Field Test of Nonintrusive Traffic Detection Technologies

1998 ◽  
Vol 1643 (1) ◽  
pp. 161-170 ◽  
Author(s):  
Stephen J. Bahler ◽  
James M. Kranig ◽  
Erik D. Minge
Keyword(s):  
Field Test ◽  
Intelligent Transportation System ◽  
General System ◽  
Weather Conditions ◽  
Test Site ◽  
Urban Traffic ◽  
Minimal Impact ◽  
Traffic Conditions ◽  
Traffic Detection ◽  
Detection Technologies

The results of a 2-year field test of nonintrusive traffic detection technologies are presented. Seventeen devices representing the following eight technologies were evaluated: passive infrared, active infrared, magnetic, radar, doppler microwave, pulse ultrasonic, passive acoustic, and video. The devices were tested in a variety of environmental and traffic conditions at both intersection and freeway test sites. Emphasis was placed on urban traffic conditions, such as heavy congestion; locations that typify temporary counting situations, such as 48-hour or peak hour counts; and performance in the wide variety of weather conditions found in Minnesota. The evaluation also focused on the ease of system set-up and general system reliability. The results show that nonintrusive technologies are capable of performing as well as conventional methods in some, but not all, situations. At the freeway test site, most nonintrusive devices counted within 3 percent of baseline data. At the intersection test site, however, congested stop-and-go traffic hindered the performance of the majority of the devices. Weather and other environmental variables were found to have minimal impact on the majority of devices. This test is the first phase of an ongoing project to evaluate new, nonintrusive technologies and devices. Further research will expand into areas such as real-time datacollection to support intelligent transportation system applications.

scholarly journals Towards an End-to-End Framework of CCTV-Based Urban Traffic Volume Detection and Prediction

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 629
Author(s):  
Maria V. Peppa ◽  
Tom Komar ◽  
Wen Xiao ◽  
Phil James ◽  
Craig Robson ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Weather Conditions ◽  
Traffic Volume ◽  
Urban Traffic ◽  
Transport Systems ◽  
Traffic Conditions ◽  
Adverse Weather ◽  
Approaches To Study ◽  
End To End

Near real-time urban traffic analysis and prediction are paramount for effective intelligent transport systems. Whilst there is a plethora of research on advanced approaches to study traffic recently, only one-third of them has focused on urban arterials. A ready-to-use framework to support decision making in local traffic bureaus using largely available IoT sensors, especially CCTV, is yet to be developed. This study presents an end-to-end urban traffic volume detection and prediction framework using CCTV image series. The framework incorporates a novel Faster R-CNN to generate vehicle counts and quantify traffic conditions. Then it investigates the performance of a statistical-based model (SARIMAX), a machine learning (random forest; RF) and a deep learning (LSTM) model to predict traffic volume 30 min in the future. Tests at six locations with varying traffic conditions under different lengths of past time series are used to train the prediction models. RF and LSTM provided the most accurate predictions, with RF being faster than LSTM. The developed framework has been successfully applied to fill data gaps under adverse weather conditions when data are missing. It can be potentially implemented in near real time at any CCTV location and integrated into an online visualization platform.

scholarly journals ResNet15: Weather Recognition on Traffic Road with Deep Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Jingming Xia ◽  
Dawei Xuan ◽  
Ling Tan ◽  
Luping Xing
Keyword(s):  
Intelligent Transportation System ◽  
Rapid Development ◽  
Weather Condition ◽  
Network Models ◽  
Weather Conditions ◽  
Severe Weather ◽  
Urban Traffic ◽  
Deep Convolutional Neural Networks ◽  
Traffic Condition ◽  
Softmax Classifier

Severe weather conditions will have a great impact on urban traffic. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. With the rapid development of deep learning, deep convolutional neural networks (CNN) are used to recognize weather conditions on traffic road. A new simplified model named ResNet15 is proposed based on the residual network ResNet50 in this paper. The convolutional layers of ResNet15 are utilized to extract weather characteristics, and then the characteristics extracted at the previous layer are shortcut to the next layer through four groups of residual modules. Finally, the weather images are classified and recognized through the fully connected layer and Softmax classifier. In addition, we build a medium-scale dataset of weather images on traffic road, called “WeatherDataset-4,” which consists of 4 categories and contains 4983 weather images covering most of the severe weather. In this paper, ResNet15 is used to train and test on the “WeatherDataset-4,” and desirable recognition results are obtained. The evaluation of a large number of experiments demonstrates that the proposed ResNet15 is superior to traditional network models such as ResNet50 in recognition accuracy, recognition speed, and model size.

The importance of public support in the implementation of green transportation in smart cities using smart vehicle bicycle communication transport

2020 ◽  
Vol 38 (5/6) ◽  
pp. 997-1011
Author(s):  
Ning Li ◽  
Parthasarathy R. ◽  
Harshila H. Padwal
Keyword(s):  
Smart Cities ◽  
Optimization Technique ◽  
Public Support ◽  
Weather Conditions ◽  
Transport Systems ◽  
Content Type ◽  
Signaling Systems ◽  
Traffic Conditions ◽  
Green Transportation ◽  
Smart Vehicle

Purpose Smart mobility is a major guideline in the development of Smart Cities’ transport systems and management. The issue of transition into green, secure and sustainable transport modes, such as using bicycles, should be implemented in this case, along with the subjectivism of management. Design/methodology/approach The proposed technology reflects the Smart Bicycle vehicle model, which tracks cyclists and weather conditions and turns to electric motors in critical circumstances. Findings This reduces the physical load and battery consumption of cyclists which affects the Smart Cities’ ecology positively. Originality/value In Smart Vehicle Bicycle Communication Transport, the vehicle movement optimization technique is used for traffic scenarios to analyze traffic signaling systems that give better results in variable and dense traffic conditions.

