Real time traffic management - challenges and solutions
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The document discusses real-time traffic management, highlighting challenges such as decision timeliness, data limitations, and operational uncertainties. It introduces the CarboTraF project aimed at reducing traffic emissions through a decision support system and outlines a decision rule approach that leverages real-time data for improved traffic operations. The findings suggest that emissions reductions and traffic optimization depend on various factors tailored to specific urban scenarios.
![-- real-time information (flow, count, speed, queue)
-- Analytical transformation with undetermined coefficients x
-- Projection onto feasible control set
--
Network performance measure (minimize)
(congestion, emission, fuel consumption)
Real-time
Information
q
Control
u
Decision Rule
Network
performance
measure
(simulation)
Φ(q,u)
f (x,q)
u = PΩ[ f (x,q)]
Decision Rule: Deterministic Formulation
q
Deterministic Formulation
Given real-time information q, find
the best decision rule (x):
u = PΩ[ f (x,q)]](https://image.slidesharecdn.com/realtimetrafficmanagement-challengesandsolutions-161213111118/85/Real-time-traffic-management-challenges-and-solutions-16-320.jpg)
![Numerical Study: Part 4
v The decision rule approach combined with metaheuristic methods allow for
sufficiently nonlinear and non-analytical objective functions, such as
v Emission (hydrocarbon, HC) is calculated based on vehicle speed, density, and
acceleration/deceleration derived from the kinematic wave model, and the
instantaneous HC emission model (Ahn et al., 2002)
f = w× Throughput - (1− w)× Total Emission w ∈ [0,1]
Great Western Rd
Great Western Rd
ByresRd
City Center
University of Glasgow
3100 3200 3300 3400 3500 3600 3700 3800 3900 4000
3.1
3.15
3.2
3.25
3.3
3.35
3.4
3.45
x 10
7
Throughput (veh)
TotalHCEmission(µg)
w=0.1
w=1.0
(no emission
consideration)](https://image.slidesharecdn.com/realtimetrafficmanagement-challengesandsolutions-161213111118/85/Real-time-traffic-management-challenges-and-solutions-28-320.jpg)
Real time traffic management - challenges and solutions
- 1. Real-Time Traffic Management: Challenges and Solutions Ke Han Lecturer (Assistant Professor) Center for Transport Studies Department of Civil and Environmental Engineering, Imperial College London k.han@imperial.ac.uk www.imperial.ac.uk/people/k.han
- 2. Outline 1. Overview 2. The CARBOTRAF Project 3. Decision Rule Approach for Real-Time Operation
- 3. Real-Time Traffic Management v Challenges Ø Timeliness of decisions Ø Nonlinear and nonconvex objective Ø Multiple objectives Ø Insufficient telecom capacity (centralized vs. distributed) Ø Uncertainties and insufficient data coverage v New Opportunities (more challenges?) Ø Multi-source and heterogeneous data (e.g. mobile data, social media) Ø New collection/communication methods (e.g. crowd sourcing) Ø Need for more robust and fundamentally new theories and methods
- 4. Outline 1. Overview 2. The CARBOTRAF Project 3. Decision Rule Approach for Real-Time Operation
- 5. The CARBOTRAF Project “A Decision Support System for reduced emissions of CO2 and Black Carbon through Adaptive Traffic Management” Ø Scenario evaluation using traffic and environmental modelling tools Ø Online status updates and decision support Ø Ambient monitoring for evaluation, feedback and learning Ø Two “Pilot Cities” (Glasgow and Graz) Ø Decision Support System with GUI for Traffic Operators
- 6. Work Flow of The CARBOTRAF Project Offline modelling & simulation Decision Support System Online Database Interfacetoreal-timedata Real-time traffic data ITS actions Catalogue of ITS actions Traffic simulation Emission models Air quality models Look-up table & database of traffic and emission scenarios Real-time pollutant concentration Real-time air quality data Real-time meteorology data Offline module Online module
