P systems can be used to model passenger flow simulation. The proposed model uses membranes to represent tram stops and trams, with objects representing passengers. Rules describe passenger behavior like boarding and disembarking, and tram movement between stops. Experimental results on sample networks show patterns in passenger and empty tram seat levels over time for different passenger disembarkation probabilities. Future work includes applying P systems to vehicular traffic and using real-world transportation network data.
ESS-Bilbao Initiative Workshop. Beam Dynamics Codes: Availability, Sophistica...ESS BILBAO
Beam Dynamics Codes: Availability, Sophistication, Limitations. P.N. Ostroumov and B. Mustapha Argonne National Laboratory, J.-P. Carneiro Fermi National Accelerator Laboratory
A REVIEW OF OPTIMUM SPEED MODEL An Assignment On Advanced Traffic Engineering...DAUDA SANUSI
A REVIEW OF OPTIMUM SPEED MODEL
An Assignment On Advanced Traffic Engineering (CIV8329)
by
Sanusi Dauda
SPS/16/MCE/00027
Submitted to
Prof. H. M. Alhassan
Highway and Transportation Engineering (Option)
Department of Civil Engineering
Faculty of Engineering
Bayero University, Kano
19TH May, 2017
ESS-Bilbao Initiative Workshop. Beam Dynamics Codes: Availability, Sophistica...ESS BILBAO
Beam Dynamics Codes: Availability, Sophistication, Limitations. P.N. Ostroumov and B. Mustapha Argonne National Laboratory, J.-P. Carneiro Fermi National Accelerator Laboratory
A REVIEW OF OPTIMUM SPEED MODEL An Assignment On Advanced Traffic Engineering...DAUDA SANUSI
A REVIEW OF OPTIMUM SPEED MODEL
An Assignment On Advanced Traffic Engineering (CIV8329)
by
Sanusi Dauda
SPS/16/MCE/00027
Submitted to
Prof. H. M. Alhassan
Highway and Transportation Engineering (Option)
Department of Civil Engineering
Faculty of Engineering
Bayero University, Kano
19TH May, 2017
This paper deals with the optimization of the capacity of a terminal railway station using the
Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
This paper deals with the optimization of the capacity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
Implementation of a lane-tracking system for autonomous driving using Kalman ...Francesco Corucci
This project was developed for a Digital Control class. It consists of a system that is able to identify and track lane marks in a video acquired by webcam. It's interesting how the Kalman filter is used in such a context in order to make the lane detection computationally feasible in the small amount of time between two subsequent video frames
A Dynamic Cellular Automaton Model for Large-Scale Pedestrian EvacuationScientific Review
An existing dynamic cellular automaton (CA) model is modified for simulating the hallway area evacuation experiment. In this proposed model, some basic parameters that plays and important role in evacuation process such as human psychology and pedestrian density around exits are considered. From the simulation and experimental results obtained, it shows that the modification provides a reasonable improvement as pedestrian also tends to select exit route according to occupant density around the exits area besides considering the spatial distance to exits. The studies on pedestrian density effects on speed during the evacuation process are performed. Comparison for both the experiment and simulation results verifies that the proposed model is able to effectively reproduce the experiment. The proposed CA model improvement is valuable for more extensive application study and aid the architectural design to increase public safety. Hence, we conclude our paper by presenting some of the application from the proposed model in conjunction to forecast the particular adjustment to the hallway area that would improve the output of the model.
A Dynamic Cellular Automaton Model for Large-Scale Pedestrian EvacuationScientific Review SR
An existing dynamic cellular automaton (CA) model is modified for simulating the hallway area
evacuation experiment. In this proposed model, some basic parameters that plays and important role in
evacuation process such as human psychology and pedestrian density around exits are considered. From the
simulation and experimental results obtained, it shows that the modification provides a reasonable improvement
as pedestrian also tends to select exit route according to occupant density around the exits area besides
considering the spatial distance to exits. The studies on pedestrian density effects on speed during the evacuation
process are performed. Comparison for both the experiment and simulation results verifies that the proposed
model is able to effectively reproduce the experiment. The proposed CA model improvement is valuable for more
extensive application study and aid the architectural design to increase public safety. Hence, we conclude our
paper by presenting some of the application from the proposed model in conjunction to forecast the particular
adjustment to the hallway area that would improve the output of the model
Application Of The Three-In-One Control Platform Based On OPC In The Lifting-...IJRES Journal
The three-in-one control platform includes MATLAB, WINCC and S7 300 PLC. In the platform, MATLAB communicates with WINCC through OPC and WINCC communicates with PLC. It is a control platform with WINCC as the bridge. The platform is designed to shorten the operating time of the lifting-sliding stereo garage, and at the same time to achieve controlling the stereo garage through monitoring interface. Genetic algorithm is designed with MATLAB for getting the optimal scheduling scheme of lifting-sliding stereo garage in the platform. Then the date was passed to WINCC through OPC. PLC conducts the scheduling of the stereo garage based on the date getting from WINCC, and through the WINCC to achieve real time picture monitoring and operating of the stereo garage. Under the same conditions, the control platform can get access to the vehicle in the shortest time.
