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Traffic Density Classification using Sound Datasets: An Empirical Study on Traffic Flow at Asymmetric Roads

Khac-Hoai Nam Bui, Hyeonjeong Oh, Hongsuk Yi
2020 IEEE Access  
In the case of the transportation domain, [13] proposed a new method for large-scale traffic analysis by developing CNN architecture for traffic image datasets which are covered from network traffic.  ...  ROAD SOUND DATASET-BASED TRAFFIC DENSITY CLASSIFICATION This study proposes a traffic density classification approach using road sound datasets.  ... 
doi:10.1109/access.2020.3007917 fatcat:3am3hokj75hudcoixbywpaziuy

Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic Congestion on Freeway in China using Ultra-low Frame Rate Videos

Zhu Sun, Ping Wang, Xiaoyu Peng, Yinli Jin, Jun Wang
2020 IEEE Access  
Traffic congestion detection plays an important role for road management. However, it is difficult to automatically report traffic congestion when it occurs in large-scale road network.  ...  The approach was tested on a self-established dataset based on empirical data, which contains images captured from 14470 surveillance cameras for monitoring 5,215 km of freeway in Shaanxi province, China  ...  road network are always resource consuming. a congestion detection system for large-scale freeway network always require massive resource investment.  ... 
doi:10.1109/access.2020.2968597 fatcat:v5svenmmqvct7dloh2wdzamkxu

RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery [article]

Armin Hadzic, Hunter Blanton, Weilian Song, Mei Chen, Scott Workman, Nathan Jacobs
2020 arXiv   pre-print
Unfortunately, collecting large-scale historical traffic speed data is expensive and time consuming.  ...  Traditional approaches for estimating free-flow speed use geometric properties of the underlying road segment, such as grade, curvature, lane width, lateral clearance and access point density, but for  ...  Additionally, we show how our approach can be used to generate large-scale free-flow speed maps, a potentially useful tool for transportation engineering and roadway planning.  ... 
arXiv:2006.08021v1 fatcat:huob754e45fohoqbko4t2mrpza

Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network

Jiasong Zhu, Ke Sun, Sen Jia, Qingquan Li, Xianxu Hou, Weidong Lin, Bozhi Liu, Guoping Qiu
2018 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
We first capture nearly an hour-long ultra high resolution traffic video at 5 busy road intersections of a modern megacity by flying an UAV during the rush hours.  ...  exploiting these technological advancements for urban traffic density estimation.  ...  stably recording road traffic at 3840 × 2178 resolution (30fps).  ... 
doi:10.1109/jstars.2018.2879368 fatcat:3adqodw6uvdzdhbz7i4tua2e7q

CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS

Teresa PAMUŁA
2016 Scientific Journal of Silesian University of Technology. Series Transport  
This approach is very demanding for the design of the computing parts of the systems. Traffic conditions determine the way in which road users perceive their ability to travel along the road network.  ...  This provides an opportunity for monitoring the conditions of traffic and related parameters, such as traffic density, traffic flow, mean traffic speed and average delays.  ... 
doi:10.20858/sjsutst.2016.92.10 fatcat:n2ybib3te5geraj4qflbyncfei

Estimating Spatial Measures of Roadway Network Usage from Remotely Sensed Data

Benjamin Coifman, Yongliang Yang
2004 Transportation Research Record  
Roadway network usage is measured for purposes as diverse as design, planning, maintenance, operation, management and research. Traditionally these measurements are made using groundbased sensors.  ...  High-resolution imagery remotely sensed from satellite or airborne platforms is an attractive alternative that can potentially supplement and enhance the existing traffic monitoring programs with a spatially  ...  If vehicles can be detected from imagery, the traffic count on an imaged roadway segment can be used for measuring traffic density on this segment at the instant the image was captured.  ... 
doi:10.3141/1870-17 fatcat:bp3n74qcajb2fj7haseejnsaxe

Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads [article]

Harish Rithish, Raghava Modhugu, Ranjith Reddy, Rohit Saluja, C.V. Jawahar
2021 arXiv   pre-print
Such methods are expensive in enforcing compliance to traffic rules and do not scale to large road networks.  ...  This paper proposes a simple mobile imaging setup to address several common problems in road safety at scale.  ...  Current approaches for addressing road safety, however, rely on manual interventions or large camera networks.  ... 
arXiv:2109.05226v1 fatcat:bnkprrbd7ndptfhd262dlzhmq4

Situation Control of Unmanned Aerial Vehicles for Road Traffic Monitoring

Nikolai Vladimirovich Kim, Mikhail Alekseevich Chervonenkis
2015 Modern Applied Science  
This paper aims to introduce an approach to the organization of road traffic monitoring by the means of unmanned aerial vehicles (UAVs), which is based on the automatic situation control of UAVs.  ...  The proposed approach contributes to the efficiency of UAV in road traffic monitoring by means of the management and detection processes automation.  ...  Below is the vehicle detection scheme for this case: 1) Image scaling and determination of the region of interest (ROI), usually one or more rectangles including the road and the roadside.  ... 
doi:10.5539/mas.v9n5p1 fatcat:h3d22c3btrfibpmzwghsdfltvq

