Model-based analysis of traffic flow in signalized intersections

Automatic multi-vehicle tracking using video cameras: An improved CAMShift approach

In this paper, the model is simplified by proposing a decomposed queuing theory to multilane-multiple intersection. The concept of queuing theory looks promising due to its simplicity and its combination with standard approach techniques. This can expand the model to be more reliable in dealing with problem associated to multilane-multiple intersections. The proposed model is simple, flexible and fit to any real case study with different structure of topology. The traffic model is developed using discrete-event simulation (DES) framework for computation and analysis the model. Simulation results have shown good correlation between the proposed traffic model and data taken from real traffic situations, and have confirmed that the proposed modeling technique based on queuing theory and standard decomposition method can be realized. In addition, simulation results give a confirmation of the model capability to correctly predict traffic performance measures.

This paper presents stereo vision-based vehicle detection approach on the road using a road feature and disparity histogram. It is not easy to detect only vehicles robustly on the road in various traffic situations, for example, a nonflat road or a multiple-obstacle situation. This paper focuses on the improvement of vehicle detection performance in various real traffic situations. The approach consists of three steps, namely obstacle localization, obstacle segmentation, and vehicle verification. First, we extract a road feature from v-disparity maps binarized using the most frequent values in each row and column, and adopt the extracted road feature as an obstacle criterion in column detection. However, many obstacles still coexist in each localized obstacle area. Thus, we divide the localized obstacle area into multiple obstacles using a disparity histogram and remerge the divided obstacles using four criteria parameters, namely the obstacle size, distance, and angle between the divided obstacles, and the difference of disparity values. Finally, we verify the vehicles using a depth map and gray image to improve the performance. We verify the performance of our proposed method by conducting experiments in various real traffic situations. The average recall rate of vehicle detection is 95.5%.

Traffic congestion has been causing many setbacks and challenges in the major developing and developed smart cities in India and all over the world. The typical traffic light sequence works on the specific switching of Red, Green and Yellow lights in a particular way with stipulated time irrespective of peak traffic hour and situation, this makes typical traffic light sequence method inefficient for present situation where density of traffic is exponentially increasing over the time. In this work, we propose and develop a design of Intelligent Traffic Light Control System to manage the road traffic according to the real time traffic situation. This work is intended to propose methods of dynamic time allocation for traffic lights according to traffic density and situation. In this paper, we acquired real time traffic density using image sensors and obstacle detector, where according to the traffic density, traffic controller dynamically generates “ON” and “OFF” time of traffic light sequence. The EDGE Spartan 6 FPGA (Field Programmable Gate Array) development board is programmed as controller to implement the proposed work and MATLAB programming in used to capture real time traffic image and generate dynamic time for the traffic light sequence to control the traffic situation in real time.

The Riemann problem and a Godunov-type scheme for a traffic flow model on two lanes with two velocities

Image sequences recorded with cameras mounted in a moving vehicle provide information about the vehicle's environment which has to be analysed in order to really support the driver in actual traffic situations. One type of information is the lane structure surrounding a vehicle. Therefore, we systematically developed and investigated driver assistance functions which make explicit use of the lane structure represented by lane borders and lane markings. With increasing computing power of standard PCs it was possible to realize more complex driver assistance with general purpose hardware. Investigations with a video-based lane departure warning system and a lane change assistant for highways will be discussed in detail. We integrated our lane keeping assistant in some experimental cars and performed systematic experiments in real traffic situations which enables the experience of video-based driver assistance on a high level—the action of a system. This allows us to assess whether a driver assistance system really understands the actual traffic situation which is the basis for reliable systems accepted by the user.

A New Human-Computer-Interface for High-Speed-Maglev Train Traffic Supervision

Deciding the Behaviour of an Autonomous Mobile Road Vehicle

Сучасний прискорений розвиток техногенних виробничих структур призвів до росту концентрації шкідливих викидів та їх об'єму в екосередовище (ґрунт, воду, атмосферу), зріс рівень забруднення сіл, міст, цілих регіонів. Ускладнення технологічних процесів, ріст виробничих потужностей теплових електростанцій, транспорту, нафтогазової промисловості, у структуру яких входять енергоактивні об'єкти, ускладнив процеси управління, що призвело до зниження в певних галузях рівня безпеки їх функціонування та підвищило ризики аварій та катастроф. При цьому рівень ризиків аварій і техногенних катастроф залежить від багатьох факторів і компонент надійності систем: надійність і якість проектів техногенних систем, моделей і алгоритмів функціонування; якість конструкцій, агрегатів, комплектуючих, способу їх монтажу; відповідність побудованих структур до проектних вимог, методів їх налагодження та випробування для введення в експлуатацію; якість стратегій, алгоритмів опрацювання даних та прийняття управлінських рішень; якість підготовки (інженерної, знаннєвої, практичної…) виробничого й адміністративного персоналу, а також їхніх позитивних і негативних ознак; підготовка ресурсів для виконання виробничого процесу та їх якості; здатність протистояти ресурсним та інформаційним атакам на техногенну систему; здатність протистояти інформаційним та ментально-психологічним атакам на оперативно-керуючий персонал при прийнятті управлінських рішень. Усі ці аспекти оцінки ризиків мають як стратегічний, так й ігровий характер і визначають динаміку процесів у техногенних системах, а також рівень і характер впливу на екологічне середовище. Для вирішення цієї проблеми виділено, розв'язано та розроблено такі задачі та методи: визначено та оцінено актуальність проблеми мінімізації ризиків техногенних систем на екологічне середовище; проаналізовано літературні джерела, в яких розглядають цю проблему; сформульовано мету дослідження та методи розв'язання задач; проаналізовано причини і фактори виникнення конфліктних ситуацій як технічного, так й інформаційного характеру; проаналізовано і побудовано ігрові моделі стратегій управління; розроблено метод розв'язання конфліктів у техногенних системах; розроблено метод структуризації системи та її агрегації; розглянуто системну гру та спосіб її представлення; побудовано загальну схему взаємодії техногенних систем, які формують шкідливі викиди, з екологічним та соціальним середовищем, як основу вироблення координаційних стратегій екозахисту та технології глибинного перероблення відходів; виявлено нові техногенні характеристики та їхній характер і вплив на екологічне середовище.

