Engineering the Neural Automatic Passenger Counter
Open accessAbstract
Automatic passenger counting (APC) in public transportation has been approached with various machine learning and artificial intelligence methods since its introduction in the 1970s. It is mainly used for revenue sharing, which (in Germany alone) is in the billions annually and supply planning, which is essential for services of general interest. While equivalence testing is becoming more popular than difference detection (Student’s t-test), the former is much more difficult to pass to ensure low user risk. On the other hand, recent developments in artificial intelligence have led to algorithms that promise much higher counting quality (lower bias). However, gradient-based methods (including Deep Learning) typically run into local optima. In this work, we explore and exploit various aspects of machine learning to increase the reliability, performance, and counting quality of the Neural APC 3D depth video-based LSTM neural network. We perform a grid search with several fundamental parameters: the selection and size of the training set, which is similar to cross-validation, and the initial network weights and randomness during the training process. Using this experiment, we show how aggregation techniques such as ensemble quantiles can reduce bias, and we give an idea of the overall spread of the results. We utilize the test success chance, a simulative metric based on the empirical distribution. We also employ a post-training Monte Carlo quantization approach and introduce cumulative summation to turn counting into a stationary method and allow unbounded counts. All in all, our numerous additions provide a major quality increase to the NAPC.
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Addressing challenges through preprocessing techniques, comprehensive training datasets, adherence to data privacy regulations, and strategic system design will be crucial for successful deployment and operation of these systems in public transport.
“Addressing these challenges through preprocessing techniques [102], comprehensive training datasets [103], adherence to data privacy regulations [104], and strategic system design will be crucial for the successful deployment and operation of these systems in public transport.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [103]
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- Authors
- Jahn, N., & Siebert, M.
- Corresponding author
- Siebert, Michael
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 114
- Pages
- 105148
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W4221147603
- Cited by
- 6
- References
- 32
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- European Regional Development Fund (10166961)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- automatic passenger counting, public transport, LSTM neural network, grid search, bias mitigation, revenue sharing, equivalence testing, training data selection
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