Evaluating safety impacts of network planning alternatives have a potential to improve safety in a proactive manner at the system level and at a minor cost of additional analysis. This paper presents a computer-implemented GIS-based method of predicting safety for planning alternatives. Although applicable to large road networks, the method predicts crashes at the road facility level (intersect...

We analytically investigate a wide cluster solution and show that it is not admitted in some of the traffic flow models in the literature. For those traffic flow models that admit the wide cluster solution, the relationship between two important control parameters and the critical densities that divide an equilibrium solution into stable and unstable regions is thoroughly discussed in detail. W...

This research deals with the investigation of the advanced driver assistance systems impact on urban traffic network conditions through the use of a traffic network simulation model. Information obtained through microscopic simulation was used as input to a network traffic simulation and assignment model (SATURN). SATURN was applied to the road network of the greater Athens area in an attempt t...

Next generation Urban Traffic Control (UTC) systems should be flexible in their ability to handle new and improved sensor information that is beginning to become available from cameras, GPS devices, and other sources. They should equally be open to supporting a variety of different traffic management policies, informed by this increasingly available sensor data, to optimise the flow of traffic ...

Inductance loop detection systems serve as a primary data source to contemporary traffic information systems. Measures like 20-second or 30-second average velocity, flow, and lane occupancy can be aggregated from individual loop detector actuation sampled at 60 Hz typically. Practically, these measures would sometimes be further aggregated into a much lower, e.g. 15-minute, resolution and then ...

The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncatio...

Analyzing motion patterns in traffic videos can be exploited directly to generate high-level descriptions of the video contents. Such descriptions may further be employed in different traffic applications such as traffic phase detection and abnormal event detection. One of the most recent and successful unsupervised methods for complex traffic scene analysis is based on topic models. In this pa...

In the Internet, Autonomous Systems (ASes) exchange traffic through interconnected links. As the volume of traffic increases, more traffic becomes concentrated on such links. To reduce the traffic concentration, an AS tries to construct a new link based on local agreements between pairs of ASes. However, the current and future traffic concentrations may not be adequately resolved, since they de...

Communication networks are nowadays crucial in our lives and the study of its traffic features yields important advantages. In both network and traffic design the understanding of the relationship between the traffic on a node and its fluctuations plays a key role. In this paper we investigate the relationship between the mean traffic flow experienced by a node and its standard deviation via nu...