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DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion [article]

Fangzhou sun and Abhishek Dubey and Jules White
2018 arXiv   pre-print
Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data.  ...  Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately.  ...  codes S t a set of traffic data that contains speed limit and real-time speed for a set of road segments S r T M C key a string representing a road segment in S r T CI traffic Condition Image, a gray-scale  ... 
arXiv:1802.00002v1 fatcat:tc27t3aamjfodhlmqq6ta3dzjy

Real-Time Traffic Analysis using Deep Learning Techniques and UAV based Video

Huaizhong Zhang, Mark Liptrott, Nik Bessis, Jianquan Cheng
2019 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)  
Realtime analysis of traffic flow information is crucial for efficiently managing urban traffic. This paper aims to conduct traffic analysis using UAV-based videos and deep learning techniques.  ...  The road traffic video is collected by using a position-fixed UAV. The most recent deep learning methods are applied to identify the moving objects in videos.  ...  The images presented here is zoom-in applied. Fig. 5 shows another video segment to demonstrate how motorbikes affect pedestrians on the road.  ... 
doi:10.1109/avss.2019.8909879 dblp:conf/avss/ZhangLBC19 fatcat:swt7z3svtbdszj5t5g45h6vjfa

Optimized CapsNet for Traffic Jam Speed Prediction Using Mobile Sensor Data under Urban Swarming Transportation

Tampubolon, Yang, Chan, Sutrisno, Hua
2019 Sensors  
In order to conduct the RNW traffic prediction, a specific data preprocessing is needed to convert traffic data into an image representing spatial-temporal relationships among RNW.  ...  The experiments were conducted using real-world urban road traffic data of Jakarta to evaluate the performance.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/s19235277 pmid:31795519 pmcid:PMC6928954 fatcat:ih5hndce6vghbatu5yncsky4ke

Autonomous driving goes downtown

U. Franke, D. Gavrila, S. Gorzig, F. Lindner, F. Puetzold, C. Wohler
1998 IEEE Intelligent Systems and their Applications  
Autonomous driving in the much more complex scenario of urban traffic or driver assistance systems like Intelligent Stop&Go are new challenges not only from the algorithmic but also from the system architecture  ...  Most computer vision systems for vehicle guidance developed in the past were designed for the comparatively simple highway scenario.  ...  For as long as possible, boundaries detected by the global analysis are tracked locally in order to estimate the vehicle's lateral po-Fig. 4.1 Urban road scenario.  ... 
doi:10.1109/5254.736001 fatcat:5r7pk24jfnb5pbfnozz2okveci

Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions [chapter]

Vlad Taran, Yuri Gordienko, Alexandr Rokovyi, Oleg Alienin, Sergii Stirenko
2019 Advances in Intelligent Systems and Computing  
Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset.  ...  The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union - mIoU) could simplify  ...  Introduction The current tendency in semantic image segmentation of traffic road conditions is making high quality images labeling to produce fine ground truth (GT) annotations for training and testing  ... 
doi:10.1007/978-3-030-16621-2_17 fatcat:q3fv56j5ajfllj322qpmko5wsa

Motion and Gray Based Automatic Road Segment Method MGARS in Urban Traffic Surveillance [chapter]

Hong Liu, Jintao Li, Yueliang Qian, Shouxun Lin, Qun Liu
2006 Lecture Notes in Computer Science  
This paper presents a novel method MGARS to automatic road area segmentation based on motion and gray feature for the purpose of urban traffic surveillance.  ...  The proposed method MGARS can effectively segment multi roads without manual initialization, and is robust to road surface pollution and tree shadow.  ...  It enables the system to adapt to different environmental conditions. In this paper, we propose a novel method MGARS for automatic road area segmentation in urban traffic video.  ... 
doi:10.1007/11821045_9 fatcat:2elj4qubx5h23dhdg3r2shafna

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

Jixin Wan, Huosai Shi, Jian Su
2021 Security and Communication Networks  
By establishing a database of urban space cases, machine learning algorithms and deep learning algorithms can be used to train computers to learn how to design urban spaces.  ...  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  ...  In this study, machine learning Extract information of urban traffic road networks from and deep learning are used for the first time to obtain urban the database, and  ... 
doi:10.1155/2021/9504188 fatcat:jx23dno5ljapfj6gly52ykra2y

Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review

Lingfei Ma, Ying Li, Jonathan Li, Cheng Wang, Ruisheng Wang, Michael Chapman
2018 Remote Sensing  
Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and  ...  Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature.  ...  in complex urban road networks.  ... 
doi:10.3390/rs10101531 fatcat:vtovzw7p45gtzdk2rrnt26apxe


