Daiheng Ni, Ph.D. - UMassFuzzy GHR Model, Kikuchi & Chakroborthy1992 Dynamic Car-Following Treiber,...

7/28/2006 7th NEITE/UMass Technical Day 1

Transportation Modeling and SimulationPast, Present, and Future

Daiheng Ni, Ph.D.

Civil and Environmental EngineeringUniversity of Massachusetts Amherst

July 20, 2006


What’s Transportation M&S?

Theo

ry

Softw

are

App

licat

ion

Transportation

M&S


What’s in Our Arsenal?

ContinuousDiscrete

System Update

DeterministicStochastic

Randomness


MacroscopicMesoscopicMicroscopic

Level of Detail

StreetFreewayIntegrated

Scope

What’s in Our Arsenal?


Mac

rosc

opic

Mesos

copi

c

Mic

rosc

opic

????

????

Level of Detail


Conservation Law

LWR

2000

1950

1960

1970

1980

1990

Bick & Newell

Munjal & Pipes

Prigigine

Payne & Whitham

Phillips

Kühne

Kerner & Konhäuser

Michalopoulos

ZhangTreiber

Shock Waves

KRONOS

KWavesCTM

Ni & LeonardSon & Hurdle

LeonardBanks

FREFLO

FREQ

CORQ

Mac

rosc

opic

Mod

els


N-KWaves-General

Macro, discrete, deterministic, freewayCompressible fluid in a pipe systemQuestions it addresses:

Given demand, what’s the throughput?Bottlenecks, queues, delaysRamp-metering, incident management


N-KWaves-Mainline

Upstream arrival

Local capacities

Downstream congestion

Departure C

umul

ativ

e #

of v

ehic

les

Time


N-KWaves-OutcomeC

umul

ativ

e #

of v

ehic

les

Time

A(x, t)

D(x, t)

Queue (veh)Delay (h)

Total Delay (veh*h)


Mesoscopic Models

Vehicles as uniform particles Cellular Automata / Particle HoppingINTEGRATION and TRANSIMSTrade-off between scale and fidelity


MITSIM

AIMSUN2

Paramics

VISSIM

CORSIM

Integrat

ion

WATSim

DRACULA

SimTraf

fic

HUTSIM

SUMO

1940 1950 1960 1970 1980 1990 2000 Current

StrategicStrategic

TacticalTactical

OperationalOperational

Deterministic

Probabilistic

Hybrid

Exponential Distribution MethodHerman et al, 1961

Impatience FunctionsMahmassani & Sheffi, 1981

Probit Behavioral ModelDaganzo, 1981

Logit Behavioral ModelCassidy et al, 1995

Parameter for “Non-Standard” Unsignalized IntersectionsGattis & Low, 1998

Neuro-Fuzzy Hybrid Model

Rossi & Meneguzzer, 2002

Gap Acceptance Models(Opposing Flow)

Stimulus-Response

Psycho-Physical

Desired Measure

Rule-Based

Intelligent Driver

Linear Car-Following ModelChandler, et al., 1958Kometani & Sasaki, 1958

Laplace Transformation based Acceleration ModelPipes, 1953

Perceptual ThresholdsMichaels, 1963

Behavioral ModelGipps, 1981

Car-Driver Unit Psycho-PhysicalWiedeman, 1974

Fuzzy GHR Model, Kikuchi & Chakroborthy1992

Dynamic Car-FollowingTreiber, 2002

Multi-Regime ModelBenekohal & Treiterer, 1989

Two-Regime ModelCeder & May, 1976

Acceleration Models(Car Following)

Shortest-Path AlgorithmWardrup, 1952

Deterministic

Probabilistic

Paired Combinatorial LogitChiu, 1981

Multinomial ProbitDaganzo, 1979

Lane Changing Models

Route Choice Models(Route Modification)

