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Congestion in large and growing cities is a significant issue that harms the economy, travelers, and the ecosystem. Forecasting the degree of congestion on a road network in advance can help to avoid it and boost the network's capacity and performance. Despite its importance, however, traffic congestion prediction is not a popular topic among researchers and traffic engineers. It's because there aren't enough effective computational traffic forecast techniques or high-quality citywide traffic data. In the existing works, there are several Artificial Intelligence (AI) models are developed for the prediction of traffic congestion in the transport networks. Yet, it facing the problems and challenges relevant to the following parameters: ineffective data handling, prediction error, high time for training and testing operations. Therefore, the proposed work aims to implement a novel AI model for accurately forecasting the traffic congestion in the transportation networks. Here, the Cascaded Transition Recurrent Feature Network (CTRFN) technique is used to extract the best and useful features related to congestion from the traffic dataset. After that, the Paramount Transfer Learning Network (PTLN) model is deployed to predict the traffic congestion based on the extracted features. During this process, the Coherent Lizard Search Optimization (CLSO) algorithm is used to compute the weight factor for constructing the covariance matrix, which supports to obtain the reduced prediction error outputs. Moreover, the congestion prediction performance of the proposed model is validated and compared by using different measures. [ABSTRACT FROM AUTHOR]

• Develop a novel two-layer coordinated multi-agent reinforcement learning algorithm. • Synergize traffic signals through local cooperation and global coordination. • Optimization of vehicle emissions and traffic congestion as simultaneous goals. • Proposed method is performed in synthetic network and Cologne urban network on SUMO. Intersection traffic signal control considering vehicle emissions has become an important topic, however, the decision complexity of traffic signal control increases dramatically in a dynamic traffic environment with multi-intersections. It is a severe challenge to coordinate traffic signals at multi-intersections based on Internet of Things information to improve the traffic condition of the road network. This paper proposes a two-layer coordination algorithm based on multi-agent reinforcement learning—Multi-agent Coordinated Policy Optimization (MACoPO), for solving traffic signal control at multi-intersections. MACoPO consists of local cooperation, which adjusts the weights of individual rewards and neighborhood agents' rewards by using local cooperation factors (LCF), and global coordination, which updates the LCF to maximize global rewards. The state and reward functions are designed in terms of the current state of the signal, waiting queue length, vehicle density and emission concentration in the lane, vehicle delay, and vehicle emissions, thus making full use of the intersection state information. The proposed method is extensively assessed through simulation experiments using artificial and real road networks and the numerical results confirm its effectiveness in complex and dynamic real-time traffic environments with multi-intersections. [ABSTRACT FROM AUTHOR]

Highlights • Greener aspects of routing were not well explored in recent pheromone schemes. • Eco-friendly Pheromone-based Green Vehicle Routing is proposed to reduce fuel burnt. • This is achieved by assigning paths with multiple green signalized intersections. • The proposed strategy can be implemented through a decentralized Multi-Agent system. Abstract The significant increase in vehicle numbers leads to adverse traffic congestion. Existing pheromone-based vehicle routing randomly assigns an alternative route to vehicles based on global distance and local pheromone intensity. Although urban congestion is reduced as vehicles are assigned paths with lower pheromone intensity, frequent stops due to numerous red signalized intersections within the paths can lead to high fuel consumption. To address this issue, a proactive Eco-friendly Pheromone-based Green Vehicle Routing (E-PGVR) strategy is proposed to prioritize the greener aspects of routing by reducing fuel consumption in addition to traffic congestion. First, paths with multiple green signalized intersections are assigned to increase the chance for vehicles to traverse multiple intersections with fewer stops, marking down fuel consumption. Second, the application of local dynamic information namely traffic light time, mean road speed, and predicted pheromone intensity encourages the implementation of a decentralized Hierarchical Multi-Agent Pheromone-based System (HMAPS). Third, a modified dynamic k-shortest path algorithm is proposed in the path assignment to reduce computational effort. Forth, Singapore traffic data that incorporates heterogeneous vehicle type is employed in the microscopic simulator (SUMO) to model real-world traffic scenario. Experiment results show that the proposed E-PGVR outperforms other approaches in reducing fuel consumption and CO 2 by 21.2%, mean waiting time by 30.0%, number of congested roads by 56.9%, mean travel time by 26.3%, and increasing number of arrived vehicles by 45.5%. [ABSTRACT FROM AUTHOR]

