scholarly journals Show Me a Safer Way: Detecting Anomalous Driving Behavior Using Online Traffic Footage

2019 ◽  
Vol 4 (2) ◽  
pp. 22
Author(s):  
Xiao Zheng ◽  
Fumi Wu ◽  
Weizhang Chen ◽  
Elham Naghizade ◽  
Kourosh Khoshelham
Keyword(s):  
Traffic Management ◽  
Traffic Accidents ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Integrated System ◽  
Navigation Systems ◽  
Driving Behaviors ◽  
Real Time Traffic ◽  
Online Traffic ◽  
Novel Applications

Real-time traffic monitoring is essential in many novel applications, from traffic management to smart navigation systems. The large number of traffic cameras being integrated into urban infrastructures has enabled efficient traffic monitoring as an intervention in reducing traffic accidents and related casualties. In this paper, we focus on the problem of the automatic detection of anomalous driving behaviors, e.g., speeding or stopping on a bike lane, by using the traffic-camera feed that is available online. This can play an important role in personalized route-planning applications where, for instance, a user wants find the safest paths to get to a destination. We present an integrated system that accurately detects, tracks, and classifies vehicles using online traffic-camera feed.

scholarly journals Travel Time Prediction on Un-Monitored Roads: A Spatial Factorization Machine Based Approach (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13855-13856 ◽  
Author(s):  
Lile Li ◽  
Wei Liu
Keyword(s):  
Traffic Congestion ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Time Of Arrival ◽  
Traffic Data ◽  
Travel Time Prediction ◽  
Large Cities ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Factorization Machine

Real-time traffic monitoring is one of the most important factors for route planning and estimated time of arrival (ETA). Many major roads in large cities are installed with live traffic monitoring systems, inferring the current traffic congestion status and ETAs to other locations. However, there are also many other roads, especially small roads and paths, that are not monitored. Yet, live traffic status on such un-monitored small roads can play a non-negligible role in personalized route planning and re-routing when road incident happens. How to estimate the traffic status on such un-monitored roads is thus a valuable problem to be addressed. In this paper, we propose a model called Spatial Factorization Machines (SFM) to address this problem. A major advantage of the SFM model is that it incorporates physical distances and structures of road networks into the estimation of traffic status on un-monitored roads. Our experiments on real world traffic data demonstrate that the SFM model significantly outperforms other existing models on ETA of un-monitored roads.

scholarly journals Effect Analysis of Intermittent Release Measures in Heavy Fog Weather with an Improved CA Model

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Jing Shi ◽  
Jinhua Tan
Keyword(s):  
Traffic Safety ◽  
Traffic Management ◽  
Traffic Accidents ◽  
Economic Losses ◽  
Time Interval ◽  
Risk Indicator ◽  
Effect Analysis ◽  
Driving Behaviors ◽  
Related Risk ◽  
Ca Model

Heavy fog may easily cause traffic accidents; thus freeway closures are frequently taken in order to ensure traffic safety in China, which not only seriously affect the travel of people, but also bring great economic losses. This paper studies the fog related risk of rear-end collisions and the intermittent release measures taken to reduce such risk; meanwhile, an improved cellular automaton model considering driving behaviors in heavy fog is proposed. The simulation results indicate that the risk indicatorfain fog is much higher than normal weather when cellular occupancyρ<0.5. After taking intermittent release measures, the magnitude offawill drop from 10−4to 10−5under the same fog condition, which greatly enhances the safety. In addition, this paper concludes the appropriate vehicle number released for each time and the time intervalhtbetween adjacent fleets and the maximum number of vehicles𝒬maxwhich can be released per hour. These results can be used as theoretical basis and reference for the traffic management departments to develop intermittent release measures.

scholarly journals Traffic Management as a Service: The Traffic Flow Pattern Classification Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Carlos T. Calafate ◽  
David Soler ◽  
Juan-Carlos Cano ◽  
Pietro Manzoni
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Intelligent Transportation System ◽  
Route Planning ◽  
Classification Problem ◽  
Time Dependent ◽  
Traffic Pattern ◽  
High Data ◽  
Real Time Traffic

Intelligent Transportation System (ITS) technologies can be implemented to reduce both fuel consumption and the associated emission of greenhouse gases. However, such systems require intelligent and effective route planning solutions to reduce travel time and promote stable traveling speeds. To achieve such goal these systems should account for both estimated and real-time traffic congestion states, but obtaining reliable traffic congestion estimations for all the streets/avenues in a city for the different times of the day, for every day in a year, is a complex task. Modeling such a tremendous amount of data can be time-consuming and, additionally, centralized computation of optimal routes based on such time-dependencies has very high data processing requirements. In this paper we approach this problem through a heuristic to considerably reduce the modeling effort while maintaining the benefits of time-dependent traffic congestion modeling. In particular, we propose grouping streets by taking into account real traces describing the daily traffic pattern. The effectiveness of this heuristic is assessed for the city of Valencia, Spain, and the results obtained show that it is possible to reduce the required number of daily traffic flow patterns by a factor of 4210 while maintaining the essence of time-dependent modeling requirements.

