UTC Case Studies Turin, Rome Quantifying the Effect of Intelligent Transport Systems on CO2 Emissions from Road Transportation Giorgio Magra, CNH-IVECO Giacomo Tuffanelli, RMA
The process Data Collection Build Model Model Calibration Scenario Run and Data analysis Up-Scale Macro Model CAMPAIGN TURIN: 6 Days Measurements ROME:3 Days Measurements TRAFFIC NORMAL (only TURIN) CONGESTED TRAFFIC MODEL TURIN: AIMSUN 4 Scenarios ROME: VISSIM (MICRO) TRANSCAD (MACRO) 2 Scenarios ON THE CORRIDOR FLOWS TRAVEL TIMES and OTHER PERFORMANCE INDEXES COLLECT SIMULATION OUTPUTS AND COMPARE THE DATA CONCLUSION UP-SCALE MACRO MODEL ROME: From test site to whole city by modifying adequately Volume-Delay functions ICT Measure UTC ON UTC OFF EMISSIONS MODELS CRUISE COPERT
UTC Case Study Turin
Turin case study Data collection Traffic model AIMSUN Direction 1: corridor in corso Lecce from Corso Regina to Via Lera Direction 2: corridor in corso Lecce from Via Lera to corso Regina Measurement of travel time via GPS equipment 4 days UTC ON 43 trips, 2 days UTC OFF 17 trips 4 vehicles (Fiat Punto, 2 Fiat Panda, Fiat 500) 2 time slots (8.00-9.00, 12.00-13.00)
Turin case study Build and run the scenario \\\ Implementation of 5 equipped intersections on the micro simulation model Traffic demand Free normal 5000 veh, congested 7500 veh Fleet composition 92,5% car, 6,9% LDV 0,2% HDV 0.4% BUS Vehicle attributes Length, average acceleration User reaction time 0,5s 30%, 0.75s 45%, 1s 20%, 1.25 s 5% + 0.25 s HDV,BUS
Turin case study Calibration of the flow AIMSUN Macro scenario AIMSUN Micro scenario Traffic demand Normal Congested
Turin case study Calibration of travel time UTC OFF UTC ON 7 1036 20 vehicles vehicles vehicles 1058 vehicles 8-9 Measurements 10 vehicles 447 23 vehicles Measurements vehicles 452 vehicles 12-13 Measurements Measurements
Turin case study Results Travel time saving 300 250 200 150 100 50 0 Basecase Travel_time -26,5% -21% UTC ON 40,0% 35,0% 30,0% 25,0% 20,0% 15,0% 10,0% 5,0% 0,0% -5,0% -10,0% -15,0% -20,0% -25,0% -30,0% -35,0% -40,0% normal congested
Turin case study Vehicle increase Average Vehicles 9000 10,0% 8000 5,0% 7000 6000 0.5% 1% 0,0% 5000 4000-5,0% normal congested 3000-10,0% 2000 1000-15,0% 0 Basecase UTC ON -20,0%
Turin case study Emission reduction CO2_avg_g_km 300 10,0% 250 5,0% 200 0,0% 150-8% -4.5% -5,0% normal congested 100-10,0% 50-15,0% 0 Basecase UTC ON -20,0%
UTC Case Study Rome
The context: Rome Municipal Area 1.285 km 2 Population Metropolitan Area 4.300.000 City residents 2.900.000 Road Network km. 5.000 Main road network km. 800 Vehicles 2.650.000 Cars 1.890.000 2 wheels 600.000 LGV-HGV 160.000 Daily Trips 6.100.000 Peak-hour Trips 560.000
Rome case study overview Via Appia test site Itinerary of 9 km Urban Road: 2 000/3 000 veh/h Direction 1 Downtown towards City Centre Via Appia Traffic Lights Controlled by UTC system with plan selection scheme Direction 2 Outwards towards Grande Raccordo Anulare 27 Traffic Lights 21 Major 2 Minor 4 Pedestrians Brussels - 2015 March, 31st
Rome case study - Surveys TUESDAY WEDNESDAY THURSDAY UTC ON UTC OFF UTC ON Floating Car Data 6 hours/day - Morning Peak (7:30/9:30) - Mid-day Off-Peak (12:30/14:30) - Afternoon Peak (17:30/19:30) 2 vehicles with GPS and Engine/fuel monitor systems - Fiat Punto - Fiat Panda 1.3 MJ Survey Numbers: - 67 trips - 410 travelled km Traffic Flows (All Intersections) 6 hours/day - Morning Peak (7:30/9:30) - Mid-day Off-Peak (12:30/14:30) - Afternoon Peak (17:30/19:30) Automatic Traffic Flows Data 24 hours/day Average Travel Time 24 hours/day
Rome case study - from Surveys to Traffic Models Modelling scenarios for AM Peak Hour URBAN - CONGESTED FLOW Field Data Simulation Data Environmental effects (CO 2 ) Survey MACRO scale UTC ON / OFF (only for test site) UTC ON / OFF (whole city) MICRO scale UTC ON / OFF (only for test site)
