Modelling individual vehicle and driver behaviours

1. Modelling individual vehicleand driver behaviours Stephen Cragg Associate – SIAS Limited

3. Overview • Methodological approach • Key achievements • Current and future challenges • Methodological approach • Key achievements • Current and future challenges

4. Influences on personal travel • Fixed • Age, sex, health • Limited Choice • Employment, income, household composition, household location • Active Choice • Lifestyle (e.g. car, motorbike, cycle ownership)

5. Influences on travel • Where am I? • Where am I going? (should I go?) • How often? (or not at all?) • How will I get there? (what’s available?) • When can / should I go? • What route to take?

6. Life, the Universe and Everything Traffic Models Traffic Models Transport Models Driver & Vehicle Behaviour

7. A model NOT in S-Paramics!

8. Behaviour model • Logic based If this situation occurs Then do this based on my vehicle and my driving style

9. Driver behaviour • Condensed into just three decisions • What lane? • Mandatory and Discretionary • What speed? • What gap?

10. What lane? • Mandatory rangers(i.e. need to be a lane or range of lanes for a manoeuvre) • When do I find out what lane(s) I should be in? • Signposting • If not in right lane(s), then ‘urgency’ to get in lane increases as I get closer to hazard

11. What lane? • Discretionary suggesters • Keep left • Vehicle behind me • Slow vehicle in front of me • Congestion • Avoidance (incident, bus) • On-slip / ramp

12. What lane? • Lane weightings applied • Seniority can be applied

13. What speed? • Acceleration suggesters – lowest value chosen • Target speed • Geometric • Following • Want lane change • Let in • Undertaking • Friction • Overtake (opposite carriageway) • End speed • Stop • Yellow box • Bus stop (for buses)

14. What speed? • Finally a set of vehicle specific modifiers • Drag and inertia • Gradient • Modifies acceleration • Modifies target speed (for GVs only)

15. What gap? • A Gap when driving is generally time-based • Junctions • Headway • Minimum gap • This is the closest distance I’ll get to the vehicle in front of me.

16. Behaviour model • Logic based If this situation occurs Then do this based on my vehicle and my driving style

17. Driver characteristics • Aggression • This determines how I behave • Awareness • This determines how I respond to others • Default is Normal Distribution • Apply a spread

18. Distribution modification • Not all distributions are normal • Apply a skew

19. Vehicle characteristics • Top speed • Physical rather than legal • Bounds of acceleration / braking • Dimensions • Length, width, height and mass

20. Overview • Methodological approach • Key achievements • Current and future challenges

21. Industry Acceptance • First Commercial Application in 1995 • First in the world (to the best of our knowledge) • Many similar products now on the market

22. Improved understanding • Not all answers are good – That’s Good! • Confidence in design • Our work is accessible to non-modellers

23. New answers • Metrics change • Journey Time can now be supplemented with Journey Time Reliability • Predictions of environmental impacts– all improved • Effect of incidents / roadworks

24. New answers • The world is changing • Managed highways • Selective vehicle priority • Driver education • Ageing Population

25. Overview • Methodological approach • Key achievements • Current and future challenges

26. Challenges • DATA, DATA, DATA • Difficult to capture individual behaviour • Difficult = Expensive! • SPEED • Richer data • Multiple runs

27. Challenges • Language • Micro and Small are NOT synonyms • Education • Different mindset

28. Challenges • Combining traffic microsimulation with other driver choices. For example: • When to travel? • How to travel (e.g. should I cycle or drive)? • Where to travel?