Outcome Assessment of Peer-to-Peer Adaptive Control Adjacent to a National Park

2017 ◽  
Vol 2620 (1) ◽  
pp. 43-53 ◽  
Author(s):  
Lucy M. Richardson ◽  
Matthew D. Luker ◽  
Christopher M. Day ◽  
Mark Taylor ◽  
Darcy M. Bullock
Keyword(s):  
Adaptive Control ◽  
Traffic Control ◽  
Traffic Management ◽  
Cost Effective ◽  
Traffic Monitoring ◽  
Peer To Peer ◽  
Control Logic ◽  
Real Time Traffic ◽  
Before And After ◽  
Adaptive Traffic Control

In the town of Moab, Utah, a combination of seasonal tourist traffic, heavy truck traffic, and high pedestrian volumes creates a unique traffic management challenge; Moab’s remote location adds additional challenges for real-time traffic monitoring and maintaining of signal timing plans. The Main Street corridor is a strong candidate for an adaptive traffic control system (ATCS). Peer-to-peer (P2P) communication and user-definable control logic were used to develop and implement a cost-effective ATCS called “P2P adaptive control” that used only the existing local controllers and detection. The adaptive control logic adjusts green time along the mainline in response to detector inputs while keeping the side streets at the minimum time needed for pedestrian service. System performance was evaluated by comparing performance measures generated from high-resolution signal controller data before and after implementation of P2P adaptive control. The P2P adaptive control increased the through bandwidth of the corridor and reduced the number of split failures (i.e., the number of phase occurrences with insufficient green). Future work will include adjusting the algorithm to improve service on side streets and expanding P2P adaptive control to additional signals expected to be constructed in the area.

scholarly journals Artificial Intelligence Enabled Traffic Monitoring System

2020 ◽  
Author(s):  
Vishal Mandal ◽  
Abdul Rashid Mussah ◽  
Peng Jin ◽  
Yaw Adu-Gyamfi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Deep Convolutional Neural Networks ◽  
Real Time Traffic ◽  
Daunting Task ◽  
The Impact

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

scholarly journals Enhanced Traffic Management System using Artificial Intelligent Congestion Control

2020 ◽  
Vol 9 (8) ◽  
pp. 1015-1018
Keyword(s):  
Urban Communities ◽  
Traffic Congestion ◽  
Smart City ◽  
Traffic Management ◽  
Traffic Information ◽  
Real Time Traffic ◽  
Suitable Site ◽  
Traffic Management System ◽  
Online Traffic ◽  
The Internet Of Things

The smart city proposed by government is providing better infrastructure with possible automated device. Every smart city proposes to provide smart transport through automated traffic management .The peak hours face the congestion road and many traffic irregularities. The congested road aids in poor Travel experience, environmental pollution and health hazards by vehicular fuel. The solution to aforesaid issues leads to traffic Automation in urban communities. To implement the traffic automation need access to real time traffic congestion information, best possible route and alternate strategy with online traffic information applicable to specific traffic stream. An more suitable site visitors manipulate and MF has been mentioned to finish short information transmission and their corresponding motion performed via artificial intelligence. The VANET scenario, congestion manage algorithm executed through mobile agent controller uniformly organizes the traffic glide by way of heading off the congestion at the smart visitors zone ,The law-enforcement bodies ,the fire opponents and the clinical and/or paramedical teams consciousness on elevated quantity of crime in addition to lifestyles losses through site visitors irregularities. The benefits of adopting the internet of things(iot)provide a new prospect for intelligent site visitors improvement.

scholarly journals Edge-Computing Video Analytics for Real-Time Traffic Monitoring in a Smart City

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2048 ◽  
Author(s):  
Johan Barthélemy ◽  
Nicolas Verstaevel ◽  
Hugh Forehead ◽  
Pascal Perez
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Pilot Project ◽  
Traffic Monitoring ◽  
Edge Computing ◽  
Urban Centers ◽  
Visual Sensor ◽  
Computing Device ◽  
Multiple Sensors ◽  
Real Time Traffic

The increasing development of urban centers brings serious challenges for traffic management. In this paper, we introduce a smart visual sensor, developed for a pilot project taking place in the Australian city of Liverpool (NSW). The project’s aim was to design and evaluate an edge-computing device using computer vision and deep neural networks to track in real-time multi-modal transportation while ensuring citizens’ privacy. The performance of the sensor was evaluated on a town center dataset. We also introduce the interoperable Agnosticity framework designed to collect, store and access data from multiple sensors, with results from two real-world experiments.

