Vehicle classification using GPS data

Sun, Zhanbo; Ban, Xuegang

doi:10.1016/j.trc.2013.09.015

Cited by 93 publications

(35 citation statements)

References 35 publications

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Section: Related Workmentioning

confidence: 99%

“…Similar to our research topic, i.e., vehicle classi cation, previous studies have used GPS trajectories to classify vehicles into delivery trucks or passenger cars [41]. In addition to di erent research objectives between ours and [41], the method used in [41] is supervised learning, while our method belongs to transfer learning [34], which can address the di culty in obtaining the labeled ridesourcing dataset in reality. Note that in traditional transportation research literature, besides GPS sensors, a variety of other sensors (e.g., radar, acoustic and computer vision-based sensors) are also used to vehicle classi cation; however, such sensors are generally deployed in xed locations and expensive to be applied in a large scale [7].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Ridesharing car detection by transfer learning

Wang

Geng

et al. 2019

Artificial Intelligence

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Ridesharing car detection by transfer learning

Wang

Geng

et al. 2019

Artificial Intelligence

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“…In [5] an enhanced, monolithic visual system using only one camera for vehicle classification is presented. In addition to camera-based systems, methods using laser scanners [6], acoustic sensors [7], accelerometers [8], magnetometers [9] and even Global Positioning System (GPS)-based approaches [10] are further existing solution approaches. Nevertheless, none of the systems is able to completely comply with the above mentioned requirements.…”

Section: B Related Workmentioning

confidence: 99%

A radio-fingerprinting-based vehicle classification system for intelligent traffic control in smart cities

Sliwa

Haferkamp

Al-Askary

et al. 2018

2018 Annual IEEE International Systems Conference (SysCon)

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The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control 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%.

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“…With the growing prevalence of GPS receivers embedded in vehicles and smartphones, there have been increasing interests in using their location updates or trajectories for monitoring traffic [10]. Even though GPS is becoming more and more used and affordable, so far only a limited number of cars are equipped with this system, typically fleet management services.…”

Section: Introductionmentioning

confidence: 99%

Analysis of GPS Based Vehicle Trajectory Data for Road Traffic Congestion Learning

Lanka

Jena

2014

Advanced Computing, Networking and Informatics- Volume 2

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Abstract. Successful developments of effective real-time traffic management and information systems demand high quality real time traffic information. In the era of intelligent transportation convergence, traffic monitoring requires traffic sensory technologies. We tabulate various realistic traffic sensors which aim to address the technicalities of both point and mobile sensors and also increase the scope to prefer an optimal sensor for real time traffic data collection. The present analysis extracted data from Mobile Century experiment. The data obtained in the experiment was pre-processed successfully by applying data mining pre-processing techniques such as data transformation, normalization and integration. Finally as a result of the availability of pre-processed Global Position System (GPS) sensors trace data a road map has been generated.

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Vehicle classification using GPS data

Cited by 93 publications

References 35 publications

Ridesharing car detection by transfer learning

Ridesharing car detection by transfer learning

A radio-fingerprinting-based vehicle classification system for intelligent traffic control in smart cities

Analysis of GPS Based Vehicle Trajectory Data for Road Traffic Congestion Learning

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