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AIMSUN Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks Adopted from Clara Fang/ Ondrej Pribyl.

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Presentation on theme: "AIMSUN Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks Adopted from Clara Fang/ Ondrej Pribyl."— Presentation transcript:

1 AIMSUN Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks Adopted from Clara Fang/ Ondrej Pribyl

2 2 Outline Introduction Practical Applications Capabilities Simulation Requirements Simulation Inputs Simulation Outputs Function Limitations

3 3 Introduction Introduction Developer –Transportation Simulation Systems (TSS), Barcelona, Spain –www.aimsun.com AIMSUN Ver 4 is integrated with GETRAM Simulation Environment Generic Environment for TRaffic Analysis and Modeling –Traffic Network Graphic Editor (TEDI) –AIMSUN –AIMSUN 3D

4 4 GETRAM External Applications Shortest Routes Component Network Database TEDI Graphical Editor Costs Routes GETRAM AIMSUN - User Interface AIMSUN2 Kernel GETRAM Extensions Simulated Data Control & Management Actions EMME/2 TRANSYT SCATS Interfaces

5 5 Microscopic Modeling Approach to Traffic Simulation Based on the movement of individual vehicle –Vehicle positions are updated using car-following logic and lane changing rules –Interactions between vehicles at unsignalized intersections are modeled by right-of-way and gap-acceptance rules –Explicit representation of control strategies at signalized intersections Detailed representation of network geometry and traffic related facilities Modeling of the variability in driver behavior and vehicle dynamics

6 6 Practical Applications Network traffic operational analysis and network design Evaluation of advanced traffic management systems and adaptive traffic control Evaluation of ramp metering at Minneapolis, USA Dynamic traffic re-routing with VMS by Halcrow Fox, UK Off-line and on-line design of traffic strategies by Momatec at Frankfurt, Hessen, Germany Traffic impact studies by Meritec at Auckland, New Zealand Actuated control system studies by DHV, Holland Public transport priority study by Elsag at Milan, Italy

7 7 Capabilities Urban networks, freeways, highways, ring roads, interchanges, roundabouts, arterials and any combination of them Public transportation Traffic incidents Vehicle types –cars,buses, trucks, trains or user-defined Fixed vs.Dynamic route choice models Interfaces –EMME/2 –TRANSYT 3D visualization

8 8 Capabilities Traffic Control and Management –Traffic signals (NEMA controllers) fixed-time, semi-actuated, fully-actuated Adaptive control –Signs –Ramp metering Green time, flow, delay –VMS Different message and Starting time –All kinds of detectors

9 9 Simulation Requirements (1) Network Geometry Vehicle’s data (2) Traffic Demand (3) Traffic Control Plan (4) Public Transportation (optional) (5) GETRAM Extensions (optional) Modelling parameters ( Default values are provided ) Scenario

10 10 Simulation Inputs (1) Network Geometry Sections (links) –length, width, number of lanes, speed limits, grade, etc. Nodes (Junctions and Joins) –turning movements for junctions Centroids –traffic source, sink or both Vehicles –type, size, vehicle characteristics Detectors, VMS, etc. Map of the area (optional)

11 11 TEDI : a user friendly graphic interface for building models TEDI : a user friendly graphic interface for building models

12 12 Network Model

13 13 Examples of Sections and Polysections

14 14 Section Properties

15 15 Examples of Joins

16 16 Examples of Junctions - Yellow Box Junction

17 17 Intersection - Turning Movements

18 18 Centroids

19 19 Vehicle Types Transfer between Library and Model

20 20 Importing backgrounds as.jpg,.bmp,.tif,...files

21 21 Simulation Inputs (2) Traffic Demand Result Based - Traffic Flows & Turning Proportions –generated at origin centroids and input into the network through the sections connected to the centroid –distributed around the network in accordance to the turning proportions defined in each section of the network. Route Based - O/D matrix and Shortest Paths –generated at origin centroids and input into the network through the sections connected to the centroid –distributed following shortest paths from input section to destination centroid.

