A Glimpse inside the Black Box Network Micro-simulation .ppt

1、A Glimpse inside the Black Box Network Micro-simulation Models,Ronghui Liu Institute for Transport Studies University of Leeds, UK r.liuleeds.ac.uk,2,Contents,Introduction Individual model components Practical implementations Summary,3,Introduction to micro-simulation,“A numerical technique for cond

2、ucting 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,Keywords:,System: the real-world process to imitate; Model: the set of assumptions, in the form of mathematical or logic

3、al relationships, put forward to help understand how the corresponding system behaves; Entities: people or vehicles that act in the system; Time: an explicit element of the system.,5,The transport road network systems,Demand for travel: activity-based trip chains and trip timing Route choice (pre-tr

4、ip and en-route) Departure-time choice Traffic movements Public transport (bus) systems,6,Interaction of the model systems:,7,Micro-simulation models of route choice,Bounded rational model Stay on the same habit route unless the alternative is well better (Mahmassani et al) Probabilistic discrete ch

5、oice From individual perceived costs on alternative routes Myopic switch Always takes the minimum cost route,8,Models of departure-time choice,Preferred arrival time (Small 1982) Probability distributions of inter-departure headways based on average flows of a given demand profile,9,Traffic micro-si

6、mulation entities,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,Traffic micro-simulation models,Car-following model Lane-changing m

7、odel Gap-acceptance model Lane-choice model Models of intersection controls,11,Car-following models,Models of individual vehicle following behaviour In a single stream of traffic (lane disciplined) No overtaking Three main types: Safety-distance model “Action-points”: different rules for different d

8、riving states Psycho-physical: Acceleration=Stimulus x Sensitivity,12,Model requirements,Agree with experimental evidence Microscopic: individual vehicle trajectories Macroscopic: q-k-u relationships Be psycho-physically feasible Posses local stability Perturbations in behaviour of lead vehicle not

9、causing following vehicle to collide Possess asymptotic stability Perturbations not magnified back over a line of vehicles,13,The Gipps car-following model,Free flow model Accelerate freely to desired speedSafety-distance model Driver maintains a speed v which will just allow him to stop in emergenc

10、y without hitting the obstacle at distance S ahead,14,Variants and constraints,Variable reaction times Variable acceleration and deceleration Variable or multiple lead vehiclesLane-disciplined Stable traffic flow: do not produce incidents,15,Gap-acceptance models,Models of individual drivers choice

11、of safety gaps to merge into or to cross other traffic streams Two elements: Gaps acceptable to drivers Gaps available to the driver,16,Variants and constraints,Time-dependent acceptable gap Courtesy yieldingIndividual gap acceptance: no shadowing effects (e.g. on approaching roundabouts) Requires d

12、istinction of major/minor flows,17,Lane-changing models,Models of individual drivers ability and propensity to change lanes 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 givewa

13、y to merging traffic 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,Variants and constraints,Variable lane-changing objectives Variable hierarchical decision tre

14、es Variable acceptable gapsLook-ahead: anticipating a lane-changing needs a link ahead Cooperative lane-changing Courtesy yieldingLane disciplined: no overtaking in between lanes or lane in opposite direction,19,Lane-choice models,Selection of the lateral position in entering or traversing a link.Pr

15、e-specified, orInstantaneous choice made in response to traffic condition and destination,20,Models of intersection traffic control,Signal controlled: fixed or responsive Priority giveway Roundabout: partially signalised Motorway merge Stop-and-go Giveway to oncoming traffic,21,Network representatio

16、n,Network topology Junction type and priority rules Link (major/minor, speed limits, ) Lanes (turning restriction, access restriction) Signal plans (stages, phases, responsive rules),22,Software implementation,Discrete time vs. discrete event Fixed time increment: used to simulate systems whose enti

17、ties change continuously with time, e.g. traffic flow, the speed of vehiclesEvent 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,Software implementation (II),Continuo

18、us space vs. cellular automata (CA) CA: represents road sections as fixed-length segments and vehicles “jump” from one segment to anotherfast, easy to implement on parallel machines; but may induce large speed changes,24,Practical micro-simulation approach,Pure traffic micro-simulation Routes drawn

19、from turning probability May lead to implausible cyclic route 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 o

20、f 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,The DRACULA approach,Dynamic Route Assignment Combining User Learing and microsimulAtion A micro-simulati

21、on 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,Summary,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. There are varied implementations in networks. Greater efforts required to adopt it as a suitable traffic model for DTA,