introduced. To make the model more lightweight, the inner and outer cross‐stage bottleneck modules, called ICB and OCB, respectively, are proposed. The proposed module reduces the quantity of parameter and floating point operations and increases the detection speed. In addition, the neighbour feature attention pooling is adopted to improve detection accuracy. The performance of the lightweight model on the bus passenger dataset is empirically demonstrated. The experiment results demonstrate that the proposed model is lightweight and efficient. Compared lightweight YOLOv5n with the original algorithm, the model weight is reduced by 31% to 2.6M, and the detection speed is increased by 6% to 40FPS without an accuracy drop.

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Bus passenger flow statistics algorithm based on deep learning

Cited by 6 publications

References 14 publications

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset

Passenger flow prediction in bus transportation system using deep learning

A lightweight bus passenger detection model based on YOLOv5

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