juts jsim urban traffic simulator 1 j-sim urban traffic simulator j-sim based, xml using grafical...
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JUTSJSim Urban Traffic Simulator
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J-Sim Urban Traffic Simulator
J-Sim based, XML using grafical and console simulation
tool.David HartmanZČU-FAV-KIV
JUTSJSim Urban Traffic Simulator
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Introduction
• Goals• Road model definition• Simulation map definition• Results collecting• XML load
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Goals
• Simulation properties– global traffic networks consideration– detailed dynamic simulation
• Simulation entities– from data configurable– easy but good graphical output
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Road model definition
• Microscopic models• Mezoscopic models• Macroscopic models
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Microscopic models
• Each vehicle consideration– system entities are objects with
specific decision-making– detailed entities interactions
simulation
• Advantages and disadvantages– difficult implementation and tune– most realistic
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Mesoscopic models
• No specific vehicle consideration– vehicles making decision itself but
like pattern (no objects)– interactions are on characteristic level
• Advantages and disadvantages– better interactions tunning– attributes of vehicle not consider
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Macroscopic models
• Vehicle flow consideration– vehicle distribution function– flow equation
• Advantages and disadvantages– microscopic details not included– Lot of calculations but fast– only for global traffic network
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Cellular Automata model
• Definition– microscopic model; detailed
interactions– Nagel-Schreckenberg(NaSch); moving
rules
• Lane Division
2 1 2 3
1 2
space between vehicles
1 cell
gab gs gab gp
speed vi < vmax
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Basic minimal NaSch rules
• (lane change decision and making)
• Acceleration vn -> min(vn+1;vmax)
• Break vn -> min(vn, gn+1)
• Randomization vn -> max(vn-1,0) [p]
• Move xn -> xn + vn
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Model extensions
• VDR model– Velocity Dependent Randomization– probability is function of
gab,speed,etc.
• Anticipation models– consider leading vehicle attributes at
speed adaptation
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JUTS road model
• VDR and anticipation types based• Cell 2.5m moving refresh period 1s• Vehicle length consideration• Leading head algorithm
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Leading head algorithm
• Head makes footmarks and other pieces are tracking
• Easy lane change and map moves
Head move Shift of the first piece Shift of the sekond piece
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Simulation map definition
• XML loadable• Global system definition with
structured approach• Easy collection of data
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Map segments
• Roads• Crossroads• Roundabouts• Parkings
• Generators• Terminators
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Segments connection
• Connection places– general segment connection– sending vehicles through
• Direction plus path– places are targets– vehicle path aiming
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Connection scheme
Crossroad
Road
terminator generator
Road
Road
Road
Road generator
generator
terminator
terminator
terminator
terminator
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Road segment
• Traffic Lanes shifts
• Rail lanes• Separation barrier• Signs container• Acceptors and
emitters
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Crossroad segment
• Crossroad places• Vehicle jumping• Lights Container
road road
road
road
lane
lane
lane
lane
lane lane
lane lane
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Roundabout segment
• Round road• Lane property• Less objects
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Generators and terminators
• Generating vehicles
• Sending to network
• Accepting vehicles from network
• Finalizing process
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Parking segment
• Storage for vehicles• Capacity and occupancy• Vehicle are not active inside
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Moving process
1. Segment server calls update function
2. Update function calls model rules for all heads of vehicles.
3. Vehicles proceed on of their moving methods according to actual place
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Traffic Lights
• Static vs. dynamic• Deep detail
– Signal program– Phase schema– Phase transitions– Signal groups
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Result collecting
• Special collector objects– Vehicle movements, traffic
characteristic– Cyclic update; time elements
• Send output to stream in XML– For each vehicle, for each step– Stream redirections
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XML structure
• Project file definitions– Map definition– Time progress definition– Initialization definition
• Project file outputs– Vehicles moves– Characteristic
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XML data loading
• XML define objects• Connections with ID• Like structure, like loading
process; all objects XML definable
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Time progress objects
• Timed value generators– Tree structure; time axis– Blocks are referenced
• Probability trends– For changing probability during time– Same tree structure
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Application
• Two regimes of running– Offline– Online
• Graphical definitions– Segments– Vehicles movements
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Future expectations
• Simulation features– Model global validation– Connection with other system
(Lights strategies,…)
• Map features– Map XML editor– GIS or other map data using
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The End