scholarly journals Measuring Fine-Scale White-Tailed Deer Movements and Environmental Influences Using GPS Collars

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Stephen L. Webb ◽  
Kenneth L. Gee ◽  
Bronson K. Strickland ◽  
Stephen Demarais ◽  
Randy W. DeYoung
Keyword(s):  
Global Positioning System ◽  
Weather Conditions ◽  
Fine Scale ◽  
Moon Phase ◽  
Minimal Impact ◽  
Gps Collars ◽  
Weather Patterns ◽  
Weather Events ◽  
Global Positioning ◽  
Tracking Devices

Few studies have documented fine-scale movements of ungulate species, including white-tailed deer(Odocoileus virginianus), despite the advent of global positioning system (GPS) technology incorporated into tracking devices. We collected fine-scale temporal location estimates (i.e., 15 min/relocation attempt) from 17 female and 15 male white-tailed deer over 7 years and 3 seasons in Oklahoma, USA. Our objectives were to document fine-scale movements of females and males and determine effects of reproductive phase, moon phase, and short-term weather patterns on movements. Female and male movements were primarily crepuscular. Male total daily movements were 20% greater during rut () than postrut (). Female daily movements were greatest during postparturition (), followed by parturition (), and preparturition (). We found moon phase had no effect on daily, nocturnal, and diurnal deer movements and fine-scale temporal weather conditions had an inconsistent influence on deer movement patterns within season. Our data suggest that hourly and daily variation in weather events have minimal impact on movements of white-tailed deer in southern latitudes. Instead, routine crepuscular movements, presumed to maximize thermoregulation and minimize predation risk, appear to be the most important factors influencing movements.

scholarly journals Factors of the safety in bicycle traffic in the City of Zagreb and its surrounding

2014 ◽  
Vol 94 (3) ◽  
pp. 55-68
Author(s):  
Josko Sindik
Keyword(s):  
Weather Conditions ◽  
Urban Traffic ◽  
Questionnaire Study ◽  
Local Authorities ◽  
Driving Style ◽  
Study Participants ◽  
Risk Driving ◽  
The City

The aim of this study was to determine the differences in underlying factors of Zagreb cycling, compared to the "types of cyclists" (driving style), i.e. different ways of using bicycles as a means of transport. The study included over 3,000 frequent participants in urban traffic cycling, sample of members of the association Cyclist Union (N = 1259) and snowball sample of "typical" of cyclists, i.e. people who are using the bike, but are not the members of the Cyclist Union (N = 1831), using the conveniently assembled questionnaire. Study participants who bike used in various applications prefer the safest driving style (only on sidewalks and bike paths / lines). Barriers of the weather conditions are ubiquitous in the safest driving style. Daily, weekly and yearly riding a bicycle are more often found in those who prefer the safest driving style. Cyclists who drive with medium secure style (roads with less traffic and lower speeds), more often ride a bike, as compared with those who prefer the safest driving style. Having a better bike line / track and other infrastructure is the most often considered at those with the highest risk driving style. The results provide the guidance for local authorities and for the cyclists to improve the conditions for a safer and more often by bicycle circulation in the City of Zagreb and its surroundings.

scholarly journals Highly Productive Pine Forests in a Changing Climate

2021 ◽  
pp. 9-23
Author(s):  
Nina F. Kuznetsova ◽  
◽  
Elena S. Klushevskaya ◽  
Elena Yu. Amineva
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Genetic Material ◽  
Weather Conditions ◽  
Test Site ◽  
Pine Forests ◽  
Soil Drought ◽  
Forest Steppe ◽  
Changing Climate ◽  
Vital State

Forest steppe of the Central Chernozem Region (CCR) of Russia belongs to the zone of highly productive pine forests. In 2015, for the first time a partial destabilization of Scots pine (Pinus sylvestris L.) was recorded within the territory of the CCR. It affected the population, organism and cellular levels of Scots pine (Pinus sylvestris L.). The destabilization was caused by the 8-year heatwave of 2007–2014 followed by a sharp drop in the water table and four severe droughts (2007, 2010, 2012, and 2014). The analysis was carried out on two sites of pine forest plantations growing in the environmentally sound region: the Stupino test site (Voronezh region, typical plantation for the CCR) and the Usman site (Lipetsk region, lands with elevated groundwater level). The results of morphological, cytogenetic and biochemical studies of model trees of the Stupino test site during the following periods are presented: 4 optimal years in terms of weather conditions, 2014 drought year and 2015 destabilization year. It was found that prolonged hydrothermal stress resulted in the transition of pine from the basic equilibrium state to a slightly nonequilibrium state. The trigger mechanism for changing their vital state was a severe autumn soil drought in 2014, after which the plants became weakened right before winter. A decrease in cone bioproductivity by the traits of seed fullness and the total number of seeds per cone, a change in population sampling structure, an increase in the number of mitosis pathologies, and an increase in proline content in needles were observed despite optimal weather conditions in 2015. The recovery of species was studied for three subsequent optimal years on the example of the Stupino and Usman populations. Experimental data indicate that the processes of vital state normalization involve profound changes in metabolism and require certain energy expenditures. It took the Stupino population longer to return to the regional norm, which indicates a different depth of destabilization of the tree genetic material of the studied populations. For citation: Kuznetsova N.F., Klushevskaya E.S, Amineva E.Yu. Highly Productive Pine Forests in a Changing Climate. Lesnoy Zhurnal [Russian Forestry Journal], 2021, no. 6, pp. 9–23. DOI: 10.37482/0536-1036-2021-6-9-23

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