Statistical physics has proven to be a fruitful framework to describe phenomena outside the realm of traditional physics. Recent years have witnessed an attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. A wide list of topics are reviewed ranging from opinion and cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, and social spreading. The connections between these problems and other, more traditional, topics of statistical physics are highlighted. Comparison of model results with empirical data from social systems are also emphasized.

Statistical physics of social dynamics

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

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

Starting with a short review of the available literature in the field of pedestrian and evacuation research, an overview is given over the observed collective phenomena in pedestrian crowds. This includes lane formation in corridors and oscillations at bottlenecks in normal situations, while different kinds of blocked states are produced in panic situations. By means of molecular-dynamic-like microsimulations based on a generalized force model of interactive pedestrian dynamics, the spatio-temporal patterns in pedestrian crowds are successfully reproduced and interpreted as self-organized phenomena. In contrast to previous socio-psychological approaches, this allows a physical understanding of the observations. Despite the significantly different phenomena occuring in normal and panic situations, the main effects can be described by a unified model containing only well interpretable and plausible terms. The transition between the “rational” normal behavior and the apparently “irrational” panic behavior is controlled by a single parameter, the “nervousness”, which influences fluctuation strengths, desired speeds, and the tendency of herding. Thereby, it causes paradoxial effects like “freezing by heating”, “faster is slower”, and the ignorance of available exits. Nevertheless, there are measures to improve pedestrian flows, both in normal and panic situations. For example, the suitable placement of columns can help, although they reduce the accessible space.

Simulation of pedestrian crowds in normal and evacuation situations

Pedestrian flow is inherently complex, more so than vehicular flow, and development of microscopic models of pedestrian flow has been a daunting task for researchers. This paper presents the use of Cellular automata (CA) microsimulation for modeling bi-directional pedestrian walkways. It is shown that a small rule set is capable of effectively capturing the behaviors of pedestrians at the micro-level while attaining realistic macro-level activity. The model provides for simulating three modes of bi-directional pedestrian flow: (a) flows in directionally separated lanes, (b) interspersed flow, and (c) dynamic multi-lane (DML) flow. The emergent behavior that arises from the model, termed CA-Ped, is consistent with well-established fundamental properties.

Cellular automata microsimulation for modeling bi-directional pedestrian walkways

The cellular automata (CA) microsimulation of pedestrians is a particle-hopping model in which a set of local rules prescribe the behavior of entities within local neighborhoods of cells. CA microsimulation has emerged as a tool for simulating traffic flow and modeling transportation networks. Pedestrian flow is inherently more complex than vehicular flow, and simulation models that are used for emulating vehicular traffic are not directly applicable to modeling pedestrian movements. In previous work the authors demonstrated that unidirectional pedestrian flow patterns consistent with well-established fundamental properties could be generated with CA microsimulation. This paper expands upon the previous effort and presents a CA microsimulation model and emergent fundamental flows for a bidirectional pedestrian walkway. Simulation experiments indicate that the basic model is applicable to walkways of various lengths and widths and across different directional shares of pedestrian movements.

Cellular automata microsimulation of bidirectional pedestrian flows

The emergence of Intelligent Transportation Systems (ITS) has fostered the development of advanced traveler information systems (ATIS). These systems are designed to assist travelers in making pre-trip and enroute travel choice decisions. It is contended that while many traveler information systems are innovative and make use of cutting edge technologies, they lack real machine intelligence and therefore may be limited in their ability to service the traveling public over the long-run. The purpose of this paper is to present a vision of the next generation traveler information system, termed Intelligent Traveler Information Systems (ITIS) in which artificial intelligence techniques are drawn upon to create systems capable of providing travelers with more personalized planning assistance.

Toward the design of intelligent traveler information systems

In recent years cellular automata (CA) have been successfully applied to modeling traffic flow. Use of a CA for modeling pedestrian flows is examined here. A particle hopping model for a single-directional pedestrian flow over a multilane walkway is presented. This model offers the advantage of effectively capturing the behaviors of pedestrians at the micro-level while attaining realistic macro-level activity. The emergent group behavior is an outgrowth of the interaction of the rule set in simulation. The results indicate that a heuristically derived minimal rule set produces flow patterns that closely resemble the accepted fundamental diagrams. Important parameters for determining the shape of the fundamental diagrams are examined. Key rules used in a vehicular traffic CA are tested for their applicability to the pedestrian CA model.

Emergent fundamental pedestrian flows from cellular automata microsimulation

This paper describes a study to investigate the effects of route guidance and traffic advisories on driver's route choice behavior. The study is a two-factor experiment with repeated measures on one factor where the between-subjects factor is the type of traveler information provided and the repeated, within-subjects factor is trips made between a specified origin and destination. Participants were recruited and randomly assigned to one of four groups: group 1 having only a basic map of the network; group 2 having access only to route guidance, group 3 having access to traffic advisory information, and group 4 having access to both route guidance and traffic advisory information. Each participant completed 15 trips between a specified origin-destination pair on a hypothetical network. The results of this study indicate that there may be significant short-term advantages to providing in-vehicle routing and navigation information to unfamiliar drivers. However, the results also indicate that the format and amount of information provided may not be significant as the benefits to having route guidance diminish when drivers become more familiar with the travel network.

Jeffrey L. Adler

Papers

Cellular automata microsimulation for modeling bi-directional pedestrian walkways

Cellular automata microsimulation of bidirectional pedestrian flows

Toward the design of intelligent traveler information systems

Emergent fundamental pedestrian flows from cellular automata microsimulation

Investigating the learning effects of route guidance and traffic advisories on route choice behavior