Why Traffic Management Works Prof. dr. Serge Hoogendoorn Chair of Traffic Operations and Management, Delft University of Technology

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1 Why Traffic Management Works Prof. dr. Serge Hoogendoorn Chair of Traffic Operations and Management, Delft University of Technology

2 Physics of a Traffic Jam Traffic jam occurs when demand exceeds road capacity This will often happen at a discontinuity: capacity locally reduces (lane-drop, bridge or tunnel, accident) additional traffic enters road (e.g. onramp, merge) Standing queue will form, in many cases having reduced outflow over free conditions (capacity drop ~ 10-15%) In standing queue, disturbances may grow out to become wide-moving jams, showing even further reduction of outflow Outflow of wide-moving-jam ~ 30% lower than free capacity Apparently: when overloading occurs, problems tend to get worse!

3 There are serious limitations to self-organization of traffic in networks Efficient self-organization Capacity drop and start-stop waves Blockades and turbulence Increasing network load Reduced network production End to efficient self-organization Dilute traffic organizes itself efficiently If traffic load increases, efficient self-organization stagnates Different phenomena self-organize which reduce throughput

4 Characterising stagnating network production Network fundamental diagram shows relation network load and exit rates Shows impacts of failing self-organisation and need to control traffic NETWORK PRODUCTION (EXIT RATES) NUMBER OF VEHICLES IN NETWORK (GEROLIMINIS AND DAGANZO, 2007)

5 Characterising stagnating network production Distribution of traffic has serious impact on network production Spatial inhomogeneity leads to reduced production (indicated by: ) NETWORK PRODUCTION (EXIT RATES) Congestion nucleation causes tendency of spatial inhomogeneity to selfincrease Congestion thus attracts more congestion NUMBER OF VEHICLES IN NETWORK P(N,σ) = α N 1 N N max n 1 σ σ 0

6 End to efficient self-organization Dilute traffic organizes itself efficiently If traffic load increases, efficient self-organization stagnates Different phenomena self-organize which reduce throughput Blockades and grid-lock impacts increase with increasing network load Capacity drop impacts increase with increasing network load Uneven distribution of traffic over network impacts increase with increasing network load Inefficient choice behavior of individual travelers

7 Causes reduction efficiency lead naturally to solutions! Four solution directions to improve network throughput Solutions apply to locations (bottlenecks), arterials and networks Effectuate solution directions by road-side and in-car measures impacts increase with increasing network load Causes Solutions

8 Causes reduction efficiency lead naturally to solutions! Four solution directions to improve network throughput Solutions apply to locations (bottlenecks), arterials and networks Effectuate solution directions by road-side and in-car measures impacts increase with increasing network load Causes Solutions Prevent blockades Increase throughput Even distribution of traffic Reduce inflow

9 Example: removal of start-stop waves using Specialist Start-stop waves reduce road capacity with 30% Specialist DSL control algorithm uses inductive loops and VMS Without Specialist, wide-moving jam propagates with fixed speed against direction of traffic Specialist reduces inflow into wide-moving jam, which shortens and eventually dissolves Pilot on A12 freeway shows effectiveness Effectiveness depends on available roadway space to apply speed limits!

10 Example: adaptive ramp metering Capacity drop at standing queues around 10-15% Adaptive ramp-metering can prevent congestion:! q m (t +1) = q m (t)+ K ( ρ * ρ d (t)) For each hour we can meter traffic, we can save about 2000 veh-h Ramp-metering is stopped when buffer space is depleted Severe limit to effectiveness (average metering duration 8 min!) bufferspace depleted No metering Local ramp-metering

11 and why Coordination works even better! Prof. dr. Serge Hoogendoorn Chair of Traffic Operations and Management, Delft University of Technology

12 Limitations of isolated measures? Many examples of cost-effective deployment of isolated measures, but also many examples showing limit effectiveness: Measures cannot always be deployed effectively for a long time due to (policy) constraints resulting in limited buffer-space Effect of measure is reduced by problems elsewhere in network Effect of single measure is insufficient! Possible solution? Network-wide coordination of measures! Deploy measures jointly and coherently to prevent above limitations (e.g. coordinated ramp-metering, intersection control, etc.) Use buffer-space elsewhere in the network Deploy measures jointly and consider network effects!! Increase throughput Prevent blockades Distribute traffic Reduce inflow

13 Coordinated ramp-metering: Use storage space on upstream ramps to meter longer To fully use storage space, all buffer space (on-ramps) needs to be depleted at the same time Master ramp starts with metering (postpones congestion or removes it), but buffer-space is limited Metering rate of Helpers is chosen such that the metering period of the Helpers = metering period of Master (all buffers filled up at same time) Predicted Bottleneck Based on bottleneck location controller chooses Master ramp ( ) Supervisor chooses Helpers to support metering ( ) Helpers create space on the freeway allowing the Master to meter longer

14 Simulation results Approach has been tested by microscopic simulation For simple testnetwerk, metering time is doubled, maintaining free flow capacity twice as long! Buffer full Buffer full Coordination starts

