1. A Glimpse inside the Black Box – Network Micro-simulation Models Ronghui Liu Institute for Transport Studies University of Leeds, UK r.liu@leeds.ac.uk

2. 2 Contents Introduction Introduction Individual model components Individual model components Practical implementations Practical implementations Summary Summary

3. 3 Introduction to micro-simulation “A numerical technique for conducting experiments on a digital computer, which may… involve mathematical models that describe the behaviour of a transportation system over extended periods of time.” (May 1990) “A numerical technique for conducting experiments on a digital computer, which may… involve mathematical models that describe the behaviour of a transportation system over extended periods of time.” (May 1990)

4. 4 Keywords: System: the real-world process to imitate; System: the real-world process to imitate; Model: the set of assumptions, in the form of mathematical or logical relationships, put forward to help understand how the corresponding system behaves; Model: the set of assumptions, in the form of mathematical or logical relationships, put forward to help understand how the corresponding system behaves; Entities: people or vehicles that act in the system; Entities: people or vehicles that act in the system; Time: an explicit element of the system. Time: an explicit element of the system.

5. 5 The transport road network systems Demand for travel: activity-based trip chains and trip timing Demand for travel: activity-based trip chains and trip timing Route choice (pre-trip and en-route) Route choice (pre-trip and en-route) Departure-time choice Departure-time choice Traffic movements Traffic movements Public transport (bus) systems Public transport (bus) systems

6. 6 Interaction of the model systems:

7. 7 Micro-simulation models of route choice Bounded rational model Bounded rational model –Stay on the same habit route unless the alternative is well better (Mahmassani et al) Probabilistic discrete choice Probabilistic discrete choice –From individual perceived costs on alternative routes Myopic switch Myopic switch –Always takes the minimum cost route

8. 8 Models of departure-time choice Preferred arrival time (Small 1982) Preferred arrival time (Small 1982) Probability distributions of inter-departure headways based on average flows of a given demand profile Probability distributions of inter-departure headways based on average flows of a given demand profile

9. 9 Traffic micro-simulation entities Driver and vehicle characteristics. Driver and vehicle characteristics. –Physical size: length and width –Mechanical capacity: maximum acceleration or deceleration –Driving behaviour: desired speed, reaction time, gap acceptance, aggressiveness, etc.

10. 10 Traffic micro-simulation models Car-following model Car-following model Lane-changing model Lane-changing model Gap-acceptance model Gap-acceptance model Lane-choice model Lane-choice model Models of intersection controls Models of intersection controls

11. 11 Car-following models Models of individual vehicle following behaviour Models of individual vehicle following behaviour –In a single stream of traffic (lane disciplined) –No overtaking Three main types: Three main types: –Safety-distance model –“Action-points”: different rules for different driving states –Psycho-physical: Acceleration=Stimulus x Sensitivity

12. 12 Model requirements Agree with experimental evidence Agree with experimental evidence –Microscopic: individual vehicle trajectories –Macroscopic: q-k-u relationships Be psycho-physically feasible Be psycho-physically feasible Posses local stability Posses local stability –Perturbations in behaviour of lead vehicle not causing following vehicle to collide Possess asymptotic stability Possess asymptotic stability –Perturbations not magnified back over a line of vehicles

13. 13 The Gipps car-following model Free flow model Free flow model –Accelerate freely to desired speed Safety-distance model Safety-distance model –Driver maintains a speed v which will just allow him to stop in emergency without hitting the obstacle at distance S ahead

14. 14 Variants and constraints Variable reaction times Variable reaction times Variable acceleration and deceleration Variable acceleration and deceleration Variable or multiple lead vehicles Variable or multiple lead vehicles Lane-disciplined Lane-disciplined Stable traffic flow: do not produce incidents Stable traffic flow: do not produce incidents

15. 15 Gap-acceptance models Models of individual drivers’ choice of safety gaps to merge into or to cross other traffic streams Models of individual drivers’ choice of safety gaps to merge into or to cross other traffic streams merge merge Two elements: Two elements: –Gaps acceptable to drivers –Gaps available to the driver

16. 16 Variants and constraints Time-dependent acceptable gap Time-dependent acceptable gap Courtesy yielding Courtesy yielding Individual gap acceptance: no shadowing effects (e.g. on approaching roundabouts) Individual gap acceptance: no shadowing effects (e.g. on approaching roundabouts) Requires distinction of major/minor flows Requires distinction of major/minor flows

