Video based adaptive road traffic signaling
01 Oct 2013-pp 1-6
TL;DR: A video based adaptive traffic signaling scheme for reducing waiting period of vehicles at road junctions without detecting or tracking vehicles is proposed and found to be a much faster and effective control strategy.
Abstract: The ability to exert real time, adaptive control, of the transportation process is the core of an intelligent traffic system. We propose a video based adaptive traffic signaling scheme for reducing waiting period of vehicles at road junctions without detecting or tracking vehicles. The traffic signal timing parameters at a given intersection are adjusted automatically as functions of the local traffic conditions. The video sequences recorded at junctions are used for generating Spatial Interest Points (SIP) and Spatio-Temporal Interest Points (STIP). The traffic congestion at the junction is estimated using SIP and STIP. The decision rules are based on a definitive analogy between road traffic and computer data traffic wherein road vehicles are compared with data packets on the network. The system is similar in approach to the technique of Weighted Round Robin (WRR) queuing, a scheduling discipline used in data communication networks. Local traffic information is used to adjust the phase split keeping the cycle time constant. Two methods have been proposed. The first method, Optimal Weight Calculator (OWC), minimizes traffic at an intersection by determining the optimal phase splits or weights. The second method, Fair Weight Calculator (FWC), calculates weights relative to the road with minimum traffic to bring more fairness. After applying the respective algorithms mathematically on varying traffic conditions, OWC was found to be more equitable in the allocation of green time which is suitable for highly weight-sensitive junctions. For traffic with road priorities, FWC was found to be a much faster and effective control strategy.
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TL;DR: A traffic state network (TSN) is presented to model the relationship among all road sections based on big GPS data, which contains mass traffic information, and effectively describes the relationship of road sections in traffic state.
Abstract: Traffic camera has become a popular sensor for traffic state estimation in Intelligent Transportation Systems (ITS). However, it is impracticable to cover the whole urban network. Then how to place sensors in a certain network to ensure the accuracy for both installed and uninstalled roads is of great importance. The GPS-equipped taxis traveling on the urban roads yield big GPS data, which contains mass traffic information. In this paper, a traffic state network (TSN) will be presented to model the relationship among all road sections based on these data. In TSN, the location problem is transformed to find a set of nodes in the network to maximize the received information. Then, it is verified NP-hard, and a local optimal solution is presented to solve it in a linear complexity time based on greedy algorithm. The experiments were carried out on the GPS data from 8007 taxis traveling on 1432 sections for 197 days. The results show that the TSN effectively describes the relationship of road sections in traffic state, and performs better than random road location method and arterial road location method.
20 citations
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TL;DR: A new approach of traffic flow-based intelligent signal timing by temporally clustering optical flow features of moving vehicles using Temporal Unknown Incremental Clustering (TUIC) model is proposed and can achieve better average waiting time and throughput as compared to the state-of-the-art signal timing algorithms.
Abstract: Computer vision-guided traffic management is an emerging area of research. Intelligent traffic signal control using computer vision is a less explored area of research. In this paper, we propose a new approach of traffic flow-based intelligent signal timing by temporally clustering optical flow features of moving vehicles using Temporal Unknown Incremental Clustering (TUIC) model. First, we propose a new inference scheme that works approximately 5-times faster as compared to the one originally proposed in TUIC in a dense traffic intersection. The new inference scheme can trace clusters representing moving objects that may be occluded while being tracked. Cluster counts of approach roads have been used for signal timing for traffic intersections. It is done by detecting cluster motion inside the regions-of-interest (ROI) marked at the entry and exit locations of intersection approaches. Departure rates are learned using Gaussian regression to parameterize traffic variations. Using the learned parameters as a function of cluster count, an adaptive signal timing algorithm, namely Throughput and Average Waiting Time Optimization (TAWTO) has been proposed. Experimental results reveal that the proposed method can achieve better average waiting time and throughput as compared to the state-of-the-art signal timing algorithms. We intend to publish two datasets as part of this work for enabling the research community to explore computer vision aided solutions for typical problems such as intelligent traffic controlling, violation detection in chaotic road intersections, etc.
18 citations
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TL;DR: The key role of real-time traffic signal control technology in managing congestion at road junctions within smart cities is explored and the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections are examined.
Abstract: The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.
12 citations
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TL;DR: In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.
Abstract: Video, rich in visual real-time content, is however, difficult to interpret and analyse. Video collections necessarily have large data volume. Video analytics strives to automatically discover patterns and correlations present in the large volume of video data, which can help the end-user to take informed and intelligent decisions as well as predict the future based on the patterns discovered across space and time. In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics.
10 citations
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TL;DR: This work proposes a parking state machine that tracks the vehicle state in a large outdoor car park area and proves to be fairly accurate, fast and robust against severe scene variations.
Abstract: Car park video surveillance systems present a huge volume of data that can be beneficial for video analytics and data analysis. We present a vehicle state tracking method for long term video surveillance with the goal of obtaining trajectories and vehicle states of various car park users. However, this is a challenging task in outdoor scenarios due to non-optimal camera viewing angle compounded by ever-changing illumination & weather conditions. To address these challenges, we propose a parking state machine that tracks the vehicle state in a large outdoor car park area. The proposed method was tested on 10 hours of continuous video data with various illumination and environmental conditions. Owing to the imbalanced distribution of parking states, we report the precision, recall and F1 scores to determine the overall performance of the system. Our approach proves to be fairly accurate, fast and robust against severe scene variations.
4 citations
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References
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TL;DR: This paper describes GHT, a Geographic Hash Table system for DCS on sensornets, and demonstrates that GHT is the preferable approach for the application workloads predicted, offers high data availability, and scales to large sensornet deployments, even when nodes fail or are mobile.
Abstract: Making effective use of the vast amounts of data gathered by large-scale sensor networks will require scalable, self-organizing, and energy-efficient data dissemination algorithms. Previous work has identified data-centric routing as one such method. In an asso-ciated position paper [23], we argue that a companion method, data-centric storage (DCS), is also a useful approach. Under DCS, sensed data are stored at a node determined by the name associated with the sensed data. In this paper, we describe GHT, a Geographic Hash Table system for DCS on sensornets. GHT hashes keys into geographic coordi-nates, and stores a key-value pair at the sensor node geographically nearest the hash of its key. The system replicates stored data lo-cally to ensure persistence when nodes fail. It uses an efficient consistency protocol to ensure that key-value pairs are stored at the appropriate nodes after topological changes. And it distributes load throughout the network using a geographic hierarchy. We evaluate the performance of GHT as a DCS system in simulation against two other dissemination approaches. Our results demonstrate that GHT is the preferable approach for the application workloads predicted in [23], offers high data availability, and scales to large sensornet deployments, even when nodes fail or are mobile.
848 citations
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TL;DR: An ongoing effort to define common APIs for structured peer-to-peer overlays and the key abstractions that can be built on them is described to facilitate independent innovation in overlay protocols, services, and applications, to allow direct experimental comparisons, and to encourage application development by third parties.
Abstract: In this paper, we describe an ongoing effort to define common APIs for structured peer-to-peer overlays and the key abstractions that can be built on them. In doing so, we hope to facilitate independent innovation in overlay protocols, services, and applications, to allow direct experimental comparisons, and to encourage application development by third parties. We provide a snapshot of our efforts and discuss open problems in an effort to solicit feedback from the research community.
578 citations
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