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DYNAMIC ODME FOR AUTOMATED VEHICLES MODELING USING ‘BIG DATA’
- Dr. Jaume Barceló, Professor Emeritus, UPC-
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DYNAMIC ODME FOR AUTOMATED VEHICLES MODELING USING BIG DATA Dr. - - PowerPoint PPT Presentation
DYNAMIC ODME FOR AUTOMATED VEHICLES MODELING USING BIG DATA Dr. - - PowerPoint PPT Presentation
DYNAMIC ODME FOR AUTOMATED VEHICLES MODELING USING BIG DATA Dr. Jaume Barcel, Professor Emeritus, UPC- Barcelona Tech, Strategic Advisor to PTV Group Shaleen Srivastava, Vice-President/Regional www.ptvgroup.com Director (PTV Group
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CONNECTED & AUTONOMOUS VEHICLES
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AUTONOMOUS VS CONNECTED VEHICLES
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SINGLE VEHICLE APPLICATIONS & COOPERATIVE APPLICATIONS
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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
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VISUALIZATION OF SHARED SELF-DRIVING CAR SIMULATION FOR LISBON
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A SELF-ORGANIZING SYSTEM OR BETTER EXTERNALLY ASSISTED?
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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.
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TESTING VACS BY MICROSOPIC SIMULATION
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.
ACC string-stability ACC traffic efficiency
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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
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HIERARCHICAL+ TM
Network Traffic Control
Link Control Link Control
V2I V2V
- Overlapping link controllers?
- Share of control tasks?
- Connect VACS and TM communities for
maximum synergy
- TM remains vital while VACS are emerging
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
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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 |)
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SENSORS, FUNCTIONS AND SURROUNDING “AWARENESS” OF PROBE CARS – DEPICTED IN RED- 4 VEHICLES DETECTED IN FRONT AND 3 BEHIND
X
- X
- Y
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 vehicles captured by the radars.
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f l dfl
(xf, yf) (xl, yl) (a)
dfl
(xf, yf) (xl, yl)
f l
(b)
f deo
(xe, ye) (xo, yo)
- (c)
A B 1 2 3 4 5 6 7 8 V2V
GPS Equipped Bluetooth Equipped Unequipped Traffic Data Center VW equipped
2 A 5 1 3 4
Time t Space x
V2V TRACKED EQUIPPED VEHICLES “AWARE” OF SURROUNDING VEHICLES TRAJECTORY RECONSTRUCTION & TRAFFIC STATE ESTIMATION
- The relative distance deo
between the equipped and the observed car
- The relative speed veo
between the equipped and the observed car
- The map-matched
position (xe,ye) and speed ve 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, 95th TRB Annual Meeting 2016, Compendium of Papers.
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(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 Methods, Kalman Filter & traffic flow based models to identify and remove outliers And to supply missing data Kernel Smoothing Methods Machine Learning Traffic Models Dynamic Flow Models OD Estimation Filtering and completion techniques Data Fusion Techniques
TRAFFIC DATA ANALYTICS
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CONCEPTUAL APPROACH TO AN ADAPTIVE AREA WIDE CONTROL STRATEGY BASED ON THE NETWORK FLOW DIAGRAM
Figure 6 Potential use of the Network Fundamental Diagram to support Active Area WideTraffic Mana URBAN AREA TO MANAGE
LARGE URBAN OR METROPOLITAN AREA
Origin r Destination s Congestion Alternative recommended route GATE-OUT GATE-IN QUEUE
Estimation algorithm for 𝒐 𝒍 ADAPTIVE FLOW CONTROL STRATEGY
A B
Critical Point in the managed area
Allow access Restrict access
C
Real-time Traffic Data Measurements from sensors Output flows n(k-1) 1)
Input flow rates (k) (k)
- M. Keyvan-Ekbatani, M. Papageorgiou,
- V. L. Knoop, Comparison of On-Line Time-Delayed
and Non-Time-Delayed Urban Traffic Control via Remote Gating, TRB 2015 Annual Meeting, Paper 15-4289
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TRAFFIC DATA ANALYTICS: DYNAMIC OD ESTIMATION
( )
− + + + + + +
+ =
k T 1 k k 1 k 1 k T 1 k k 1 k 1 k
R F P F F P G
( )
( )
k 1 k 1 k 1 k 1 k
g F G d
+ + + +
− + = 1 z k
( )
k 1 k 1 k 1 k 1 k 1 k
P F G I P
+ + + + +
− =
k T k k k 1 k
W D DP P + =
+
k k k 1 k
Dg g =
+ Initialization KF recursive dynamics
1
+ =
+ + + + 1 k k k 1 k 1 k
d g g
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 ICT traffic measurements. TRR Transportation Research Records: Journal of the Transportation Research Board, No. 2344, pp. 31-39.
