This work was supported by Latvian state research program project The next generation of information and communication technologies (NexIT) (2014-2017) MODELLING AS DECISION SUPPORT TOOL FOR SUSTAINABLE TRANSPORT SYSTEM PLANNING: CASE-STUDY OF RIGA D R. S C. I N G. M I H A I L S S A V R A S O V S http://www.tsi.lv http://las.tsi.lv Seminārs Modernās informācijas un komunikācijas tehnoloģijas transporta sistēmu vadībai
Introduction Outline Case-study 1: highway P133 and Ziemelu street intersection simulation and capacity estimation Case-study 2: pedestrian and transport flows analysis for pedestrian street creation in Riga Case-study 3: freight traffic flow research and rerouting from Riga city center Conclusions 2
Requirements to the modern transport system Transport key element of modern economics 3 Ecological requirements Safety Effectiveness Commission of European Communities, "White paper- European transport policy," Luxembourg, 92-894-0341-1, 2011.
Sustainable development Sustainable development is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs Transport sustainable development tools: use of ITS (Intelligent Transportation Systems) use of P&R (Park and Ride) optimization of existing transport infrastructure development of intermodality implementation of new transport infrastructure elements base on strict analysis of development consequences use of sound tax policy etc. 4
Model-based decision-making 5 E. Yurshevich, Methodology of decision-making support based on urban transportation system microscopic models repositories, doctoral thesis, Riga, TTI, 2013
Hierarchy of TS modelling approaches Microscopic models Each vehicle is moved through the network of transportation facilities on a split second by split second basis according to the physical characteristics of the vehicle (length, maximum acceleration rate, etc.) Mesoscopic models models simulate individual vehicles, but describe their activities and interactions based on the aggregate (macroscopic) relationships Macroscopic models simulate traffic flow, taking into consideration cumulative traffic stream characteristics (speed, flow, and density) and their relationships to each other 6
Case-study 1: highway P133 and Ziemelu street intersection simulation and capacity estimation 7 M I H A I L S S A V R A S O V S V L A D I M I R V E N T I R I N A Y A T S K I V
Highway P133 and Ziemelu street intersection simulation and capacity estimation Goal: to test different scenarios on P133 and Ziemelu street intersection capacity improvement 8 Tasks: to develop microsocpic model of the intersection; to test different development scenarios to estimate level of services (LOS) for crossroads according to HCM standard to produce 3D animation of the model
Research object 9 Ziemeļu street To city center To airport to Mārupe
Development plans Cargo and passenger growth 10 Territory development Expedition hall Business center
Input data (traffic flow intensity) 11
Input data (traffic light data) 12
Simulation model development PTV VISION VISSIM 5.6 162 links and connectors 76 - conflict areas 5 traffic input point 2 public transport stops 1 public transport route 12 traffic lights > 100 3D objects 13
Particularities of the model Some directions are controlled by detectors 14
Scenarios Title Description 1 Scenario 1: P Intensity: Forecasted 2 Scenario 2: P+2J Intensity: Forecasted 2 lanes from Ziemeļu iela 3 Scenario 3: P+2J+L1 Intensity: Forecasted 2 lanes from Ziemeļu iela Fix traffic light cycle (1 st variant) 7 Scenario 7: P+2J+L2 Intensity: Forecasted 2 lanes from Ziemeļu iela Fix traffic light cycle (2nd variant) 11 Scenario 11: P175+2J+L3 15 Intensity: Forecasted (Marupe direction 175 v/h) 2 lanes from Ziemeļu iela Fix traffic light cycle (3rd variant)
Level of Service 16 LOS LOS Время задержки (с) Delay time (s) A 10 B > 10 20 C > 20 35 D >35 55 E > 55-80 F >80
Simulation results 17 LOS Krustojums Virziens Braukšana 1 NB (Developing territory) EB (Airport) SB (Marupe) WB (City center) Left 2 Through Right 2 Scenāriji 1 2 3 4 5 6 7 8 9 10 11 Total F F F F F E F F E D E Left 2 Through Right 1 Total C C C C C C D D D D D Left 2 Through Total F F F F F F E E E F E Left 2 Through Right 2 Total D D D D D D D D D D D Total F F F F E E E E E E E
Simulation results 18 Delay time, s
Simulation results 19 Average speed, km/h
3D animation 20
Case-study 2: pedestrian and transport flows analysis for pedestrian street creation in Riga 21 I R I N A Y A T S K I V E L E N A Y U R S H E V I C H M I H A I L S S A V R A S O V S
Goal: Pedestrian and transport flows analysis for pedestrian street creation in Riga city to test different development scenarios of the Terbatas street Tasks: to make a traffic counts survey to obtain information about current use of the street and around lying region; to estimate public transport loading level and to make public transport passenger questionnaire about origin, destination, travel goal and preferences; to develop and calibrate microscopic model, which describes current situations; to test different development scenarios; to forecast traffic flow redistribution and to estimate level of services (LOS) for crossroads according to HCM standard. 22
