![]() These mesoscopic models are not individual-based, as set out by (e.g. These models were often stochastic: a velocity statistical distribution is generally computed, but the individuality of each vehicle is not really modeled. In the 1970s, some diversity was provided to the vehicles/particles in models often inspired by gas kinetics. These models therefore do not allow the study of individual or collective road traffic practices that do not follow either international conventions (never drive against the traffic flow) or national regulations (only one vehicle per lane). However, by their nature, they do not allow to study the individual behavior of vehicles and neglect the diversity of drivers in the flow. These models are also able to reproduce the phenomenon of congestion. These macroscopic models reproduce the shock waves corresponding to vehicle braking and acceleration with the decrease in the density of vehicles usually observed in the traffic. Keeping these equations solvable or step-based and approximable requires the features modeled to be kept sparse: the geometry of the road is generally ignored, it is often a single road composed of a single lane, with equations that model the collective evolution of vehicle described by average speed, average behavior, average characteristics. These analytical models focused on the study of traffic phases (in terms of thermodynamics): at first two (free flow and congested), then three phases (free flow, wide jam and synchronized). These models would later be known as macroscopic. ![]() Principles of conservation of mass and energy could then lead to the proof of algebraic and differential equations describing the links between these variables. The reasoning was the same: although it was known that traffic was composed of cars, like gases were composed of molecules, with their own set of defining characteristics, it was more productive to consider the resulting aggregations like mean speed, flow or density. Based on the best methodology available at the time, they mirrored the practice of physics and in particular, fluid mechanics. Modeling traffic is about as old as car traffic itself : the first ideas date back to the 1920s (like Frank Knight traffic equilibrium) and the first models to the 1950s. An experiment carried out shows that the model can accurately reproduce the traffic in Hanoi, Vietnam. Moreover, the model allows to carry out city-level simulations with tens of thousands of driver agents. ![]() In particular, it allows modelling road infrastructures and traffic signals, change of lanes by driver agents and less normative traffic mixing car and motorbike as in some South East Asian countries. ![]() In this paper, we present a built-in model integrated into the GAMA open-source modeling and simulation platform, allowing the modeler to easily define traffic simulations with a detailed representation of the driver’s operational behaviors. Among them, agent-based frameworks are now appropriate for studying ordinary traffic conditions at city-scale, but remain difficult to adapt, especially for non-computer scientists, to more specific application contexts (e.g., car accidents, evacuation following a natural disaster), that require integrating particular behaviors for the agents. Continuous improvement in computing power allowed for an increase of the scales micro-traffic models can be used at.
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