DYNAMIC ODME FOR AUTOMATED VEHICLES MODELING USING ‘BIG DATA’ Dr. Jaume Barceló, Professor Emeritus, UPC- Barcelona Tech, Strategic Advisor to PTV Group Shaleen Srivastava, Vice-President/Regional www.ptvgroup.com Director (PTV Group Americas) www.ptvgroup.com
INTRODUCTORY REMARKS Connected vehicle systems and autonomous vehicles likely to be major game changers in traffic and mobility. No longer a question of if, but of when, in what form, at what rate. And through what kind of evolution path… … operational regimes in which vehicles are connected to each other and to the infrastructure, and augmented with autonomous capabilities. (Hani Mahmassani, Workshop 134: Emerging Needs for Improving Simulation Models, TRB 96 th Annual Meeting, Washington, January 8, 2017) www.ptvgroup.com
CONNECTED & AUTONOMOUS VEHICLES www.ptvgroup.com
AUTONOMOUS VS CONNECTED VEHICLES www.ptvgroup.com
SINGLE VEHICLE APPLICATIONS & COOPERATIVE APPLICATIONS www.ptvgroup.com
WILL AV REPLACE CURRENT AUTOMOTIVE TECHNOLOGIES FOR INDIVIDUAL MOTORIZED MOBILITY? OR, WILL MOSTLY BE USED FOR COLLECTIVE MOBILITY? http://www.traffictechnologytoday.com/video-audio.php?v=GATEwayTeam www.ptvgroup.com
VISUALIZATION OF SHARED SELF-DRIVING CAR SIMULATION FOR LISBON www.ptvgroup.com
A SELF-ORGANIZING SYSTEM OR BETTER EXTERNALLY ASSISTED? www.ptvgroup.com
COOPERATIVE DRIVING WITH THE HELP OF V2X COMMUNICATIONS Source: D. Jia, D. Ngoduya, Enhanced cooperative car- following traffic model with the combination of V2V and V2I communication Transp. Res. B, March 2016 Source: L. Zhao, J. Sun, Simulation Framework for Vehicle Platooning and Car-following Behaviors under Connected-Vehicle Environment, Procedia - Social and Behavioral Sciences 96 ( 2013 ) 914 – 924. www.ptvgroup.com
TESTING VACS BY MICROSOPIC SIMULATION ACC string-stability ACC traffic efficiency From: Ntousakis, I.A., Nikolos, I.K., Papageorgiou, M.: On microscopic modelling of adaptive cruise control systems. 4th Intern. Symposium of Transport Simulation (ISTS’14), 1 -4 June 2014, Corsica, France. Transportation Research Procedia 6 (2015), pp. 111-127. www.ptvgroup.com
ACC/CACC: STABILITY/EFFICIENCY Macroscopic simulation of traffic flow (spatio-temporal evolution of traffic density) close to an on-ramp using the GKT model, combined with a novel ACC/CACC modeling approach. Left: manual cars; Middle: ACC- equipped cars; Right: CACC-equipped cars. Source: Delis, A.I., Nikolos, I.K., Papageorgiou, M.: Macroscopic traffic flow modeling with adaptive cruise control: development and numerical solution. Computers & Mathematics with Applications , 2015 www.ptvgroup.com
HIERARCHICAL+ TM Network Traffic Control Link Control Link Control V2I • Connect VACS and TM communities for V2V maximum synergy • TM remains vital while VACS are emerging • Overlapping link controllers? • Share of control tasks? Papageorgiou, M., Diakaki, C., Nikolos, I., Ntousakis, I., Papamichail, I., Roncoli, C. : Freeway traffic management in presence of vehicle automation and communication systems (VACS). In Road Vehicle Automation 2, G. Meyer and S. Belker, Editors, Springer International Publishing, Switzerland, 2015, pp. 205-214 www.ptvgroup.com
EQUIPPED VEHICLES, V2V & V2I BECOME RICH DATA SOURCES TO SUPPORT MANAGEMENT AND GUIDANCE Autonomous vehicles rely on knowing the roadway they are traveling on, changes to the roadside such as new development or construction will require the type of real-time exchange of information that CV technology provides including valuable information about the road ahead — allowing rerouting based on new information such as a lane closures, or congestion growing. ATKINS “Autonomous vs connected vehicles – what’s the difference?” (Suzanne Murtha | 02 Oct 2015 |) www.ptvgroup.com
SENSORS, FUNCTIONS AND SURROUNDING “AWARENESS” OF PROBE CARS – DEPICTED IN RED- 4 VEHICLES DETECTED IN FRONT AND 3 BEHIND Y Each of the vehicles measures each half second and stores its position, heading, speed, and acceleration, as well as distances and relative speeds to “visible” surrounding -X X vehicles captured by the radars. -Y www.ptvgroup.com
V2V TRACKED EQUIPPED VEHICLES “AWARE” OF Traffic Data Center SURROUNDING VEHICLES TRAJECTORY RECONSTRUCTION & TRAFFIC STATE ESTIMATION d fl f l 4 1 f 6 (a) d eo (x o , y o ) (x f , y f ) (x l , y l ) (x e , y e ) 5 A V2V 8 o d fl (c) f l 3 B 2 7 (b) (x f , y f ) (x l , y l ) GPS Equipped Unequipped Bluetooth Equipped VW equipped • The relative distance d eo between the equipped Space x 3 and the observed car 5 4 A 2 1 • The relative speed v eo between the equipped and the observed car • The map-matched position (x e ,y e ) and speed v e of the equipped car. Source: L. Montero, J. Barceló et al., A case study on cooperative car data for traffic state estimation in an urban network, Paper 16-4959, 95 th TRB Time t www.ptvgroup.com Annual Meeting 2016, Compendium of Papers.