17. 17 Lane-changing models Models of individual drivers’ ability and propensity to change lanes Models of individual drivers’ ability and propensity to change lanes Lane-changing objectives, e.g. Lane-changing objectives, e.g. –To overtake a slower moving vehicle –To bypass an obstacle –To move off/into a reserved bus lane –To get-in-lane for next junction turning –To giveway to merging traffic Decision-making behaviour: Decision-making behaviour: –Is it possible to change lane? (physically & safely) –Is it necessary to change lane? (for junction turning?) –Is it desirable to change lane? (to overtake?)

18. 18 Variants and constraints Variable lane-changing objectives Variable lane-changing objectives Variable hierarchical decision trees Variable hierarchical decision trees Variable acceptable gaps Variable acceptable gaps Look-ahead: anticipating a lane-changing needs a link ahead Look-ahead: anticipating a lane-changing needs a link ahead Cooperative lane-changing Cooperative lane-changing Courtesy yielding Courtesy yielding Lane disciplined: no overtaking in between lanes or lane in opposite direction Lane disciplined: no overtaking in between lanes or lane in opposite direction

19. 19 Lane-choice models Selection of the lateral position in entering or traversing a link. Selection of the lateral position in entering or traversing a link. Pre-specified, or Pre-specified, or Instantaneous choice made in response to traffic condition and destination Instantaneous choice made in response to traffic condition and destination choice choice

20. 20 Models of intersection traffic control Signal controlled: fixed or responsive Signal controlled: fixed or responsive Priority giveway Priority giveway Roundabout: partially signalised Roundabout: partially signalisedt: partially signalisedt: partially signalised Motorway merge Motorway merge Stop-and-go Stop-and-go Giveway to oncoming traffic Giveway to oncoming traffic

21. 21 Network representation Network topology Network topology Junction type and priority rules Junction type and priority rules Link (major/minor, speed limits,..) Link (major/minor, speed limits,..) Lanes (turning restriction, access restriction) Lanes (turning restriction, access restriction) Signal plans (stages, phases, responsive rules) Signal plans (stages, phases, responsive rules)

22. 22 Software implementation Discrete time vs. discrete event Discrete time vs. discrete event –Fixed time increment: used to simulate systems whose entities change continuously with time, e.g. traffic flow, the speed of vehicles –Event scanning: the system is updated every time a main event takes place. Used to model systems whose entities change instantaneously at separate points in time, e.g. traffic signals

23. 23 Software implementation (II) Continuous space vs. cellular automata (CA) Continuous space vs. cellular automata (CA) –CA: represents road sections as fixed-length segments and vehicles “jump” from one segment to another –fast, easy to implement on parallel machines; but –may induce large speed changes

24. 24 Practical micro-simulation approach Pure traffic micro-simulation Pure traffic micro-simulation –Routes drawn from turning probability –May lead to implausible cyclic route Traffic micro-simulation with no or simple route choice Traffic micro-simulation with no or simple route choice –Route choice from static equilibrium model, or based on aggregated feedback loop from the micro-simulation –Lack of consistency in route choice mechanism Day-to-day micro-simulation of route and departure-time choice Day-to-day micro-simulation of route and departure-time choice –Based on simple traffic model: u-k relationships –Lack of junction modelling –Difficult to deal with mixed traffic, bus lane, traffic controls at intersections

25. 25 The DRACULA approach Dynamic Route Assignment Combining User Learing and microsimulAtion Dynamic Route Assignment Combining User Learing and microsimulAtion A micro-simulation model of the full supply systems: A micro-simulation model of the full supply systems: –Micro-simulation of day-to-day route and departure-time choices –Micro-simulation of traffic movements –Micro-simulation of public transport systems and passenger route choice

26. 26 Summary Traffic micro-simulation models deal naturally with time-dependent queues, lane sharing problems, variability, etc Traffic micro-simulation models deal naturally with time-dependent queues, lane sharing problems, variability, etc They are based on simple mathematical models and logical rules. They are based on simple mathematical models and logical rules. There are varied implementations in networks. There are varied implementations in networks. Greater efforts required to adopt it as a suitable traffic model for DTA Greater efforts required to adopt it as a suitable traffic model for DTA