We report on simulations of chaotic traffic flow in freeways based on a fuzzy knowledge based model of driver behavior. Our simulation approach is a microscopic method similar to Q. Yang and H.N. Koutsopoulos (1996), where individual vehicles are separately modeled. The knowledge base consists of an extensive and exhaustive set of fuzzy IF-THEN rules depicting all possible traffic situations that a driver is likely to encounter in a highway. The principal motivation behind using a fuzzy knowledge based approach to model the driver behavior is because fuzzy modeling provides an effective means to break up any highly nonlinear system into IF-THEN rules (J.M. Mendel, 1995). In addition, fuzzy logic is well equipped to handle uncertainties that are present in real world traffic situations (P. Chakraborty and S. Kikuchi, 1992; S. Kikuchi and M. Pursula, 1998). Therefore using fuzzy rules, it is possible to incorporate linguistic descriptions of scenarios such as 'speed is moderate' or 'adjacent lane gap is quite acceptable'. Instead of quantifying variables into crisp classes as in a traditional expert system, this is the manner in which a driver is more likely to perceive any situation. Fuzzy logic techniques are receiving recent attention as important tools in transportation engineering studies and such approaches have met with a great deal of success.

Successful design and efficient performance of future networks are closely related to the appropriate selection of traffic source models. The source model presented in this paper provides a simple way for characterizing the comportment of a telecommunication user recognizing the multilevel discipline of a telecommunication session. The traffic model is general enough to fit a variety of different services by appropriately varying the values of the parameters defining it but also it is simple enough because it is described by a small number of parameters and the defining parameters make a good intuitive sense by being closely related to actual physical quantities. Through careful selection of input parameters, the model capability to describe real traffic situations (e.g., LAN traffic) is demonstrated.

Comparison of road traffic emission models in Madrid (Spain)

On the determination of the aerodynamic coefficients of highway tunnels

In order to quantify the degree of influence of weather on traffic situations in real time, this paper proposes a terminal traffic situation prediction model under the influence of weather (TSPM-W) based on deep learning approaches. First, a feature set for predicting traffic situations is constructed based on data such as weather, traffic demand, delay conditions, and flow control strategies. When constructing weather data, a terminal area weather quantification method (TAWQM) is proposed to quantify various weather feature values. When constructing the traffic situation label, fuzzy C-means clustering (FCM) is used to perform cluster analysis on the traffic situation, and the traffic situation is marked as bad, average, or good. Accordingly, the multi-source data is fused as the input vector, based on the combined prediction model of convolutional neural network (CNN) and gated recurrent unit (GRU), TSPM-W is constructed. Finally, based on the historical operation data of the Guangzhou Baiyun International Airport terminal area, the proposed data set is used to predict the traffic situation time series at intervals of 1 h, 3 h, and 6 h. The comparative experimental results show that the proposed time series prediction model has higher prediction accuracy than other existing prediction methods. The proposed dataset is able to more accurately predict the traffic situation in the terminal area.

When dealing with traffic noise propagation over large distances, models assuming flat and homogeneous grounds and homogeneous atmosphere are not sufficient to have an accurate prediction. A first improvement was obtained by many authors who worked on new theoretical approaches individually dealing with the influence of mixed grounds, stratified atmosphere, noise barriers, etc. on sound propagation. Thus, many models were built. In order to predict sound pressure levels for complex conditions representative of realistic traffic noise situations, a model based on the resolution of the wave equation through a parabolic approximation has been developed. The problem is numerically solved for various conditions including mixed grounds, upward and downward refracting turbulent atmospheres, and the presence of noise barriers producing a variation of the sound celerity profile along the propagation path between the source and the receiver, over several hundred metres. The numerical approach is based on the split-step Padé technique mainly developed and recently improved by the acoustic group of the Ecole Centrale in Lyon, France [Chevret et al., J. Acoust. Soc. Am. 100, 3587–3599 (1996)]. Interesting comparative results with other theoretical approaches and experimental data have been obtained and will be widely developed in the paper.