A Privacy-Preserving Traffic Monitoring Scheme via Vehicular Crowdsourcing

The explosive number of vehicles has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles’ speed information is an effective way to monitor the traffic conditions and avoid vehicles’ congestion, however it may threaten vehicles’ location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle’s speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles’ speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles’ speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles’ privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles’ identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme.

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INFLUENCE OF BUS BAY AND CURBSIDE BUS STOP IN AN URBAN ROAD

International Journal of Students Research in Technology & Management ◽

10.18510/ijsrtm.2020.833 ◽

2020 ◽

Vol 8 (3) ◽

pp. 14-19

Author(s):

Saurav Barua

Keyword(s):

Traffic Congestion ◽

Simulation Models ◽

Good Alternative ◽

Road Segment ◽

Dhaka City ◽

Average Speed ◽

Urban Road ◽

Traffic Conditions ◽

Bus Stop ◽

Bus Stops

Purpose of Study: The purpose of this study is to investigate the efficiency of bus bay compare to the curbside bus stop in a midblock road segment of Dhaka city. Methodology: Vehicle composition and traffic volume were counted on-peak hours for the midblock of Azimpur road near the existing bus stop. Simulation models were developed in VISSIM, where Model 1 represented the existing road scenario with curbside bus stop, and Model 2 represented the same road segment with a bus bay. Main findings: The simulation result showed that Model 2 outperformed Model 1 due to the presence of bus bay. Comparing Model 1, travel time and delay reduced by varying 1.80% to 12.5% and 6.25% to 100% respectively in Model 2 during the simulation. Similarly, average speed increased by 1.39% and density decreased by 61.29% in model 2. Application of this study: Curbside bus stops result in abrupt halt, disrupt traffic flow, and queuing of the small-sized vehicle behind buses. These bus stops caused traffic congestion and delays in urban roads which can be alleviated by alternatives, such as, bus bay. The novelty of this study: The bus bay is a good alternative to the curbside bus stop, which can improve existing traffic conditions in urban roads.

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IoT based Real Time Traffic Monitoring System Using M/G/1 Queuing Research

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9397.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3818-3822

Keyword(s):

Intelligent Transportation Systems ◽

Traffic Congestion ◽

Queuing Theory ◽

Traffic Monitoring ◽

Transportation Systems ◽

Theory Approach ◽

The Road ◽

Traffic Conditions ◽

Performance Framework ◽

Real Time Traffic

Vehicular Traffic crowding is paramount worry in urban cities. The use of technologies like Intelligent Transportation systems and Internet of Things can solve the problem of traffic congestion to some extent. The paper analyses the traffic conditions on a particular urban highway using queuing theory approach. It researches on performance framework such as time for waiting and queue length. The results can provide significant analysis to predict traffic congestion during peak hours. A congestion controlling action can be generated to utilize the road capacity fully during peak hours by using these results

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Smart Driving System for Improving Traffic Flow

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0174 ◽

2017 ◽

Vol 7 (7) ◽

pp. 236

Author(s):

Rajesh Kumar Gupta ◽

L. N. Padhy ◽

Sanjay Kumar Padhi

Keyword(s):

Traffic Flow ◽

Traffic Congestion ◽

Urban Areas ◽

Adaptive Cruise Control ◽

Human Perception ◽

Cruise Control ◽

Driving System ◽

V2v Communication ◽

Traffic Conditions ◽

Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay. For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

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Detecting Different Road Infrastructural Elements Based on the Stochastic Characterization of Speed Patterns

Journal of Advanced Transportation ◽

10.1155/2017/3802807 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Mario Muñoz-Organero ◽

Ramona Ruiz-Blázquez

Keyword(s):

Total Variation ◽

Traffic Congestion ◽

Automatic Detection ◽

Mass Function ◽

Total Variation Distance ◽

Statistical Moments ◽

Probability Mass Function ◽

Traffic Lights ◽

Traffic Conditions ◽

Variation Distance

The automatic detection of road related information using data from sensors while driving has many potential applications such as traffic congestion detection or automatic routable map generation. This paper focuses on the automatic detection of road elements based on GPS data from on-vehicle systems. A new algorithm is developed that uses the total variation distance instead of the statistical moments to improve the classification accuracy. The algorithm is validated for detecting traffic lights, roundabouts, and street-crossings in a real scenario and the obtained accuracy (0.75) improves the best results using previous approaches based on statistical moments based features (0.71). Each road element to be detected is characterized as a vector of speeds measured when a driver goes through it. We first eliminate the speed samples in congested traffic conditions which are not comparable with clear traffic conditions and would contaminate the dataset. Then, we calculate the probability mass function for the speed (in 1 m/s intervals) at each point. The total variation distance is then used to find the similarity among different points of interest (which can contain a similar road element or a different one). Finally, a k-NN approach is used for assigning a class to each unlabelled element.

