In this paper, we present our work on clustering and prediction of temporal evolution of global congestion configurations in a large-scale urban transportation network. Instead of looking into temporal variations of traffic flow states of individual links, we focus on temporal evolution of the complete spatial configuration of congestions over the network. In our work, we pursue to describe the...

-There have been a number of methods presented by various researchers for traffic prediction, some of which involve modeling the problem of traffic prediction as a time series. It has been observed that Artificial Neural Networks (ANN) perform better than statistical methods for time series forecasting. The network performance and complexity varies with the choice of algorithm used. Back propag...

Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from experience to classify, process and pre...

This study presented a method of real-time traffic condition identification based on the fuzzy c-means clustering and travel time prediction on urban expressway. The traffic flow characteristics of expressway were analyzed and the traffic flow states were divided into four classes. Then the fuzzy c-means clustering technique was used to classify the sampled historical data and the clustering ce...

Environmental impact studies are strongly related to road traffic noise, especially in urban areas. A long term exposure to road traffic noise, in fact, can lead to relevant effects, both auditory (e.g. sleep disturbance, hearing loss, etc.) or not auditory (e.g. stress, anxiety, cardiovascular problems, etc.). A proper modelling of noise production and propagation is a challenging issue, espec...

In this paper we present three theoretically grounded methods: prediction, reconstruction and interpolation, for measuring cross traffic on the bottleneck link of an end-toend path. The objective is to infer cross traffic as accurately as possible, while not injecting a significant amount of probe packets into the network. In the prediction-based method, we take advantage of the LRD characteris...

Many species use acoustic signals to attract mates, and such signals can be degraded by anthropogenic noise. Anuran abundance has been shown to be negatively correlated with road traffic which could be due in part to the interruption of mate attraction by traffic noise. However, this impact could be small if anurans can alter their vocalization characteristics to avoid masking of their calls by...

This paper presents a binary neural network algorithm for short-term traffic flow prediction. The algorithm can process both univariate and multivariate data from a single traffic sensor using time series prediction (temporal lags) and can combine information from multiple traffic sensors with time series prediction ( spatial-temporal lags). The algorithm provides Intelligent Decision Support (...