ITS simulation in ITS Modeller

Transcription

1 PLATOS

2 2 Simulation of ITS Simulation of ITS is used in design or evaluation of ITS applications Important differences with standard traffic management and planning issues (like traffic lights, new roads): Driver behaviour is important In-car systems may change the way in which vehicles drive In particular, different vehicles (equipped, non-equipped, ) behave differently Requires suitable microscopic simulation tools ITS Modeller Next: some examples

3 3 Changing the way vehicles drive Shockwave damping Eco driving Cooperative merging assistance

4 4 Driver behaviour is important 3.0% Speed distributions for 3 versions of ISA and for unequipped cars Distribution des vitesses Vitesse de consigne : 50 km/h 2.5% % 2.0% 1.5% 1.0% Neutre Informatif Débrayable Contraint without ISA informative intervening overrideable, intervening non-overrideable 0.5% 0.0% Vitesses (km/h) Source: LAVIA

5 5 Does it matter? 60 ~ 50 ~ LED v Speed profile Advice via speed signs, LED and in-car Guaranteed green if advice is followed But it is not always followed x

6 6 Yes, it does! Adaptation 1,0 0,8 0,6 0,4 0,2 Not everyone follows the advice compliance rate Not all followers fully adopt advice adaptation rate Driving behaviour and penetration rate have big influence on outcome! 0,0 0% 20% 40% 60% 80% 100% Compliance ODYSA +LED +OBU Potential Large effect Medium effect Small effect

7 7 Effect of penetration rate - example Cooperative shockwave damping application. Effect linear in penetration rate? Speed diagram (kph) % Speed diagram (kph) % location (every 250m) location (every 250m) time (mins) time (mins) Speed diagram (kph) time (mins) % location (every 250m) Speed Increase 16% 14% 12% 10% 8% 6% 4% 2% Post Bottleneck Speed Increase 0% 0% 20% 40% 60% 80% 100% Shockwave damping %

8 8 Why a new tool? Classic simulation packages do not have much built-in support for ITS There typically is an API, but Difficult to scope functionality (One More All) Black box So may be hard to simulate (certain) ITS need more flexible tool Requirements: Models fully modifiable by user White box modelling Stable simulation engine (not open to user)

9 9 ITS Modeller Input Network Traffic demand (A to B) Sources: Models for vehicles, drivers and roadside controllers VISSIM ITS Modeller NG Each time step: Update vehicles, drivers and controllers Output Network level effects of ITS: traffic efficiency traffic safety environment Vehicle trajectories Distributions of speed, headway, acceleration,

10 10 Features - Modelling Users of ITS Modeller NG can Develop and include their own models for driver behaviour vehicle dynamics road side controller decisions Use existing models Adapt model parameters to their needs Select and combine models All through an easy-to-use GUI And in a flexible model framework that allows combinations of models to function properly Linked to Driver Model Library Model configurations Vehicle types ITS applications Model Driver categories types and state Routing Roadside behaviour controllers Lane change behaviour Longitudinal behaviour Vehicle dynamics Sensors Wireless communication Controllers for Active Traffic models lights, Defined Dynamic per speed model signs, configuration Variable message signs Framework has rules to combine them Parameters of a model

11 11 Modelling architecture: add your own models ITS Modeller NG Model interfaces (API) Default models - Drivers - Vehicles - Controllers User defined models - Drivers - Vehicles - Controllers JAR More user defined models - Drivers - Vehicles - Controllers JAR

12 12 API Driver Desired acceleration Lane change Route choice Sensors Communication Vehicle Acceleration Roadside systems Controller Collaboration between models in a model configuration (vehicle + driver type): No hidden models (white box) no movement without models Desired acceleration = minimum of all desired accelerations Lane change = max priority lane change Route choice: one cost model, multiple cost information suppliers One vehicle model General model structure: getdesiredacceleration(drivercontext) desired acceleration, delay

13 13 Components NG

14 14 Use cases / Scenarios The scenario describes: Geographic region Network (road types, ) Time period (year, day, rush hour, ) Time point (e.g. the base year) Traffic demand Functionality of the ITS under investigation Penetration rate of this ITS Driver behaviour with and without this ITS Presence of other ITS Scenarios describe baseline and alternative Baseline is not the same as do nothing : Alternative Baseline Do nothing Time

15 15 Example Green speed for cyclists Master thesis project Joeri van Egmond, TU Delft, 2013 Master thesis project Theofili Apostola, TU Delft, ongoing

16 16 Green speed for cyclists What is effect of speed advice on number of stops and travel times? How does it depend on controller type: predictable (fixed time) or not acceptance by cyclist: utility of green versus disutility of higher or lower speed; accounting for uncertainty (several models)

17 Scenario 17

18 18 Modelling Controller Acceleration Signal timings Road side infra Signal timings or speed advice Speed advice Sensor

19 19 Results Fixed time controller ~20% fewer stops Requires significant speed range (15-23 km/h) Effect depends on speed range that cyclists accept

20 20 Example Parking guidance Master thesis project Manos Chaniotakis, TU Delft, 2014

21 21 Parking guidance Search traffic: 10-60% of all (inner) city traffic Intelligent parking system (IPS) Personalized service (on smart phone, navigation device) Real-time monitoring of vehicle position Finds parking place at lowest generalized cost (travel time, walking distance, monetary cost) Parking lots and optionally on-street Reservation, route advice and payment What is effect on traffic?

22 22 Parking guidance how does driver behave? Compliant driver with IPS follows advice of system System advice is based on personalized generalized cost of available alternatives Non-compliant driver & driver without IPS: default parking behavior This is much harder to model! Strategic behavior: trip, destination, departure time and mode choice depending on parking characteristics. Tactical behavior: route choice and parking destination choice (parking type, parking location) Operational behavior: searching/waiting time for parking destination and turning decisions at an intersection (for on-street parking) Familiar and unfamiliar users

23 23 Decision model for default parking behaviour Source: Thesis Manos Chaniotakis

24 24 Modelling Controller Capacity, price Controller Capacity, price Destination & route choice Generalized costs Back office Controller Capacities, prices Road side infra Capacities, prices Route cost Capacities, prices Sensor

25 25 Results Assen network Average total travel times Large decrease if unfamiliar drivers switch to smart parking To From AllFamiliar Unfamiliar5 Unfamiliar15 Smart20 Smart40 AllFamiliar % -6.5% 0.3% 1.5% Unfamiliar5 2.2%- -4.5% 2.5% 3.6% Unfamiliar15 6.9% 4.7%- 7.2% 8.5% Smart20-0.3% -2.4% -6.8%- 1.2% Smart40-1.4% -3.5% -7.8% -1.1%-

26 26 How do you get the system and driver behaviour? Or vice versa: determine relation between behaviour parameters and effect Simulation Lab Test site FOT +Controlled +Cost effective +Versatile +Large scale +Choice levels -Realism -Driver behaviour +Controlled +Realism -Driver beh. -Small scale -Operational only Realism, driver behavior, cost +Controlled +Realism -Driver beh. -Small scale -Tactical only +Realism +Driver beh. +Large scale +Choice levels -Uncontrolled -Costly Versatility, control Choice levels, scale

27 27 Conclusions Simulation of ITS requires tools that allow modelling of driver behaviour and ITS applications Especially if ITS does not force behaviour and only persuades Behaviour can have large influence on outcome This may be difficult with classical tools ITS Modeller has stable simulation engine but fully modifiable models for drivers, vehicles, controllers in white box set-up.

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