- 7. Off-Line Modeling Microsimulation Emission Model Dispersion Model • Network model • Traffic flows • Signal plans • Vehicle composition • Vehicle dynamics • Vehicle emission categories • Road elevation • Weather data • Building heights S-Paramics, VISSIM AIRE IFDM
- 8. Test Site in West Glasgow Key Performance Indicators v Traffic Ø Travel time Ø Speed Ø Delay v Environment Ø Black Carbon Ø CO2 Ø Nox v Spatial references Ø Network wide Ø Corridor Ø Junction Great W estern Rd. KelvinWay University Av. VMS TSC city center
- 10. Reduction of BC Concentration with ITS Actions BC conc (µg/m3) ITS – Base Scenario Boundary condition 1 BC conc (µg/m3) ITS – Base Scenario Boundary condition 2
- 11. Managerial Insights Gained from Offline Modeling & Simulation v The effectiveness of ITS actions depends on many factors, which need to be determined and telecommunicated in real time Ø Dynamic demand profile Ø Weather condition Ø Fleet composition v Benefits of the ITS actions are more pronounced at the local level Ø Network level: below 3% Ø Corridor/junction level: 5-30% v In an urban environment, emission is closely related to Ø Traffic flow dynamics (not merely “flow” or “volume”) Ø Fleet composition (bus/LGV/HGV)
- 12. Decision Support System v The DSS combines streaming data and the off-line LUT to rank different candidate ITS actions v Input: Ø Current ITS action deployed Ø Probability distributions of KPIs for the complete set of alternative actions (LUT) Ø Operational constraints on the set of ITS actions v Minimization problem (in real time): v Potential issues: Ø Resolution of the Look-Up Table Ø Expectation highly susceptible to outliers and errors Ø Computationally expensive, with additional lags -- Traffic Prediction Tool (Min and Wynter, 2011)
- 13. Outline 1. Overview 2. The CARBOTRAF Project 3. Decision Rule Approach for Real-Time Operation
- 14. Analytical/ closed-form transformation Decision Rule Approach for Real-Time Traffic Management v Real-time control: Challenges - Timeliness - Nonlinear and nonconvex objective - Distributed vs. centralized control - Uncertainties v Heuristic (genetic algorithm, fuzzy logic), inexact and sub-optimal v Decision Rule (DR) approach for real-time traffic management ü Historical and real-time data ü Within-day and day-to-day variations ü Distributionally Robust Optimization (DRO) to ensure performance in the most adversarial situation ü Efficient on-line operation ü Compatible with analytical computations and microsimulation Real-time system state Control parameters Not optimal? Decision rule
- 15. Decision Rule: Concept Off-line module On-line module Analytical/ closed-form transformation Real-time traffic state Real-time decision Decision rule Offline training Real-time traffic state Historical traffic state Look-up table Traffic prediction tool Real-time decision Historical traffic state Offline simulation Decision rule approach CARBOTRAF approach Stochastic optimization Offline simulation
- 16. -- real-time information (flow, count, speed, queue) -- Analytical transformation with undetermined coefficients x -- Projection onto feasible control set -- Network performance measure (minimize) (congestion, emission, fuel consumption) Real-time Information q Control u Decision Rule Network performance measure (simulation) Φ(q,u) f (x,q) u = PΩ[ f (x,q)] Decision Rule: Deterministic Formulation q Deterministic Formulation Given real-time information q, find the best decision rule (x): u = PΩ[ f (x,q)]
- 17. Linear Decision Rule Time Location/ data type past T observations REAL-TIME DATA CONTROL COEFFICIENT
- 18. Nonlinear Decision Rule (Artificial Neural Network) REAL-TIME DATA CONTROL ANN . . . . . . Artificial Neural Network v : a neural network with m hidden layers and n neurons v Activation function pre-determined (e.g. sigmoid functions) v x represents the weights of the connections between neurons
