Linn County Regional Planning Commission, 6th Floor City Hall, Cedar Rapids, Iowa 52401. Transportation agencies often determine what the annual average daily traffic (AADT) count is on streets and highways by counting traffic for short time periods (usually for 24 hours) and then estimating the AADT based on this count and a numerical factor that takes into account dayof-week and/or seasonal v...

this paper presents the prediction of vehicle's velocity time series using neural networks. for this purpose, driving data is firstly collected in real world traffic conditions in the city of tehran using advance vehicle location devices installed on private cars. a multi-layer perceptron network is then designed for driving time series forecasting. in addition, the results of this study a...

For many years intelligent transportation systems (ITS) have been collecting and processing huge amounts of data from numerous sensors to generate a ground truth of urban traffic. Such data has set the foundation of traffic theory, planning and simulation to create rule-based systems. It has also been used in many different studies in data-driven short-term traffic flow forecasting with promisi...

Received Sep 15, 2017 Revised Nov 10, 2017 Accepted Nov 20, 2017 Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic pre...

A green transportation system composed of transit, busses and bicycles could be a significant in alleviating traffic congestion. However, the inaccuracy of current transit ridership forecasting methods is imposing a negative impact on the development of urban transit systems. Traffic Analysis Zone (TAZ) delineating is a fundamental and essential step in ridership forecasting, existing delineati...

The studies carried out with the objective of minimizing the effects of congestion, delay and environment problems on the transportation network have gained increasing importance in the last years. Among these studies, short-term traffic flow and average vehicle speed forecasting methods have come into prominence due to their easy implementations, efficient usage on different areas and cost-eff...

Forecasting the flow of crowds is of great importance to traffic management and public safety, and very challenging as it is affected by many complex factors, including spatial dependencies (nearby and distant), temporal dependencies (closeness, period, trend), and external conditions (e.g. weather and events). We propose a deep-learning-based approach, called ST-ResNet, to collectively forecas...

Active traffic management aims to dynamically manage congestion based on existing and predicted traffic conditions. A challenge in this is that it is not usually possible to process data in real-time and use the output in control algorithms or in traveler information systems. A solution to this is to predict the traffic state based on assessments of current and past measurements. The work descr...

This paper addresses the problem of determining the number and placement of signals on traffic networks for long-range urban travel forecasting. An algorithm for determining the signalization strategy was developed and given a large-scale test on the network for a metropolitan area of about 150,000 people. The algorithm attempts to mimic the actions of traffic engineers as they make adjustment ...