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Most special-use freeway lanes in the US, whether reserved for carpools, toll-paying commuters or both, are physically separated from the adjacent regular-use lanes by some form of barrier. Vehicle movements in and out of a special lane of this type are permitted only at select access points along the route. The barrier at each select point might open for a distance of 400 m or so. Limiting access in this way is said to reduce the “turbulence” that might otherwise occur were the special lane not to have a buffer, such that vehicles could instead enter or exit that lane anywhere along its length. Yet, real freeway traffic studied in spatiotemporal fashion shows that access points are prone to become bottlenecks. The problem occurs when traffic in the regular lanes becomes dense, as commonly happens during a rush. Drivers then seek refuge in the special lane in greater numbers. Since the vehicular maneuvers through the access point are focused within a limited physical space, they can become disruptive and further degrade traffic. Degradation can occur both in the special lane and in the adjacent regular ones. The damage can be worse than what occurs when barrier are not used to limit special-lane ingress and egress. Policy implications are discussed.

In this study, we investigated an alternative technique to automatically classify road traffic congestion with high accuracy aligning with travelers' opinions. The method utilized an intelligent traffic camera system orchestrated with a web survey system to collect the traffic conditions and travelers' opinions. A large number of human perceptions were used to train the artificial neural network (ANN) model that classify velocity and traffic flow into three congestion levels: light, heavy, and jam. The learning parameters were heuristically optimized to gain highest prediction accuracy. The outcomes were a practical method and the model achieving as high as 94.99% accuracy. The model was then compared to the Occupancy Ratio (OR) technique, currently in service in the Bangkok Metropolitan Area. The comparison indicated that our model could determine the traffic congestion levels 12.15% more accurately than the existing system. The analysis revealed that the derived model classified congestion levels based mainly on the vehicle velocity, suggesting that the model could be modified and broadly used with various types of traffic sensors. The methodology, though conceived for use in Bangkok, is a general Intelligent Transportation System (ITS) practice that can be applied to any part of the world. [ABSTRACT FROM AUTHOR]

We proposed a technique to identify road traffic congestion levels from velocity of mobile sensors with high accuracy and consistent with motorists' judgments. The data collection utilized a GPS device, a webcam, and an opinion survey. Human perceptions were used to rate the traffic congestion levels into three levels: light, heavy, and jam. Then the ratings and velocity were fed into a decision tree learning model (J48). We successfully extracted vehicle movement patterns to feed into the learning model using a sliding windows technique. The model achieved accuracy as high as 91.29%. By implementing the model on the existing traffic report systems, the reports will cover on comprehensive areas. The proposed method can be applied to any parts of the world. [ABSTRACT FROM AUTHOR]

Real-time segmentation of moving regions in image sequences is a fundamental step in many vision systems including automated visual surveillance and human-machine interface. In this paper we present a framework for detecting some important but unknown knowledge like vehicle identification and traffic flow count. The objective is to monitor activities at traffic intersections for detecting congestions, and then predict the traffic flow which assists in regulating traffic. The present algorithm for vision-based detection and counting of vehicles in monocular image sequences for traffic scenes are recorded by a stationary camera. The method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Background subtraction is used which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. The resulting system robustly identifies vehicles at intersection, rejecting background and tracks vehicles over a specific period of time. Real-life traffic video sequences are used to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This research is intended to increase drivers’ utility by reducing traffic congestion. To attain our purpose, we propose a simple route guidance mechanism based on route information sharing (RIS). Drivers using the RIS give planned route details to the route information server, which then sends accumulated traffic information based on those routes to drivers. Multiagent simulation in a lattice network and a radial and ring network confirmed that: i) as the drivers using the RIS increased, the travel time of both the drivers using the RIS and the other drivers decreased; ii) the travel times of drivers using the RIS were substantially shorter than those of drivers using other mechanisms. [ABSTRACT FROM AUTHOR]

This article presents information on network traffic control methods. Flow and congestion control is a distributed algorithm to share network resources among competing users. Like a transportation network, congestion can build up in a telecommunications network when traffic load exceeds network capacity. As load increases in a packet network, queues build up until packets are delayed by an excessive amount or even lost and need to be retransmitted. Packets that are transmitted multiple times or at upstream nodes only to be discarded at downstream nodes, waste network resources and intensify congestion.

Discusses myths about diversity in cities in the U.S. Esthetic problems related to homogeneity in cities; Analysis of the issue that diversity causes traffic congestion; Description of Fifth Avenue in New York.

The article presents the Chinese peasants as an example which French philosopher Jean-Paul Sartre gives to illustrate the notion of the practico-inert. An example in Western civilization is the motorist caught in a series of traffic jams created by the increased availability of cars whose original intention was to enable people like himself to move about more freely.

A definition of the term "traffic intensity" is presented. It refers to the average load of each server in a queuing system.