scholarly journals Smart Real-Time Traffic Optimization System

2019 ◽  
Vol 8 (2S4) ◽  
pp. 56-58
Keyword(s):  
Real Time ◽  
Traffic Signals ◽  
Fixed Time ◽  
Traffic Data ◽  
Optimization System ◽  
The Road ◽  
Real Time Traffic ◽  
Yellow Orange ◽  
Optimization Methodology ◽  
Traffic Optimization

Today’s traffic situations in busy cities require a smarter traffic signal optimization methodology to regulate the traffic in an orderly manner. Traffic signals are set to a fixed time limit in most of the road junctions. This leads to a dead lock in many junctions because they do not recognize the density of traffic in each road. People want to reach their destinations as quickly as possible. Our proposed Smart Real-Time Traffic Optimization System (SRTOS) provides optimization of traffic signals based on real time Google traffic data. In Google maps, traffic data is indicated through different colors (dark red, red, yellow, orange and green). These colors are processed to find out the traffic around a particular traffic junction and preference is given to the road that has a maximum traffic in the traffic junction, thereby easing out the congestion in that junction. Following similar patterns of activities for all other traffic junctions, wherever possible, will be helpful to drastically ease the flow of traffic patterns of a particular city. This is to fulfill the objective of smoother traffic flow without making people to wait and waste their valuable time

An Optimization on Traffic Signals Sequence Scheduling

2013 ◽  
Vol 765-767 ◽  
pp. 1709-1712
Author(s):  
Bin Bin Zhou ◽  
Lu Yi Chen ◽  
Ping Xu ◽  
Jing Jing Liu ◽  
Guo Yong Dai
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Traffic Signals ◽  
Wireless Sensor ◽  
Traffic Information ◽  
Traffic Data ◽  
Real Time Traffic ◽  
Traffic Environment

We research the issue of traffic signals scheduling, based on the use of real-time traffic information gathered by a wireless sensor network. In this paper, an optimization on traffic signals sequence scheduling has been put forward, which can be achieved based on the instant traffic data collected in the dynamically traffic environment. Afterwards, simulations have been conducted in several scenarios, and show that the proposed approach can achieve better performance in terms of traffic throughput.

Crash risk analysis during fog conditions using real-time traffic data

2018 ◽  
Vol 114 ◽  
pp. 4-11 ◽  
Author(s):  
Yina Wu ◽  
Mohamed Abdel-Aty ◽  
Jaeyoung Lee
Keyword(s):  
Risk Analysis ◽  
Real Time ◽  
Crash Risk ◽  
Traffic Data ◽  
Real Time Traffic

Feasibility Study of Traffic Signals at Intersections in Peshawar, Pakistan

2012 ◽  
Vol 253-255 ◽  
pp. 1645-1649
Author(s):  
Rawid Khan ◽  
Ghulam Dastagir ◽  
Omar Shahid ◽  
Zeeshan Ahmed ◽  
Bashir Alam
Keyword(s):  
Traffic Management ◽  
Management Strategies ◽  
Traffic Signals ◽  
Level Of Service ◽  
Signal Timing ◽  
Traffic Data ◽  
Ongoing Research ◽  
The Road ◽  
Micro Simulation ◽  
Signal Time

The paper is part of an ongoing research project on traffic management strategies for Peshawar Pakistan. Traffic data collected and warrant tests checked at selected intersections. Peak hour vehicular volume warrant test selected and performed at intersections. Signal timing capacity and delay analysis performed and level of service determined for selected intersection. It was found that “for the same width of the road” the delay and level of service is different at different locations and the corresponding signal time is also different. Some data also analysed in 3D micro simulation.

Real-Time Traffic Measurement from Single Loop Inductive Signatures

2002 ◽  
Vol 1804 (1) ◽  
pp. 98-106 ◽  
Author(s):  
Seri Oh ◽  
Stephen G. Ritchie ◽  
Cheol Oh
Keyword(s):  
Real Time ◽  
The United States ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Vehicle Class ◽  
Traffic Measurement ◽  
Single Loop ◽  
Loop Detectors ◽  
Real Time Traffic ◽  
Traffic Data Collection

Accurate traffic data acquisition is essential for effective traffic surveillance, which is the backbone of advanced transportation management and information systems (ATMIS). Inductive loop detectors (ILDs) are still widely used for traffic data collection in the United States and many other countries. Three fundamental traffic parameters—speed, volume, and occupancy—are obtainable via single or double (speed-trap) ILDs. Real-time knowledge of such traffic parameters typically is required for use in ATMIS from a single loop detector station, which is the most commonly used. However, vehicle speeds cannot be obtained directly. Hence, the ability to estimate vehicle speeds accurately from single loop detectors is of considerable interest. In addition, operating agencies report that conventional loop detectors are unable to achieve volume count accuracies of more than 90% to 95%. The improved derivation of fundamental real-time traffic parameters, such as speed, volume, occupancy, and vehicle class, from single loop detectors and inductive signatures is demonstrated.

scholarly journals Ant Colony Optimization for Real Time Traffic Lights Control on a Single Intersection

2020 ◽  
Vol 14 (02) ◽  
pp. 196 ◽  
Author(s):  
Nouha Rida ◽  
Mohammed Ouadoud ◽  
Aberrahim Hasbi
Keyword(s):  
Road Traffic ◽  
Green Light ◽  
Optimal Solution ◽  
Ant Colony ◽  
Traffic Data ◽  
Traffic Light ◽  
Traffic Lights ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

In this paper, we present a new scheme to intelligently control the cycles and phases of traffic lights by exploiting the road traffic data collected by a wireless sensor network installed on the road. The traffic light controller determines the next phase of traffic lights by applying the Ant Colony Optimazation metaheuristics to the information collected by WSN. The objective of this system is to find an optimal solution that gives the best possible results in terms of reducing the waiting time of vehicles and maximizing the flow crossing the intersection during the green light. The results of simulations by the SUMO traffic simulator confirm the preference of the developed algorithm over the predefined time controller and other dynamic controllers.

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