Rome case study - MICRO Model Calibration MODELLED TRAVEL TIME [s] 1.500 1.250 1.000 750 500 250 CARS y = 0,915x + 16,16 R² = 0,958 2200 2000 CARS y = 0,9668x - 0,3027 R² = 0,9942 0 0 250 500 750 1.000 1.250 1.500 MEASURED AVERAGE TRAVEL TIMES [s] 1800 MODELLED FLOWS [veh/h] 1600 1400 1200 1000 800 600 400 200 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 2200 MEASURED FLOWS [veh/h]
Rome case study MICRO Model - ICT Measures Effects Microsimulation results Average of 15 runs for each scenario (with 15 different seeds) Parameters UTC ON UTC OFF Abs.diff. % Average Network Speed [km/h] 20,3 18,7-1,6-7,5% Total number of stops 105 888 114 720 + 8 832 +8,3% Average number of stops per vehicle 5,5 6,1 + 0,5 + 9,6% Average lost time per vehicle [s] 204,9 232,2 + 27,7 + 13,4% Note: Results for the UTC main itinerary (via Appia) and related intersections Results in line with real data
Rome case study MICRO Model - Environmental Effects CO2 absolute (kg) 13150 13100 13050 13000 12950 12900 12850 12800 12750 12700 12650 CO2 abs kg CO2 rel g/km 355 350 340-2,2% -4,8% 335 325 320 congested flow congested flow basecase UTC on basecase UTC on CO2 relative (g/km) 345 330 Note: Results for the UTC main itinerary (via Appia) and related intersections
Rome case study - MACRO Model Calibration Calibration Process Based on comparison between real data and model data for Traffic Flows and Travel Time
Rome case study MACRO Model Parameters Definition Modelling impacts of UTC measure by matching travel time differences on Via Appia due to UTC ON/OFF condition (+14%) Definition of new set of parameters that modify Volume-Delay functions of links Resulting new free flow speeds, capacities and saturation flows for links Main Assumption: UTC measure mainly affects free flow speeds and road capacity Saturation Flow: SAT = f(l) Modification of saturation flow through L parameter (road width) Free Flow Speed: V 0 = f(l,d) Modification of free flow speed through L,D parameters
Rome case study MACRO Model Definition and Traffic Effects SURVEYS Dir. DOWNTOWN: ICT off - ICT on = +14% SIMULATIONS Dir. DOWNTOWN: ICT off - ICT on = +14%
Rome case study MACRO Model - Environmental Effects UTC status ON Vehicular Class CO2 Emissions (kg) Diff % vs OFF Buses 24.193-0,06% Heavy Duty Trucks 213.024-0,03% Light Commercial Vehicles 96.344-0,05% Passenger Cars 912.879-0,05% Total 1.246.440-0,05% OFF (base case) Buses 24.208 Heavy Duty Trucks 213.096 Light Commercial Vehicles 96.395 Passenger Cars 913.319 Total 1.247.019 Emissions variations due to the application of the ICT measure only to the main itinerary of via Appia (Rome test site) UTC on corridor allows a small gain in CO 2 overall reduction
Rome case study Upscaling Process From Test Site to Whole City 22 urban axis under UTC Emissions variations due to the application of the ICT measure to all the main itineraries of the city controlled by UTC and the consequent traffic redistribution effects TRAFFIC: flow increase on UTC (on) axis Avg Speed +4% UTC status Vehicular Class CO2 (kg) ON ENVIRONMENTAL Diff % vs OFF Buses 24.193-0.33% Heavy Duty Trucks 213.024-0.37% Light Commercial Vehicles 96.344-0.28% Passenger Cars 912.879-0.26% OFF (base case) Total 1.247.019-0,28% Buses Heavy Duty Trucks Light Commercial Vehicles Passenger Cars 24,272 213,808 96,610 915,208 Total 1.249.897
Conclusions UTC increases the Level of Service where it is applied, mostly in normal condition Increases the capacity of the road mostly in congested condition Decreases the emissions in both the cases
Thank you for your attention Contacts: Giorgio Magra Giacomo Tuffanelli giorgio.magra@cnhind.com giacomo.tuffanelli@agenziamobilita.roma.it