- 19. Decision Rule: Stochastic Extension v In reality, q is stochastic, subject to within-day & day-to-day variations v Stochastic programming – exact probability distribution required v Ambiguous information on the distribution with finite samples v Distributionally robust optimization (DRO) Ø Worst-case scenario (‘max’), Ø among all candidate distributions Ø Subsumes stochastic optimization Ø Data-driven calibration of Distributionally Robust Formulation Given stochastic input q, find the best decision rule coefficient x: “Uncertain distributions (DRO) instead of uncertain parameters (RO)”
- 20. Advantages of the Decision Rule Approach v Finding the best responsive signal strategy è Finding x v Feasible and efficient on-line operation - Off-line: Distributionally robust optimization (expensive) - On-line: Linear transformation and projection (inexpensive) v Flexible sensor location, data type, and control resolution v User-defined feasible set for signal control parameters v Two solution procedures for the off-line problem: - Mixed integer linear program - Metaheuristic search Distributionally Robust Optimization
- 21. v Kolmogorov-Smirnov (K-S) goodness-of-fit test (Massey, 1951; Bertsimas et al., 2013): v Random variable: ,parameterized by v Uncertainty set: ,parameterized by v Fix ,and consider K samples (historical data) v Does a distribution well capture a finite set of sampled data? v Reject H0 at the level α if Data-Driven Calibration of the Uncertainty Set Set of candidate distributions
- 22. Formulation of the Uncertainty Set
- 23. Evaluating the Objective Function v Random Variable (objective): , parameterized by v Lower and upper bounds of : , partitioned into W intervals v Fix (control), g1Lf Uf g2 gi-1 gi . . . . . . K-S test
- 24. Numerical Study, Part 1 Great Western Rd Great Western Rd ByresRd City Center University of Glasgow § West end of Glasgow § 5 signalized intersections § 35 directed links § LWR network model Network Data § Turn-by-turn flow count § 8-9 am, 7 June 2010 § Daily variations are generated synthetically, using a variety of distributions Benchmarks § Fixed signal timing (deterministic & DRO) § Field signal parameters (Glasgow City Council)
- 25. Numerical Study: Part 1 Particle Swarm Optimization Great Western Rd Great Western Rd ByresRd City Center University of Glasgow § Zeroth-order information on the objective and constraints § LWR-based network simulation model § Flexible trade-off between solution quality and computational cost § Off-line computational time: 24h § On-line computational time: negligible Criteria Deterministic Fixed timing DRO Fixed timing Field parameter (Glasgow City) LDR-DRO NDR-DRO Objective (maximize) 1.61 3.81 4.14 4.28 4.34 Throughput 1498 (veh) 3382 (veh) 3576 (veh) 3910 (veh) 3951 CPU time (offline/online) 24h/- 24h/- -/- 24h/0.01s 24h/0.03s
- 26. Numerical Study: Part 2 v 4-by-4 grid network in S-Paramics v 8 zones, 56 O-D pairs v Dynamic route assignment v Fleet: passenger car, LGV, MGV, HGV, coach v 4-stage signal plan at all four junctions v 30 random seeds for generating samples 72 74 76 78 80 82 1 AverageDelay(s) 3.0% improvement
- 27. 210 215 220 225 230 235 240 245 250 0 1 2 3 4 5 6 7 Average Vehicle Delay (s) Count 215 220 225 230 235 240 245 1 2 AverageVehicleDelay(s) Numerical Study: Part 3 v West Glasgow in S-Paramics v 21 zones, 420 O-D pairs v Dynamic route assignment v Fleet: passenger car, LGV, MGV, HGV, coach v 30 random seeds for generating samples v 80 PSO major iterations v Signal optimization at the key junction Byres Rd. & University Ave. NDR-DRO Webster 1.3% improvement
- 28. Numerical Study: Part 4 v The decision rule approach combined with metaheuristic methods allow for sufficiently nonlinear and non-analytical objective functions, such as v Emission (hydrocarbon, HC) is calculated based on vehicle speed, density, and acceleration/deceleration derived from the kinematic wave model, and the instantaneous HC emission model (Ahn et al., 2002) f = w× Throughput - (1− w)× Total Emission w ∈ [0,1] Great Western Rd Great Western Rd ByresRd City Center University of Glasgow 3100 3200 3300 3400 3500 3600 3700 3800 3900 4000 3.1 3.15 3.2 3.25 3.3 3.35 3.4 3.45 x 10 7 Throughput (veh) TotalHCEmission(µg) w=0.1 w=1.0 (no emission consideration)