scholarly journals RFID Based Vehicle Toll Collection System for Toll Roads

2021 ◽  
Vol 12 (2) ◽  
pp. 2104-2113
Author(s):  
Piyush Singhal, Et. al.
Keyword(s):  
Control Device ◽  
Toll Roads ◽  
Moving Vehicle ◽  
Toll Road ◽  
Collection Device ◽  
Toll Collection ◽  
The Individual ◽  
Machine Readable ◽  
Moving Vehicle Detection ◽  
Collection Program

The RFID-based vehicle collection program is intended to better handle toll operations through technology that aims to streamline the flow of vehicles. The purpose of this work is to plan, introduce and promote the automated operation of the car selection system (VTS). The Vehicle Toll Collection Device in this paper automatically detects vehicles and gathers machine-readable details on tolls for automobiles driving in the toll road. This knowledge is instigated by the modification and installation of at least one vehicle with a moving vehicle detection device. The computerized control device located along the toll line will transmit the registration signal as the car is reaching the registration point and will determine the toll to be debited and transfer the toll electronically to the account of the individual vehicle. This device helps a car to proceed beyond the scan point with no halting, thereby providing commuters with optimum comfort, speeding up traffic movement and reducing the need for human capital on highways

Measuring Drivers’ Attitudes Toward Use of Electronic Toll Collection Systems in Spain

2017 ◽  
Vol 2670 (1) ◽  
pp. 1-8
Author(s):  
Javier Heras-Molina ◽  
Juan Gomez ◽  
Jose Manuel Vassallo
Keyword(s):  
Nationwide Survey ◽  
Minor Role ◽  
Toll Roads ◽  
Electronic Toll Collection ◽  
Explanatory Variables ◽  
Toll Road ◽  
Road Users ◽  
Effective Policy ◽  
Toll Collection ◽  
A Minor

Electronic payment is increasing inexorably as a means of paying for the use of toll roads worldwide. However, it is necessary to increase the effectiveness and expediency of this mechanism, as well as improve policy issues such as privacy or data management. The state of the art in this field typically analyzes users’ perceptions and willingness to pay to use toll roads. However, research addressing drivers’ attitudes toward the use of electronic toll collection (ETC) systems is not sufficiently developed. The aim of this paper is to identify the explanatory variables influencing toll road users’ use of ETC technologies. For that purpose, based on a nationwide survey directed toward road users of interurban toll roads in Spain, a binomial logit model was developed to explore users’ attitudes toward the use of ETC systems. The research concludes that drivers’ tag ownership is related mainly to trip-related attributes, while personal socioeconomic characteristics play a minor role. In addition, free tags would be an effective policy measure to increase toll road use, given that a majority of respondents show a positive response.

A Novel AMS-DAT Algorithm for Moving Vehicle Detection in a Satellite Video

2020 ◽  
pp. 1-5
Author(s):  
Xu Chen ◽  
Haigang Sui ◽  
Jian Fang ◽  
Mingting Zhou ◽  
Chen Wu
Keyword(s):  
Vehicle Detection ◽  
Moving Vehicle ◽  
Moving Vehicle Detection

scholarly journals Pelanggaran Kecepatan Kendaraan pada Ruas Jalan Tol Cipularang. (Hal. 39-49)

2019 ◽  
Vol 5 (1) ◽  
pp. 39
Author(s):  
Rizki Intan Mauliza ◽  
Tania Bonita Sabrina ◽  
Wahyu Maulana
Keyword(s):  
Search Method ◽  
Primary Data ◽  
Maximum Speed ◽  
Speed Limit ◽  
Toll Roads ◽  
Passenger Cars ◽  
Passenger Car ◽  
Average Speed ◽  
Toll Road ◽  
Road Speed