TRAFFIC DATA ANALYTICS (Extracting the most useful & valuable information from traffic measurements) Dealing with heterogeneous traffic data from varied technological sources (conventional detectors, Bluetooth, GPS, cooperative & autonomous vehicles …): - Data filtering, completion and fusion techniques - Processing huge amounts of data (Big Data Ad hoc Data Base Management Techniques ) Kernel Smoothing Kernel Smoothing Methods, Methods Filtering and Kalman Filter & traffic flow Data Machine Learning completion based models to identify and Fusion Traffic Models techniques remove outliers Techniques Dynamic Flow Models And to supply missing data OD Estimation www.ptvgroup.com
CONCEPTUAL APPROACH TO AN ADAPTIVE AREA WIDE CONTROL STRATEGY BASED ON THE NETWORK FLOW DIAGRAM LARGE URBAN OR METROPOLITAN AREA Alternative recommended route Origin r Congestion GATE-IN Destination s QUEUE GATE-OUT URBAN AREA TO MANAGE Real-time Critical Point in Traffic Data Measurements the managed area B from sensors Input flow Output flows C rates (k) (k) n(k-1) 1) A M. Keyvan-Ekbatani, M. Papageorgiou, V. L. Knoop, Comparison of On-Line Time-Delayed and Non-Time-Delayed Urban Traffic Control via Allow access Restrict access Remote Gating, TRB 2015 Annual Meeting, Paper Estimation algorithm for 𝒐 𝒍 15-4289 ADAPTIVE FLOW CONTROL STRATEGY Figure 6 Potential use of the Network Fundamental Diagram to support Active Area WideTraffic Mana www.ptvgroup.com
TRAFFIC DATA ANALYTICS: DYNAMIC OD ESTIMATION Identification of time-dependent mobility patterns in terms of Origin-Destination (OD) State equations AR(r) on deviates: D(w) transition matrices describing the Matrices Exploiting ICT measurements r ( ) ( ) ( ) ( ) effects of previous OD path flow deviates + = − + + g k 1 D w g k w 1 w k g ijc ( k-w+1 ) on current flows g ijc (k+1) = w 1 Destination T A U (k) v (k) 1 1 1 ( ) = + = + T z k A U (k) g(k) v (k) F(k) g(k) v(k) Origin 2 2 2 τp t Observation equations: number of trips from Origin i to ij E(k) v (k) Destination j in time period for 3 purpose p First block: deviates of observations at sensor locations Second block: conservation flows for each time interval k = + = k k T k k P DP D W g Dg + Initialization + k 1 k k k 1 k ( ) + = − k 1 k P I G F P + + + + k 1 k 1 k 1 k 1 ( ) − = + k T k T G P F F P F R + + + + + + k 1 k 1 k 1 k 1 k 1 k 1 k KF recursive MLU MLU OD OD path OD ICT Entry OD path OD ICT Entry dynamics OD Path id links pair sensor id id links pair sensor id + = + k 1 k g g d 0 Path id + + + k 1 k 1 k 1 id ( ) ( ) = + − k d G F g z k 1 1 1-6-11 1=(1,8) 6, 1,5 1 6 3-6-11 3=(2,8) 7,1,5 2 + + + + k 1 k 1 k 1 k 1 2-7-9-11 1=(1,8) 6,2,3,5 1 4-7-9-11 3=(2,8) 7,2,3,5 2 2 7 3 2-8-13 1=(1,8) 6,2,4 1 8 4-8-13 3=(2,8) 7,2,4 2 1-5-10-14 2=(1,9) 6,1,3,4 1 3-5-10-14 4=(2,9) 7,1,3,4 2 4 9 5 2-8-14 2=(1,9) 6,2,4 1 10 4-8-14 4=(2,9) 7,2,4 2 Ad Hoc Kalman Filter to estimate the time dependent OD J.Barceló, L.Montero, M.Bullejos, M.P. Linares, O. Serch (2013), Robustness and computational efficiency of a Kalman Filter estimator of time dependent OD matrices exploiting www.ptvgroup.com ICT traffic measurements. TRR Transportation Research Records: Journal of the Transportation Research Board, No. 2344, pp. 31-39.
DATA COLLECTION FROM AUTONOMOUS/CONNECTED VEHICLES Assumption: travel times T rq of drivers departing from origin r during time interval t going through POI q follow a distribution (not stationary under congestion), no matter the selected path. Approximate travel time distributions by discrete distributions with bin proportions updated according to collected on-line ICT data. www.ptvgroup.com
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