An innovative idea of vehicle tracking for video-based intelligent traffic management system is known to bring significant socioeconomic impact. A successful vehicle tracking method is always in demand to monitor different traffic parameters such as the average speed, strange movements, and congestion of vehicles or even to detect accidents automatically on highways or freeways. The challenges of traditional video-based vehicle tracking methods include the initialization of tracking to tackle an unknown number of targets and the reduction of the drift sensitivity of targets from true positions mainly caused by the variations in lighting condition, occlusions and camera position. To address these challenges, this paper presents a novel vehicle tracking method for a traffic management system that introduces the multiple time-spatial images (MTSIs)-based detection in the stochastic filter-based tracking. The MTSI-based tracking employs the concept of multiple numbers of key vehicular frames (KVFs) for each of the vehicular-objects in the traffic. These KVFs provide highly accurate positional information of the vehicles due to the fact that the shape and texture of the vehicles are comparable on the same scale and do not depend on the speed of the traffic. The spatial correspondence of a vehicle in successive KVFs is then incorporated as a low-complexity data association technique to alleviate the common problem of drifting in the stochastic filter-based method and thereby increasing the accuracy in tracking trajectory. Comprehensive experimentations are carried out using two publicly available video databases (EBVT and GRAM-RTM) that have traffics of varying environments to evaluate the vehicle tracking performance of the proposed method as compared to the existing methods. Experimental results demonstrate that the introduction of MTSIs not only automates the initialization of tracking, but also significantly increases the accuracy of the tracking trajectories of the vehicles on roads evaluated both in the presence and absence of ground truths. [ABSTRACT FROM AUTHOR]

Highlights: [•] We model the traffic density conduction situation intuitively. [•] We use temporal-spatial time related rule mining methods with conduction rules. [•] The obtained model chooses route avoiding the road have congestion in future, and aims to avoid the congestion conduction. [Copyright &y& Elsevier]

Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adjust the signal phasing and timings for different traffic movements at a signalized intersection with ASCS. The accuracy of the queue length prediction model varies based on many factors, such as the stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather, and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and saturated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to develop short-term queue length prediction models for signalized intersections that can be leveraged by adaptive traffic signal control systems using six variations of Grey systems: (i) the first-order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst model (GVM), (iv) GVM with Fourier error corrections (EGVM), (v) the Grey model with cosine term (GM(1,1 | c o s (ω t) or GMC), and (vi) GMC with Fourier error corrections (EGMC). The efficacy of the Grey models is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they can adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with adaptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models were compared with linear, nonlinear time series models, Adaboost (ADA), Bagging (BAG), Gradient Boost (GB), Neural Network (NN), Random Forest (RF), Support Vector Regression (SVR), and Long Short Term Memory (LSTM) models. Based on our analyses, we found that GMC can produce better or competing performance compared to more complex (i.e., LSTM and NN models) in predicting average and maximum queue lengths in terms of root mean squared and mean absolute errors. GMC model needs only 4 data values and can be used in real-time as the computational time to generate a prediction in less than 0.001 s. • Six Grey System theory models for queue length prediction studied at traffic arterials. • The Grey Cosine models demonstrated better accuracy than other compared models. • The Cosine model with Fourier error correction demonstrates the best accuracy. • Grey models do not require a large amount of data or computational time. • Grey models are compared to machine learning methods. [ABSTRACT FROM AUTHOR]