15 Using buffer-space elsewhere in the network Use storage space on upstream ramps to meter longer (coordinated ramp-metering) Use storage space on urban arterial (only if effective!) Storage space on urban arterials is used based on: relation with bottleneck (indicated by %) policy objectives (function of road, public transit, etc.)! Buffers also depend on: Prevailing network traffic conditions 70% 80% 60% Bottleneck 80% 60% 30% Example shows how which buffers can be used to reduce inflow into bottleneck and prevent on-set of congestion

16 Field Operational Test Integrated Network Management Amsterdam Field deployment of concepts discussed here in PPA project Functional architecture shows monitoring functions (including prediction modules) and control functions (isolated and coordination supervisors) A8 A10 S116 S102 N220 N200 S114 A10 S100 S112 A9 RAI Data collection for Behavioral Modeling - ICEM 2012 A1

17 Field Operational Test Integrated Network Management Amsterdam All functions have been prototyped and tested in simulation Currently, production software is tested and first field test results are becoming available showing effectiveness of concept A8 A10 S116 S102 N220 N200 4 Snelheidscontour (07 Nov 2013) x S A S100 Ruimte (hm) RAI S112 15:30 16:00 16:30 17:00 Tijd (u) 17:30 18:00 0 Data collection for Behavioral Modeling - ICEM 2012 A1

18 Field Operational Test Integrated Network Management Amsterdam Second part of the PPA project deals with pilots using in-car technology Focus is on route information and guidance and integration with road-side traffic management systems

19 Transition by Integration Instead of step-wide abandoning road-side systems (inductive loops, ramp-meters, etc.) for sake of costs (in-car is done by the market ) Integration of road-side and in-car yields giant leaps in effectiveness! A8 A10 Traffic data N220 Road authority S102 Signal status OD information FCD traces N200 Signal settings DATA EXCHANGE A9 Service providers Information S114 OD and location info A10 Road-side systems S116 S100 S112 Road users In-car device RAI Data collection for Behavioral Modeling - ICEM 2012 A1

20 Opportunities for integration Data and monitoring: Using FCD data provides data on routing patterns essential for effective intersection control and ramp-metering Integrated control: Support guidance provided by in-car systems using adapting control Testing new measures that in the end can be done by in-car devices only (e.g. dynamic unraveling, mainline metering using dynamic speeds) Transition phase to in-car traffic management provides unprecedented opportunities for effective control of traffic Speed estimate err. Data fusion: combining loop data with FCD data substantially increases quality of state estimate Floating car percentage X

21 Changing policies lead to changes in traffic management Belief that with advanced (commercial!) in-car information and guidance services, need for advanced traffic management is limited Does TomTom make Traffic Management obsolete? A Bridge 1 x / 100 Route 1 15 Route 2 15 Bridge 2 x / 100 B Minimum travel time: 50% of travelers use route 1 and 50% use route 2 Data collection for Behavioral Modeling - ICEM 2012 With 1000 travelers in total, each route has travel time of 20 minutes

22 Does TomTom make Traffic Management obsolete? TomTom informs all 1000 traveller about rat-running route This route has a shorter travel time than the other routes, so travelers are likely to follow the advice and choose that route A Bridge 1 x / 100 Route 1 15 Rat-running route 5 Route 2 15 Bridge 2 x / 100 B All 1000 travelers choose rat-running route, travel time equals 25 minutes Data collection for Behavioral Modeling - ICEM 2012 Inherent market failure causes increase overall travel time to increase

23 Changing policies lead to changes in traffic management Possible solution: use (centralized) traffic management to bring user optimum closer to system optimum (e.g. using pricing or traffic control) Reverse Stackleberg game! A Bridge 1 x / 100 Route 1 15 Increase cost of route > 10 Route 2 15 Bridge 2 x / 100 B Increasing the (perceived) route cost (or travel time) brings system back to Data collection for Behavioral Modeling - ICEM 2012 the system optimal state

24 Main contributions: Show why traffic management works and can improve network traffic operations (cost effectively) Discuss limitations of isolated measures and need for coordination Show prospective future of traffic management and limits to selforganisation due to inherent market failure Importance to steer system from User Optimum to System Optimum! Research directions: Transition towards more in-car based traffic management High fidelity models for traffic prediction capturing key characteristics of network dynamics General theory of (second-best) optimal traffic control Increase throughput Prevent blockades Distribute traffic Reduce inflow

25 Discussion: ITS 2.0 causes fundamental system changes Increased role of ICT (travel information and guidance, e-society, connected vehicles) yield fundamental changes at all levels (operations, tactical and strategic behaviour of travellers) Example traffic flow operations: Traffic flow characteristics of connected vehicles / automated driver are qualitatively and quantitatively different Example shows (a) normal disturbance propagation and (b) disturbance propagation and stability characteristics for cooperative vehicles

26 Comments and Questions? Prof. dr. Serge Hoogendoorn Chair of Traffic Operations and Management, Delft University of Technology