Workshop Modelling Futures Lisboa 14 Julho de 2014 1 TRAFFIC MODELLING PLATFORMS IN TEMA-TT Jorge Bandeira & Margarida C. Coelho University of Aveiro, R&D Group on TRANSPORTATION TECHNOLOGY, Centre for Mechanical Technology and Automation (TEMA) Department of Mechanical Engineering, Aveiro - Portugal
Outline 2 Presentation Modelling platforms Case studies Future work
Transportation Technology Group: 3 Research lines 1. Impacts of transportation systems Traffic Energy consumption Pollutants Emissions Road Safety 2. Eco-routing & ITS 3. Life cycle assessment for alternative fuels
4 TT Group - Partnerships
Traffic-emissions modelling platforms 5 Traffic TRANUS Emissions CORINAIR Real world DTA VISSIM VSP c++ Optimization platforms Air quality
6 1 ARE ECO-LANES A SUSTAINABLE OPTION TO REDUCING EMISSIONS IN A MEDIUM-SIZED EUROPEAN CITY? Tânia Fontes, Paulo Fernandes, Hugo Rodrigues, Jorge Bandeira, Sérgio Pereira, Asad J. Khattak, Margarida Coelho University of Aveiro, PORTUGAL Old Dominion University, VA, USA.
1.1 Objectives 7 To develop an integrated microscale modeling platform calibrated with real world data to assess the impact of future TMS in an urban area; To evaluate the introduction of eco-lanes in different types of roads in a medium-sized European city and its effects in terms of emissions and traffic performance.
1.2 Study area: Aveiro, Portugal 8 Aveiro
1.3 Overall methodology 9 BASELINE SCENARIOS Road configuration INPUT Data collection Vehicle dynamics Microsimulation traffic model Microsimulation traffic model Traffic volumes MODEL OUTPUT Microscale emissions model Calibration and Validation Microscale emissions model Data output Average speed model Traffic signals Evaluation of eco-lanes: Traffic Emissions volumes Speed Travel profiles time AOV Travel Network times Modal distribution performance
-50-38 -26-14 -2 10 22 34 46 58 Time (s) Seconds X VSPi / X VSP 14 1.4 Traffic and emissions modeling 10 VISSIM 5.4 VSP=v[1.1a+9,81 sin (arctan(grade) ) +0.132]+0.000302 v 3 20000 15000 10000 5000 Second-by-Second Speed, Acceleration, Grade 0 600 2.5 400 2 200 1.5 0 1 1 2 3 4 5 6 7 8 9 1011121314 0.5 VSP (Kw/ton) VSP mode 0 VSP modal distribution 3.5 3 NOX LDDV CO LDGV CO2 LDDV 1 3 5 7 9 11 13 EP = 14 1 t i XP i CORINAIR F(Av. Speed)
Travel time (s) 1.5 Evaluation 11 700 600 500 400 300 200 Observed Estimated 95% CI 100 0 A CS B CS C CS A SC B CS C SC ROUTE
Frequency Frequency 1. 6 Evaluation VSP distribution 12 240 210 180 240 210 180 Observed Estimated 150 150 120 120 90 90 60 60 30 30 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 VSP Modes 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 VSP Modes FREEWAY S-C URBAN (S-C) Kolmogorov-Sminorv - 97.5% CI no significant differences
1.7 Conclusions 13 Eco-lane: No significant impacts on network performance. Freeway - majority of passengers can reduce their travel (=5%) and (-3% CO 2, -14% CO, -8% NO X ); Urban corridor - reduction of emissions => only if the AOV 1.50; Arterial road - no significant time savings advantage; Incorporation of green vehicles in the HOV => little impact on the corridors performance.
