Video Analytics for Railroad Safety Research: An Artificial Intelligence Approach

2018 ◽  
Vol 2672 (10) ◽  
pp. 269-277 ◽  
Author(s):  
Asim Zaman ◽  
Xiang Liu ◽  
Zhipeng Zhang
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Decision Support Tool ◽  
Video Data ◽  
Near Miss ◽  
Support Tool ◽  
Near Misses ◽  
Railroad Industry ◽  
Safety Research ◽  
Railroad Safety

The volume of video data in the railroad industry has increased significantly in recent years. Surveillance cameras are situated on nearly every part of the railroad system, such as inside the cab, along the track, at grade crossings, and in stations. These camera systems are manually monitored, either live or subsequently reviewed in an archive, which requires an immense amount of human resources. To make the video analysis much less labor-intensive, this paper develops a framework for utilizing artificial intelligence (AI) technologies for the extraction of useful information from these big video datasets. This framework has been implemented based on the video data from one grade crossing in New Jersey. The AI algorithm can automatically detect unsafe trespassing of railroad tracks (called near-miss events in this paper). To date, the AI algorithm has analyzed hours of video data and correctly detected all near-misses. This pilot study indicates the promise of using AI for automated analysis of railroad video big data, thereby supporting data-driven railroad safety research. For practical use, our AI algorithm has been packaged into a computer-aided decision support tool (named AI-Grade) that outputs near-miss video clips based on user-provided raw video data. This paper and its sequent studies aim to provide the railroad industry with next-generation big data analysis methods and tools for quickly and reliably processing large volumes of video data in order to better understand human factors in railroad safety research.

Artificial Intelligence-Aided Automated Detection of Railroad Trespassing

2019 ◽  
Vol 2673 (7) ◽  
pp. 25-37 ◽  
Author(s):  
Asim Zaman ◽  
Baozhang Ren ◽  
Xiang Liu
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Video Clip ◽  
Text Message ◽  
The United States ◽  
Video Data ◽  
Surveillance Video ◽  
Railroad Industry ◽  
Real Time Analysis ◽  
Safety Research

Trespassing is the leading cause of rail-related deaths and has been on the rise for the past 10 years. Detection of unsafe trespassing of railroad tracks is critical for understanding and preventing fatalities. Witnessing these events has become possible with the widespread deployment of large volumes of surveillance video data in the railroad industry. This potential source of information requires immense labor to monitor in real time. To address this challenge this paper describes an artificial intelligence (AI) framework for the automatic detection of trespassing events in real time. This framework was implemented on three railroad video live streams, a grade crossing and two right-of-ways, in the United States. The AI algorithm automatically detects trespassing events, differentiates between the type of violator (car, motorcycle, truck, pedestrian, etc.) and sends an alert text message to a designated destination with important information including a video clip of the trespassing event. In this study, the AI has analyzed hours of live footage with no false positives or missed detections yet. This paper and its subsequent studies aim to provide the railroad industry with state-of-the-art AI tools to harness the untapped potential of an existing closed-circuit television infrastructure through the real-time analysis of their data feeds. The data generated from these studies will potentially help researchers understand human factors in railroad safety research and give them a real-time edge on tackling the critical challenges of trespassing in the railroad industry.

A Decision Support Tool for Evaluating Big Data Investment in Transport: Empirical evidence from European use cases of NOESIS project

2020 ◽  
Author(s):  
Christos Katrakazas ◽  
Natalia Sobrino ◽  
Ilias Trochidis ◽  
Jose Manuel Vassallo ◽  
Stratos Arampatzis ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Empirical Evidence ◽  
Decision Support Tool ◽  
Use Cases ◽  
Support Tool

An integrated parallel big data decision support tool using the W-CLUS-MCDA: A multi-scenario personnel assessment

2020 ◽  
Vol 195 ◽  
pp. 105749 ◽  
Author(s):  
Abtin Ijadi Maghsoodi ◽  
Dara Riahi ◽  
Enrique Herrera-Viedma ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support Tool ◽  
Support Tool ◽  
Personnel Assessment

Real-Time Monitoring of Intercity Passenger Flows Based on Big Data

2017 ◽  
Vol 8 (4) ◽  
pp. 120-128 ◽  
Author(s):  
Zheng Li ◽  
Yan Wang ◽  
Qin Chen
Keyword(s):  
Big Data ◽  
Real Time ◽  
Public Transport ◽  
Low Cost ◽  
Decision Support Tool ◽  
Population Mobility ◽  
Population Movements ◽  
Policy Makers ◽  
Support Tool ◽  
Passenger Flows

Managing population mobility is a key to urban growth and sustainable development. This study uses administrative and business data from a number of trustworthy and publicly-available websites for public transport to access passenger flows in a real-time manner. A case study is used to illustrate the application, with intercity passenger flows by public transport mode (rail or air), by rail service type and by time. Moreover, a model is developed for monitoring the implications of population movements, which can be a decision support tool for governments and policy makers to manage population mobility. The big-data approach to accessing public transport passenger movement has the following characteristics: (1) low cost, (2) a population scale, (3) instantaneous data collection/update, and (4) high quality.

scholarly journals Attitudes towards Trusting Artificial Intelligence Insights and Factors to Prevent the Passive Adherence of GPs: A Pilot Study

2021 ◽  
Vol 10 (14) ◽  
pp. 3101
Author(s):  
Massimo Micocci ◽  
Simone Borsci ◽  
Viral Thakerar ◽  
Simon Walne ◽  
Yasmine Manshadi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Online Survey ◽  
Decision Support Tool ◽  
Skin Lesions ◽  
Management Plan ◽  
Individual Characteristics ◽  
System Efficiency ◽  
Support Tool ◽  
The Difference

Artificial Intelligence (AI) systems could improve system efficiency by supporting clinicians in making appropriate referrals. However, they are imperfect by nature and misdiagnoses, if not correctly identified, can have consequences for patient care. In this paper, findings from an online survey are presented to understand the aptitude of GPs (n = 50) in appropriately trusting or not trusting the output of a fictitious AI-based decision support tool when assessing skin lesions, and to identify which individual characteristics could make GPs less prone to adhere to erroneous diagnostics results. The findings suggest that, when the AI was correct, the GPs’ ability to correctly diagnose a skin lesion significantly improved after receiving correct AI information, from 73.6% to 86.8% (X2 (1, N = 50) = 21.787, p < 0.001), with significant effects for both the benign (X2 (1, N = 50) = 21, p < 0.001) and malignant cases (X2 (1, N = 50) = 4.654, p = 0.031). However, when the AI provided erroneous information, only 10% of the GPs were able to correctly disagree with the indication of the AI in terms of diagnosis (d-AIW M: 0.12, SD: 0.37), and only 14% of participants were able to correctly decide the management plan despite the AI insights (d-AIW M:0.12, SD: 0.32). The analysis of the difference between groups in terms of individual characteristics suggested that GPs with domain knowledge in dermatology were better at rejecting the wrong insights from AI.

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