22 22 Traffic Flows and Turning Proportions Define “State”

23 23 Simulation Inputs Signal –Signal types –Signal groups (turning movements are grouped) –Phases sequences and associated signals groups –Duration of each phase –Offset –Actuated parameters Unsignalized –Priority rules - Yield and Stop sign –Parameters that affect the Gap-acceptance model Ramp metering –Control parameters (green time, flow or delay time)

24 24 OD Matrix

25 25 Traffic Generation Arrival Distributions Exponential –Route based modeling Uniform –Result based modeling Normal Constant Other Arrival Models –ASAP (as soon as possible) –External (GETRAM Extensions)

26 26 Simulation Inputs (3) Traffic Control Signal –Signal types –Signal groups (turning movements are grouped) –Phases sequences and associated signals groups –Duration of each phase –Offset –Actuated parameters Unsignalized –Priority rules - Yield and Stop sign –Parameters that affect the Gap-acceptance model Ramp metering –Control parameters (green time, flow or delay time)

27 27 Typical signal plan

28 28 Uncontrolled Fixed External Actuated SCATS Traffic Signal Control Types

29 29 Traffic Signal Control Plan

30 30 Simulation Inputs (4) Public Transport (Optional) Public transport lines –Routs Reserved lanes –Bus stops –Vehicle type Timetables –Departure frequency –Fixed schedule PT Plan

31 31 Simulation Inputs (5) GETRAM Extensions (Optional) Application Programming Interface (API) –Develop external applications (traffic control systems) –Access to the statistical data produced by simulated detectors, VMS or ramp metering –Keep track of a guided vehicle throughout the network and directly control a vehicle movement –Programming in C/C++,or using Python scripting language

32 32 GETRAM extensions Principle of data exchange

33 33 Simulation Inputs (6) Modelling parameters Global –Reaction time, queue up speed and queue leaving speed, etc. –Car-following model Maximum number of vehicles, maximum distance, etc. –Lane changing model Percent overtaken, percent recover, etc. Local –Speed limit, turning speed, visibility distance at intersection, distance zone, etc. Vehicle Attributes

34 34 Simulation Algorithms Vehicle Arrivals Vehicle Attributes Global Simulation Parameters Car-Following Lane Changing Gap Acceptance

35 35 Vehicle Arrivals User may select among the following models: –Exponential –Uniform –(Truncated) Normal –ASAP –Constant –External Source

36 36 Vehicle Attributes Length Width (considered for graphics only) Maximum Desired Speed Maximum Acceleration Normal Deceleration Maximum Deceleration Speed Limit Acceptance Minimum spacing

37 37 Global Simulation Parameters Turning Speed Effects of Grade on Vehicle Performance Drivers’ reaction time - also the simulation time step - affects capacity! Reaction time when vehicle is stopped – affects queuing Speed to join the queue Speed to depart from a queue

38 38 Models of vehicle movements The vehicle movements is computed based on particular sub-models such as –Car-following model –Lane changing The vehicles are aiming to get to the desired speed… But are constrained by environment –Adjacent vehicles, speed limits, signal light, …

39 39 Principle In every simulation step (time based triggering) are the parameters recomputed according to following principle: if (it is necessary to change lanes) then Apply Lane-Changing Model endif if (the vehicle has not changed lanes) then Apply Car-Following Model endif

40 40 Car-Following Based on Gipps Model, which is a function of: –Type of Driver –Geometry of the Section Uses a 2-lanes car-following model to consider the effects of adjacent lanes as a function of: –Area to be considered –Number of vehicles in area

41 41 Computation of acceleration and deceleration (Source AIMSUN Manual)

42 42 Lane Changing Uses the Gipps Lane Changing Model It is based on: –Necessity of Lane Change –Desirability –Feasibility Algorithm “asks” each vehicle at every update: Is it necessary, desirable, feasible to change lanes?

43 43 Gap-Acceptance Used to answer the question: Is it “feasible” to change lanes? Evaluates gap to both upstream and downstream vehicles. There are segemetn sections defined in order to achieve more representative behavior

44 44 Gap acceptance model (Source AIMSUN Manual)

45 45 Simulation Outputs Statistical measures In network, O/D matrix, stream, section, turning, type of vehicles level –Mean Flow –Density –Mean Speed and Harmonic Mean Speed –Travel Time –Delay Time –Stop Time and Number of Stops –Queue Length (Mean and Maximum) –Total Travel Length –Fuel Consumed and Pollution Emitted

46 46 Simulation Outputs Storage of simulation outputs –ASCII files –Database (e.g. Microsoft Access) User- defined time interval Multiple runs Record simulation Comparisons of two sets of time series data (Validation) –Provides charts and statistical indicators –e.g., flows measured at a detector at each interval (5 minutes) vs. field data

47 47 Function Limitations No signal optimization No control delay output No signal phase switch information output

48 48 Thank you ! Questions & Comments?


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