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Traffic control through video/image processing is a trending research topic within Intelligent Transportation Systems. The number of moving vehicles, the density of vehicles at the junctions, traffic anomalies, and traffic flow directly influence real-time traffic congestion. The primary goal of this work is to design an architecture that is simple and fast enough to use in real-time heterogeneous traffic scenes. The proposed methodology uses user-defined zones for determining vehicle occupancy and count based on traffic surveillance video. The angular integral projection function accompanies the background subtraction method for better localization. Traffic anomaly detection is implemented by incorporating intelligent sensor technology. Also, the skipping of frames at fixed intervals has increased the processing speed with minimal data loss. The compatibility with any Internet Protocol camera further distinguishes this approach from other state-of-the-art methods. The proposed algorithm has been tested on a publicly available traffic surveillance video dataset and real-time surveillance feeds. Experimental results from the real-time intersection scenes show an overall accuracy of 97.14% and an average processing speed of 92.91%. The accuracy of the detection module in the custom-made dataset is 98.73%. [ABSTRACT FROM AUTHOR]

Efficient traffic signal management is crucial for regulating traffic flow and fostering sustainable development within road transportation systems. To address the challenges in traffic management, numerous studies have applied the Adaptive Traffic Signal Control (ATSC) technology, using Deep Reinforcement Learning (DRL) to decrease vehicles' average waiting times. Nonetheless, the intricate nature of DRL, characterized by its extensive parameter connections, often complicates the assurance of real-time responsiveness. Additionally, by prioritizing reduced waiting times, these methods may overlook potential rises in queue lengths, risking congestion. In this paper, we propose an Efficient Double Broad Reinforcement Learning (E-DBRL) algorithm based on a Double Broad Q-Network (Double BQN) to alleviate the overestimation of action values common in Broad Reinforcement Learning (BRL). To enhance the Quality of Experience (QoE) of drivers, we develop a new reward function that optimizes the average waiting time and the range between the longest and shortest waiting times, thus avoiding the need for dimension normalization. Moreover, we conduct simulation experiments using actual traffic data collected from Hangzhou, China. The experimental results indicate that, compared to the traditional Double DQN, the proposed E-DBRL algorithm achieves a 45.78% reduction in the average training time per round and a 5.57% increase in the average rewards. [ABSTRACT FROM AUTHOR]

Traffic lights are crucial for urban traffic management, as they significantly impact congestion reduction and travel safety. Traditional methods relying on hand-crafted rules and operator experience are limited in their ability to adapt to changing traffic environments. To address this challenge, we have been exploring intelligent traffic light control using deep reinforcement learning techniques. However, current approaches often suffer from inadequate training data and unstable training processes, leading to suboptimal performance and real-world consequences. In this study, we propose RELight, a novel random ensemble reinforcement learning-based traffic light control framework. RELight effectively utilizes collected empirical data, ensuring a stable and efficient training process. To evaluate the performance of our proposed framework, we conducted a comprehensive set of experiments on a variety of datasets, including four synthetic datasets and a real traffic dataset collected from surveillance cameras at an intersection in Hangzhou, China. The results show that RELight outperforms existing approaches, demonstrating its superiority and potential for practical traffic light control applications. [ABSTRACT FROM AUTHOR]

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Cooperation between intersections in large-scale road networks is critical in traffic congestion. Currently, most traffic signals cooperate via pre-defined timing phases, which is extremely inefficient in real-time traffic scenarios. Most existing studies on multi-agent reinforcement learning (MARL) traffic signal control have focused on designing efficient communication methods, but have ignored the importance of how agents interact in cooperative communication. To achieve more efficient cooperation among traffic signals and alleviate urban traffic congestion, this study constructs a Graph Cooperation Q-learning Network Traffic Signal Control (GCQN-TSC) model, which is a graph cooperation network with an embedded self-attention mechanism that enables agents to adjust their attention in real time according to the dynamic traffic flow information, perceive the traffic environment quickly and effectively in a larger range, and help agents achieve more effective collaboration. Moreover, the Deep Graph Q-learning (DGQ) algorithm is proposed in this model to optimize the traffic signal control strategy according to the spatio-temporal characteristics of different traffic scenes and provide the optimal signal phase for each intersection. This study also integrates the ecological traffic concept into MARL traffic signal control, which aims to reduce traffic exhaust emissions. Finally, the proposed GCQN-TSC is experimentally validated both in a synthetic traffic grid and a real-world traffic network using the SUMO simulator. The experimental results show that GCQN-TSC outperforms other traffic signal control methods in almost all performance metrics, including average queue length and waiting time, as it can aggregate information acquired from collaborative agents and make network-level signal optimization decisions. [ABSTRACT FROM AUTHOR]