This paper deals with the optimization of the capacity of a terminal railway station using the
Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
This paper deals with the optimization of the capacity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
Implementation of a lane-tracking system for autonomous driving using Kalman ...Francesco Corucci
This project was developed for a Digital Control class. It consists of a system that is able to identify and track lane marks in a video acquired by webcam. It's interesting how the Kalman filter is used in such a context in order to make the lane detection computationally feasible in the small amount of time between two subsequent video frames
A Dynamic Cellular Automaton Model for Large-Scale Pedestrian EvacuationScientific Review
An existing dynamic cellular automaton (CA) model is modified for simulating the hallway area evacuation experiment. In this proposed model, some basic parameters that plays and important role in evacuation process such as human psychology and pedestrian density around exits are considered. From the simulation and experimental results obtained, it shows that the modification provides a reasonable improvement as pedestrian also tends to select exit route according to occupant density around the exits area besides considering the spatial distance to exits. The studies on pedestrian density effects on speed during the evacuation process are performed. Comparison for both the experiment and simulation results verifies that the proposed model is able to effectively reproduce the experiment. The proposed CA model improvement is valuable for more extensive application study and aid the architectural design to increase public safety. Hence, we conclude our paper by presenting some of the application from the proposed model in conjunction to forecast the particular adjustment to the hallway area that would improve the output of the model.
A Dynamic Cellular Automaton Model for Large-Scale Pedestrian EvacuationScientific Review SR
An existing dynamic cellular automaton (CA) model is modified for simulating the hallway area
evacuation experiment. In this proposed model, some basic parameters that plays and important role in
evacuation process such as human psychology and pedestrian density around exits are considered. From the
simulation and experimental results obtained, it shows that the modification provides a reasonable improvement
as pedestrian also tends to select exit route according to occupant density around the exits area besides
considering the spatial distance to exits. The studies on pedestrian density effects on speed during the evacuation
process are performed. Comparison for both the experiment and simulation results verifies that the proposed
model is able to effectively reproduce the experiment. The proposed CA model improvement is valuable for more
extensive application study and aid the architectural design to increase public safety. Hence, we conclude our
paper by presenting some of the application from the proposed model in conjunction to forecast the particular
adjustment to the hallway area that would improve the output of the model
Application Of The Three-In-One Control Platform Based On OPC In The Lifting-...IJRES Journal
The three-in-one control platform includes MATLAB, WINCC and S7 300 PLC. In the platform, MATLAB communicates with WINCC through OPC and WINCC communicates with PLC. It is a control platform with WINCC as the bridge. The platform is designed to shorten the operating time of the lifting-sliding stereo garage, and at the same time to achieve controlling the stereo garage through monitoring interface. Genetic algorithm is designed with MATLAB for getting the optimal scheduling scheme of lifting-sliding stereo garage in the platform. Then the date was passed to WINCC through OPC. PLC conducts the scheduling of the stereo garage based on the date getting from WINCC, and through the WINCC to achieve real time picture monitoring and operating of the stereo garage. Under the same conditions, the control platform can get access to the vehicle in the shortest time.
