Road Traffic Monitoring

IEE Review ◽  
1989 ◽  
Vol 35 (5) ◽  
pp. 188
Author(s):  
P.L. Belcher
Keyword(s):  
Road Traffic ◽  
Traffic Monitoring

scholarly journals LPWAN-based hybrid backhaul communication for intelligent transportation systems: architecture and performance evaluation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Taghi Shahgholi ◽  
Amir Sheikhahmadi ◽  
Keyhan Khamforoosh ◽  
Sadoon Azizi
Keyword(s):  
Low Power ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Area Network ◽  
Emergency Vehicle ◽  
Wide Area Network

AbstractIncreased number of the vehicles on the streets around the world has led to several problems including traffic congestion, emissions, and huge fuel consumption in many regions. With advances in wireless and traffic technologies, the Intelligent Transportation System (ITS) has been introduced as a viable solution for solving these problems by implementing more efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide long range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In this paper, we have proposed an architecture to employ the LPWAN as a backhaul infrastructure for ITS and to understand the feasibility of the proposed model, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end-to-end latency per user. Simulation of Urban MObility has been used for realistic traffic generation and a Python-based program has been developed for evaluation of the communication system. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT.

Night-time disability glare of constant-light LED traffic monitoring fill light

2021 ◽  
pp. 147715352098226
Author(s):  
X Cai ◽  
L Quan ◽  
J Wu ◽  
Y He
Keyword(s):  
Dynamic Range ◽  
Road Traffic ◽  
Reaction Times ◽  
High Dynamic Range ◽  
Luminance Contrast ◽  
Traffic Monitoring ◽  
Constant Light ◽  
Night Time ◽  
Disability Glare ◽  
Small Targets

Fill light, used to helps cameras capture road traffic conditions at night, can lead to serious visual consequences for drivers. Research on disability glare from LED fill light is scarce and therefore this study explored strategies for controlling disability glare of constant-light LED traffic monitoring fill light. The threshold increment was used as an index to evaluate disability glare. The effective disability glare area of LED traffic monitoring fill light was determined based on high dynamic range technology. According to visual efficacy theory, there is a relationship between disability glare conditions and reaction times. The influencing factors include background luminance, luminance contrast and fill light luminance. The results showed that disability glare was the most intense in a range of 20 m to 30 m in front of LED fill light. To reduce the effect of disability glare on drivers, luminance contrast between small targets and the road surface should be greater than 0.5. The fill light luminance should not be greater than 100,000 cd/m2.

scholarly journals High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

2019 ◽  
Author(s):  
Daoyuan Yang ◽  
Shaojun Zhang ◽  
Tianlin Niu ◽  
Yunjie Wang ◽  
Honglei Xu ◽  
...  
Keyword(s):  
Urban Area ◽  
Real World ◽  
Vehicle Emissions ◽  
Large Scale ◽  
Emission Inventory ◽  
Road Traffic ◽  
Dispersion Model ◽  
Traffic Monitoring ◽  
Atmospheric Pollutants ◽  
Link Model

Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link, based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOX), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that non-local HDTs could for 29 % and 38 % of estimated total on-road emissions of NOX and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOX concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 μg m−3 of NOX in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

scholarly journals Research on Traffic Acoustic Event Detection Algorithm Based on Sparse Autoencoder

2020 ◽  
Vol 308 ◽  
pp. 05002
Author(s):  
Xiaodan Zhang ◽  
Yongsheng Chen ◽  
Guichen Tang
Keyword(s):  
Feature Vector ◽  
Road Traffic ◽  
Intelligent Transportation ◽  
Research Direction ◽  
Traffic Monitoring ◽  
Detection Algorithm ◽  
Acoustic Event ◽  
Traffic State ◽  
Sparse Autoencoder ◽  
New Research

Road traffic monitoring is very important for intelligent transportation. The detection of traffic state based on acoustic information is a new research direction. A vehicles acoustic event classification algorithm based on sparse autoencoder is proposed to analysis the traffic state. Firstly, the multidimensional Mel-cepstrum features and energy features are extracted to form a feature vector of 125 features; Secondly, based on the computed features, the five-layers autoencoder is trained. Finally, vehicle audio samples are collected and the trained autoencoder is tested. The experimental results show that detection rate of the traffic acoustic event reaches 94.9%, which is 12.3% higher than that of the traditional Convolutional Neural Networks (CNN) algorithm.

scholarly journals Improving local air quality in cities by reducing nitrogen dioxide pollution from road traffic

2019 ◽  
Vol 122 ◽  
pp. 05002
Author(s):  
Spiru Paraschiv
Keyword(s):  
Air Quality ◽  
Nitrogen Dioxide ◽  
Temporal Variation ◽  
Air Pollutants ◽  
Road Traffic ◽  
Traffic Monitoring ◽  
Monitoring Station ◽  
Daily Minimum

Trucks and buses play a major role in our lives, transporting goods and thousands of people to cities every day. But these vehicles, although in a much smaller number than the car generates a significant amount of air pollutants. The daily NO2 concentrations measured by a traffic monitoring station over a period of two years are used to identify the temporal variation of NO2 pollution as a result of measures to ban the circulation of trucks that do not meet the EURO 6 standard on Stresemannstrase Street in Hamburg. The data shows a decrease in NO2 concentration due to the measure taken so that in January 2017 the maximum daily NO2 concentration was 86 µg/m3 compared to 63 µg/m3 in 2019. There was also a difference between the daily minimum concentrations during the same period, being approximately 28 µg/m3 in 2017 and 10 µg/m3 in 2019. The daily NO2 observations show a significant decrease in concentration since May 2018 when the non-EURO 6 trucks were banned. The largest decrease in daily concentrations was recorded in March 2019 compared with levels in March 2018, with a lower concentration for 28 days. A different situation was observed in October 2018, when compared to October 2017, showed an increase in concentration for 23 days.

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