Sharif Digital Repository

Finding the optimal location for sensors is a key problem in flow estimation. There are several location models that have been developed recently for vehicle identification (ID) sensors. However, these location models cannot be applied to large networks because there are many constraints and integer variables. Based on a property of the location problem for vehicle ID sensors, given the initial vehicle ID sensors that are pre-installed and fixed on the network, this article presents a solution that greatly reduces the size of this location problem. An applied example demonstrates that when 8% of the arcs from a real network that are randomly selected have a vehicle ID sensor, the reductions... 

Finding the optimal location for sensors is a key problem in flow estimation. There are several location models that have been developed recently for vehicle identification (ID) sensors. However, these location models cannot be applied to large networks because there are many constraints and integer variables. Based on a property of the location problem for vehicle ID sensors, given the initial vehicle ID sensors that are pre-installed and fixed on the network, this article presents a solution that greatly reduces the size of this location problem. An applied example demonstrates that when 8% of the arcs from a real network that are randomly selected have a vehicle ID sensor, the reductions... 

The traditional approach to origin-destination (OD) estimation based on data surveys is highly expensive. Therefore, researchers have attempted to develop reasonable low-cost approaches to estimating the OD vector, such as OD estimation based on traffic sensor data. In this estimation approach, the location problem for the sensors is critical. One type of sensor that can be used for this purpose, on which this paper focuses, is vehicle identification sensors. The information collected by these sensors that can be employed for OD estimation is discussed in this paper. We use data gathered by vehicle identification sensors that include an ID for each vehicle and the time at which the sensor... 

After several decades of work by several talented researchers, estimation of the origin–destination matrix using traffic data has remained very challenging. This paper presents a set of innovative methods for estimation of the origin–destination matrix of large-scale networks, using vehicle counts on links, partial path data obtained from an automated vehicle identification system, and combinations of both data. These innovative methods are used to solve three origin–destination matrix estimation models. The first model is an extension of Spiess’s model which uses vehicle count data while the second model is an extension of Jamali’s model and it uses partial path data. The third model is a... 

This paper presents an online highly accurate system for automatic number plate recognition (ANPR) that can be used as a basis for many real-world ITS applications. The system is designed to deal with unclear vehicle plates, variations in weather and lighting conditions, different traffic situations, and high-speed vehicles. This paper addresses various issues by presenting proper hardware platforms along with real-Time, robust, and innovative algorithms. We have collected huge and highly inclusive data sets of Persian license plates for evaluations, comparisons, and improvement of various involved algorithms. The data sets include images that were captured from crossroads, streets, and... 

This paper presents an online scale-invariant license plate detection (LPD) system with high accuracy for the automatic license plate recognition (ALPR) systems. A dataset of Persian plates is accumulated with more than 44,000 images of plates and 9000 frames of real world roads. For the plate detection and localization, a multi-stage classifier is trained with local binary pattern (LBP) features and a multi-scale algorithm to detect plates with any size within a frame. Besides, we proposed multiple algorithms to boost the performance and accuracy of our solution, including two-stage detection, background subtraction for non-moving areas elimination, and a sophisticated method for estimating...