Popelka, S: Space-Time-Cube for Visualization of Eye-tracking data
Janoska in dog
1. P Systems for Passenger Flow Simulation
P Systems for Passenger Flow Simulation
Zbynˇk Janoˇka
e s
Department of Geoinformatics, Palack´ University in Olomouc
y
October 30, 2012
2. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
◮ Computational model from the family of natural computing
◮ Inspired by the living cell
◮ its structure
◮ its functionality
◮ Gheorghe P˘un (1998) - Computing with membranes
a
◮ Research concerned with computational power, not biological
modelling
◮ No application to spatial phenomena (so far)
3. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Main components of P systems
◮ membrane structure
◮ objects
◮ rules
Basic features
◮ maximal paralelism
◮ non-determinism
4. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Step 1
◮ environment – #
◮ membrane 1 – #
◮ membrane 2 – #
◮ membrane 3 – ac
◮ a → ab
◮ a → bδ
◮ c → cc
ac → abcc
5. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Step 2
◮ environment – #
◮ membrane 1 – #
◮ membrane 2 – #
◮ membrane 3 – abcc
◮ a → ab
◮ a → bδ
◮ c → cc
abcc → bbccccδ
6. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Step 3
◮ environment – #
◮ membrane 1 – #
◮ membrane 2 – bbcccc
◮ b→d
◮ d → de
◮ (cc → c) > (c → δ)
bbcccc → ddcc
◮ membrane 3 – dissolved
7. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Step 4
◮ environment – #
◮ membrane 1 – #
◮ membrane 2 – ddcc
◮ b→d
◮ d → de
◮ (cc → c) > (c → δ)
ddcc → ddcee
◮ membrane 3 – dissolved
8. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Step 5
◮ environment – #
◮ membrane 1 – #
◮ membrane 2 – ddcee
◮ b→d
◮ d → de
◮ (cc → c)4 > (c → δ)
ddcee → ddeeeeδ
◮ membrane 3 – dissolved
9. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Step 6
◮ environment – #
◮ membrane 1 – ddeeee
◮ e → eOUT
[ddeeee]1 → [dd]1 [eeee]ENV
◮ membrane 2 – dissolved
◮ membrane 3 – dissolved
10. P Systems for Passenger Flow Simulation
Introduction
P systems – Introduction
Final configuration
[dd]1 [eeee]ENV
Calculation succesfull – no other rule
can be applied
11. P Systems for Passenger Flow Simulation
Transportation modelling
Transportation modelling
Three levels of traffic flow models (Hoogendoorn & Bovy, 2001)
◮ microsimulation
◮ mesosimulation
◮ macrosimulation
Public transportation models – meso-models – detailed passenger
flow simulation, vehicle modelling omitted (Peeta &
Ziliaskopoulos, 2001)
12. P Systems for Passenger Flow Simulation
Proposed model
Informal description
◮ tram stops – membranes
◮ road network – graph
topology
◮ trams – membranes
◮ passengers – objects
◮ behaviour – rules
◮ passengers getting on
and off the tram
◮ tram moving between
stops
◮ passenger decisions
13. P Systems for Passenger Flow Simulation
Proposed model
Formal description
Rules describing passengers getting on and off the tram
◮ [tram empty ]− people → [tram people ]tram
tram
−
p1 ≤1
◮ [tram people ]− − → [tram empty ]− people OUT
tram − − tram
p2 ≤1
◮ [tram people ]− − → [tram people ]−
tram − − tram
14. P Systems for Passenger Flow Simulation
Proposed model
Formal description
Rules describing movement of the trams
t≥1
◮ [i [tram ]+ @j ]i − → [j [tram ]− ]j
tram − tram
◮ [i [tram ]− ]i → [i [tram ]+ ]i
tram tram
15. P Systems for Passenger Flow Simulation
Proposed model
Formal description
Rules describing passenger arrival and departure from tram stops
◮ [i ]i → [i people ∗ N ]i
◮ [i people OUT ]i → [i ]i
16. P Systems for Passenger Flow Simulation
Proposed model
Parameters of the model
◮ topology of the network
◮ number of vehicles, their schedule
◮ capacity of vehicles
◮ number of passengers using the system
◮ probabilities of passengers getting off the tram
17. P Systems for Passenger Flow Simulation
Experimental results
Model 1
Experimental results - model 1
◮ topology of the network – circular
◮ number of vehicles, their schedule – 3
trams, 5 mins between stops
◮ capacity of vehicles - 55 passengers
◮ number of passengers using the
system – Poisson dist. with λ = 3
◮ probabilities of passengers getting off
the tram – 0.50, 0.55, 0.60
18. P Systems for Passenger Flow Simulation
Experimental results
Model 1
Experimental results – probability 0.50
passengers waiting at the stop
250
200
