real time traffic simulation using cellular automata

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  • 7/31/2019 Real Time Traffic Simulation Using Cellular Automata

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    EG UK Theory and Practice of Computer Graphics (2010)John Collomosse, Ian Grimstead (Editors)

    Real-Time Traffic Simulation Using Cellular Automata

    C. S. Applegate, S. D. Laycock and A. M. Day

    Crowd Simulation Group, School of Computing Sciences, University of East Anglia, Norwich, United Kingdom. NR4 7TJc.applegate@uea.ac.uk, s.laycock@uea.ac.uk, a.day@uea.ac.uk

    Abstract

    In this paper, we present a method to simulate large-scale traffic networks, at real-time frame-rates. Our novel

    contributions include a method to automatically generate a road graph from real-life data, and our extension to a

    discrete traffic model, which we use to simulate traffic, demonstrating continuous vehicle motion between discrete

    locations. Given Ordnance Survey data, we automatically generate a road graph, identifying roads, junctions,

    and their connections. We distribute cells at regular intervals throughout the graph, which are used as discrete

    vehicle locations in our traffic model. Vehicle positions are then interpolated between cells to obtain continuous

    animation. We test the performance of our model using a 500 x 500m2 area of a real city, and demonstrate that

    our model can simulate over 600 vehicles at real-time frame-rates (> 80% network density).

    Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geome-try and Object ModelingPhysically based modeling; I.6.8 [Simulation and Modeling]: Types of SimulationAnimation

    Keywords: traffic simulation, mesoscopic simulation, cellular automata, road generation, vehicle animation

    1. Introduction

    For more than 50 years, researchers from a diverse set of dis-ciplines have been studying the behaviour of traffic flow tobetter understand the causes of traffic congestion, accidentsand related phenomena. As the worlds population continuesto increase, there is high demand for effective and efficienttransport infrastructures that alleviate these problems. Typ-ically, cost-effective solutions are designed and developedusing simulation packages, which allow transportation engi-neers to accurately model road networks and evaluate theirdesigns though visual simulation. These packages providethree-dimensional representations of different traffic scenar-ios prior to their actual construction, allowing professionals,

    local authorities and the general public to instantly under-stand the expected impact on the surrounding environment.However, to better portray this information, methodologiesto model larger-scale networks at higher visual fidelity aredesirable.

    One of the major challenges faced by researchers in thecomputer graphics industry, involves the design and imple-mentation of algorithms that allow large-scale simulations torun at real-time frame-rates. This is a typical requirement of

    traffic simulators, as it enables designers to modify and in-teract with an environment in real-time, reducing the devel-opment time and overall cost of a project. However, to ob-tain the required performance, many applications will com-promise behavioural accuracy or visual quality. These chal-lenges also translate to the entertainment industry, as real-time traffic simulation is required in computer games, filmsand virtual tourism applications.

    Over the years, there has been extensive research into thedevelopment of realistic traffic simulations, with the ma-jority of literature either focusing on agent-based micro-scopic models, where individual vehicles have their own de-

    tailed behaviour, or continuum-based macroscopic models,which focus on the collective behaviour of vehicles, mod-elled through density conservation equations. A third typeof traffic simulation, mesoscopic models, combines micro-scopic and macroscopic model properties, simulating indi-vidual vehicles that show collective behaviour. However,there has been relatively little literature that considers ex-tending mesoscopic traffic models to produce traffic simula-tions, which exhibit both accurate behaviour and continuousanimation.

    c The Eurographics Association 2010.

    mailto:c.applegate@uea.ac.ukmailto:c.applegate@uea.ac.ukmailto:s.laycock@uea.ac.ukmailto:s.laycock@uea.ac.ukmailto:a.day@uea.ac.ukmailto:a.day@uea.ac.ukmailto:s.laycock@uea.ac.ukmailto:c.applegate@uea.ac.uk
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    C. S. Applegate, S. D. Laycock and A. M. Day / Real-Time Traffic Simulation Using Cellular Automata

    In this paper, we propose a system to simulate large-scaletraffic networks in real-time. Our novel contributions includea method to automatically create a road graph from real-life data, and our extension to Nagel and Schreckenbergsoriginal mesoscopic traffic model [NS92], which we use tosimulate a large-scale traffic network, demonstrating contin-uous animation between discrete locations. We test the per-formance of our model using a 500 x 500m2 area of a realcity, and demonstrate that our model can simulate over 600vehicles at real-time frame-rates (> 80% network density).

