Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ``phantom traffic jams'' even though drivers all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ``freeze by heating''? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to self-driven many-particle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for self-driven many-particle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.

Traffic and related self-driven many-particle systems

Nowadays traffic flow and congestion is one of the main societal and economical problems related to transportation in industrialized countries. In this respect, managing traffic in congeste...

State-of-the-art of vehicular traffic flow modelling

Cellular automata models of road traffic

Simple cellular automata models are able to reproduce the basic properties of highway traffic. The comparison with empirical data for microscopic quantities requires a more detailed description of the elementary dynamics. Based on existing cellular automata models, we propose an improved discrete model incorporating anticipation effects, reduced acceleration capabilities and an enhanced interaction horizon for braking. The modified model is able to reproduce the three phases (free-flow, synchronized, and stop-and-go) observed in real traffic. Furthermore we find a good agreement with detailed empirical single-vehicle data in all phases.

https://iopscience.iop.org/article/10.1088/0305-4470/33/48/103/pdf

Towards a realistic microscopic description of highway traffic

This study investigates the effectiveness of simultaneous and staged evacuation strategies using agent-based simulation. In the simultaneous strategy, all residents are informed to evacuate simultaneously, whereas in the staged evacuation strategy, residents in different zones are organized to evacuate in an order based on different sequences of the zones within the affected area. This study uses an agent-based technique to model traffic flows at the level of individual vehicles and investigates the collective behaviours of evacuating vehicles. We conducted simulations using a microscopic simulation system called Paramics on three types of road network structures under different population densities. The three types of road network structures include a grid road structure, a ring road structure, and a real road structure from the City of San Marcos, Texas. Default rules in Paramics were used for trip generation, destination choice, and route choice. Simulation results indicate that (1) there is no evacuation strategy that can be considered as the best strategy across different road network structures, and the performance of the strategies depends on both road network structure and population density; (2) if the population density in the affected area is high and the underlying road network structure is a grid structure, then a staged evacuation strategy that alternates non-adjacent zones in the affected area is effective in reducing the overall evacuation time.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.jors.2602321.pdf

Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies

Saturated capacities in traffic systems evoke increasing interest in simulations of complex networks serving as laboratory environment for developing management strategies. Especially for urban areas questions concerning overall traffic control have to be considered with regard to their impacts on the whole network. Modeling traffic flow dynamics using cellular automata allows us to run large network traffic simulations with only comparatively low computational efforts. We present a traffic simulation tool for urban road networks which is based on the Nagel–Schreckenberg Model. Arbitrary kinds of roads and crossings are modeled as combinations of only a few basic elements. Furthermore parking capacities are considered as well as circulations of public transports. The vehicles are driven corresponding to route plans or at random depending on the available data. The application of this network simulation covers investigations on the field of traffic planning as well as online simulations based on real-time traffic data as basis for dynamic traffic management systems.

Microscopic Simulation of Urban Traffic Based on Cellular Automata

We present a simulation tool to predict urban traffic based on a cellular automaton model for traffic flow supplemented by real-traffic data. This tool is applied to the road network of the inner city of Duisburg. The quality of the reproduced traffic states is investigated with regard to vehicle densities and key features of urban traffic like rush-hours which are resembled quite well.

A cellular automaton traffic flow model for online simulation of traffic

In this paper a concept for online simulations of urban road networks is developed, with real-time traffic data from induction loops serving as input for microscopic simulations From the simulations, estimates of traffic conditions for regions which are not adequately covered by measurements are determined The quality of the reproduced traffic states with regard to vehicle densities and link travel times is investigated The dynamic data are processed by a route guidance system based on a fuzzy logic approach As an example of dynamic traffic management, different strategies for individual en-route guidance systems and their efficiencies are studied The study area is the road network of Duisburg

Microscopic online simulations of urban traffic

The development of dynamic traffic information systems is one of the most important challenges in the field of Intelligent Transportation Systems ITS. A promising way is to couple simulations of the actual traffic state together with online counting data stemming from detection devices, e.g. induction loops. The aim is to generate a one-to-one image of the individual on-road car motion in the computer and to derive the relevant information from the simulation results. In addition, the simulation serves as an “interpolation” for regions which are not adequately covered by measurements. In this contribution the method of online simulations is discussed and some results are reported.

J. Esser

Papers

Microscopic Simulation of Urban Traffic Based on Cellular Automata

A cellular automaton traffic flow model for online simulation of traffic

Microscopic online simulations of urban traffic

Online Traffic Simulation with Cellular Automata