M. Mohammadi, F. Tabib Mahmoudi, M. Hedayatifard
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise.  ...  However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images.  ...  INTRODUCTION Considering the speed of construction in large urban areas, traffic management is investigated as one of the most challenging issues in urban management.  ... 
doi:10.5194/isprs-archives-xlii-4-w18-741-2019 fatcat:lnvzoqoeczdpbkrosknzujvql4

Predicting the impact of urban change in pedestrian and road safety [article]

Cristina Bustos, Daniel Rhoads, Agata Lapedriza, Javier Borge-Holthoefer, Albert Solé-Ribalta
2022 arXiv   pre-print
Increased interaction between and among pedestrians and vehicles in the crowded urban environments of today gives rise to a negative side-effect: a growth in traffic accidents, with pedestrians being the  ...  In this paper, by considering historical accident data and Street View images, we detail how to automatically predict the impact (increase or decrease) of urban interventions on accident incidence.  ...  In this section, we combine the hazard index of sidewalks and road segments with network features to provide a practical analysis of urban safety.  ... 
arXiv:2202.01781v1 fatcat:ojnga7b2ifcn5f3yha2jz42ao4

Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting

2020 Algorithms  
The idea of transferring video prediction models to the urban traffic forecasting domain is validated using a large real-world traffic data set.  ...  Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area.  ...  Acknowledgments: We thank Taek Kwon for his public archive of MnDoT traffic data [57] .  ... 
doi:10.3390/a13020039 fatcat:ofmz444gq5hhll7zn7bkm4jcoq

Extraction of contextual information for automotive applications

Andrea Beoldo, Alessio Dore, Carlo S. Regazzoni
2009 2009 16th IEEE International Conference on Image Processing (ICIP)  
In this work, lanes detection, vehicle position and traffic analysis are the information extracted to characterize the driving situation and the proposed techniques try to cope with the above mentioned  ...  The presented framework is tested using an on-board camera in real-world scenario respecting the real-time constraint and showing good performances in highways and urban roads.  ...  In Section 3 information about traffic, type of road (highway or urban road) and stop of the vehicle are extracted.  ... 
doi:10.1109/icip.2009.5413519 dblp:conf/icip/BeoldoDR09 fatcat:zcdpgxuasfhxfmgguitkbqnwqe

Road Traffic Monitoring from UAV Images Using Deep Learning Networks

Sungwoo Byun, In-Kyoung Shin, Jucheol Moon, Jiyoung Kang, Sang-Il Choi
2021 Remote Sensing  
In this paper, we propose a deep neural network-based method for estimating speed of vehicles on roads automatically from videos recorded using unmanned aerial vehicle (UAV).  ...  The proposed method includes the following; (1) detecting and tracking vehicles by analyzing the videos, (2) calculating the image scales using the distances between lanes on the roads, and (3) estimating  ...  Conclusions In this paper, we propose a method for grasping the flow of traffic from UAV images using various deep learning techniques developed for image analysis.  ... 
doi:10.3390/rs13204027 fatcat:cqusyffgvza47bwtat24fcitsi

Predicting Fine-Grained Traffic Conditions via Spatio-Temporal LSTM

Xiaojuan Wei, Jinglin Li, Quan Yuan, Kaihui Chen, Ao Zhou, Fangchun Yang
2019 Wireless Communications and Mobile Computing  
MapLSTM first obtains the historical and real-time traffic conditions of road segments via map-matching. Then LSTM is used to predict the conditions of the corresponding road segments in the future.  ...  Predicting traffic conditions for road segments is the prelude of working on intelligent transportation.  ...  Reference [18] is another image-based learning to measure traffic density using a deep convolutional neural network.  ... 
doi:10.1155/2019/9242598 fatcat:qsut6ankozdwvokrqpnyyzcyoa

High-Level Traffic-Violation Detection for Embedded Traffic Analysis

Julien A. Vijverberg, Nick A.H.M de Koning, Jungong Han, Peter H.N. de With, Dion Cornelissen
2007 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07  
We use background segmentation and a novel road-model to obtain the candidate traffic participants.  ...  This paper presents the design of a robust and real-time traffic-violation detection system for cameras on intersections.  ...  In this paper, we have presented a multi-level video analysis system for violation detection on urban intersections.  ... 
doi:10.1109/icassp.2007.366355 dblp:conf/icassp/VijverbergKHWC07 fatcat:urtoxypnana2rh4uxfld2nmxdi
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