Context-Aware Intelligent Traffic Light Control through Secure Messaging

In this paper, we propose STCM, a context-aware secure traffic control model to manage competing traffic flows at a given intersection by using secure messages with real-time traffic information. The vehicle is modeled as a virtual sensor which reports the traffic state, such as its speed and location, to a traffic light controller through a secure and computationally lightweight protocol. During the reporting process, a vehicle’s identity and location are kept anonymous to any other vehicle in the system. At an intersection, the traffic light controller receives the messages with traffic information, verifies the identities of the vehicles, and dynamically implements and optimizes the traffic light phases in real-time. Moreover, the system is able to detect the presence of emergency vehicles (such as ambulances and fire fighting trucks) in the communication range and prioritize the intersection crossing of such vehicles to in order to minimize their waiting times. The simulation results demonstrate that the system significantly reduces the waiting time of the vehicles in both light and heavy traffic flows compared to the pretimed signal control and the adaptive Webster’s method. Simulation results also yield effective robustness against impersonating attacks from malicious vehicles.

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Mobile Road Traffic Management System Using Weighted Sensor

International Journal of Interactive Mobile Technologies (iJIM) ◽

10.3991/ijim.v11i5.6745 ◽

2017 ◽

Vol 11 (5) ◽

pp. 147 ◽

Cited By ~ 2

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

Vehicular traffic is continuously increasing around the world, especially in urban areas, and the resulting congestion has become 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 was simulated and the performance was assessed using response time, delay and throughput. Results showed that, mobile devices are capable of assisting road users’ in faster decision making by providing real-time traffic information and recommending alternative routes.

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Intelligent traffic light control system based on real time traffic flows

Kahn at Penn ◽

10.1109/ccnc.2017.7983196 ◽

2017 ◽

Author(s):

Zhijun Li ◽

Chunxiao Li ◽

Yanan Zhang ◽

Xuelong Hu

Keyword(s):

Control System ◽

Real Time ◽

Traffic Flows ◽

Light Control ◽

Traffic Light ◽

Real Time Traffic ◽

Traffic Light Control

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Chaotic Traffic Flows on Two Crossroads Caused by Real-Time Traffic Information

Traffic and Granular Flow’05 ◽

10.1007/978-3-540-47641-2_65 ◽

2007 ◽

pp. 667-673 ◽

Cited By ~ 1

Author(s):

Minoru Fukui ◽

Katsuhiro Nishinari ◽

Yasushi Yokoya ◽

Yoshihiro Ishibashi

Keyword(s):

Real Time ◽

Traffic Information ◽

Traffic Flows ◽

Real Time Traffic

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Computing Traffic Information in the Cloud

Cloud Technology ◽

10.4018/978-1-4666-6539-2.ch049 ◽

2015 ◽

pp. 1062-1079

Author(s):

Po-Ting Wei ◽

Tai-Chi Wang ◽

Shih-Yu Chang ◽

Yeh-Ching Chung

Keyword(s):

Ad Hoc Networks ◽

Real Time ◽

Traffic Safety ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Traffic Information ◽

Traffic Light ◽

Real Time Traffic ◽

Hoc Networks ◽

Commercial Applications

Vehicular ad hoc networks have been envisioned to be useful in road safety and commercial applications. In addition, in-vehicle capabilities could be used as a service to provide a variety of applications, for example, to provide real-time junction view of road intersections or to address traffic status for advanced traffic light control. In this work, the authors construct a cloud service over vehicular ad hoc networks to provide event data including capturing videos or Global Positioning System (GPS) data. Moreover, the authors integrate the GPS receiver and the navigation software equipped over On Board Unit to create a Geographic Information System digital map and to offer a traffic safety application. The hardware is implemented by Eeepad for integrating camera and GPS. Furthermore, the cyclic recording scheme has been addressed for data transmission and query. With the design, people can get real-time traffic information including traffic videos or geographical data in the cloud.