passengers
150
100
50
0
0 200 400 600 800 1000
time units
19. P Systems for Passenger Flow Simulation
Experimental results
Model 1
Experimental results – probability 0.50
empty spaces in tram
50
40
30
empty spaces
20
10
0
0 200 400 600 800 1000
time units
20. P Systems for Passenger Flow Simulation
Experimental results
Model 1
Experimental results – probability 0.55
passengers waiting at the stop
80
60
passengers
40
20
0
0 200 400 600 800 1000
time units
21. P Systems for Passenger Flow Simulation
Experimental results
Model 1
Experimental results – probability 0.55
empty spaces in tram
50
40
empty spaces
30
20
10
0
0 200 400 600 800 1000
time units
22. P Systems for Passenger Flow Simulation
Experimental results
Model 1
Experimental results – probability 0.60
passengers waiting at the stop
50
40
30
passengers
20
10
0
0 200 400 600 800 1000
time units
23. P Systems for Passenger Flow Simulation
Experimental results
Model 1
Experimental results – probability 0.60
empty spaces in tram
50
40
30
empty spaces
20
10
0
0 200 400 600 800 1000
time units
24. P Systems for Passenger Flow Simulation
Experimental results
Model 2
Experimental results - model 2
◮ topology of the network – line
◮ number of vehicles, their schedule – 2
trams, 5 mins between stops
◮ capacity of vehicles - 55 passengers
◮ number of passengers using the
system – Poisson dist. with λ = 3
◮ probability of passengers getting off
the tram – 0.95
25. P Systems for Passenger Flow Simulation
Experimental results
Model 2
Experimental results – stop 1
passengers waiting at the stop
1200
1000
800
passengers
600
400
200
0
0 200 400 600 800 1000
time units
26. P Systems for Passenger Flow Simulation
Experimental results
Model 2
Experimental results – stop 2
passengers waiting at the stop
80
60
passengers
40
20
0
0 200 400 600 800 1000
time units
27. P Systems for Passenger Flow Simulation
Experimental results
Model 2
Experimental results – stop 3
passengers waiting at the stop
70
60
50
40
passengers
30
20
10
0
0 200 400 600 800 1000
time units
28. P Systems for Passenger Flow Simulation
Experimental results
Model 2
Experimental results – empty spaces
empty spaces in tram
50
40
30
empty spaces
20
10
0
0 200 400 600 800 1000
time units
29. P Systems for Passenger Flow Simulation
Future work
Future and related work
Future work
◮ P systems for vehicular
Related work
flow simulation ◮ Population dynamics
◮ Dvorsk´ et al, 2012 –
y modelling using P systems
first ideas, XML
specification, software
◮ superior for small
◮ real data aquisition -
populations
Bˇeclav city
r
◮ previous research
(population 25 000, 5
available
traffic lights)
◮ experimental results
◮ Background model for proven usefull
traffic optimisation
30. P Systems for Passenger Flow Simulation
Conclusion
Conclusion
◮ P systems are computational models inspired by the living cell
◮ Enable hierarchical representation of modelled system,
behavior is ruled by ’chemical equations’
◮ Expressive and efficient
◮ Simple to extend existing models
31. P Systems for Passenger Flow Simulation
Conclusion
Conclusion
Drawbacks of proposed model
Advantages of proposed model
◮ objects are not inteligent
◮ discrete representation of ◮ can not incorporate
vehicles, passengers representation of world by
◮ expressive the means of physical laws
◮ easy to extend
◮ detail of the model is
limited
32. P Systems for Passenger Flow Simulation
Bibliography
[Dvorsk´ et al, 2012] J. Dvorsk´, Z. Janoˇka & L. Voj´ˇek.
y y s ac
P systems for traffic flow simulation,
Lecture Notes in Computer Science Volume 7564,, 2012.
[Hoogendoorn & Bovy, 2001] S.P. Hoogendoorn & P.H.L.
Bovy.
State-of-the-art of vehicular traffic flow modelling,
Delft University of Technology, Delft,, 2001.
[P˘un, 1998] Gh. P˘un.
a a
Computing with membranes,
TUCS Report 208, Turku Center for Computer Science, 2000.
[P˘un, 2004] Gh. P˘un.
a a
Introduction to membrane computing,
33. P Systems for Passenger Flow Simulation
Bibliography
First brainstorming Workshop on Uncertainty in Membrane
Computing, 2004.
[Peeta & Ziliaskopoulos, 2001] S. Peeta & A. Ziliaskopoulos
Foundations of dynamic traffic assignment: The past, the
present and the future,
Networks and Spatial Economics, 2001.
[P systems web page]
http://ppage.psystems.eu/