Adaptive real time traffic prediction using deep neural networks

Parinith R Iyer, Shrutheesh Raman Iyer, Raghavendran Ramesh, Anala M.R., K.N. Subramanya
2019 IAES International Journal of Artificial Intelligence (IJ-AI)  
The detection and classification are done using SSD Neural Network object detection algorithm.  ...  This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.</span>  ...  ACKNOWLEDGEMENTS We acknowledge the support of NVIDIA Corporation for donating Jetson TX1 and Jetson TX2 kits used to carry out this project.  ... 
doi:10.11591/ijai.v8.i2.pp107-119 fatcat:gmthcbiqbnaqldomcqiu4pklim

Research on Urban Renewal Public Space Design Based on Convolutional Neural Network Model

Jixin Wan, Huosai Shi, Jian Su
2021 Security and Communication Networks  
Based on the basic concepts of machine learning and deep learning and their procedural logic, this paper explores the generation mode of traffic road network, neighborhood space form, and building function  ...  space generation design method and provide a new idea for the innovative development of urban design methods.  ...  of artificial the optimal urban traffic road network.  ... 
doi:10.1155/2021/9504188 fatcat:jx23dno5ljapfj6gly52ykra2y

What Makes London Work Like London?

Sawsan AlHalawani, Yong-Liang Yang, Peter Wonka, Niloy J. Mitra
2014 Computer graphics forum (Print)  
Commonly used computational approaches focus on geometric descriptors, both for images and for laser scans.  ...  In contrast, in urban planning, a large body of work has qualitatively evaluated street networks to understand their effects on the functionality of cities, both for pedestrians and for cars.  ...  The modeling of large-scale road networks has been tackled by Galin et al. [GPGB11] . At a lower level, Maréchal et al. [GPGB11] studied how a single road adapts to the terrain.  ... 
doi:10.1111/cgf.12441 fatcat:s23hguc6bfdwbanemtz34zab4q

Categorical Vehicle Classification and Tracking using Deep Neural Networks

Deependra Sharma, Zainul Abdin Jaffery
2021 International Journal of Advanced Computer Science and Applications  
The capabilities of generative adversarial networks framework to compensate for weather variability, Gaussian models to look for roadway configurations, single shot multibox detector for categorical vehicle  ...  This research offers a categorical vehicle classification and tracking system based on deep neural networks to overcome these difficulties.  ...  This allows the network to handle several object scales at the same time.  ... 
doi:10.14569/ijacsa.2021.0120964 fatcat:k2a6dtf3grepdnflyg3yrjpxzu

Estimation of fundamental diagrams in large-scale traffic networks with scarce sensor measurements

O. L. Quintero Montoya, Carlos Canudas-de-Wit
2018 2018 21st International Conference on Intelligent Transportation Systems (ITSC)  
The first author also thanks INRIA for partial support.  ...  Acknowledgements This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (CNRS, ERC Scale-freeBack grant agreement  ...  Introduction Estimation problems in large-scale networks with scarce sensors sets have been studied during the last decade in order to obtain accurate models of the urban traffic networks for simulation  ... 
doi:10.1109/itsc.2018.8569817 dblp:conf/itsc/MontoyaW18 fatcat:nenzdx4awzcblfmbmjlkvlw6qu

Vehicle detection and traffic density monitoring from very high resolution satellite video data

George Kopsiaftis, Konstantinos Karantzalos
2015 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
In particular, at every subregion the number of the detected vehicles is calculated and the density is then estimated for the entire road network at every frame.  ...  In this paper an automated vehicle detection and traffic density estimation algorithm has been developed and validated for very high resolution satellite video data.  ...  the same time satellite imagery cover large areas, providing an integrated picture of the traffic conditions at a suburb or city spatial scale.  ... 
doi:10.1109/igarss.2015.7326160 dblp:conf/igarss/KopsiaftisK15 fatcat:phkstrfrojg4lom4lr7dbge6se

Road traffic congestion in the developing world

Vipin Jain, Ashlesh Sharma, Lakshminarayanan Subramanian
2012 Proceedings of the 2nd ACM Symposium on Computing for Development - ACM DEV '12  
In this paper, we first present a simple automated image processing mechanism for detecting the congestion levels in road traffic by processing CCTV camera image feeds.  ...  Our algorithm is specifically designed for noisy traffic feeds with poor image quality.  ...  As cities grow in an ad-hoc manner, no provision is made towards scaling road capacities, eventually resulting into several bottleneck roads, which remain congested for extended periods of time.  ... 
doi:10.1145/2160601.2160616 dblp:conf/dev/JainSS12 fatcat:hqqvsqsjk5fgvp5wz6wqifkur4
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