An expensive and prevalent problem worldwide, fare evasion in proof-of-payment transit systems introduces a need for randomized patrol strategies that effectively deter fare evasion and maximize transit system revenue. The Tactical Randomizations for Urban Security in Transit Systems (TRUSTS) approach addresses this challenge by using Bayesian Stackelberg games to model the transit patrolling problem and efficiently solving for the optimized patrol strategy for each patrol officer shift. In order to implement the TRUSTS approach in real-world transit systems, the METRO mobile app presented in this paper is being developed to work with TRUSTS to (i) provide officers with real-time TRUSTS-generated patrol schedules, (ii) provide recovery from unexpected schedule interruptions that can occur in real-world patrolling domains, and (iii) collect valuable patrol data for system analysis. An innovation in transit system patrolling technology, the METRO mobile app is an online agent that interacts with the user as an interface between the patrol officer and TRUSTS. In this paper, we propose a demonstration of the TRUSTS system, composed of the TRUSTS and METRO app components, focusing on the mobile app for user interaction. Providing a brief overview of the problem setting being addressed and the system components, this demonstration showcases how the TRUSTS system works and enables successful and robust deployment in the Los Angeles Metro System.
Fpga implementation of mass public transit facility for smart security systemeSAT Journals
Abstract With the increasing number of bus users, many initiatives have been undertaken to enhance customer’s satisfaction. The RFID-based luggage tracking system would make the process of luggage handling easier and faster. We are going to fix RF Transmitter into the luggages, once the destination is reached, and then he/she should enter the user ID. If the code matches with database then the luggage is delivered to corresponding passenger. In case of lost or mishandled luggage, the alarm will be activated and the information is passed to the control station and the message will be sent to the passenger regarding their luggage status via GSM communication. For the security tendency of the vehicle is improved by scanning the person entering into the bus using Gas sensor. If a person is suspected of carrying any illegal materials, then the buzzer will be activated and the information is displayed. To ensure the security features, we have introduced alert switches nearby every seat. Whenever the switch is pressed, automatically the vehicle will be turned off and all the lights will be turned on and high alarm is activated so that the public can interfere and avoid the misbehavior. At the same time, the information contains a bus number or an ID will be sent to the Police station using SMS facility. All these processes are controlled by an FPGA controller with the high speed of processing using Xilinx ISE. Keywords- FPGA, RFID, GSM Communication, Gas sensor, SMS
this ppt addresses the current urban transportation crisis that India is going through and provides the possible remedies which might help in improving the situation.
Fpga implementation of mass public transit facility for smart security systemeSAT Journals
Abstract With the increasing number of bus users, many initiatives have been undertaken to enhance customer’s satisfaction. The RFID-based luggage tracking system would make the process of luggage handling easier and faster. We are going to fix RF Transmitter into the luggages, once the destination is reached, and then he/she should enter the user ID. If the code matches with database then the luggage is delivered to corresponding passenger. In case of lost or mishandled luggage, the alarm will be activated and the information is passed to the control station and the message will be sent to the passenger regarding their luggage status via GSM communication. For the security tendency of the vehicle is improved by scanning the person entering into the bus using Gas sensor. If a person is suspected of carrying any illegal materials, then the buzzer will be activated and the information is displayed. To ensure the security features, we have introduced alert switches nearby every seat. Whenever the switch is pressed, automatically the vehicle will be turned off and all the lights will be turned on and high alarm is activated so that the public can interfere and avoid the misbehavior. At the same time, the information contains a bus number or an ID will be sent to the Police station using SMS facility. All these processes are controlled by an FPGA controller with the high speed of processing using Xilinx ISE. Keywords- FPGA, RFID, GSM Communication, Gas sensor, SMS
this ppt addresses the current urban transportation crisis that India is going through and provides the possible remedies which might help in improving the situation.
Game-theoretic Patrol Strategies for Transit Systems: the TRUSTS System and i...Samantha Luber
Published at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013).
