This Project addresses an approach to intelligent safety control of passengers on escalators. The aim is to improve the accuracy of detecting threatening situations on escalators in the subway to make decisions to prevent threats and eliminate the consequences. The novelty of the approach li
intelligent escalator passenger safety management
This Project addresses an approach to intelligent safety control of passengers on escalators. The aim is to improve the accuracy of detecting threatening situations on escalators in the subway to make decisions to prevent threats and eliminate the consequences. The novelty of the approach lies in the complex processing of information from three types of sources (video, audio, sensors) using machine learning methods and recurrent neural networks with controlled elements. The conditions and indicators of safety assurance efficiency are clarified. New methods and algorithms for managing the safety of passengers on escalators are proposed. The architecture of a promising safety software system is developed, and implementation of its components for cloud and fog computing environments is provided. Modeling results confirm the capabilities and advantages of the proposed technological solutions for enhancing the safety of escalator passengers, efficiency of control decision making, and system usability. Due to the proposed solutions, it has become possible to increase the speed of identifying situations 3.5 times and increase the accuracy of their determination by 26%. The efficiency of decision making has increased by almost 30%.
The Objectives are as follows:
Ensuring safety of the passenger in case of malfunction of escalator equipment or its parts (electricity, mechanics, etc.).
Detect defect/fault in Escalator.
Measures to prevent a threat or eliminate its consequences.
Overload Alert.
Provision of timely assistance to the passenger in case of a sharp deterioration of health.
Timely assistance to low-mobility passengers (disabled persons).
The proposed system has a three-level architecture:
1. End user application is installed on smartphones under iOS or Android.
2. The program for data processing control is deployed in a fog computing environment.
3. Data processing program is deployed in the cloud.
Processing of all types of data takes place in the cloud using the high-performance system Microsoft Azure;
obtaining data characteristics necessary for control and carrying necessary data prepossessing is performed in
a fog environment, and the result is delivered to the safety operator’s smartphone or tablet. Cloud technologies
involve the use of the control console (through a website or command line), which simplifies to a large extent the
deployment of applications. Options for selection and configuration of different versions of operation systems
(Windows, Linux, Mac, FreeBSD, CentOS, etc.), necessary software (various web servers, machine learning
libraries, network administration programs, remote control, etc.) are provided via control console. At the fog
level, virtual machines are used, which, unlike the traditional VMware Workstation (https://www.vmware. com),
Virtual PC from Microsoft, can be adapted to certain technical means (taking into account storage parameters
and systems, chip-sets, central and graphics processors). Adaptation to hardware parameters enhances the efficiency of virtual machine resources by the users of applications.
Ready-made servers can be adapted to solve certain problems in the fog environment. For example, a Cisco
UCS C480 ML M5 server that differs from a user home computer by price (20 or even 30 times more expensive)
and by preinstalled software, necessary drivers, components (discrete and analog input boards for video, audio,
sensors), has a processor with a powerful NVIDIA Tesla V100 RAM graphics core, 96GB of RAM, a control
console that optimizes the resource consumption of a fog computing environment, is offered for use for solving
tasks using machine learning and neural networks. Servers that are adapted for other tasks (production process
control, banking operations, etc.) are also provided.
Intelligent escalator passenger safety management will be used by subways and train stations, shopping malls, underground
passages and other objects of public infrastructure.
Improve the accuracy of detecting threatening situations on escalators in the subway to make decisions to prevent threats and eliminate the consequences.
Registration of a chronology of events in case of an emergency.
Ensuring safety of the passenger in case of malfunction of escalator equipment or its parts (electricity, mechanics, etc.)
Call an operator to assist a disabled person in case of his inability to move independently and absence of an
accompanying specialist.
• Stop the escalator in case of items stuck, in particular shoes, clothes or body parts in escalator equipment
mechanisms (comb, side parts of balustrade, handrails, etc.).
• Call an ambulance if the passenger falls from the escalator and is injured.
• Call the police and special services in case of a terrorist threat.
• Call the fire fighter, stop the escalator, turn on the fire prevention system, de-energize the escalator in case
of smoke, fire or open burning, previously persuading in the high degree of danger of the situation that has
arisen, i.e., making sure that the smoke is not the result of cigarette smoking or the actions of a minor bully
who has set fire to an article or other objects.
• De-energize the escalator and call emergency services in case of electrostatic failure.
• Switch on the backup brake system in case of failure of the main brakes or damage of the escalator engine or
side handrails
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| documents in hard copy | Miscellaneous | 5 | 2000 | 10000 |
| Autonic BJ1M-DDT | Equipment | 2 | 11500 | 23000 |
| Escalator sensor | Equipment | 1 | 12000 | 12000 |
| Drive machine | Equipment | 2 | 5000 | 10000 |
| Gear Reducer | Equipment | 1 | 10000 | 10000 |
| Handrail System | Equipment | 1 | 15000 | 15000 |
| Total in (Rs) | 80000 |
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