Crowd flow prediction is one of the most remarkable issues in a wide range of areas, from traffic control to public safety, and aims to forecast the inflow and outflow of crowds in each region of a city. Most existing studies adopt CNN and its variants to discover the spatial patterns of grid maps (each grid represents a region) while ignoring the correlation between distant regions that may share similar temporal patterns. In this paper, we propose a gnn-based prediction method, called STGs, for crowd flow prediction, which jointly constructs spatial and temporal graphs from grid maps and then implements graph neural networks to directly capture the relationship between regions. Additionally, we introduce a gated fusion mechanism to combine spatial and temporal embedding from the corresponding graph, which further improves the performance of our STGs. We conduct numerical experiments to compare STGs with other baseline models using two real-world datasets, BikeNYC and TLC. Experimental results demonstrate the superiority of our STGs model; specifically, our model reduces the mean absolute error (MAE) of crowd flow prediction by approximately 7-8% compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Short-term traffic flow forecasting is a key problem in the area of intelligent transportation systems (ITS). Timely and accurate traffic state prediction is also the prerequisite of realizing proactive traffic control and dynamic traffic assignment effectively. In this paper, a new hybrid model for short-term traffic flow forecasting, which is built based on multifractal characteristics of traffic flow time series, is proposed. The hybrid model decomposes traffic flow series into four different components, namely a periodic part, a trend part, a stationary part and a volatility part, to unearth the traffic features hidden behind the data. Four parts are treated and modeled separately by using different methods, such as spectral analysis, time series and statistical volatility analysis, to further explore the underlying traffic patterns and improve forecasting accuracy. Performance of the proposed hybrid model is investigated with traffic flow data from freeway I-694 EB in the Twin Cities. The experimental results indicate that the proposed model outperforms in capturing nonlinear volatility and improving forecasting accuracy than traditional forecasting methods, especially for the multi-step ahead forecasting. Compared with the ARIMA-GARCH model, it gets an improvement of 8.23% in RMSE for one-step ahead forecasting and 10.69% for ten-step ahead forecasting. It is better than the hybrid model newly proposed in literature (Zhang et al. Transp Res Part C: Emerg Technol 43(1):65-78 2014) and gets an improvement of 1.27% in forecasting accuracy. [ABSTRACT FROM AUTHOR]

As vehicular networking has recently been developed and commercialized, vehicular cloud computing has received much attention in various research areas, such as intelligent transportation systems and vehicular ad hoc networks. An efficient intersection traffic control using vehicular cloud computing is one of the key research topics in intelligent transportation systems. To efficiently deal with intersection traffic control via vehicle-to-vehicle communications, we design a distributed mutual exclusion algorithm that does not rely on broadcast, which introduces communication overheads; instead, our algorithm use point-to-point messages sent between the vehicles to keep network traffic load lower. In our algorithmic design, to pass an intersection, the lead vehicle on a lane must get permissions from a subset of other vehicles and its following vehicles on the same lane can follow the lead vehicle without permissions unlike the previous research. To evaluate the performance of our distributed mutual exclusion algorithm, we conduct extensive experiments. The results show that our algorithmic design is both effective and efficient. [ABSTRACT FROM AUTHOR]