An expensive and prevalent problem worldwide, fare evasion in proof-of-payment transit systems introduces a need for randomized patrol strategies that effectively deter fare evasion and maximize transit system revenue. The Tactical Randomizations for Urban Security in Transit Systems (TRUSTS) approach addresses this challenge by using Bayesian Stackelberg games to model the transit patrolling problem and efficiently solving for the optimized patrol strategy for each patrol officer shift. In order to implement the TRUSTS approach in real-world transit systems, the METRO mobile app presented in this paper is being developed to work with TRUSTS to (i) provide officers with real-time TRUSTS-generated patrol schedules, (ii) provide recovery from unexpected schedule interruptions that can occur in real-world patrolling domains, and (iii) collect valuable patrol data for system analysis. An innovation in transit system patrolling technology, the METRO mobile app is an online agent that interacts with the user as an interface between the patrol officer and TRUSTS. In this paper, we propose a demonstration of the TRUSTS system, composed of the TRUSTS and METRO app components, focusing on the mobile app for user interaction. Providing a brief overview of the problem setting being addressed and the system components, this demonstration showcases how the TRUSTS system works and enables successful and robust deployment in the Los Angeles Metro System.
Demo video: http://www.youtube.com/embed/_lUG08ODqTI
"Upgrade existing elevators for improved efficiency, safety, and user experience, ensuring compliance with modern standards, reducing operational costs, and minimizing downtime."
In deze lezing worden recent afgeronde TRAIL proefschriften besproken, met focus op de relevantie voor de praktijk. We bespreken recente ontwikkeling in verkeersmanagement en coöperatieve systemen, crowd- en evacuatiemanagement en transport security. We bespreken ook kort de verschuiving van de focus binnen de leerstoel Traffic Operations and Management.
IVPower is a software dedicated to real-time monitoring of transmission and distribution grids.
IVPower provides essential information about power system disturbances for control and maintenance purpose.
Using mainly disturbance records and event logs from protection devices, IVPower offers innovative and eay-to-use web-based applications dedicated to operators in control centers, post mortem analysis experts and asset managers.
IVPower uses COMTRADE files.
Worldsensing: A Real World Use Case for Flux by Albert Zaragoza, CTO & Head o...InfluxData
Albert Zaragoza, Fuad Mimoun and Daniel Lázaro from Worldsensing will be sharing how their team built an end-to-end IoT solution for cities — from traffic flow management and smart parking to emergency & security response and critical infrastructure monitoring. They will focus this talk on how they used Flux to pull together lots of data sources into their real-time platform to provide alerts to the many constituents of the data.
Self-adaptive container monitoring with performance-aware Load-Shedding policies, by Rolando Brondolin, PhD student in System Architecture at Politecnico di Milano
Predicting air quality is necessary step to be taken by government as it is becoming the major concern among the health of human beings. Air quality Index measure the quality of air. Various air pollutants causing air pollution are Carbon dioxide, Nitrogen dioxide, carbon monoxide etc that are released from burning of natural gas, coal and wood, industries, vehicles etc. Air Pollution can cause severe disease like lungs cancer, brain disease and even lead to death. Machine learning algorithms helps in determining the air quality index. Various research is being done in this field but still results are still not accurate. Dataset are available from Kaggle, air quality monitoring sites and divided into two Training and Testing. Machine Learning algorithms employed for this are Linear Regression, Decision Tree, Random Forest, Artificial Neural Network, Support Vector Machine.
This is a overview of currently available adaptive signal control systems in the US, presented by authors at the WSDOT Traffic Engineers annual meeting, May 2016, Leavenworth, WA.
Write-up of final project for Multimedia Systems Design grad course. We implemented a content-based image search engine using color histograms, back projection, and Bhattacharyya distance.
Game-theoretic Patrol Strategies for Transit Systems: the TRUSTS System and i...Samantha Luber
Published at the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013).
An expensive and prevalent problem worldwide, fare evasion in proof-of-payment transit systems introduces a need for randomized patrol strategies that effectively deter fare evasion and maximize transit system revenue. The Tactical Randomizations for Urban Security in Transit Systems (TRUSTS) approach addresses this challenge by using Bayesian Stackelberg games to model the transit patrolling problem and efficiently solving for the optimized patrol strategy for each patrol officer shift. In order to implement the TRUSTS approach in real-world transit systems, the METRO mobile app presented in this paper is being developed to work with TRUSTS to (i) provide officers with real-time TRUSTS-generated patrol schedules, (ii) provide recovery from unexpected schedule interruptions that can occur in real-world patrolling domains, and (iii) collect valuable patrol data for system analysis. An innovation in transit system patrolling technology, the METRO mobile app is an online agent that interacts with the user as an interface between the patrol officer and TRUSTS. In this paper, we propose a demonstration of the TRUSTS system, composed of the TRUSTS and METRO app components, focusing on the mobile app for user interaction. Providing a brief overview of the problem setting being addressed and the system components, this demonstration showcases how the TRUSTS system works and enables successful and robust deployment in the Los Angeles Metro System.
