Sharif Digital Repository

Estimation of travel time in transportation engineering has always been an important issue. So far, most of travel time studies have focused on freeways and highways, and very few research have been done on travel time estimation for arterials. In this study, by using shock wave concept, a probabilistic model is developed for estimating the travel time of a signalized arterials consisting of two pre-timed signalized intersections. This model can be used in both coordinated and non-coordinated signals. In this study, travel time and traffic volume data were collected for Fatemi Ave. in Tehran, Iran. By using these data, some parameters of VISSIM simulation package were calibrated to reflect... 

Nowadays, investigating the factors affecting traffic models from different aspects such as metropolitan planning according to the present conditions can help high-level decision-makers and also, at the micro-level, help the travelers to make appropriate decisions for scheduling affairs, route selection, and vehicle type selection. Given the importance of this topic, a framework will be presented in this study that will evaluate the impact of some identified factors such as travel distance, climate, and urban events, and then all these factors will be presented in mathematical formulas. In the end, based on the model, the travel time will be predicted. In this framework, gene expression... 

Our surrounding environment has valuable information about our life. We live in technological societies where we are leaving digital footprints in, continuously. Growing development and popularity of mobile phones, has turned them into the most important global sensors. Mobile phone tracking via GPS points is one of the data types which can be collected from these sensors and used for traffic monitoring, intelligence traffic control and travel time estimation.
In order to use these devices, we need to collect data, detect transportation mode and estimate travel time. By evaluation of previous works, we can feel the need to a comprehensive approach for detecting transportation mode of... 

Travel time estimation is a central issue in the urban transportation industry and is the basis of many analyses and services in businesses related to this area. In the past few years, various statistical approaches have been devised to solve this problem. The purpose of this dissertation is to review existing methods by focusing on segment-based approaches for urban travel time estimation. A big challenge is the small amount of data in hand compared to the size of the urban network. Exploring historical data and extracting correlation between urban network segments leads to modeling the urban traffic condition and travel time estimation in one specific time interval of the day  

Real-time travel time estimation is a major requirement in many transportation related systems. One of the main challeges is to estimate the traffic speed and then forecast it for a short time. A valuable data source for this task is instant location of moving cars that is captured using global positioning system (GPS) and sent through internet in online manner. The main problem is that the resulting traffic data is severely sparse and also contains a lot of noise. Previous researchs on this type of data are mostly based on matrix or tensor factorization. In this work it is shown that despite the large fraction of missing value it is possible to use neural network for this problem with some...