Traffic and Related Self-Driven Many-Particle Systems

Since the subject of traffic dynamics has captured the interest of physicists, many astonishing effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by so-called ``phantom traffic jams'', although they 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 of the traffic volume cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize in lanes, while similar systems are ``freezing by heating''? Why do self-organizing systems tend to reach an optimal state? Why do panicking pedestrians produce dangerous deadlocks? All these questions have been answered by applying and extending methods from statistical physics and non-linear dynamics to self-driven many-particle systems. This review article on traffic introduces (i) empirically data, facts, and observations, (ii) the main approaches to pedestrian, highway, and city traffic, (iii) 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 like a general modelling framework for self-driven many-particle systems, including spin systems. Subjects such as the optimization of traffic flows and relations to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are discussed as well.Comment: A shortened version of this article will appear in Reviews of Modern Physics, an extended one as a book. The 63 figures were omitted because of storage capacity. For related work see http://www.helbing.org

Drivers' use of deceleration and acceleration information in car-following process

Understanding driver behavior is important for the development of many applications such as microscopic traffic simulation models and advanced driver assistance systems. The car-following process is an important phase of driving behavior and takes place when a driver follows a lead vehicle and tries to maintain distance and relative speed within an acceptable range. A key to improving knowledge of driver behavior during this process is determining the information perceived by drivers that could influence their decisions. It has been believed for some time that the main kinematic parameters that affect driver judgment in car following are the relative speed, the distance separation, and the absolute speed. The research described investigated whether drivers are also able to use information on the lead vehicle's deceleration or acceleration during the car-following process through experimental validation of current car-following hypotheses. For this research, an instrumented vehicle was used to collect a large database of car-following time sequences, the analysis of which showed strong evidence that drivers are able to perceive information such as the deceleration or acceleration of the vehicle being followed, although no empirical relationship was determined. An example demonstrating the importance of such perception shows that modeling a driver trying to avoid a collision with a lead vehicle would lose 20% of its fit accuracy if the lead-vehicle acceleration state were not considered

Modeling Driver Behavior in Work and Nonwork Zones

A new multidimensional framework for modeling car following on the basis of statistical evaluation of driver behavior in work and nonwork zones is presented. The models developed as part of this multidimensional framework use psychophysical concepts for car following that are close in character to the Wiedemann model used in popular traffic simulation software such as VISSIM. The authors hypothesized that with an instrumented research vehicle (IRV) in a living laboratory (LL) along a roadway, the parameters of models developed from the multidimensional framework could be derived statistically and calibrated for driver behavior in work zones. This hypothesis was validated with data collected from a group of 64 random participants who drove the IRV through an LL set up along a work zone on I-95 near Washington, D.C. For this validation, the IRV was equipped with sensors, including radar, and an onboard data collection system to record the vehicle performance. One of the limitations of current car-following models is that they account for only one overall behavioral condition. This study demonstrated that there are four different categories of car-following behavior models, each with different parameter distributions: the four categories are divided by traffic condition (congested versus noncongested) and by roadway condition (work versus nonwork zone). Calibrated threshold values for each of these four categories are presented. Furthermore, this new framework for modeling car-following behavior is described in a multidimensional setting and can be used to enhance vehicle behavior in microsimulation models