Dynamic Scheduling of Traffic Signal (DSTS) Management in Urban Area Network
19 Sep 2019-pp 132-143
TL;DR: This work proposes an approach to optimize the timing of traffic light dynamically by using scheduling algorithm to reduce the congestion in various junction points in urban area network by using V2I connectivity system via road side unit (RSU).
Abstract: Nowadays vehicular ad hoc network (VANET) is a promising area of research. One of the aspects of this area is traffic congestion control. Due to the limited capacity of road networks, road traffic congestions are becoming a vital problem in most of the metropolitan cities or large cities throughout the world. That creates the chances of casualties and other types of losses related to time, fuel, finance etc. Congestion also causes a considerable amount of pollution. In this paper, we concentrate on traffic light scheduling in the intersection or junction point of road network for congestion control. We propose an approach to optimize the timing of traffic light dynamically by using scheduling algorithm to reduce the congestion in various junction points in urban area network. The proposed mechanism is used for connected intersection system where every objects and traffic lights will be connected with each other and can share information. We use V2I connectivity system via road side unit (RSU) for our methodology. The Traffic Management controller (TMC) is able to collect the traffic related information of an intersection from RSU. Several researchers worked on this problem. But the performance of the proposed method is simulated and the results show that the proposed method performing better in terms of queue length and the waiting time of vehicle in intersection area with respect to other methodologies.
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TL;DR: This work explores the vision of edge intelligence for optimal urban traffic monitoring, and proposes a scheme that effectively reduces the estimation error of network tomography compared to common approaches with either uniform or random strategy.
Abstract: Efficient urban traffic monitoring is a key enabler for intelligent planning and management of modern cities. Network tomography can monitor the urban traffic with a comparably small number of traffic detectors like cameras, and has become an appealing technique for urban traffic management. However, previous work on network tomography based traffic monitoring focuses primarily on developing estimators using the given end-to-end travel time measurements, while the design of data collection for efficiently distributed collecting and processing the raw monitoring videos to such measurements is often neglected. We fill this gap by exploring the vision of edge intelligence for optimal urban traffic monitoring, and tackle the following two problems in regard of limited telecommunications resources: 1) when the total number of monitoring videos that are successfully processed into the end-to-end travel time measurements is pre-bounded, we employ a Fisher Information Matrix (FIM) to help determine the best quota scheme for the monitoring videos that each traffic detector need to generate and 2) when the centralised processing of monitoring videos alone is insufficient, we make use of the computation capabilities from these edge devices, i.e., traffic detectors, and employ a multi-agent reinforcement learning approach to help them conduct intelligent computation offloading individually. Extensive simulations demonstrate that our proposed scheme effectively reduces the estimation error of network tomography compared to common approaches with either uniform or random strategy.
20 citations
01 Sep 2022
TL;DR: A trajectory deviation point embedding and deep clustering method for outlier detection and LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures.
Abstract: Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased F-measure by 5.06% and 2.4% while compared with generic Node2Vec and Struct2Vec methods.
10 citations
TL;DR: In this article , a trajectory deviation point embedding and deep clustering method for outlier detection is proposed, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres.
Abstract: Cooperative Intelligent Transport Systems (C-ITS) are emerging in the field of transportation systems, which can be used to provide safety, sustainability, efficiency, communication and cooperation between vehicles, roadside units, and traffic command centres. With improved network structure and traffic mobility, a large amount of trajectory-based data is generated. Trajectory-based knowledge graphs help to give semantic and interconnection capabilities for intelligent transport systems. Prior works consider trajectory as the single point of deviation for the individual outliers. However, in real-world transportation systems, trajectory outliers can be seen in the groups, e.g., a group of vehicles that deviates from a single point based on the maintenance of streets in the vicinity of the intelligent transportation system. In this paper, we propose a trajectory deviation point embedding and deep clustering method for outlier detection. We first initiate network structure and nodes’ neighbours to construct a structural embedding by preserving nodes relationships. We then implement a method to learn the latent representation of deviation points in road network structures. A hierarchy multilayer graph is designed with a biased random walk to generate a set of sequences. This sequence is implemented to tune the node embeddings. After that, embedding values of the node were averaged to get the trip embedding. Finally, LSTM-based pairwise classification method is initiated to cluster the embedding with similarity-based measures. The results obtained from the experiments indicate that the proposed learning trajectory embedding captured structural identity and increased F-measure by 5.06% and 2.4% while compared with generic Node2Vec and Struct2Vec methods.
7 citations
TL;DR: In this paper , the authors proposed a fuzzy contrast-based model that identifies deviation points using the deep network for weighted position nodes and extracted the embedding of the contrast set using a pairwise classification approach based on similarity metrics.