ABSTRAKSalah satu faktor penyebab kecelakaan yang signifikan adalah tidak sesuainya kecepatan kendaraan dengan kondisi jalan, lingkungan dan kegiatan, dalam hal ini adalah kecepatan yang terlalu tinggi. Jalan tol/jalan bebas hambatan merupakan salah satu jalan yang berpotensi memiliki banyak pelanggaran dalam kecepatan kendaraan. Batasan kecepatan jalan tol telah di atur dalam PM Hub 111/2015 yaitu 40 km/jam untuk tol dalam kota dan 60 km/jam - 100 km/jam untuk tol luar kota. Untuk memastikan kecepatan rata-rata kendaraan dan menentukan tingkat pelanggaran kendaraan yang melintasi ruas jalan tol Cipularang maka penelitian menggunakan metode pengumpulan data primer/pengamatan secara langsung. Hasil analisis secara keseluruhan didapatkan bahwa rata-rata kecepatan kendaraan mobil penumpang sebesar 88 km/jam, truk 62 km/jam dan bus 72 km/jam dengan persentasi kecepatan rata-rata untuk mobil penumpang, truk dan bus berturut-turut sebesar 43%, 5% dan 22%. Hal ini menunjukan terdapat pelanggaran batas kecepatan maksimum untuk kendaraan mobil penumpang dengan prosentase yang tinggi (lebih dari 30%) atau kecepatan rata-rata lebih dari 80 km/jam.Kata kunci: kecelakaan, batas kecepatan, jalan tol ABSTRACTOne factors of a significant accident is not according to the speed of the vehicle with the environment, environment and activities, in this case the speed is too high. Toll road / freeway is one of the roads that has many roads in the vehicle. The toll road speed limit has been set in PM Hub 111/2015, which is 40 km/hour  for city tolls and 60 km/hour  100 km/hour for out-of-city toll roads. To determine the average speed of a vehicle and determine the level of the vehicle passing through the Cipularang toll road, the study uses the primary data / direct search method. The overall analysis results are obtained that the average speed of passenger car vehicles is 88 km/hour, trucks 62 km/hour and buses 72 km/hour with the percentage of average speed for passenger cars, trucks and buses being helped-along by 43%, 5% and 22%. This shows the maximum speed limit for passenger car vehicles with a higher percentage (more than 30%) or an average speed of more than 80 km/hour.Keywords: accidents, speed limits, toll roads

Moving Vehicle Detection Using Time Domain Statistical Features

2013 ◽  
Vol 471 ◽  
pp. 208-212 ◽  
Author(s):  
M.P. Paulraj ◽  
Hamid Adom Abdul ◽  
Marhainis Othman Siti ◽  
Sundararaj Sathishkumar
Keyword(s):  
Probabilistic Neural Network ◽  
Hearing Impaired ◽  
Statistical Features ◽  
Experimental Protocol ◽  
Moving Vehicle ◽  
Vehicle Type ◽  
Moving Vehicles ◽  
Machine Interface ◽  
Moving Vehicle Detection ◽  
Sound Signature

The Hearing Impaired People (HIP) cannot distinguish the sound from a moving vehicle approaching from their behind. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoor. If HIPs can successfully get sound information through some machine interface, dangerous situation will be avoided. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. This paper presents, simple statistical features are used to classify the vehicle type and its distance based on sound signature recorded from the moving vehicles. An experimental protocol is designed to record the vehicle sound under different environment conditions and also at different speed of vehicles. Basic statistical features such as the standard deviation, Skewness, Kurtosis and frame energy have been used to extract the features. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation.

User Profiles for Personalizing Digital Libraries

2009 ◽  
pp. 149-158 ◽  
Author(s):  
Giovanni Semeraro ◽  
Pierpaolo Basile ◽  
Marco de Gemmis ◽  
Pasquale Lops
Keyword(s):  
Machine Learning ◽  
Digital Libraries ◽  
Machine Learning Techniques ◽  
Linguistic Knowledge ◽  
User Profiles ◽  
User Interests ◽  
Learning Techniques ◽  
Document Filtering ◽  
The Individual ◽  
Machine Readable

Exploring digital collections to find information relevant to a user’s interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the individual interests of the user. Algorithms designed to solve this problem base their relevance computations on user profiles in which representations of the users’ interests are maintained. The main focus of this chapter is the adoption of machine learning to build user profiles that capture user interests from documents. Profiles are used for intelligent document filtering in digital libraries. This work suggests the exploiting of knowledge stored in machine-readable dictionaries to obtain accurate user profiles that describe user interests by referring to concepts in those dictionaries. The main aim of the proposed approach is to show a real-world scenario in which the combination of machine learning techniques and linguistic knowledge is helpful to achieve intelligent document filtering.

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