State equations AR(r) on deviates: Observation equations: ( ) ( ) ( ) ( )
k w 1 w k g w D 1 k g
1
+ + − = +
= r w
D(w) transition matrices describing the effects of previous OD path flow deviates gijc(k-w+1) on current flows gijc(k+1)
First block: deviates of observations at sensor locations Second block: conservation flows for each time interval k
( )
(k) v (k) v (k) v k z
3 2 1 2 2 1 1
v(k) g(k) F(k) g(k) E(k) (k) U A (k) U A
T T
+ = + =
Origin Destination
τp ij
t
number of trips from Origin i to Destination j in time period for purpose p
Identification of time-dependent mobility patterns in terms of Origin-Destination (OD) Matrices Exploiting ICT measurements
MLU OD Path id OD path links OD pair ICT sensor id Entry id
MLU OD Path id
OD path links OD pair ICT sensor id Entry id 1 1-6-11 1=(1,8) 6, 1,5 1 6 3-6-11 3=(2,8) 7,1,5 2 2 2-7-9-11 1=(1,8) 6,2,3,5 1 7 4-7-9-11 3=(2,8) 7,2,3,5 2 3 2-8-13 1=(1,8) 6,2,4 1 8 4-8-13 3=(2,8) 7,2,4 2 4 1-5-10-14 2=(1,9) 6,1,3,4 1 9 3-5-10-14 4=(2,9) 7,1,3,4 2 5 2-8-14 2=(1,9) 6,2,4 1 10 4-8-14 4=(2,9) 7,2,4 2
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DATA COLLECTION FROM AUTONOMOUS/CONNECTED VEHICLES
Assumption: travel times Trq 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.
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EKF APPROACH FOR NETWORKS (III) : FLOW
ESTIMATES AND ERROR CORRECTIONS (SHALEEN SRIVASTAVA, 2010)
- Traffic flow at a location
- Flows – y(t)
- Assume Gaussian distributed measurements
- Model simulation (virtual detectors) – traffic flow
- Measurement (real detectors) – traffic flow
- Flows measurement from the model at t1:
Mean = z1 Variance = σz1
- Optimal estimate of traffic flows: ŷ(t1) = z1
- Variance of error in estimate: σ2x (t1) = σ2z1
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EKF APPROACH FOR NETWORKS (III) : FLOW
ESTIMATES AND ERROR CORRECTIONS (SHALEEN SRIVASTAVA, 2010)
- So we have the prediction ŷ-(t2)
- Detector data measurement at t2: Mean
= z2 and Variance = σz2
- Need to correct the prediction by model
due to measurement to get ŷ(t2)
- Closer to more trusted measurement –
linear interpolation
- Corrected mean is the new optimal
estimate of traffic flows (basically we have ‘updated’ the predicted flows by model using detector data)
- New variance is smaller than either of
the previous two variances
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EKF APPROACH FOR NETWORKS (III) : FLOW
ESTIMATES AND ERROR CORRECTIONS (SHALEEN SRIVASTAVA, 2010)
- If measurement is preferred:
- Measurement error covariance decreases to zero
- Weights residual more heavily than prediction
- If prediction is preferred:
- Prediction error covariance decreases to zero
- Weights prediction more heavily than residual
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MEASURING THE QUALITY OF THE ESTIMATES ESTIMATED (KF APPROACH) VS TARGET FLOWS IN OD PAIRS FOR A 15 MINUTES INTERVAL
50 100 150 200 250 300 350 400 450 500 1 4 6 7 8 14 19 21 22 30 36 43 44 45 55 56 58 61 68 70 76 77 85 2 9 10 15 16 17 24 27 29 31 34 35 38 50 52 54 71 72 75 78 80 3 5 11 12 13 23 26 37 40 41 48 49 53 57 63 64 66 67 73 82 18 20 25 28 32 33 39 42 46 47 51 59 60 62 65 69 74 79 81 83 84 Estimated vs Target OD Flows - 1h (veh/h OD Pair Target OD flow Estimate OD flow
y = 1,04x - 2,9644 R² = 0,9387
50 100 150 200 250 300 350 400 450 500 50 100 150 200 250 300 350 400 450
Demand Set 2: Estimated vs Target OD flows - 1h
Barcelona’s Central Business District (CBD), Eixample, 2111 sections, 1227 nodes 120 generation centroids, 130 destination centroids (877 non-zero OD pairs) 116 Loop detector Stations & 50 Bluetooth Antennas
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