Purpose: Road traffic incidents cause delay, affect public safety and the environment. The CEDR PRIMA project aims to extend practical guidance for traffic managers in pro-active Traffic Incident Management (TIM) techniques to reduce the impacts and associated costs of incidents. Methods: The paper describes modelling methods used in the project for assessing the effect of different management techniques on incident duration and travel delay under various scenarios, including collision, adverse weather, heavy vehicle breakdown and other obstruction, assuming various management strategies and generic impacts of novel technologies. Macroscopic simulations of 178 variations of 13 basic scenarios have been performed using a flexible and computationally efficient macroscopic queue model, results being verified by simulation using a velocity-based Cell Transmission Model (CTM-v). Results: The results of the two modelling methods are broadly consistent. While delays estimated by the two methods can differ by up to 20%, this is small compared to the factor of 30 range of modelled delays caused by incidents, depending on their nature and circumstances, and is not sufficient to affect general conclusions. Under the peak traffic conditions assumed, the most important factor affecting delay is whether running lanes can be kept open, but quick clearance of carriageway is not always feasible. Conclusions: Comparison of two very different modelling methods confirms their consistency within the context of highly scenario-dependent results, giving confidence in the results. Future research and data needs include further validation of the models, potential application to traffic flow and conflict prediction and incident prevention, and more complete and consistent recording of incident timelines and impacts. [ABSTRACT FROM AUTHOR]

In this study, dynamic traffic control strategies, namely dynamic ramp metering and dynamic speed limit control, have been examined through microscopic traffic simulation based on site measurements. In this context, the traffic flow data at a particular highway intersection have been analyzed to determine the pattern of the traffic. Then, the traffic model has been built in a traffic micro-simulation software and calibrated with the field data. The foci of the study are to measure the efficiency of the dynamic traffic control strategies and to compare it with the uncontrolled case considering various performance indicators such as total travel time, average delay time per vehicle, and average number of stops per vehicle. For the dynamic ramp metering strategies, the ALINEA (Asservissement Lineaire d'Entree Autoroutiere - French for Linear Utilization for Highway Entrances) control algorithm is implemented with different fixed-time cycle lengths. It has been observed that various ramp metering implementations decreased the average delay time per vehicle up to 30%. The dynamic speed limit control strategies are set according to the occupancy rates that are measured at the bottleneck downstream. The examined speed limit control strategies decreased the average delay time per vehicle to around 7%. The results also revealed that the implemented dynamic traffic control strategies help alleviate congestion by increasing the capacity of the bottleneck section. [ABSTRACT FROM AUTHOR]

Dynamic traffic assignment (DTA) is at the heart of much ITS research. Assigning traffic, whether for planning purposes or for real time route guidance, is a difficult problem. Recent advances in user-optimal dynamic traffic assignment have built on the methods developed for static user-optimal assignments. Since assignment models are complex, they will not use many of the network analysis functions found in commercial GIS packages. Custom software will have to be developed. In this paper we explore the problems faced in solving static and dynamic assignments and relate those problems to information that is likely to be based in a traffic control center’s GIS database. Because of the size of the problem and the need for faster-than-real-time analysis, how and when data is transferred between a GIS to analysis modules is important. Further, many approaches for GIS software design and spatial data handling, such as OOP and dynamic segmentation, may impose too much overhead to be of much use in time-sensitive ITS applications. [ABSTRACT FROM AUTHOR]

Effective path finding has been identified as an important requirement for dynamic route guidance in Intelligent Transportation Systems (ITS). Path finding is most efficient if the all-pair (shortest) paths are precomputed because path search requires only simple lookups of the precomputed path views. Such an approach however incurs path view maintenance (computation and update) and storage costs which can be unrealistically high for large ITS networks. To lower these costs, we propose a Hierarchical Path View Model (HPVM) that partitions an ITS road map, and then creates a hierarchical structure based on the road type classification. HPVM includes a map partition algorithm for creating the hierarchy, path view maintenance algorithms, and a heuristic hierarchical path finding algorithm that searches paths by traversing the hierarchy. HPVM captures the dynamicity of traffic change patterns better than the ITS path finding systems that use the hierarchical$A$ * approach because: (1) during path search, HPVM traverses the hierarchy by dynamically selecting the connection points between two levels based on up-to-date traffic, and (2) HPVM can reroute the high-speed road traffic through local streets if needed. In this paper, we also present experimental results used to benchmark HPVM and to compare HPVM with alternative ITS path finding approaches, using both synthetic and real ITS maps that include a large Detroit map (> 28,000 nodes). The results show that the HPVM incurs much lower costs in path view maintenance and storage than the non-hierarchical path precomputation approach, and is more efficient in path search than the traditional ITS path finding using $A$* or hierarchical $A$* algorithms. [ABSTRACT FROM AUTHOR]

Introduction: Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Purpose: Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field. Methods: In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper. Conclusion: Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas. [ABSTRACT FROM AUTHOR]