scholarly journals Data Acquisition and Mining Algorithm of Car Networking under Big Data Background

2019 ◽  
Vol 15 (01) ◽  
pp. 4
Author(s):  
Guohua Xiong
Keyword(s):  
Big Data ◽  
Data Acquisition ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Mining Algorithm ◽  
Mining Algorithms ◽  
Traffic Information Collection ◽  
Networking Technology

In order to solve the problem of traffic jams, intelligent traffic technology and car networking technology were applied. In the context of big data, data acquisition and mining algorithms for vehicular network were studied. First, the overall architecture of the system was introduced. Then, the data acquisition technology based on the car network and the data mining technology based on the cloud plat-form were introduced. Finally, simulation experiments of real-time traffic information collection and recognition algorithms were performed. The results showed that the proposed mining algorithm had better data repair effect and better clustering effect, and the probability of misjudgment was smaller. Therefore, the algorithm can obtain accurate road traffic conditions.

scholarly journals Mobile Road Traffic Management System Using Weighted Sensor

2017 ◽  
Vol 11 (5) ◽  
pp. 147 ◽  
Author(s):  
Solomon Adegbenro Akinboro ◽  
Johnson A Adeyiga ◽  
Adebayo Omotosho ◽  
Akinwale O Akinwumi
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Traffic Information ◽  
Traffic Light ◽  
Real Time Traffic ◽  
Traffic Management System

<p><strong>Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion ha</strong><strong>s</strong><strong> be</strong><strong>come</strong><strong> a major concern to automobile users. The popular static electric traffic light controlling system can no longer sufficiently manage the traffic volume in large cities where real time traffic control is paramount to deciding best route. The proposed mobile traffic management system provides users with traffic information on congested roads using weighted sensors. A prototype of the system was implemented using Java SE Development Kit 8 and Google map. The model </strong><strong>was</strong><strong> simulated and the performance was </strong><strong>assessed</strong><strong> using response time, delay and throughput. Results showed that</strong><strong>,</strong><strong> mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.</strong></p>

scholarly journals Sistem Pengamatan Kondisi Lalu Lintas Berbasis Data GPS pada Smartphone (Studi Kasus : Kota Denpasar)

2020 ◽  
Vol 7 (4) ◽  
pp. 667
Author(s):  
Gede Herdian Setiawan ◽  
I Ketut Dedy Suryawan
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Data Retrieval ◽  
Traffic Information ◽  
Vehicle Speed ◽  
Observation Model ◽  
Economic Activities ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Real Time Traffic

<p>Pertumbuhan jumlah kendaraan yang semakin meningkat setiap tahunnya mengakibatkan volume kendaraan yang melintasi ruas jalan semakin padat yang kerap mengakibatkan kemacetan lalu lintas. Kemacetan lalu lintas dapat menjadi beban biaya yang signifikan terhadap kegiatan ekonomi masyarakat. Informasi lalu lintas yang dinamis seperti informasi kondisi lalu lintas secara langsung <em>(real time)</em> akan membantu mempengaruhi aktivitas masyarakat pengguna lalu lintas untuk melakukan perencanaan dan penjadwalan aktivitas yang lebih baik. Penelitian ini mengusulkan model pengamatan kondisi lalu lintas berbasis data GPS pada <em>smartphone</em>, untuk informasi kondisi lalu lintas secara langsung. GPS <em>Receiver</em> pada <em>smartphone</em> menghasilkan data lokasi secara instan dan bersifat mobile sehingga dapat digunakan untuk pengambilan data kecepatan kendaraan secara langsung. Kecepatan kendaraan diperoleh berdasarkan jarak perpindahan koordinat kendaraan dalam satuan detik selanjutnya di konversi menjadi satuan kecepatan (km/jam) kemudian data kecepatan kendaraan di proses menjadi informasi kondisi lalu lintas. Secara menyeluruh model pengamatan berfokus pada tiga tahapan, yaitu akuisisi data kecepatan kendaraan berbasis GPS pada <em>smartphone</em>, pengiriman data kecepatan dan visualisasi kondisi lalu lintas berbasis GIS. Pengujian dilakukan pada ruas jalan kota Denpasar telah mampu mendapatkan data kecepatan kendaraan dan mampu menunjukkan kondisi lalu lintas secara langsung dengan empat kategori keadaan lalu lintas yaitu garis berwarna hitam menunjukkan lalu lintas macet dengan kecepatan kendaraan kurang dari 17 km/jam, merah menunjukkan padat dengan kecepatan kendaraan 17 km/jam sampai 27 km/jam, kuning menunjukkan sedang dengan kecepatan kendaraan 26 km/jam sampai 40 km/jam dan hijau menunjukkan lancar dengan kecepatan kendaraan diatas 40 km/jam.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The growth in the number of vehicles that is increasing every year has resulted in the volume of vehicles crossing the road increasingly congested which often results in traffic congestion. Traffic congestion can be a significant cost burden on economic activities. Dynamic traffic information such as information on real time traffic conditions will help influence the activities of the traffic user community to better plan and schedule activities. This study proposes a traffic condition observation model based on GPS data on smartphones, for information on real time traffic conditions. The GPS Receiver on the smartphone produces location and coordinate data instantly and is mobile so that it can be used for direct vehicle speed data retrieval. Vehicle speed is obtained based on the displacement distance of the vehicle's coordinates in units of seconds and then converted into units of speed (km / h), the vehicle speed data is then processed into information on traffic conditions. Overall, the observation model focuses on three stages, namely GPS-based vehicle speed data acquisition on smartphones, speed data delivery and visualization of GIS-based traffic conditions. Tests carried out on the Denpasar city road segment have been able to obtain vehicle speed data and are able to show traffic conditions directly with four categories of traffic conditions, namely black lines indicating traffic jammed with vehicle speeds of less than 17 km / h, red indicates heavy with speed vehicles 17 to 27 km / h, yellow indicates medium speed with vehicles 26 km/h to 40 km / h and green shows fluent with vehicle speeds above 40 km / h.</em></p><p><em><strong><br /></strong></em></p>

