Running the Simulation
The master html file, for example onramp.html, starts the actual simulation by the canvas tag:
<canvas id="canvas_onramp" width="800" height="600">some text for old browsers </canvas>
What to do with this canvas is specified in the init() procedure of onramp.js which starts the simulation and is assocoated with this canvas by the first command of the init procedure,
canvas = document.getElementById("canvas_onramp");
(for ring.html, the init procedure of ring.js would be associated with the canvas of that file, and so on). At the end of the initialization, init() starts the actual simulation thread by the command
return setInterval(main_loop, 1000/fps);
The initial canvas dimensions are overridden depending on the actual browser's viewport size by additional controls in canvasresize.js implementing a responsive design.
Just download all the html files and the js/, figs/, css/, and info/ directories and load, e.g., index.html in your favourite browser. For convenience and also to have access to the German version not contained in the repository, you can also download the zip file offlineVersion.zip containing all the ressources
Programm Files and Structure
<scenario>.js (ring.js, onramp.js etc)
the top-level simulation code for the corresponding scenario called in ring.html, onramp.html etc. Initializes the road network elements needed for the corresponding scenario (e.g. mainroad and onramp for the onramp scenario), starts/stops the simulation, controls the simulation updates in each time step depending on the scenario, draws everything, and implements the user controls defined in ring_gui.js, onramp_gui.js etc.
<scenario_gui>.js (ring_gui.js, etc.)
represents a road network element (road link) and organizes the vehicles on it. Contains an array of vehicles and methods to get the neighboring vehicles for a given vehicle, to update all vehicles for one time step, to interact with/get information of neighboring road network elements.
It also provides methods to draw this network element and the vehicles on it. These drawing methods depend on the road geometry functions
traj_y to be provided by the calling pseudoclasses <scenario>.js
each vehicle has (i) properties such as length, width, type, (ii) dynamic variables such as position and speed, and (iii) instances of the acceleration/lane changing methods from models.js.
a collection of pseudo-classes for the longitudinal models (presently, the IDM), and lane-changing decision models (presently, MOBIL).
a set of traffic-related objects that can be dragged by the user
from a "depot" to a network link (road) and back.
The main data element of this class is an array
of the traffic objects. At present, any array element
represent one of three types of traffic objects:
- traffic lights
- speed limits
Any object has one of two states at any time specified by the object's
traffObj.isActive=true: The object is on the road:
- in case of obstacles or traffic lights, real or virtual vehicle objects are added to the road at dropping time
- in case of speed limits, no new objects are generated but the vehicle's models are changed.
- in all cases, the visual appearance changes at dropping time
traffObj.isActive=false: the object is either in the "depot", or dragged, or zooming back to the depot
The traffic light and speed limit objects also have values:
The main unique component of the objects is its
In case of active traffic light or obstacle objects,
the id of the generated vehicle objects on the road are the same
as that of the
traffObj and in the range 50-199 (all special
vehicles have ids < 200). The complete list of traffObj and vehicle
id ranges is
veh.id=1: ego vehicle
veh.id=10..49: vehicles that are disturbed by clicks
traffObj.id=`veh.id=50..99: objects and generated vehicles of type obstacle
traffObj.id=`veh.id=100..149 objects of type trafficLight and generated vehicles (one per lane) of type obstacle
traffObj.id=150..199 speed limits ( no generated virtual vehicles)
veh.id>=200: normal vehicles and fixed (non-depot) obstacles
Helper-class providing some speed and type-dependent color maps to draw the vehicles.
callback (implementation) of the buttons for the different scenarios on the <scenario>.html simulation pages
The underlying car-following model for the longitudinal dynamics providing the accelerations (Intelligent-Driver Model, IDM, or extensions thereof) is time-continuous, so a numerical update scheme is necessary to get the speeds and positions of the vehicles as approximate integrals over the accelerations. For our purposes, it turned out that following ballistic scheme is most efficient in terms of computation load for a given precision. Its pseudo-code for an update of the speeds speed and positions pos over a fixed time interval dt reads
where acc(t) is the acceleration calculated by the car-following model at the (old) time t.
Lane-changing is modelled by the discrete model MOBIL, so no
integration is needed there. In order to reuse the accelerations
needed by MOBIL (Minimizing Obstructions By Intelligent
Lane-changes") for calculating the lane-changing decisions, lane
changing is performed after evaluating all
accelerations. Furthermore, since MOBIL anticipates the future
situation, the actual speed and positional update is performed after
the lane changing. Hence the central update sequence performed for all
road instances of the simulated network is given by
roadInstance.calcAccelerations(); roadInstance.changeLanes(); roadInstance.updateSpeedPositions();
in the main simulation file of the given scenario (
onramp.js etc). The main method is either
updateU() (the other scenarios).
Notice that the update is in parallel, i.e., updating all accelerations on a given road, then all lanes, all speeds, and all positions sequentially (if there are interdependencies between the road elements of the network, this sequentiality should also be traversed over all road instances which, presently, is not done).
The central update step is prepended by updating the model parameters as a response to user interaction, if vehicles reach special zones such as the uphill region, or if they reach mandatory lane-changing regions before lane closing and offramps.
For closed links (ring road), the central update step is prepended by changing the vehicle population (overall density, truck percentage) as a response to user interaction.
For open links, the central method is appended by applying the boundary conditions
roadInstance.updateBCupfor all non-closed network links. For further information on boundary conditions, see the info link Boundary Conditions at
The implementation of the actual models is given in
models.js. Presently (as of November 2016), an extension of the Intelligent-Driver Model ("ACC model") is used as acceleration model, and MOBIL as the lane-changing model. We use the ACC model rather than the "original" IDM since the former is less sensitive to too low gaps which makes lane changing easier. For the same reason, we have modified MOBIL somewhat by making its
bSafeparameter depending on the speed. Thus, we make lane changes more aggressive in congested situations. For further information, see the scientific references below, or the info links below the heading Traffic Flow Models at
The drawing is essentially based on images:
The background is just a jpeg image.
Each road network element is composed of typically 50-100 small road segments. Each road segment (a small png file) represents typically 10m-20m of the road length with all the lanes. By transforming this image (translation, rotation,scaling) and drawing it multiple times, realistically looking roads can be drawn.
The vehicles are drawn first as b/w. images (again translated, rotated, and scaled accordingly) to which an (appropriately transformed) semi-transparent rectangle is added to display the color-coding of the speeds.
 M. Treiber and A. Kesting. Traffic Flow Dynamics, Data, Models and Simulation. Springer 2013. Link
 A. Kesting, M. Treiber, and D. Helbing. General lane-changing model MOBIL for car-following models. Transportation Research Record, 86-94 (2007). Paper
 A. Kesting, M. Treiber, and D. Helbing. Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity. Philosophical Transactions of the Royal Society A, 4585-4605 (2010). Preprint
 M. Treiber, and A. Kesting. An open-source microscopic traffic simulator. IEEE Intelligent Transportation Systems Magazine, 6-13 (2010). Preprint
 M. Treiber and V. Kanagaraj. Comparing Numerical Integration Schemes for Time-Continuous Car-Following Models Physica A: Statistical Mechanics and its Applications 419C, 183-195 DOI 10.1016/j.physa.2014.09.061 (2015). Preprint