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Smart Traffic Light System

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.38053 ◽

2021 ◽

Vol 9 (9) ◽

pp. 679-686

Author(s):

Rashi Maheshwari

Keyword(s):

Traffic Congestion ◽

Traffic Signals ◽

Traffic Monitoring ◽

Traffic Signal ◽

Traffic Signal Control ◽

Traffic Light ◽

Mathematical Functions ◽

Light System ◽

Smart Traffic ◽

And Control

Abstract: Traffic signal control frameworks are generally used to monitor and control the progression of cars through the intersection of roads. Moreover, a portable controller device is designed to solve the issue of emergency vehicles stuck in overcrowded roads. The main objective of this paper is to design and implement a suitable algorithm and its simulation for an intelligent traffic signal simulator. The framework created can detect the presence or nonappearance of vehicles within a specific reach by setting appropriate duration for traffic signals to react accordingly. By employing mathematical functions and algorithms to ascertain the suitable timing for the green signal to illuminate, the framework can assist with tackling the issue of traffic congestion. The explanation relies on recent fixed programming time. Keywords: Smart Traffic Light System, Smart City, Traffic Monitoring.

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Privacy-Preserving Sorting Algorithms Based on Logistic Map for Clouds

Security and Communication Networks ◽

10.1155/2018/2373545 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10

Author(s):

Hua Dai ◽

Hui Ren ◽

Zhiye Chen ◽

Geng Yang ◽

Xun Yi

Keyword(s):

Data Privacy ◽

Logistic Map ◽

Security Analysis ◽

Privacy Preserving ◽

Service Recommendation ◽

Sensitive Data ◽

Encrypted Data ◽

Sorting Algorithms ◽

Common Operation ◽

Cloud Servers

Outsourcing data in clouds is adopted by more and more companies and individuals due to the profits from data sharing and parallel, elastic, and on-demand computing. However, it forces data owners to lose control of their own data, which causes privacy-preserving problems on sensitive data. Sorting is a common operation in many areas, such as machine learning, service recommendation, and data query. It is a challenge to implement privacy-preserving sorting over encrypted data without leaking privacy of sensitive data. In this paper, we propose privacy-preserving sorting algorithms which are on the basis of the logistic map. Secure comparable codes are constructed by logistic map functions, which can be utilized to compare the corresponding encrypted data items even without knowing their plaintext values. Data owners firstly encrypt their data and generate the corresponding comparable codes and then outsource them to clouds. Cloud servers are capable of sorting the outsourced encrypted data in accordance with their corresponding comparable codes by the proposed privacy-preserving sorting algorithms. Security analysis and experimental results show that the proposed algorithms can protect data privacy, while providing efficient sorting on encrypted data.

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TOWARDS AN EFFICIENT TRAFFIC CONGESTION PREDICTION METHOD BASED ON NEURAL NETWORKS AND BIG GPS DATA

IIUM Engineering Journal ◽

10.31436/iiumej.v20i1.997 ◽

2019 ◽

Vol 20 (1) ◽

pp. 108-118 ◽

Cited By ~ 3

Author(s):

Wiam Elleuch ◽

Ali Wali ◽

Adel M. Alimi

Keyword(s):