Using Smartphones as a Tool to Capture Road Traffic Attributes

2013 ◽  
Vol 432 ◽  
pp. 513-519 ◽  
Author(s):  
Giuseppe Guido ◽  
Alessandro Vitale ◽  
Frank Fedel Saccomanno ◽  
Demetrio Carmine Festa ◽  
Vittorio Astarita ◽  
...  
Keyword(s):  
Road Traffic ◽  
Transportation Systems ◽  
Vehicle Tracking ◽  
Traffic Information ◽  
Critical Conditions ◽  
Navigation Systems ◽  
Tracking Data ◽  
Safety Issues ◽  
Real Time Traffic ◽  
Special Events

Road network management under critical conditions is achievable by adopting technologies that trace vehicles and capture unsafety events to provide users with real time traffic information. Most common approaches used to acquire vehicle tracking data are based on video image processing algorithms and satellite navigation systems. However, many studies are increasingly focused on the emerging smartphone technologies for tracking vehicles. The aim of this study is to present a procedure for acquiring vehicle tracking data from smartphone sensors, supporting managers of transportation systems to take effective decisions on their networks, especially in conjunction with special events and/or critical road safety issues.

scholarly journals Detecting anomalous road traffic conditions using VGG19 CNN Model

2021 ◽  
Vol 309 ◽  
pp. 01226
Author(s):  
M. Rajeshwari ◽  
CH. MallikarjunaRao
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Road Traffic ◽  
Dynamic Mode ◽  
Static Mode ◽  
Vehicle Accidents ◽  
Traffic Conditions ◽  
Real Time Traffic ◽  
Street Traffic ◽  
Vehicle Movement

Detection on the real time road traffic has tremendous application possibilities in metropolitan road safety and traffic management. Due to the effect of numerous factors, for example: climate, viewpoints and road conditions in real-time traffic scene, Anomaly detection actually faces many difficulties. There are many reasons for vehicle accidents, for example: crashes, vehicle on flames and vehicle breakdowns, which exhibits distinctive and obscure behaviours. In this paper, we approached with a model to identify oddity in street traffic by monitoring the vehicle movement designs in two unmistakable yet associated modes which is 1. The vehicle’s dynamic mode and 2. The vehicle’s Static mode. The vehicle’s static mode investigation is gained using the background modelling after the detection of a vehicle, this strategy is useful to locate the unusual vehicle movement which keep still out and about. The dynamic mode vehicle examination is gained from identified and followed vehicle directions to locate the strange direction which is distorted from the predominant movement designs. The outcomes from the double mode investigations are at long last fused together by driven a distinguishing proof model to get the last peculiarity. For this research we are using traffic-net Dataset, VGG19 CNN model along with ImageNet weights and OpenCV.

scholarly journals Real-Time Traffic Information Collection Using Multiple Virtual Detection Lines

2008 ◽  
Vol 15B (6) ◽  
pp. 543-552 ◽  
Author(s):  
Eui-Chul Kim ◽  
Soo-Hyung Kim ◽  
Guee-Sang Lee ◽  
Hyung-Jeong Yang
Keyword(s):  
Real Time ◽  
Traffic Information ◽  
Information Collection ◽  
Real Time Traffic ◽  
Traffic Information Collection

scholarly journals Study on the Accessibility and Recreational Development Potential of Lakeside Areas Based on Bike-Sharing Big Data Taking Wuhan City as an Example

2019 ◽  
Vol 12 (1) ◽  
pp. 160
Author(s):  
Jing Wu ◽  
Changlong Ling ◽  
Xinzhuo Li
Keyword(s):  
Big Data ◽  
Road Traffic ◽  
Water Environment ◽  
Wuhan City ◽  
Dense Area ◽  
Development Potential ◽  
Traffic Conditions ◽  
Spatial Measurement ◽  
Bike Sharing ◽  
Distribution Laws

Accessibility is an important factor in measuring the recreational development potential of Wuhan lakeside areas where people like bike-sharing services for leisure. By using bike-sharing big data, this paper visualizes the spatiotemporal distribution characteristics and depicts the free flows of OD (Original Points and Destination Points) points of the bike-sharing activities taking place within 4 km of 21 lakes in the Wuhan Third Ring Road on an important holiday. Based on these distribution laws, statistics and spatial measurement are used to measure and compare the theoretical accessibility and actual accessibility of these lakeside areas at different grid scales in order to estimate the recreational development potential and explore the causes and possible suggestions behind the recreational potential. Results show that Ziyang Lake, Shai Lake, and South Lake have great recreational potential in improving their accessibility, whereas the Hankou lake dense area has a saturated recreational development potential due to its high accessibility characteristics. The differences in the water environment, surrounding road traffic conditions, and construction situations in these lakes influence their accessibility. Some differences are also observed between the actual and theoretical accessibility of most of these lakes, and there is a long way to go for real improvement of their recreational development potential. To better exploit the recreational development potential, improving the accessibility of these lakes remains an important issue that needs to be addressed as soon as possible.

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