    2. Related Work

    The realistic behaviour of vehicles has been studiedextensively in literature by researchers from a varietyof disciplines. The pioneering work of Lighthill andWhitham [LW55] and Richards [Ric56], investigated the be-haviour of traffic flow as a density continuum, modelling themacroscopic or continuous behaviour of traffic using the-ory from fluid-dynamics. The Lighthill-Whitham-RichardsModel (LWR Model) uses aggregate variables representingthe average velocity of vehicles, traffic density and trafficflow, to model the collective behaviour of vehicles fromthe defined relationship between these attributes. The maindisadvantage of macroscopic traffic models is that they donot differentiate between individual vehicles, and it is there-fore difficult to accurately simulate complex urban networkswhere there are a diverse range of vehicle types and driverbehaviours. An advantage of modelling the collective be-haviour of traffic is that simulations operate at high-speeds.For these reasons, macroscopic models are often used tosimulate large-scale networks where detail is less important.

    Recently, Sewall et al. [SWML10] extended a macroscopictraffic model to produce detailed three-dimensional anima-tions of traffic flow on large-scale networks. Their systemuses continuum dynamics to model traffic, but maintains in-dividual vehicle information to display each vehicle. Theirmethod is scalable with multi-core/multi-processor architec-tures and runs at real-time frame-rates.

    Microscopic or discrete traffic simulations are the mostpopular form of traffic simulation, and model the behaviourof individual vehicles with detailed behaviours. Microscopicmodels are often preferred to macroscopic alternatives, asthese systems can accurately simulate traffic on both urbanand rural road networks, using car-following and lane-

    changing rules, which model individual vehicle attributessuch as speed, acceleration, braking forces, reaction times,etc. In these rules, the movement of each vehicle is calcu-lated based on the position or velocity of the vehicle in front.A disadvantage of microscopic models is that they typicallyrequire more computation time when compared with macro-scopic models. Significant contributions in this area can befound in the works of Gerlough [Ger55], Newell [New61]and Gipps [Gip81]. Popular microscopic traffic simulatorsinclude, Quadstone Paramics and PTV AGs VISSIM.

    Mesoscopic traffic simulators combine microscopic andmacroscopic model properties, simulating individual vehi-cles that show aggregated behaviour at high computationspeeds. In 1992, Nagel and Schreckenberg [NS92] proposeda mesoscopic model for traffic simulation using cellular au-tomata. The authors used a one-dimensional array to repre-sent a single-lane carriageway, which was segmented intoa finite number of cells. Each cell contained a maximumof one vehicle, and vehicles moved between consecutivecells using simple rules. Updates were performed per-celland in parallel, thus the performance of this model is scal-able with multi-core/multi-processor architectures. Rickertet al. [RNSL96] extended Nagel and Schreckenbergs modelto incorporate two lanes of traffic, where vehicles were per-mitted to change lanes, based on their distance to neigh-bouring vehicles. Nishinari and Takahashi [NT99] use Burg-ers Cellular Automaton (BCA), an ultra discrete version ofthe Burgers equation to model traffic flow. In their model,each cell can contain a number of vehicles, and movement

    between cells is defined by the evolution of the BCA. InNishinari and Takahashis model, individual lane-changingrules are neglected, but the macroscopic effects of lanechanges are preserved. More recently, cellular approacheshave been used to model the interactions in mixed bi-cycle flow [JJW04], the interactions between pedestriansand low-speed vehicles (e.g. bicycles, tricycles and bar-rows) [JW06b, JW06a] and between different types of ve-hicle [ZJG07, MDDZ07, CYZ08, LGJZ09].

    Although cellular approaches combine the advantagesof macroscopic and microscopic models, their discrete na-ture is not directly suitable for animating the continuousmovement of traffic, as vehicles appear to pop between

    disjoint cells. Animation quality could be improved us-ing finer discretisations, but at the cost of reducing per-formance. Alternative techniques for simulating the be-havioural animation of vehicles have been investigated inthe literature; Go et al. [GVK06], introduce a frameworkfor animating autonomous agents in interactive applications,simulating the behaviour of vehicles and spacecraft, bycombining Reynolds steering behaviours for autonomousagents [Rey99

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