Overview of the CONDUITS framework

Transcription

1 Overview of the CONDUTS framework CONDUTS training workshop 26 November 2014 Madrid oannis Kaparias, City University London Niv Eden, Technion srae nstitute of Technoogy

2 Outine CONDUTS KPs framework Requirements Framework Appication to rea-word case studies Specific case studies Paris, Te Aviv, Munich Genera assessment Rome Further deveopment: CONDUTS-DST Approach and framework Case studies in Brusses, Stuttgart and Te Aviv 2

3 Requirements of KPs Easy to use and communicate No or very itte extra work for users Making use of existing data Cear to poitica decision-makers and the genera pubic Adaptabe to cities individuaity and needs Geographica scae (e.g. sections, zones, roads, network, ) Enabing the use of a types of urban data, that are reevant to quantify performance Offering the abiity to contro and customise the measure of performance 3

4 CONDUTS KPs framework 4

5 CONDUTS KPs framework 5

6 Appication case studies Paris mpementation of a priority system to 3 bus ines: Assessment of traffic efficiency (mobiity KP) and traffic safety (accidents KP) Rome Genera network-wide assessment of traffic efficiency using the mobiity and reiabiity KPs Te Aviv mpementation of new signa strategies: Assessment of traffic efficiency (reiabiity KP) Munich/ngostadt Appication of feedback signs for drivers, and impementation of adaptive signa contro : Assessment of traffic safety (direct safety impacts and indirect safety impacts KPs) 6

7 Appication case study 1: Paris Assessment of impementation of bus priority system Priority on bus ines 26, 91 and 96 in 2006, with anticipated savings of 30 s per traveer on average, i.e. saving of one vehice Suppied data (before and after): Bus trave times on the 3 bus ines Vehice traffic speeds on roads affected by the priority system Casuaty numbers due to accidents on roads affected by the priority Vehice traffic fows on roads affected by the priority 7

8 Appication case study 1: Paris Assessment of traffic efficiency Mobiity KP minutes/km, weighted for pubic and private transport Resuts Separatey for pubic and private transport Combined, with w PT = 0.7, w PV = 0.3 8

9 Appication case study 1: Paris Assessment of traffic safety Accdents KP casuaties per miion vehices, weighted by severity Resuts For each severity eve Tota, with w DEAD = 0.85, w SER = 0.1, w SL =

10 Appication case study 2: Rome Genera network-wide assessment of traffic efficiency Severa techniques and technoogies, incuding TS, are used for traffic management in the entire Greater Rome area, incuding the Mobiity Contro Centre Suppied data: Trave times by private and pubic transport between a zones in the city, aong with route engths Occurrences of congestion and their average duration on a number of key routes on the road network over one year 10

11 Appication case study 2: Rome Assessment of traffic efficiency Mobiity KP Reiabiity KP minutes/km, weighted for pubic and private transport Resuts dimensioness, weighted by ink and mode Mobiity separatey for pubic and private transport, and combined, with w PT = 0.7, w PV = 0.3 Reiabiity as a whoe (with a routes weighted equay): REL =

12 Appication case study 3: Te Aviv Traffic efficiency assessment of new signa strategies Depoyment of new signa strategies (AVVM) to dea with recurrent congestion during the afternoon/evening peak 30 Trave Time TCongestion TCongestion 5 Data used: Leve of Service (LOS) and duration of congestion on inks of the Ha Shaom Arteria eights according to time of day, ink category and direction of trave Hour 12

13 Appication case study 3: Te Aviv Assessment of traffic efficiency Reiabiity KP Resuts dimensioness, weighted by ink and mode Comparing the index during the afternoon peak hours, two months before the impementation of the new signa programs with two months after, shows an average index increase of 36% The decrease in congestion duration was higher (~41%) Figure supported by the genera perception of representative traveers ithin a few months the decreasing tendency of the index vaue stopped and within one year the index vaue became stabe 13

14 Appication case study 4: Munich Traffic safety assessment of speed feedback signs Two feedback signs during a test period, measuring speeds at two urban streets (speed imit 50 and 30 km/h) in both directions, and showing fashing messages: if exceeding the speed threshod Sow down!, or otherwise Thank you! Suppied data (before, during and after): Time and speed of each vehice passing the ocation Daiy traffic voume and number of vehices exceeding speed imit 14

15 Appication case study 4: Munich Assessment of traffic safety Direct safety impacts KP Resuts For each road site and direction number of safety interventions (warning messages) per vehice Combined, with w L1 = 0.2, w L2 = 0.2, w L3 = 0.3, w L4 =