Traffic counts 23 Date and time: 29 September from 7:30 till 9:30 29 September from 16:30 till 18:30 Wednesday 30 September from 7:30 till 9:30 30 September 16:30 till 18:30 Thursday 150 TTI students (traffic counts) 10 students (data processing, analysis, visualization)
Object 37 Crossroads: 24 Brīvības iela Elizabetes iela Brīvības iela Dzirnavu iela Brīvības iela Blaumaņa iela Brīvības iela Lāčplēša iela Brīvības iela Ģertrūdes iela Brīvības iela Stabu iela Brīvības iela Bruņinieku iela Brīvības iela Matīsa iela Brīvības iela Tallinas iela Brīvības iela Cēsu iela Tērbatas iela Elizabetes iela Tērbatas iela Dzirnavu iela Tērbatas iela Blaumaņa iela Tērbatas iela Lāčplēša iela Tērbatas iela Ģertrūdes iela Tērbatas iela Stabu iela Tērbatas iela Bruņinieku iela Tērbatas iela Matīsa iela Tērbatas iela Tallinas iela Krišjāņa Barona iela Elizabetes iela Krišjāņa Barona iela Dzirnavu iela Krišjāņa Barona iela Blaumaņa iela Krišjāņa Barona iela Lāčplēša iela Krišjāņa Barona iela Ģertrūdes iela Krišjāņa Barona iela Stabu iela Krišjāņa Barona iela Bruņinieku iela Krišjāņa Barona iela Matīsa iela Krišjāņa Barona iela Tallinas iela Marijas iela Elizabetes iela Marijas iela Dzirnavu iela Aleksandra Čaka iela Blaumaņa iela Aleksandra Čaka iela Lāčplēša iela Aleksandra Čaka iela Ģertrūdes iela Aleksandra Čaka iela Stabu iela Aleksandra Čaka iela Bruņinieku iela Aleksandra Čaka iela Matīsa iela Aleksandra Čaka iela Tallinas iela
Traffic count results 25
Simulation Results (1/3) 26
Simulation Results (2/3) 27
Simulation Results (3/3) 28
Case-study 3: freight traffic flow research and rerouting from Riga city center 29 I R I N A Y A T S K I V M I H A I L S S A V R A S O V S I R I N A P T I C I N A I L Y A C I G R I N S N A D E Z H D A B O R O V I K O V A R I T A S T R O D E
Transport system 30 GDP 53%
Cargo turnover in Riga port 31
Cargo turnover by type 32
Average daily traffic flow intensity (2013) 33
Traffic counts 34 Date and time: 19 February from 10:00 till 12:00 19 February from 14:00 till 16:00 Wednesday 20 February from 10:00 till 12:00 20 February from 14:00 till 16:00 Thursday
Counted types of vehicles 35
Counting sites 19 counting sites: Vanšu tilts Akmens tilts Salu tilts Dienvidu tilts Maskavas iela Krasta iela 11. novembra krastmala Ziepniekkalna iela Mūkusalas iela Gustava Zemgale gatve Daugavgrīvas iela Raņķa dambis Slāvu iela Lubānas iela Hanzas iela Skanstes iela Vienības gatve Kārļa Ulmaņa gatve Viestura prospekts 36
Counting results (1/2) 37 Virziens: 5A / Maskavas iela (uz centru) Datums: 19.02.2014 (Transportlīdzekļu skaits) 400 350 300 250 200 150 100 50 0 326 311 318 326 268 274 293 311 296 317 269 281 26 22 28 275 277 280 32 20 32 35 251 23 38 42 20 32 19 12 32 19 230 228 232 248 238 237 277 263 285 281 275 266 246 254 223 239 C4 C3 C2 C1 V
Counting results (2/2) 38 Virziens: 5A / Maskavas iela (uz centru) Datums: 19.02.2014 Transporta plūsmu struktura 3% 2% 0% 9% 86% V C1 C2 C3 C4
5000 4500 4000 4609 4500 Total intensity 39 3500 3250 3109 2798 3000 2500 2000 2490 2333 2091 1959 1846 1750 1648 1637 1553 1548 1443 1368 1318 1500 1000 1156 500 0
Total cargo flow intensity 40 1400 1200 1155 1000 993 879 800 600 400 200 774 713 712 651 639 583 580 498 464 459 403 376 358 349 269 218 0
Macromodel development 170 - nodes 41 470 - links 27 transport zones 2 OD matrix
Calibration and validation (1/2) 42
Calibration and validation 43 3000 2500 R² = 0,939 2000 1500 1000 500 0 0 500 1000 1500 2000 2500 3000
Scenario Changes in transport system Changes in traffic intensity 2014_B NO NO 2014_C1 Cargo flow restriction from Salu bridge to Hanzas iela NO 2014_C2 2014_C3 2014_VC1 2014_VC2 2014_VC3 Cargo flow restriction from Salu bridge to Hanzas iela and on Akmens bridge Cargo flow restriction from Salu bridge to Hanzas iela and on Akmens bridge and Sala bridge Transport flow restriction from Akmens bridge and Vanšu bridge Transport flow restriction from Turgeņeva street to Dzelzceļa bridge Transport flow restriction from Vanšu bridge Turgeņeva street NO NO All types of vehicles: - 5% All types of vehicles: - 5% All types of vehicles: - 5% 44
Project Austrumu maģistrāle 45
km/st Simulation results 46 22 21.3 21 20.6 20 19 18 19.9 19.75 18.9 19.7 19.2 18.2 19.7 19.55 18.6 19.5 19.3 18.1 18.3 17 16 Scenārijs
Level of service (HCM) 47
Conclusions 48 Traffic flow simulation is a modern approach of TS quantitative analysis, which could be used for decision making Different types of models could be used in different level of decision making Use of model require prove of data validity, model validity, correctness of evaluation Use of standalone models is effective, but not for city authorities The important parts of DSS are: data collection procedures and technical implementation; database of the models; procedures of linkage different models issues related to data and model access
This work was supported by Latvian state research program project The next generation of information and communication technologies (NexIT) (2014-2017) 49