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Modeling Individual Vehicle and Driver Behaviors: Insights and Future Challenges

This presentation by Stephen Cragg from SIAS Limited explores the complexities of modeling individual vehicle and driver behaviors. It covers methodological approaches, key achievements, and ongoing challenges in the field. Key influences on personal travel, including demographic factors and lifestyle choices, are examined. The discussion also highlights traffic and transport models, behavior models, and the importance of driver characteristics in decision-making. With the ever-changing landscape of transportation, the presentation emphasizes the need for richer data and effective predictions regarding journey time reliability and environmental impacts.

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Modeling Individual Vehicle and Driver Behaviors: Insights and Future Challenges

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Presentation Transcript

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

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

  3. 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)

  4. 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?

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

  6. A model NOT in S-Paramics!

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

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

  9. 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

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

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

  12. 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)

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

  14. 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.

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

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

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

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

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

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

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

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

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

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

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

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

  27. 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?

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