Demo video: http://www.youtube.com/embed/_lUG08ODqTI
"Upgrade existing elevators for improved efficiency, safety, and user experience, ensuring compliance with modern standards, reducing operational costs, and minimizing downtime."
In deze lezing worden recent afgeronde TRAIL proefschriften besproken, met focus op de relevantie voor de praktijk. We bespreken recente ontwikkeling in verkeersmanagement en coöperatieve systemen, crowd- en evacuatiemanagement en transport security. We bespreken ook kort de verschuiving van de focus binnen de leerstoel Traffic Operations and Management.
IVPower is a software dedicated to real-time monitoring of transmission and distribution grids.
IVPower provides essential information about power system disturbances for control and maintenance purpose.
Using mainly disturbance records and event logs from protection devices, IVPower offers innovative and eay-to-use web-based applications dedicated to operators in control centers, post mortem analysis experts and asset managers.
IVPower uses COMTRADE files.
Worldsensing: A Real World Use Case for Flux by Albert Zaragoza, CTO & Head o...InfluxData
Albert Zaragoza, Fuad Mimoun and Daniel Lázaro from Worldsensing will be sharing how their team built an end-to-end IoT solution for cities — from traffic flow management and smart parking to emergency & security response and critical infrastructure monitoring. They will focus this talk on how they used Flux to pull together lots of data sources into their real-time platform to provide alerts to the many constituents of the data.
Self-adaptive container monitoring with performance-aware Load-Shedding policies, by Rolando Brondolin, PhD student in System Architecture at Politecnico di Milano
Predicting air quality is necessary step to be taken by government as it is becoming the major concern among the health of human beings. Air quality Index measure the quality of air. Various air pollutants causing air pollution are Carbon dioxide, Nitrogen dioxide, carbon monoxide etc that are released from burning of natural gas, coal and wood, industries, vehicles etc. Air Pollution can cause severe disease like lungs cancer, brain disease and even lead to death. Machine learning algorithms helps in determining the air quality index. Various research is being done in this field but still results are still not accurate. Dataset are available from Kaggle, air quality monitoring sites and divided into two Training and Testing. Machine Learning algorithms employed for this are Linear Regression, Decision Tree, Random Forest, Artificial Neural Network, Support Vector Machine.
This is a overview of currently available adaptive signal control systems in the US, presented by authors at the WSDOT Traffic Engineers annual meeting, May 2016, Leavenworth, WA.
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31. Metro App Response
• Current location is
updated
• A new schedule is
generated from the
TRUSTS-generated
strategy
• The officer can
continue their shift with
the new schedule
32. End of the Shift
• The officer submits
their shift data report
34. Robust Deployment in the
L.A. Metro System
• Successful in L.A. Metro System simulation and trial
testing
• Deter fare evasion and crime
• Maximize transit system revenue
• Valuable data collection for analysis by TRUSTS
research team and L.A. Police Department
• Efficient violation data reporting for LAPD officers
In this AAMAS(ahh-mus) 2013 demonstration video, we present the TRUSTS system, an interactive agent-based software system that generates randomized patrol strategies for transit systems.
Part 1 of the demonstration provides background context for the TRUSTS system and an overview of its two main components: TRUSTS and its mobile app. This demonstration focuses on the mobile app for user interaction.
First, we’ll introduce the problem our system addresses. In proof of payment transit systems, passengers are legally required to purchase a ticket before boarding a metro train or bus. However, resource limitations prevent patrol officers from actually verifying that every passenger has done so. Instead, patrol officers inspect a subset of passengers based on some patrol strategy. Violations and fines are issued to any fare evaders they catch.
Successful fare evaders, on the other hand, cost the transit system their ticket fare in revenue loss, accumulating to potentially significant amounts over time. In 2007 alone, the Los Angeles Metro system, where our demonstration simulation takes place, lost an estimated $5.6 million in revenue due to fare evasion [2].In order to address this costly issue, there is a need for effective patrol strategies that are unpredictable by passengers and that maximize transit system revenue revenue. Due to the complexity of this patrolling problem, human schedulers cannot manually generate these optimized patrol strategies.
