3,051 research outputs found
A Radio-fingerprinting-based Vehicle Classification System for Intelligent Traffic Control in Smart Cities
The measurement and provision of precise and upto-date traffic-related key
performance indicators is a key element and crucial factor for intelligent
traffic controls systems in upcoming smart cities. The street network is
considered as a highly-dynamic Cyber Physical System (CPS) where measured
information forms the foundation for dynamic control methods aiming to optimize
the overall system state. Apart from global system parameters like traffic flow
and density, specific data such as velocity of individual vehicles as well as
vehicle type information can be leveraged for highly sophisticated traffic
control methods like dynamic type-specific lane assignments. Consequently,
solutions for acquiring these kinds of information are required and have to
comply with strict requirements ranging from accuracy over cost-efficiency to
privacy preservation. In this paper, we present a system for classifying
vehicles based on their radio-fingerprint. In contrast to other approaches, the
proposed system is able to provide real-time capable and precise vehicle
classification as well as cost-efficient installation and maintenance, privacy
preservation and weather independence. The system performance in terms of
accuracy and resource-efficiency is evaluated in the field using comprehensive
measurements. Using a machine learning based approach, the resulting success
ratio for classifying cars and trucks is above 99%
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
A traffic monitoring system is an integral part of Intelligent Transportation
Systems (ITS). It is one of the critical transportation infrastructures that
transportation agencies invest a huge amount of money to collect and analyze
the traffic data to better utilize the roadway systems, improve the safety of
transportation, and establish future transportation plans. With recent advances
in MEMS, machine learning, and wireless communication technologies, numerous
innovative traffic monitoring systems have been developed. In this article, we
present a review of state-of-the-art traffic monitoring systems focusing on the
major functionality--vehicle classification. We organize various vehicle
classification systems, examine research issues and technical challenges, and
discuss hardware/software design, deployment experience, and system performance
of vehicle classification systems. Finally, we discuss a number of critical
open problems and future research directions in an aim to provide valuable
resources to academia, industry, and government agencies for selecting
appropriate technologies for their traffic monitoring applications.Comment: Published in IEEE Acces
Promoting Public Health and Safety: A Predictive Modeling Software Analysis on Perceived Road Fatality Contributory Factors
Extensive literature search was conducted to computationally analyze the relationship between key perceived road fatality factors and public health impacts, in terms of mortality and morbidity. Heterogeneous sources of data on road fatality 1970-2005 and that based on
interview questionnaire on European road drivers’ perception were sourced. Computational analysis was performed on these data using the Multilayer Perceptron model within the dtreg predictive modeling software. Driver factors had the highest relative significance.
Drivers played significant role as causative agents of road accidents. A good degree of correlation was also observed when compared with results obtained by previous researchers. Sweden, UK, Finland, Denmark, Germany, France, Netherlands, and Austria, where road safety targets were set and EU targets adopted, experienced a faster and sharper reduction of road fatalities. However, Belgium, Ireland, Italy, Greece and Portugal experienced slow, but little reduction in cases of road fatalities. Spain experienced an increase in road fatalities
possibly due to road fatalities enhancing factors. Estonia, Slovenia, Cyprus, Hungry, Czech Republic, Slovakia and Poland experienced a fluctuating but decreasing trend. Enforcement of road safety principles and regulations are needed to decrease the incidences of fatal
accidents. Adoption of the EU target of -50% reductions of fatalities in all countries will help promote public health and safety
DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning
A traffic monitoring system (TMS) is an integral part of Intelligent
Transportation Systems (ITS). It is an essential tool for traffic analysis and
planning. One of the biggest challenges is, however, the high cost especially
in covering the huge rural road network. In this paper, we propose to address
the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a
low-cost, portable, and non-intrusive solution that is built only with two WiFi
transceivers. It exploits the unique WiFi Channel State Information (CSI) of
passing vehicles to perform detection and classification of vehicles. Spatial
and temporal correlations of CSI amplitude and phase data are identified and
analyzed using a machine learning technique to classify vehicles into five
different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and
large trucks. A large amount of CSI data and ground-truth video data are
collected over a month period from a real-world two-lane rural roadway to
validate the effectiveness of DeepWiTraffic. The results validate that
DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4%
and the average classification accuracy of 91.1% in comparison with
state-of-the-art non-intrusive TMSs.Comment: Accepted for publication in the 16th IEEE International Conference on
Mobile Ad-Hoc and Smart Systems (MASS), 201
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