The potential of monitoring traffic conditions up to 15 times a day using sub-meter resolution EO images

2021 ◽  
Author(s):  
Refiz Duro ◽  
Georg Neubauer ◽  
Alexandra-Ioana Bojor
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Traffic Monitoring ◽  
Movement Direction ◽  
Time Lags ◽  
Time Data ◽  
Traffic Lights ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Spatial Coverage

&lt;p&gt;Urbanization and the trend of people moving to cities often leads to problematic traffic conditions, which can be very challenging for traffic management. It can hamper the flow of people and goods, negatively affecting businesses through delays and the inability to estimate travel times and thus plan, as well as the environment and health of population due to increased fuel consumption and subsequent air pollution. Many cities have a policy and rules to manage traffic, ranging from standard traffic lights to more dynamic and adaptable solutions involving in-road sensors or cameras to actively modify the duration of traffic lights, or even more sophisticated IoT solutions to monitor and manage the conditions on a city-wide scale. The core to these technologies and to decision making processes is the availability of reliable data on traffic conditions, and better yet real-time data. Thus, a lot of cities are still coping with the lack of good spatial and temporal data coverage, as many of these solutions are requiring not only changes to the infrastructure, but also large investments.&lt;/p&gt;&lt;p&gt;One approach is to exploit the current and the forthcoming advancements made available by Earth Observation (EO) satellite technologies. The biggest advantage is EOs great spatial coverage ranging from a few km&amp;#178; to 100 km&amp;#178; per image on a spatial resolution down to 0.3m, thus allowing for a quick, city-spanning data collection. Furthermore, the availability of imaging sensors covering specific bands allows the constituent information within an image to be separated and the information to be leveraged.&lt;/p&gt;&lt;p&gt;In this respect, we present the findings of our work on multispectral image sets collected on three occasions in 2019 using very high resolution WorldView-3 satellite. We apply a combination of machine learning and PCA methods to detect vehicles and devise their kinematic properties (e.g., movement, direction, speed), only possible with satellites with a specific design allowing for short time lags between imaging in different spectral bands. As these data basically constitute a time-series, we will discuss how the results presented fully apply to the forthcoming WorldView-Legion constellation of satellites providing up to 15 revisits per day, and thus near-real time traffic monitoring and its impact on the environment.&lt;/p&gt;

scholarly journals Assessments of Real-Time Communications over TSN Automotive Networks

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 556
Author(s):  
Lucia Lo Bello ◽  
Gaetano Patti ◽  
Giancarlo Vasta
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Communication System ◽  
Traffic Management ◽  
Mac Layer ◽  
Automated Driving ◽  
Real Time Traffic ◽  
Management Scheme ◽  
Event Driven ◽  
Automotive Networks

The IEEE 802.1Q-2018 standard embeds in Ethernet bridges novel features that are very important for automated driving, such as the support for time-driven communications. However, cars move in a world where unpredictable events may occur and determine unforeseen situations. To properly react to such situations, the in-car communication system has to support event-driven transmissions with very low and bounded delays. This work provides the performance evaluation of EDSched, a traffic management scheme for IEEE 802.1Q bridges and end nodes that introduces explicit support for event-driven real-time traffic. EDSched works at the MAC layer and builds upon the mechanisms defined in the IEEE 802.1Q-2018 standard.

scholarly journals Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles

2021 ◽  
Vol 11 (15) ◽  
pp. 7132
Author(s):  
Jianfeng Xi ◽  
Shiqing Wang ◽  
Tongqiang Ding ◽  
Jian Tian ◽  
Hui Shao ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Operating Data ◽  
Detection Model ◽  
Driving Behaviors ◽  
K Nearest Neighbor Algorithm ◽  
Fatigue Driving ◽  
Freight Vehicles

Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The k-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the k-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the k-nearest neighbor algorithm is more reliable.

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