C-LogitCascetta, 1996

Multinomial LogitDaganzo, 1977

Dynamic

Binary Logit, bounded rationalJayakrishnan & Mahmassani, 1991

KeyKey

Model Implementation

Partial Model Implementation

Major Model/Research

Major Research Thread

Economics

Physics

Psychology

Computer Science

Operations Research/Other

Binary LogitMahmassani, 1990

Multinomial Logit, ProbitModelsKhattak, 1993

Latent reliability perceptionMadanot, 1995

Logit, Information TypesPolydoropoulou, 1997

Latent driver class analysisPal, 1998

Non-Linear Optimal VelocityNewell, 1961

Discrete Multi-Regime ModelKosonen, 1999

Non-Linear Car-Following ModelGazis, et al., 1961

Opposing Flow ModelRaff et al, 1950

Lognormal Distribution MethodDrew et al, 1967

Siegloch MethodSiegloch, 1973

Parameter for Two-Way Left Turn LanesTRB, 1997

Parameter for U-Turns

Al-Masaeid, 1997

Parameter for Vehicle Wait TimeVelan and Van Aerde, 1996

Queueing Time ModelMadanat, 1993

Hewitt’s ModelHewitt, 1993

Driver Characteristics Study Hamed et al, 1997

Mandatory & Dicretionary (MLC/DLC)Risk Factor AnalysisHalati, et al., 1997

Stochanstic Lane ChangingZhang, et al., 1998

Discrete ChoiceAhmed, et al., 1996

Deterministic Lane ChangingGipps, 1981

Cooperative Lane Changing Hidas and Behbahanizadeh, 1998

Traffic Pressure FunctionKosonen, 1999

Rule-BasedYang & Koutsopoulos, 1996

Adaptive Acceleration MLC/DLC

Autonomous Vehicle Control

Situational AwarenessSuthankar, 1997

Research Influence

ARTEMIS

Implementation SnapshotImplementation Snapshot

Tactical Lane ChangingToledo, 2002

StochasticDial, 1971

Transm

odeler

Multi-Regime ModelsCar-Following Models

Microscopic ModelsSource: Next Generation Simulation (NGSIM) program, FHWA


Car-Following

)]()([)]()([

)]([)( 11

11 txtx

txtxTtxTtx nnl

nn

mn

n ++

++ −

−+

=+ &&&

&&α

nx& nx&& 1+nx& 1+nx&&

nx 1+nx

Stimulus-response


Lane-Changing

Note: Pictures borrowed from H. Jula et al’s paper: Collision Avoidance Analysis for Lane Changing and Merging

Car and its surroundingSafe distance (M, Ld)Safe distance (M, Fd)Safe distance (M, L0)Safe distance (M, F0)


Gap-Acceptance

Source: Aimsun Microscopic Traffic Simulator: A Tool for The Analysis and Assessment of ITS SystemsTSS-Transport Simulation Systems, Paris 101, 08029 Barcelona, Spain

VEHY approaching Yield JCTObtain VEHPDetermine TCPCalculate TP1Calculate ETP1Calculate TP2

Calculate ETP2IF TP2 < ETP1, VEHY crossesElse IF ETP2 < TP1, search for

Next VEHP and go to step 2Else, VEHY yields


Future Directions

Nanoscopic simulationModeling scopeDistributed simulationReal-time simulationDDDAS


Nanoscopic Simulation

Natural extensionMacroscopicMesoscopicMicroscopicNanoscopic

Modeling techniquesVehicle modelingDriver modelingMovement modeling


Modeling ScopeSc

ope

Level of Detail

Regional

Interchange

Corridor

Segment

Macro Meso Micro Nano

Idea borrowed from Wunderlich's article: Scale and Complexity Tradeoffs in Surface Transportation Modeling

“too easy”

“too hard”


Distributed Simulation

Share workloadPast: one computer does allNow: share and synchronizeAchieve scale and speed

Centralize vs. delegatePast: data report to centerNow: computing powerdelegates to local


Real-time Simulation


DDDAS

Dynamic Data-Driven Application Simulation

Past approach:driven by model or timecomplete before updateapprox. stepwise

DDDAS:driven by datareal-time updatingapprox. incrementally

Real world process


Comments and Questions?

Daiheng Ni, Ph.D.

Assistant ProfessorCEE, UMass Amherst

Phone: (413) 545-5408Fax: (413) 545-2840E-mail: [email protected]://www.ecs.umass.edu/cee/ni/

mailto:[email protected]

http://www.ecs.umass.edu/cee/ni/

Daiheng Ni, Ph.D. - UMassFuzzy GHR Model, Kikuchi & Chakroborthy1992 Dynamic Car-Following Treiber,...

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