Modelling individual vehicle and driver behaviours Stephen Cragg Associate – SIAS Limited.

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Presentation transcript:

Modelling individual vehicle and driver behaviours Stephen Cragg Associate – SIAS Limited

Overview Methodological approach Key achievements Current and future challenges Methodological approach Key achievements Current and future challenges

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)

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?

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

A model NOT in S-Paramics!

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

Driver behaviour Condensed into just three decisions What lane? ~Mandatory and Discretionary What speed? What gap?

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

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

What lane? Lane weightings applied Seniority can be applied

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)

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

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.

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

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

Distribution modification Not all distributions are normal Apply a skew

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

Overview Methodological approach Key achievements Current and future challenges

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

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

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

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

Overview Methodological approach Key achievements Current and future challenges

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

Challenges Language Micro and Small are NOT synonyms Education Different mindset

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?