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Developing countries such as India face many problems when it comes to the management of existing traffic system and its inefficient planning and scheduling leads to traffic congestion, increased pollution level and transit delays. This requires a significant amount of attention to build a smart traffic management system. Taking this into consideration a framework is proposed to determine traffic density and use the corresponding data to further modulate the traffic signal proficiently. This will be carried out by applying two different methodologies namely Background Subtractor MOG and TensorFlow Object Detection API. A video from surveillance camera is taken as input which will be converted into frames using OpenCV and after processing the detected vehicles are counted, thus the density is obtained. This is followed by comparing the results obtained by the mentioned methodologies. A vital application of the determined traffic densities can be in controlling traffic signals in a smarter way, therefore leading in decreased traffic congestion and uniform flow of traffic. [ABSTRACT FROM AUTHOR]

The main instrument for urban traffic control is the traffic light settings, which is applied in this research in order to improve the traffic dynamics. The goal is to develop a model for urban traffic control with bigger outgoing traffic flows than ingoing ones in order to be decreased the vehicle queues at crossroads. As a difference from the usual practice of using only one control influence of the traffic light settings, here two control settings - traffic light cycles and green light durations are implemented as control influences of an arterial network of five crossroads. The implementation of this approach is on the basis of the bi-level optimization methodology. It integrates two goal functions (of both hierarchical levels), more variables and constraints in comparison with the one-level optimization. The optimization problems of the bi-level optimization have been defined. The bi-level solutions have been compared with the one-level optimization ones. The numerical simulations give preference to bi-level optimization. [ABSTRACT FROM AUTHOR]

A formal model of urban traffic management in a network of four intersections is developed. The aim of the study is to reduce the queues of cars in front of traffic lights and increase the outflow of cars from each intersection in the network. The methodology used for the formalization is the application of bi-level optimization. It integrates and coordinates more goal functions, constraints, and optimization variables. This allows greater closeness between the developed model and reality. For the bi-level model, the optimization problems for the two hierarchical levels are defined. An additional logical modification is applied in the optimization algorithm. It allows synchronizing the traffic control in the network. The presented numerical results of the simulations are compared with the solutions of a classical nonlinear problem (one-level problem). The comparisons give preferences to the bi-level optimization. [ABSTRACT FROM AUTHOR]

Traffic control is crucial as modern cities experience regular jams, air pollution peaks and waste of time. In this paper we present a flexible and fast approach toward optimizing city traffic with the support of available monitoring cameras, intelligent video processing algorithm and analysis of the vehicles passing by. We argue that by estimating economic impact of different optimization procedures and introducing real-time metrics on different points in the transportation network it is possible to perform a large-scale optimization and improvement of traffic with the use of available technology and minimal investments. We also suggest and test a simple technology for handling rotating cameras and automatically adjusting the tracking algorithms with regard to changes in the angle of view. [ABSTRACT FROM AUTHOR]

Rapidly rising vehicle count is a significant indicator of a country's cost-effective development. The increase in vehicle count results in increasing the traffic congestion. It leads to waste of electricity, time and contamination of the atmosphere. Due to the fact that traditional traffic lights have specific time intervals for transitioning from red to green phase, they cannot meet the demands of an overgrowing city. This is a big problem that most cities are dealing with despite efforts to alleviate and eliminate it. In recent years, traffic congestion has emerged as one of the most significant problems for engineers, planners, and politicians worldwide, not just in urban areas. Several attempts have been made to regulate traffic lights based on the density of vehicles on the road using Intelligent Transportation Systems (ITS). This paper compares and contrasts various sensor frameworks in terms of cost, reliability, accuracy, performance, and maintenance. The number of automobiles on the road has risen dramatically in recent years. Traffic Congestion is a rising issue that everybody deals with on a daily basis. Manual traffic control by traffic cops has not proven to be successful. The signal's fixed set time in all conditions (low and high traffic density) hasn't solved the issue. The use of the Internet of Things (IoT) is proposed as a model for effectively solving the problems described above. For internet-based computing, we use the cloud, which provides various resources such as server, storage, and application for traffic management. [ABSTRACT FROM AUTHOR]

For successful planning of traffic flows and road networks in an urban environment are very important information's about prior, current and planned information on traffic flows for an urban transport planner or traffic engineer. In the field of traffic engineering is one of most common approaches to research traffic flow distribution on base of travel behaviour covering connections, patterns and nodes. In the case of a disruption on the urban road network, different equilibriums are used for predict future behavior on in the area of influence of the road network in planning process. In order to plan the future induction and reduction of traffic flows in the area of the anticipated disruption of the road network, the planners rely on the declared data of the users of the disrupted network. The article deals with the aspect of comparing the measured values that occurred as a disturbance in the urban road network and the data obtained from drivers who used the same area of disturbance in the urban road network. Measured values where optained with automatic pressurize traffic counters in year 2015 when the first disruption on the observed network in urban area occurred meanwhile so called reported reviewed responses from user who used the same observed network was made via personal interviews in year 2018. Reported reviewed responses are also equipped with the emotional response of drivers/users and separated by common characteristics such as gender, age, status, origin and purpose. In this way, the engineering approach of obtaining data at counting points and the sociological approach are connected with the obtained statements on the traffic behavior of drivers in the past, present and future. It is also possible to compare the common characteristics of drivers who make similar decisions in response to a disruption in the transport network. Additionally, the results of the statements are accompanied by the emotional aspect of drivers getting used to changes in traffic behavior as a result of disruption of the urban road network. The responses of the observed drivers showed that they roughly mimic the form of a graph of the general description of reactions to changes, where the satisfaction rating begins to rise over a certain period of time, but it cannot yet be fully claimed that emotional reactions directly influence the decision to use specific alternative road a disruption in the transport network. It was detected clear connections between the decisions made on alternative routes based on the common characteristics of the drivers and as well with measured values obtained with automatic pressurize traffic counters which used as validation. [ABSTRACT FROM AUTHOR]

The present paper focuses on features of application Macroscopic Fundamental Diagram (MFD) of traffic flow. One of the approaches is to use the MFD to develop urban traffic management algorithms. In our study we analysed traffic perimeter control scheme for the two-region inside city. In framework of investigation were collected traffic data on the boundaries of regions for obtaining MFDs parameters. The empirical results were using for evaluation stable and unstable conditions in case of dynamic perimeter control on the base of MFDs parameters. Combinations of traffic demand have been identified to ensure the effectiveness of two-region perimeter control. [ABSTRACT FROM AUTHOR]

The control object is the urban traffic of network of eight crossroad sections as a part of density populated area in Sofia. The goal is minimization of vehicle queue lengths in front of the junctions, which leads to faster driving, reducing the air and noise pollution and better ecological picture. The paper analyzes two types of modeling and optimizations, applied for the traffic control: classical optimization (one-level) and bi-level one. The bi-level optimization has the priority to co-ordinate the two-level hierarchical problem and to take into account the interconnections between the crossroad sections. It contains two goal functions, which are considered for the control process. The bi-level and classical one-level linear-quadratic optimizations are numerically applied for the traffic light control of urban network. The comparison gives preferences to the bi-level optimization, confirmed by numerical simulations. [ABSTRACT FROM AUTHOR]