Identification of critical traffic conditions based on analysis of traffic variables in a route allows the planners to react timely under such conditions. In this paper, a Decision Support System (DSS) is proposed to determine traffic condition in a route based on the data traffic variables received online in a route such as speed and flow. A knowledge based system is established, which consists of a series of rules (If-Then) to identify the critical traffic conditions when the traffic variables in a route decrease or increase as compared with the previous means, significantly. Statistical control charts are used to identify significant deviations of traffic variables from their means, which are usually caused by events such as accidents. The data for traffic variables, namely flow and speed in 60 hours was collected from one of the main highways of Tehran (Iran’s Capital) in peak hours (17 and 18). 35 observations were used to design control limits for two traffic variables, namely flow and speed and the remaining 25 observations were used to monitor traffic conditions in the future (execution of phase 2 of statistical control charts). The results of traffic monitoring in the intended route indicate that the decision support system has a good performance to identify critical traffic conditions.

Due to the importance and complexity of the portfolio management issue, over 100 different techniques have already been presented. In general, the final result of these tools is to create a prioritized list of the projects that must be selected for allocating resources. The use of financial strategies may be misleading in some cases, and it is necessary to combine these methods with other methods such as strategic approaches in order to guarantee a balanced portfolio toward the organizational strategies. On the other, categorizing projects into different baskets allows the organizations to select, evaluate and prioritize the projects in a subset using a set of similar criteria and techniques. In this article, by choosing agriculture sector as a case study, an attempt has been made to study the evaluation, ranking and management of projects with investment classifying strategy of the projects using Delphi, TOPSIS and AHP methods. The results reveal that in similar cases we can use the presented model by determining the type of activity and investment and localization of the indexes.