Predicting highly-resolved traffic noise
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1 Predicting highly-resolved traffic noise (using data available as a by-product of Urban Traffic Management and Control systems) Dr Shadman Marouf, Professor Margaret C. Bell, CBE Future Mobility Group, Newcastle University Presented by Dr Paul Goodman, Environmental Engineering Group, Newcastle University Presented at MATTS 218, TU Delft, 18 th October 218
2 Background Modelling noise has been a topic since the 196s. Since the END 22, there has been the need for noise maps every 5 years in agglomerations last 217 Recognition that we can calculate noise very well for freely-flowing traffic conditions, but less well for interrupted flows Can we use the wealth of UTMC information to assist in calculation and mapping? Can we control for noise? (and would we want to?)
3 A Spatio-Temporal Model Spatially: We have a network made of nodes and links We have a fleet of multiple vehicle types (car, MGV, HGV/Bus) Our vehicles have four operational modes Cruising, Accelerating, Decelerating, Idling A cruising acceleration queueing deceleration W cruising Y acceleration B receiver (P) Temporally, we have two operational modes: Uninterrupted operation under a green signal Interrupted operation by a red signal
4 Calculating Sound Use CNOSSOS-EU based procedure Calculate Sound Power Level (SPwL) 2x sources on an individual vehicle (Rolling & Power) 8x octave bands for broadband total Calculate look-up table of Sound Exposure Level values (SELs) for individual vehicle modes Scale by vehicle flow in mode Propagate and convert contributions from all sources to get L Aeq levels
5 Calculating Sound (Simplified ) Basic CNOSSOS-EU Sound Power (speed and vehicle type dependent): L Veh = f(l power + L rolling ), L rolling = a + b log 1 [ V V ref ], L power = c + d [ V V ref V ref ] Correction of Sound Power to SELs (speed and geometry dependent): SEL Veh = L Veh 1log 1 v + 1log 1 d + 1log 1 a 1log 1 4πd 2 Lg Sound Energy contribution from a vehicle class in a period: E Veh = 1log 1 [ 1.1E Veh,idl. Q Veh + 1.1E Veh,cru. Q Veh + 1.1E Veh,acc. Q Veh + 1.1E Veh,dec. Q Veh ] Correction to L Aeq,T : L Aeq T = 1log 1 [ 1 T (E car + E MGV + E HGV + T Q car Q LGV Q HGV. 1.1L back] We re glossing over loads of assumptions regarding CNOSSOS parameters (e.g. road surface) here! see also Paoprayoon et al., 25 and Watts et al., 24
6 Where to get Traffic Data? Use Newcastle City Council s SCOOT (Split, Cycle, Offset, Optimisation) system (TRL UK, Hunt et al., 1981) Data either measured or generated from SCOOT used in this research include: Flow: an estimate of stop-line arrival flow in veh/h or veh/5min; Delay: an estimate of the total delay experienced by all vehicles arriving at the stop-line, in units of 1/1th vehicle hours/hour; Occupancy: the number of quarter-second intervals a traffic loop detector embedded in the road pavement is occupied by vehicles during the overall time period (in this study 5 min) Collected via NUIDAP (Newcastle University Integrated Data Access Platform)
7 How to Calibrate/Validate? Use data collected from inexpensive emote sensors See for yourself at:
8 Study Area emotes 177, 173 City Centre F J K G 173 N A189/Bath Lane emote173 NODE 1151 Q B N M A D 177 A189/Westgate Road emote177 NODE 1141 X E Z P Y St. James Boulevard S W NODE 1121 B A N A189 St. James Boulevard Region
9 Occupancy Occupancy Flow (veh/5min) Flow (veh/5min) N1141P N1141Z Speed (km/h) N1141P Speed (km/h) Flow (veh/5min) N1141Z Speed (km/h) N1141Z Speed (km/h) Flow (veh/5min) Sample SCOOT data (Flow, Occupancy, Speed) Occupancy 177 N1141P F J K NODE 1151 A189/Bath Lane B D A M Q emote A189/Westgate Road Occupancy emote177 Link 1141Z is straight-ahead flow, 1141P is a right-turn pocket Mote 177 is near the stop-line whilst 173 is mid-link. NODE 1141 X F F F P Z Y W S A NODE 1121 E
10 NUIDAP and Flow States (Hodges et al., 29) Smooth flow Start-Stop Tuesday Wednesday Friday Saturday Sunday Monday Quiet Congestion Occupancy (.25s/5min) States provided for each 5 minutes namely Quiet (1) Smooth Flow (2) Start-Stop (3) and Congestion (4) for each five minutes used in CNOSSUS
11 Models Tested Three Scenarios (Weekdays 7: 19:): Free-Flow - assumed average flows and speed Free-Flow and speed with CNOSSUS junction corrections assuming constant periods of SCOOT state. Each state has defined proportions of traffic mode N1141Z (straight-ahead) quiet and smooth states 4% and 6% respectively N1141P (right-turn movement only) quiet (67%), smooth (24%) and busy (9%). Using SCOOT derived flow and speed with known flow regime for each five minute period and corrected for the spatial changes upstream and downstream of the link.
12 Results: Site Stopline 1. Free Flow 2. +CNOSSOS Junction Correction 3. Spatio-Temporal using SCOOT data Absolute error: 3.7dBA RMSE: 4.1dBA Absolute error: 2.dBA RMSE: 2.dBA Absolute error:.5dba RMSE: 1.9dBA
13 Results: Site 173 Mid-Link 1. Free Flow 2. +CNOSSOS Junction Correction 3. Spatio-Temporal using SCOOT data Absolute error: 4.3dBA RMSE: 4.7dBA Absolute error: 3.9dBA RMSE: 4.3dBA Absolute error: 1.4dBA RMSE: 1.9dBA
14 Difference (predicted-measured) in L Aeq,5-min 1. Free Flow Difference between 1. and CNOSSOS Junction Correction Difference between 1. and Spatio-Temporal using SCOOT data
15 Conclusions and Limitations Three variants of a noise prediction model developed each using UTMC (SCOOT) data in a different way Most effective model used breakdown of links into four sections and included flow regimes. Using free-flow, average speed underestimated noise Also can obtain distributions of noise, rather than just single values Limited by simple calculations of speed from SCOOT occupancy and delay e.g. masking can occur using inductive loops No way (yet) of getting different attributes for different vehicle classes from SCOOT, or in assumptions Other effects? Road surface? Site characterstics? Non-traffic noise? Nearside links only considered
16 Thank you for listening Any questions? Contacts: Professor Margaret C. Bell, CBE Science City Professor of Transport and Environment Future Mobility Research Group School of Engineering, Cassie Building, Newcastle University, Newcastle upon Tyne, NE1 7RU Dr. Paul Goodman Researcher in Transport and Environment Environmental Engineering Research Group School of Engineering, Cassie Building, Newcastle University, Newcastle upon Tyne, NE1 7RU
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