Based on an approach successfully deployed in other security applications, such the police patrolling at LAX, TRUSTS uses Stackelberg games to model the L.A. metro system. In this model, the patrol officer, represented by the leader, commits to a patrol strategy and the potential fare evaders, represented by the followers, observe this patrol strategy and select a counter strategy accordingly [4].
Unique from the other applications, transit systems impose both temporal and spatial constraints on the domain model (shown here in the LA Metro Gold Line timetable and route map), making the model too complicated to be efficiently solved in this form.
The TRUSTS approach addresses this problem by reducing the constraints into a single transition graph, representing all possible patrol office action movements in the transit system as flows from each station node in the graph. In addition, TRUSTS introduces duplicate history station nodes into this graph to represent a state for the past action, allowing the current patrol schedule to be recovered if the patrol officer unexpectedly misses a patrol action.
Finally, TRUSTS extracts the maximum patrol strategy, or optimal flow through the transition graph shown here, using the Decomposed Optimal Bayesian Stackelberg Solver (DOBSS). This solution describes the optimized patrol action from each state node in the graph [4].
For robust deployment of TRUSTS in real-world transit systems, we have developed the METRO app, an innovation in transit system patrol scheduling. The METRO app is a software agent carried by each patrol officer that provides an interface for interaction between the user and TRUSTS. Shown in the this figure, The METRO app provides three principal features: a TRUSTS-generated patrol schedule for the current shift, a tracking system for reporting passenger violations, and a shift statistics summary report.The TRUSTS component runs on a server machine and uses the specified transit system parameters, including route schedules, shift start times, and number of patrol officers, to compute patrol strategies f or each patrol officer’s shift. These patrol strategies are stored in the TRUSTS database for retrieval by the METRO app. At the beginning of an officer’s shift, the METRO app queries the TRUSTS database for the user’s patrol strategy for the current shift. At the end of the shift, the officer submits the patrol and violations data captured throughout their shift. This information is stored in the same TRUSTS database, associated with its generated patrol strategy.
These are the three main views of the METRO app: Schedule View, Reporting View, and Summary View.
Schedule View allows the patrol officer to see their current patrol action as well as upcoming patrol actions. When the end time of the current action is reached, the current action is completed and the view is updated for the next action. As previously discussed, TRUSTS supports schedule interruption recovery. If an unexpected event causes the patrol officer to miss an action, the METRO app allows users to update their current location, triggering a reschedule for the corresponding station node in the TRUSTS-generated patrol strategy and a new schedule to be shown.
Reporting View allows the officer to enter violation data for the current patrol action. This violation data is shown below in the view and included in the information sent back to TRUSTS at the end of the officer’s shift.
Summary View shows a generated statistics report for the officer’s shift. This view also allows patrol officers to view and edit the violation data of past actions. Finally, at the end of their shift, patrol officers use the submit report button to submit their shift patrol data to the TRUSTS database.
The interactive simulation, the focus of the demonstration, showcases the TRUSTS system running over the course of a shortened shift of a patrol officer in the L.A. Metro System. The participants experience the real-world deployment of the TRUSTS system first-hand from the perspective of the patrol officer using the METRO app.
For the demonstration setup, we have a laptop functioning as the TRUSTS server that is connected to a monitor, displaying what the TRUSTS system is doing at the present state in the shift simulation. Throughout the shift simulation, the monitor prompts the user with various event occurrences and instructions on how to use the METRO app in response to these real-world scenarios.The laptop runs TRUSTS on an L.A. Metro Gold Line-based dataset, modified to support a two-minute patrol officer shift for the demonstration, to generate a patrol strategy for the simulated shift. The METRO app, deployed on the mobile phone shown here, has also been modified for the demonstration to continuously communicate the METRO app’s reporting data to the TRUSTS server for display on the monitor.
To conclude our demonstration, we now briefly touch on our expectations from deploying this system in the LA Metro.
The TRUSTS approach has already shown to be successful in increasing fare deterrence in LA Mero simulation and trial testing.By working with the LAPD to deploy our robust system in the real world, we expect to address their fare evasion problem with this novel application. In addition, through analysis on the METRO app-collected patrol data, we expect to gain valuable insight on the L.A. Metro System patrolling domain, such as follower behavior patterns, and be able to evaluate the effectiveness of TRUSTS system deployment in the transit system. This patrol data is also useful for transit systems that manually record violations data or perform their own analysis on this information.