Transportation Modeling and Simulation Past, Present, and Future. Daiheng Ni, Ph.D.
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1 Transportation Modeling and Simulation Past, Present, and Future Daiheng Ni, Ph.D. Civil and Environmental Engineering University of Massachusetts Amherst July 20, /28/2006 7th NEITE/UMass Technical Day 1
2 What s Transportation M&S? Application Software Theory Transportation M&S 7/28/2006 7th NEITE/UMass Technical Day 2
3 What s in Our Arsenal? Randomness Deterministic Stochastic System Update Continuous Discrete 7/28/2006 7th NEITE/UMass Technical Day 3
4 What s in Our Arsenal? Scope Street Freeway Integrated Level of Detail Macroscopic Mesoscopic Microscopic 7/28/2006 7th NEITE/UMass Technical Day 4
5 Level of Detail???????? Microscopic Mesoscopic Macroscopic 7/28/2006 7th NEITE/UMass Technical Day 5
6 Conservation Law 1950 Macroscopic Models Shock Waves KRONOS KWaves CTM LWR Bick & Newell Munjal & Pipes FREQ CORQ Prigigine Payne & Whitham Phillips FREFLO Kühne Kerner & Konhäuser Michalopoulos 2000 Leonard Son & Hurdle Ni & Leonard Banks Zhang Treiber 7/28/2006 7th NEITE/UMass Technical Day 6
7 N-KWaves-General Macro, discrete, deterministic, freeway Compressible fluid in a pipe system Questions it addresses: Given demand, what s the throughput? Bottlenecks, queues, delays Ramp-metering, incident management 7/28/2006 7th NEITE/UMass Technical Day 7
8 N-KWaves-Mainline Local capacities Upstream arrival Departure Downstream congestion Cumulative # of vehicles Time 7/28/2006 7th NEITE/UMass Technical Day 8
9 N-KWaves-Outcome Cumulative # of vehicles A(x, t) Delay (h) Queue (veh) D(x, t) Total Delay (veh*h) Time 7/28/2006 7th NEITE/UMass Technical Day 9
10 Mesoscopic Models Vehicles as uniform particles Cellular Automata / Particle Hopping INTEGRATION and TRANSIMS Trade-off between scale and fidelity 7/28/2006 7th NEITE/UMass Technical Day 10
11 Key Research Influence Microscopic Models Source: Next Generation Simulation (NGSIM) program, FHWA Major Research Thread Major Model/Research Model Implementation Partial Model Implementation Computer Science Economics Physics Psychology Operations Research/Other Implementation Snapshot AIMSUN2 ARTEMIS CORSIM DRACULA HUTSIM Integration MITSIM Paramics SimTraffic SUMO Transmodeler VISSIM WATSim Deterministic Route Choice Models (Route Modification) Strategic Shortest-Path Algorithm Wardrup, 1952 Probabilistic Multinomial Logit Daganzo, 1977 Stochastic Dial, 1971 Multinomial Probit Daganzo, 1979 Paired Combinatorial Logit Chiu, 1981 Dynamic Multinomial Logit, Probit Models Khattak, 1993 C-Logit Cascetta, 1996 Logit, Information Types Polydoropoulou, 1997 Binary Logit Mahmassani, 1990 Binary Logit, bounded rational Jayakrishnan & Mahmassani, 1991 Latent reliability perception Madanot, 1995 Latent driver class analysis Pal, 1998 Autonomous Vehicle Control Situational Awareness Suthankar, 1997 Stochanstic Lane Changing Lane Changing Models Adaptive Acceleration MLC/DLC Zhang, et al., 1998 Tactical Lane Changing Toledo, 2002 Tactical Mandatory & Dicretionary (MLC/DLC) Rule-Based Yang & Koutsopoulos, 1996 Cooperative Lane Changing Hidas and Behbahanizadeh, 1998 Traffic Pressure Function Kosonen, 1999 Deterministic Lane Changing Gipps, 1981 Discrete Choice Ahmed, et al., 1996 Risk Factor Analysis Halati, et al., 1997 Gap Acceptance Models (Opposing Flow) Deterministic Opposing Flow Model Raff et al, 1950 Probabilistic Exponential Distribution Method Herman et al, 1961 Lognormal Distribution Method Drew et al, 1967 Siegloch Method Siegloch, 1973 Probit Behavioral