14 2 Emissions impact of road traffic incidents using Advanced Traveller Information Systems in a regional scale T. Fontes, A. Lemos, P. Fernandes, S.R. Pereira, P. Fernandes, J.M. Bandeira and M.C. Coelho University of Aveiro, Centre for Mechanical Technology and Automation (TEMA) / Department of Mechanical Engineering, Aveiro - Portugal
15 2.1 Methodology: Simulation Framework INPUTS BASELINE Road and zones characteristics; traffic counts; O/D matrix Road and zones characteristics; traffic counts; O/D matrix SCENARIOS Average Speeds; Traffic Counts MODEL Traffic modelling (DTALite) Traffic modelling (DTALite) Emission modelling (EMEP/EEA) Calibration and validation OUTPUTS Average speed Traffic counts NO x, HC, PM, CO, FC, CO 2
Flow Rate (vphpl) Speed (kph) 2.2 Methodology: Traffic Modelling 16 MESOSCOPIC TRAFFIC MODEL DTALite Newell s simplified kinematic wave model o Triangular flow-density relationship 2000 80 60 1000 40 0 0 100 200 20 0 0 50 100 150 200 Density (vph/kmpl) Density (vph/kmpl)
17 2.3 Methodology: Calibration and Validation Calibration Validation Observed and estimated counts Model Performance: R-square; GEH statistic. Observed and estimated travel times 11 routes (7 highway; 4 motorway)
Estimated Values [vph] Estimated Values [vph] 2. Results: Calibration and 18 Validation Calibration Validation 6000 6000 R 2 = 0.805 R 2 = 0.744 4000 4000 2000 2000 0 0 2000 4000 6000 Observed Values [vph] 0 0 2000 4000 6000 Observed Values [vph] GEH<10: 56% of points
Relative diference (%) Relative diference (%) Relative diference (%) Relative diference (%) 2. Results: Scenarios Evaluation 19 2.5 1.5 0.5-0.5 Impacts on A29 incident 1.0 0.5 0.0 Impacts on N109-1.5-0.5-2.5 Scenario 2 Scenario 4 Scenario 5 Scenario 7-1.0 Scenario 2 Scenario 4 Scenario 5 Scenario 7 2.5 1.5 Impacts on A1 2.5 1.5 Impacts on IC2 0.5 0.5-0.5-0.5-1.5-1.5-2.5-2.5 Scenario 2 Scenario 4 Scenario 5 Scenario 7 Scenario 2 Scenario 4 Scenario 5 Scenario 7 Legend: NO X HC CO PM CO 2 FC
20 3 AN ECO-TRAFFIC MANAGMENT TOOL J. Bandeira a, S. R. Pereira a, T. Fontes a, P. Fernandes a, A. Khattak b and M.C. Coelho a A University of Aveiro, Centre for Mechanical Technology and Automation / Dep. Mechanical Engineering. Research GROUP ON TRANSPORTATION TECHNOLOGY b Old Dominion University, Civil & Environmental Engineering Department, VA USA
3.1 Objectives 21 o Tool => most sustainable traffic distribution in a given corridor depending on: i. total demand ii. n routes linking an OD pair iii. individual and integrated criteria and assignment methods.
3.2 Methodology 22 Overall structure of the optimization platform Toll cos ts Link Level Development of Volume dependent functions VCF / VDF Network Level User equilibrium assignment Traffic Model / GPS Data Emissions Model VEF Volume- Emissions Functions Weighing Criteria System Equitable Or System Optimum Assignment 22
Cost ( ) 3.3 Case-study 23 Network characteristics R1. R2 R3 0.2 0.1 R4 0 1 km Layout of alternative routes the alternative routes. 0,08 0,06 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0 2000 4000 600 Volume on route (vph) Volume-cost functions for COST R1 COST R2 COST R3 COST R4
Relative Flow Environmental Impacts ( ) Total users cost 24 3.4 Results (environmental costs / Moderate demand) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% U.E. S.O. S.E. Moderate (4000 vph) R4 R3 R2 R1 450 400 350 300 250 200 150 100 50 0 U.E. S.O. S.E. Moderate (4000) vph 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
25 4 Modelling Futures: Room for improvement... Validation of scenarios / ex-post analysis (are we predicting well?) Scripting language / APIs (Trade off: Time vs. results) National/regional database on model parameters (micro and macro), fleet data, OD patterns... Data resolution (interoperability) for model integration (traffic and emissions to Air Quality - GIS Interface; external optimization models) Real time data availability (considering the requirements for input to new modelling procedures)
Contactos Telef: 234 370 830 (ext. 23882) Fax: 234 370 953 E-mail: margarida.coelho@ua.pt 26 http://transportes-tema.web.ua.pt/ Universidade de Aveiro Departamento de Engenharia Mecânica Campus Universitário de Santiago 3810-193 Aveiro