Abstract: Cooperative intelligent transportation systems (ITS) are on the rise in the field of transportation. The trajectory-based knowledge graph enables the ITS to have semantic and connectivity capabilities. This article presents the approach of embedding trajectory deviation points and deep clustering. We constructed the structural embedding by maintaining the relationship between the nodes based on the network structure and the neighbors of the nodes. This approach was then used to learn the latent representation based on the deviation points in the road network structure. We generated a set of sequences using a hierarchical multilayer network and a biased random walk. This research proposes a fuzzy contrast-based model that identifies deviation points using the deep network for weighted position nodes. This sequence is used to fine-tune the embedding of the nodes. We then averaged the embedding values of the nodes to obtain the travel embedding. Next, we extracted the embedding of the contrast set using a pairwise classification approach based on similarity metrics. Numerical studies show that the proposed learning trajectory embedding approach successfully captures the structural identity and outperforms competing strategies. The deep contrast set approach enables highly accurate detection of outliers in the trajectory and deviation locations.
3 citations
TL;DR: In this article , the authors proposed a fuzzy contrast-based model that identifies deviation points using the deep network for weighted position nodes and extracted the embedding of the contrast set using a pairwise classification approach based on similarity metrics.
Abstract: Cooperative intelligent transportation systems (ITS) are on the rise in the field of transportation. The trajectory-based knowledge graph enables the ITS to have semantic and connectivity capabilities. This article presents the approach of embedding trajectory deviation points and deep clustering. We constructed the structural embedding by maintaining the relationship between the nodes based on the network structure and the neighbors of the nodes. This approach was then used to learn the latent representation based on the deviation points in the road network structure. We generated a set of sequences using a hierarchical multilayer network and a biased random walk. This research proposes a fuzzy contrast-based model that identifies deviation points using the deep network for weighted position nodes. This sequence is used to fine-tune the embedding of the nodes. We then averaged the embedding values of the nodes to obtain the travel embedding. Next, we extracted the embedding of the contrast set using a pairwise classification approach based on similarity metrics. Numerical studies show that the proposed learning trajectory embedding approach successfully captures the structural identity and outperforms competing strategies. The deep contrast set approach enables highly accurate detection of outliers in the trajectory and deviation locations.
2 citations
References
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TL;DR: Two secure intelligent traffic light control schemes using fog computing whose security are based on the hardness of the computational DiffieHellman puzzle and the hash collision puzzle are proposed, which can avoid the problem of single-point failure and is fog device friendly.
135 citations
01 Jan 2015
TL;DR: Different techniques and algorithms used in both infrastructure-based and infrastructure-free density estimation methods are explored, and the main features, limitations and critical issues for each density estimation method are depicted.
Abstract: Nowadays, vehicular Ad hoc Networks (VANETs) are gaining enormous research interest. Even though the leading reason for developing VANETs is traffic safety, many applications such as traffic status monitoring, road traffic management, routing and distribution of data, have been considered. Road traffic density estimation provides important information for such applications. Various infrastructure-based mechanisms were proposed to estimate traffic density relying on vehicle detection devices that require pre-deployment. However, as utilizing wireless communication capabilities in vehicles is developing rapidly, more accurate and real-time infrastructure-free density estimation methods are emerging day-to-day. Some of the infrastructure-free methods depend mainly on statistical estimations, while others are based on VANET communications and traffic flow information. In addition, clustering and group members counting are the main characteristic of a third type of infrastructure-free methods. This review paper explored different techniques and algorithms used in both infrastructure-based and infrastructure-free density estimation methods. Furthermore, the main features, limitations and critical issues for each density estimation method are depicted.
106 citations
TL;DR: The ITLC algorithm reduces, at each isolated traffic light, the queuing delay and increases the traffic fluency by 30% compared with the online algorithm (OAF) traffic light scheduling algorithm, and the ATL controlling algorithm decreases the average delay at each traffic light by 10%.
Abstract: In this paper, we propose an intelligent traffic light controlling (ITLC) algorithm. ITLC is intended to schedule the phases of each isolated traffic light efficiently. This algorithm considers the real-time traffic characteristics of the competing traffic flows at the signalized road intersection. Moreover, we have adopted the ITLC algorithm to design a traffic scheduling algorithm for an arterial street scenario; we have thus proposed an arterial traffic light (ATL) controlling algorithm. In the ATL controlling algorithm, the intelligent traffic lights installed at each road intersection coordinate with each other to generate an efficient traffic schedule for the entire road network. We report on the performance of ITLC and ATL algorithms for several scenarios using NS-2. From the experimental results, we infer that the ITLC algorithm reduces, at each isolated traffic light, the queuing delay and increases the traffic fluency by 30% compared with the online algorithm (OAF) traffic light scheduling algorithm. The latter algorithm achieved the best performance when compared with the OAF traffic light scheduling algorithm. On the other hand, the ATL controlling algorithm increases the traffic fluency of traveling vehicles at arterial street coordinations by 70% more than the random and separate traffic light scheduling system. Furthermore, compared with the previously introduced traffic scheduling ART-SYS, the ATL controlling algorithm decreases the average delay at each traffic light by 10%.
99 citations
TL;DR: A dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection, to consider the presence of emergency vehicles.
Abstract: Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles' efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.
70 citations
TL;DR: A taxonomy of adaptive traffic signal control strategies achieved through various levels of vehicular communications is presented, as the prevalence of smartphones has suggested supplementing legacy traffic monitoring with traffic-related reports submitted by the driving public.
66 citations