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Novo método de cálculo das frações de verde para controle semafórico em tempo real

TRANSPORTES ◽

10.14295/transportes.v19i1.489 ◽

2011 ◽

Vol 19 (1) ◽

pp. 87

Author(s):

Werner Kraus Junior ◽

José Dolores Vergara Dietrich ◽

Felipe Augusto De Souza ◽

Eduardo Camponogara

Keyword(s):

Real Time ◽

Traffic Control ◽

Original Method ◽

New Method ◽

Superior Performance ◽

Control Parameters ◽

Real Time Traffic ◽

Simulation Results

Resumo: Apresenta-se um método de cálculo de frações de verde a ser usado em sistemas de controle de tráfego em tempo real por amostragem cíclica. A base do método origina-se da estratégia TUC, brevemente revisada neste trabalho. O objetivo é substituir um procedimento empírico de ajuste de parâmetros auxiliares do controle por uma metodologia que seja facilmente entendida e utilizável na prática. Resultados de simulação indicam o desempenho superior do método quando comparado com os ajustes empíricos do método original.Palavras-chave: controle semafório em tempo real; amostragem cíclica; método TUC.Abstract: A new method for the computation of splits for real-time traffic control with cyclic sampling is presented. The method is based on the TUC strategy, which is briefly reviewed in this paper. The goal is to replace an empirical tuning procedure of auxiliary control parameters by a method that is easy to understand and implement. Simulation results indicate the superior performance of the method when compared to the empirical adjustments of the original method.Keywords: real-time traffic control; cyclic sampling; TUC method.

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Traffic Signal Control for a Single Intersection-Based Intelligent Transportation System

Digital Transformation and Innovative Services for Business and Learning - Advances in Business Strategy and Competitive Advantage ◽

10.4018/978-1-7998-5175-2.ch009 ◽

2020 ◽

pp. 159-180

Author(s):

Nouha Rida ◽

Mohammed Ouadoud ◽

Abderrahim Hasbi

Keyword(s):

Real Time ◽

Waiting Time ◽

Intelligent Transportation Systems ◽

Intelligent Transportation ◽

Transportation Systems ◽

Traffic Information ◽

Traffic Signal Control ◽

Traffic Light ◽

The Road ◽

Real Time Traffic

Traffic optimization at an intersection, using real-time traffic information, presents an important focus of research into intelligent transportation systems. Several studies have proposed adaptive traffic lights control, which concentrates on determining green light length and sequence of the phases for each cycle in accordance with the real-time traffic detected. In order to minimize the waiting time at the intersection, the authors propose an intelligent traffic light using the information collected by a wireless sensors network installed in the road. The proposed algorithm is essentially based on two parameters: the waiting time in each lane and the length of its queue. The simulations show that the algorithm applied at a network of intersections improves significantly the average waiting time, queue length, fuel consumption, and CO2 emissions.

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A Fast Connection Construction and Update Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.2506 ◽

2013 ◽

Vol 712-715 ◽

pp. 2506-2509

Author(s):

Feng Liu

Keyword(s):

Real Time ◽

Heavy Traffic ◽

Hash Table ◽

Traffic Analysis ◽

Internet Traffic ◽

Research Area ◽

The Internet ◽

Traffic Flows ◽

Real Time Traffic ◽

Traffic Environment

In the research area of network security, we often need to analyze the Internet traffic in real time. But the Internet traffic is usually very heavy, so it is very hard for us to analysis each packet one by one. Alternatively, analyzing the Internet traffic on flow level is often employed. Before analyzing the traffic flows, how we can fast construct and update these flows is a key issue. To solve this issue, in this paper, we propose a fast connection construction and update algorithm. Firstly, we define bidirectional flows as connection. Then, we use hash table to store the connection records and use this connection construction and update algorithm to ensure that these connection records can to be stored in memory in heavy traffic environment to achieve real-time traffic analysis. At last, the experiments show that the algorithm we proposed is efficient and can meet the traffic analysis need.

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A Sarsa(λ)-Based Control Model for Real-Time Traffic Light Coordination

The Scientific World JOURNAL ◽

10.1155/2014/759097 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Xiaoke Zhou ◽

Fei Zhu ◽

Quan Liu ◽

Yuchen Fu ◽

Wei Huang

Keyword(s):

Real Time ◽

Traffic Flow ◽

Traffic Control ◽

Learning Algorithm ◽

Control Model ◽

Traffic Signal ◽

Signal Timing ◽

Traffic Light ◽

Real Time Traffic ◽

Traffic Signal Timing

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

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