Model Daganzo, 1981 Impatience Functions Mahmassani & Sheffi, 1981 Hewitt s Model Hewitt, 1993 Queueing Time Model Madanat, 1993 Parameter for Vehicle Wait Time Velan and Van Aerde, 1996 Neuro-Fuzzy Hybrid Model Rossi & Meneguzzer, Hybrid 2002 Logit Behavioral Model Cassidy et al, 1995 Parameter for Two-Way Left Turn Lanes TRB, 1997 Driver Characteristics Study Hamed et al, 1997 Parameter for U- Turns Al-Masaeid, 1997 Parameter for Non-Standard Unsignalized Intersections Gattis & Low, 1998 Dynamic Car-Following Treiber, 2002 Operational Car-Following Models Multi-Regime Models Intelligent Driver Rule-Based Fuzzy GHR Model, Kikuchi & Chakroborthy1992 Discrete Multi-Regime Model Kosonen, 1999 Acceleration Models (Car Following) Psycho-Physical Perceptual Thresholds Michaels, 1963 Car-Driver Unit Psycho- Physical Wiedeman, 1974 Desired Measure Laplace Transformation based Acceleration Model Pipes, 1953 Non-Linear Optimal Velocity Newell, 1961 Behavioral Model Gipps, 1981 Multi-Regime Model Benekohal & Treiterer, 1989 Stimulus-Response Linear Car-Following Model Chandler, et al., 1958 Kometani & Sasaki, 1958 Non-Linear Car-Following Model Gazis, et al., 1961 Two-Regime Model Ceder & May, Current 7/28/2006 7th NEITE/UMass Technical Day 11
12 Car-Following Stimulus-response x& n & n+ 1 x& n x& & x& n+ 1 x n x n+1 && x m α[ x& n+ 1( t + T )] 1( t + T ) = [ x& ( t) x& n n 1( t)] l [ x ( t) x ( t)] n + + n n+ 1 7/28/2006 7th NEITE/UMass Technical Day 12
13 Lane-Changing Car and its surrounding Safe distance (M, L d ) Safe distance (M, F d ) Safe distance (M, L 0 ) Safe distance (M, F 0 ) Note: Pictures borrowed from H. Jula et al s paper: Collision Avoidance Analysis for Lane Changing and Merging 7/28/2006 7th NEITE/UMass Technical Day 13
14 Gap-Acceptance VEHY approaching Yield JCT Obtain VEHP Determine TCP Calculate TP1 Calculate ETP1 Calculate TP2 Calculate ETP2 IF TP2 < ETP1, VEHY crosses Else IF ETP2 < TP1, search for Next VEHP and go to step 2 Else, VEHY yields Source: Aimsun Microscopic Traffic Simulator: A Tool for The Analysis and Assessment of ITS Systems TSS-Transport Simulation Systems, Paris 101, Barcelona, Spain 7/28/2006 7th NEITE/UMass Technical Day 14
15 Future Directions Nanoscopic simulation Modeling scope Distributed simulation Real-time simulation DDDAS 7/28/2006 7th NEITE/UMass Technical Day 15
16 Nanoscopic Simulation Natural extension Macroscopic Mesoscopic Microscopic Nanoscopic Modeling techniques Vehicle modeling Driver modeling Movement modeling 7/28/2006 7th NEITE/UMass Technical Day 16
17 Modeling Scope Regional too hard Scope Corridor Segment Interchange too easy Macro Meso Micro Nano Level of Detail 7/28/2006 7th NEITE/UMass Technical Day 17 Idea borrowed from Wunderlich's article: Scale and Complexity Tradeoffs in Surface Transportation Modeling
18 Distributed Simulation Share workload Past: one computer does all Now: share and synchronize Achieve scale and speed Centralize vs. delegate Past: data report to center Now: computing power delegates to local 7/28/2006 7th NEITE/UMass Technical Day 18
19 Real-time Simulation 7/28/2006 7th NEITE/UMass Technical Day 19
20 DDDAS Dynamic Data-Driven Application Simulation Past approach: driven by model or time complete before update approx. stepwise DDDAS: driven by data real-time updating approx. incrementally Real world process 7/28/2006 7th NEITE/UMass Technical Day 20
21 Comments and Questions? Daiheng Ni, Ph.D. Assistant Professor CEE, UMass Amherst Phone: (413) Fax: (413) /28/2006 7th NEITE/UMass Technical Day 21
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