Traffic Congestion ◽

Prediction Method ◽

Traffic Information ◽

Gps Data ◽

Traffic Patterns ◽

Dynamic Changes ◽

Traffic Conditions ◽

Congestion Prediction ◽

Different Types ◽

Nonparametric Techniques

ABSTRACT: The prediction of accurate traffic information such as speed, travel time, and congestion state is a very important task in many Intelligent Transportations Systems (ITS) applications. However, the dynamic changes in traffic conditions make this task harder. In fact, the type of road, such as the freeways and the highways in urban regions, can influence the driving speeds and the congestion state of the corresponding road. In this paper, we present a NNs-based model to predict the congestion state in roads. Our model handles new inputs and distinguishes the dynamic traffic patterns in two different types of roads: highways and freeways. The model has been tested using a big GPS database gathered from vehicles circulating in Tunisia. The NNs-based model has shown their capabilities of detecting the nonlinearity of dynamic changes and different patterns of roads compared to other nonparametric techniques from the literature. ABSTRAK: Ramalan maklumat trafik yang tepat seperti kelajuan, masa perjalanan dan keadaan kesesakan adalah tugas yang sangat penting dalam banyak aplikasi Sistem Pengangkutan Pintar (ITS). Walau bagaimanapun, perubahan keadaan lalu lintas yang dinamik menjadikan tugas ini menjadi lebih sukar. Malah, jenis jalan raya, seperti jalan raya dan lebuh raya di kawasan bandar, boleh mempengaruhi kelajuan memandu dan keadaan kesesakan jalan yang sama. Dalam makalah ini, kami membentangkan model berasaskan NN untuk meramalkan keadaan kesesakan di jalan raya. Model kami mengendalikan input baru dan membezakan corak trafik dinamik dalam dua jenis jalan raya yang lebuh raya dan jalan raya. Model ini telah diuji menggunakan pangkalan data GPS yang besar yang dikumpulkan dari kenderaan yang beredar di Tunisia. Model berasaskan NNs telah menunjukkan keupayaan mereka untuk mengesan ketiadaan perubahan dinamik dan pola jalan yang berbeza berbanding dengan teknik nonparametrik yang lain dari kesusasteraan.

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Dynamic Traffic Light Control

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8609.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 3228-3231

Keyword(s):

Traffic Congestion ◽

Traffic Management ◽

Red Light ◽

Traffic Monitoring ◽

Intelligent Transport System ◽

Light Control ◽

Traffic Light ◽

Traffic Lights ◽

Traffic Light Control ◽

Traffic Management System

Intelligent Transport System (ITS) is blooming worldwide. The Traditional Traffic management system is a tedious process and it requires huge man power, to overcome this we have proposed an automatic Traffic monitoring system that has effective fleet management. The current transportation system at intersections and junctions has Traffic Lights with Fixed durations which increase the unnecessary staying time which intern harms the environment. An Adaptive traffic light control is implemented using SUMO simulator, that changes the duration of Green and Red light according to the traffic flow. This is an effective and efficient way to reduce the Traffic congestion. The traffic congestion is determined by taking the object count using deep learning approach (Convolutional Neural Network).

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PEDESTRIAN TRAFFIC CONDITIONS OF RURAL ROADS AND BUILT-UP AREAS: THE ANALYSIS OF THE EXISTING SITUATION AND POSSIBILITIES OF IMPROVING TRAFFIC CONDITIONS

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2010.054 ◽

2010 ◽

Vol 2 (3) ◽

pp. 60-66

Author(s):

Nemunas Abukauskas ◽

Egidijus Skrodenis

Keyword(s):

Traffic Safety ◽

Road Safety ◽

Traffic Accidents ◽

Road Traffic ◽

Rural Roads ◽

Traffic Conditions ◽

Foreign Countries ◽

Pedestrian Traffic ◽

Main Activity

The results of lengthy thorough investigations into traffic safety situation show that the percentage of pedestrians getting involved in road traffic accidents on Lithuanian roads is significantly higher (more than 33 % of the total number of injury and fatal accidents) than that compared to the other European Union member-states. The article studies traffic safety problems and their factors causing the largest influence on the occurrence of these accidents. Considering valuable experience gained by foreign countries, investigation was carried out to establish general and main factors causing insufficient road safety conditions and significance of these factors to road safety. The article also shows the main activity improving road safety in Lithuania and discusses the effectiveness of strategic and local (temporary and long term) measures to improve conditions for pedestrian road safety.

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

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020741909 ◽

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

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 (real time) 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 smartphone, untuk informasi kondisi lalu lintas secara langsung. GPS Receiver pada smartphone 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 smartphone, 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. AbstractThe 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.

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