16 Appication case study 5: ngostadt Traffic safety assessment of adaptive signa contro Appication of two onine agorithms for optimising green waves (hi-cimbing and genetic), as opposed to the existing static green wave, to dea with oversaturated junctions during peak hours and fuctuating demand during off-peak hours Suppied data (before, during and after): Foating Car Data (FCD) from a representative period by GPS-tracking of a sma feet of vehices Daiy traffic voume from oop detectors 16

17 Appication case study 5: ngostadt Assessment of traffic safety ndirect safety impacts KP S U Resuts For each of three routes separatey Combined, with a routes weighted equay before hicimbing genetic Route 1 3,7 3,2 3,0 Route 2 4,7 4,2 4,1 Route 3 4,4 4,0 3,5 before S-U is-u hicimbing genetic Network 4,3 3,8 3,5 5,00 4,50 4,00 3,50 3,00 2,50 2,00 1,50 1,00 0,50 0,00 L L w CS DTV number of critica situation occurrences (e.g. speed imit occurrences) per vehice S-U on route eve Route 1 Route 2 Route 3 static hicimbing genetic 4,50 4,00 3,50 3,00 2,50 2,00 1,50 1,00 0,50 0,00 S-U on network eve static hicimbing genetic 17

18 From CONDUTS to CONDUTS-DST Concusion of CONDUTS: KPs found to refect major phenomena, offering the abiity to perform independent and educated decision-making, based on rea and avaiabe data but rea data are not aways avaiabe and often it is too ate to perform an evauation when rea data become avaiabe (e.g. accidents) Next step: predictive performance evauation Need to be abe to evauate impacts in advance Deveopment of CONDUTS-DST (decision-support too), aowing ex ante cacuation of CONDUTS KPs using data from traffic simuation toos 18

19 CONDUTS DST Framework Rea Life Appications AVVM Municipa DB SCOOT Regiona DB Transportation Modeing Toos VSSM VSUM Aimsun TransCAD Traffic Efficiency Poution Socia ncusion & Land Use Safety 19

20 CONDUTS DST Framework VSSM Links Data Vehice Data Links Evauation (STR) Vehice Records (FZP) CONDUTS_DST P ET etet ET etet et Q et, et Poution KP Emission Aggregation Emissions Cacuation Vehice Data ARE nput fies ARE Emission Data MOB REL 1 R L L L rr R L ATT D r r CT T w Traffic Efficiency KP Traffic Reiabiity KP Routes Trave Times Aggregation Routes Trave Times Aggregation Emission Data Routes Generation Trave Times Cacuation Vehice Data nc. emissions 20

21 CONDUTS KPs P ET etet ET etet et Q et MOB, et 1 R rr R ATT D r r REL L L Measured/ Cacuated L L Contro CT T w 21

22 Modeing Measures P ET etet ET etet et Q et MOB, et 1 R rr R ATT D r r REL L L Measured/ Cacuated L L Contro CT T w 22

23 The ARE Mode (Anaysis of nstantaneous Road Emissions) nstantaneous Emissions Mode (EM) Passenger car & Heavy duty Emissions Mode (PHEM) Graz Technica University High accuracy for fue consumption, CO 2, NO x, PM in the traffic micro-simuation modes 23

24 Estimation of the poutant emissions by ARE EM Tabes Vehices records Gradients Loads Engine type Acceeration Speed Location Estimation of the poutants emissions 24

25 Distributions used in ARE (1) Vehice Types car gv hgv rigid hgv artic bus 1 deck bus mini bus 2 decks bus bendy tram coach taxi motorcyce Axes Location urban rura motorway Fue Types petro diese pg eectricengine type 2-stroke 4-stroke Gross vehice weight under 2.5t over 2.5 t t Engine capacity under 150cc cc Euro standard pre-euro V Vehice oads unaden haf-aden fuy-aden V V cc over 750cc under under 2.0 over t 12-14t 14-20t 20-26t 20-28t 26-28t 28-32t 28-34t over 32t 34-40t 40-50t Years

26 Modeing Measures P ET etet ET etet et Q et MOB, et 1 R rr R ATT D r r REL L L Measured/ Cacuated L L Contro CT T w 26

27 Modeing Measures ARE Routes Generation Average Emissions Trave Times Per Route Poution KP Traffic KPs 27

28 CONDUTS KPs - eights P ET etet ET etet et Q et MOB, et 1 R rr R ATT D r r REL L L Measured/ Cacuated L L Contro CT T w 28

29 CONDUTS KP s Controing the eights Poutant Vehice Types Routes 29

30 CONDUTS DST Scenarios 30

31 Thank you! oannis Kaparias City University London Northampton Square, London EC1V 0HB, United Kingdom Niv Eden Transportation Research nstitute Technion City, Haifa, srae tri.technion.ac.i 31