Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...

In allusion to the influence of traffic flow change on vehicle routing selection, the article aims at finding the method for planning the trip route under the condition of knowing traffic flow change. The method combining particle swarm algorithm and dynamic planning is used for routing optimization in order to obtain the excellent route for various vehicles influenced by traffic flow. The road...

Due to steady increase in traffic density on roads, the traffic phenomena become non-linear and complex. For the traffic engineers and policy makers to regularize the traffic flow, we need to explore the traffic behavior in detail. In the current paper, the usefulness of CA to traffic flow modeling has been determined. Also, some of the existing CA models have been extended to study characteris...

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...

Eulerian models are used to represent the air traffic environment as traffic flows between interconnected control volumes representing the airspace system. While these models can be manually derived for simple air traffic patterns, computer-based approaches are essential for modeling realistic airspaces involving multiple traffic streams. A computeraided methodology for deriving large-dimension...

There have been two kinds of traffic flow theories presented thus far in this monograph. Chapter 2 discussed the relations among aggregated quantities of flow rate, concentration and mean speeds for stationary vehicular traffic (these relations are also referred to as the equations of state in traffic flow), and Chapter 3 described the dynamic evolution of microscopic quantities of vehicle spac...

Congestions of the traffic flow within the urban traffic network have been a challenging task for all the urban developers. Many approaches have been introduced into the current system to solve the traffic congestion problems. Reconfiguration of the traffic signal timing plan has been carried out through implementation of different techniques. However, dynamic characteristics of the traffic flo...

The paper designs a new technique for correlating network flows, which they call RAINBOW. The core idea of RAINBOW is to slightly delay network packets in a way to modulate a secret signal, called the watermark signal, into the packet timings of network flows. The proposed technique is different from previous designs by being “non-blind”, i.e., the traffic analysis parties use a side channel to...

Knowledge of flow distributions from a stream of packets passing through a network link helps a network operator infer traffic demands, characterize source traffic, and detect traffic anomalies. Flow monitoring provides critical information for many network applications, and some approaches have been proposed to address flow monitoring. However, they either lack accuracy, or require intensive c...

The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient typ...