Fare evasion is a problem for public transport companies, with LSTM models
this issue can help companies get an analytical insight into where this issue
occurs the most, to prevent capital loss. In addition to the financial burden
this problem causes, having more inspectors is not enough to alleviate the
problem. The purpose of this study is to find a different way to predict fare
evasion in the public transport sector. Through the use of keypoint extractions
of passengers in video footage, an LSTM model is trained on those keypoints to
help predict the actions of passengers between payments and evasions. The
results were promising when it came to predicting the actions of passengers on
real-time footage. Thus a sophisticated approach can help to decrease the fare
evasion problem. A ReID model can be used alongside the LSTM model for better
accuracy, as there is always the chance that a person might only pay for the
fare at a later stage. With both models, it is possible for public transport
companies to start narrowing down where the root of their fare evasion problems
emerges.

使用 LSTM 模型和关键点提取，对公共交通领域的乘客进行行为预测，以减少逃票问题。同时，结合 ReID 模型提高准确性，帮助公共交通公司定位逃票问题的根源。

深度神经网络方法用于逃票

A Deep Neural Network Approach to Fare Evasion

Nowadays, it is possible to collect precise data describing movements of
public transport. Specifically, for each bus (or tram) geoposition data can be
regularly collected. This includes data for all buses in Warsaw, Poland.
Moreover, this data can be downloaded and analyzed. In this context, one of the
simplest questions is: can a model be build to represent behavior of busses,
and predict their delays. This work provides initial results of our attempt to
answer this question.

本研究使用波兰华沙的公交车定位数据，探讨了建立公交车行为模型及预测公交车延误的问题，并给出了初步的研究结果。

应用机器学习预测波兰华沙市公交运输行为

Applying machine learning to predict behavior of bus transport in Warsaw, Poland

The majority of public transport vehicles are fitted with Automatic Vehicle
Location (AVL) systems generating a continuous stream of data. The availability
of this data has led to a substantial body of literature addressing the
development of algorithms to predict Estimated Times of Arrival (ETA). Here
research literature reporting the development of ETA prediction systems
specific to busses is reviewed to give an overview of the state of the art.
Generally, reviews in this area categorise publications according to the type
of algorithm used, which does not allow an objective comparison. Therefore this
survey will categorise the reviewed publications according to the input data
used to develop the algorithm. The review highlighted inconsistencies in
reporting standards of the literature. The inconsistencies were found in the
varying measurements of accuracy preventing any comparison and the frequent
omission of a benchmark algorithm. Furthermore, some publications were lacking
in overall quality. Due to these highlighted issues, any objective comparison
of prediction accuracies is impossible. The bus ETA research field therefore
requires a universal set of standards to ensure the quality of reported
algorithms. This could be achieved by using benchmark datasets or algorithms
and ensuring the publication of any code developed.

该研究综述了公交模式下预测到站时间算法的发展，强调对公共数据标准的需求以解决报道标准的不一致性和算法预测精度的测量统一性，推进社会公共交通的规范化发展。