scholarly journals Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China

2021 ◽  
Vol 13 (4) ◽  
pp. 1695
Author(s):  
Yao Wu ◽  
Yanyong Guo ◽  
Wei Yin
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Red Light ◽  
Volume Ratio ◽  
Pedestrian Safety ◽  
Traffic Data ◽  
Red Light Running ◽  
The Real ◽  
Comparison Results ◽  
Safety Models

The traditional way to evaluate pedestrian safety is a reactive approach using the data at an aggregate level. The objective of this study is to develop real-time safety models for pedestrian red-light running using the signal cycle level traffic data. Traffic data for 464 signal cycles during 16 h were collected at eight crosswalks on two intersections in the city of Nanjing, China. Various real-time safety models of pedestrian red-light running were developed based on the different combination of explanatory variables using the Bayesian Poisson-lognormal (PLN) model. The Bayesian estimation approach based on Markov chain Monte Carlo simulation is utilized for the real-time safety models estimates. The models’ comparison results show that the model incorporated exposure, pedestrians’ characteristics and crossing maneuver, and traffic control and crosswalk design outperforms the model incorporated exposure and the model incorporated exposure, pedestrians’ characteristics, and crossing maneuver. The result indicates that including more variables in the real-time safety model could improve the model fit. The model estimation results show that pedestrian volume, ratio of males, ratio of pedestrians on phone talking, pedestrian waiting time, green ratio, signal type, and length of crosswalk are statistically significantly associated with the pedestrians’ red-light running. The findings from this study could be useful in real-time pedestrian safety evaluation as well as in crosswalk design and pedestrian signal optimization.

Research on the Traffic Simulation Platform Based on the Real-Time Mobile Phone Data

2012 ◽  
Vol 253-255 ◽  
pp. 1365-1368
Author(s):  
Ge Qi Qi ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Traffic Simulation ◽  
Rapid Development ◽  
Mobile Phone Data ◽  
Simulation Platform ◽  
Traffic Data ◽  
The Real ◽  
Real Time Traffic ◽  
Wide Range

With the rapid development of the society, the transportation system has become more complicated and vulnerable. For simulating the real-time traffic condition of the whole city, a wide range of OD matrix data are needed which are hard to collect in whole based on the present conventional methods. The paper raises a feasible design of the traffic simulation platform based on the real-time mobile phone data. The popularity and development of mobile phones make the vast amounts of real-time traffic data can be collected and usable. With the help of the GIS module, dynamic OD traffic generation module and other related modules, the real-time mobile phone data will be converted to the valuable traffic data and applied to the traffic simulation platform.

AN AGENT BASED APPROACH TO THE REAL TIME AIR TRAFFIC CONTROL

2005 ◽  
Vol 38 (1) ◽  
pp. 126-131 ◽  
Author(s):  
Ludovica Adacher ◽  
Carlo Meloni
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Air Traffic ◽  
Agent Based ◽  
The Real

scholarly journals Design and Development of Traffic Control System using Image Processing

2021 ◽  
Vol 9 (VI) ◽  
pp. 3247-3252
Author(s):  
G. Kalyan
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Red Light ◽  
Traffic Density ◽  
The Road ◽  
Real Time Traffic ◽  
On The Road

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

scholarly journals Deep Reinforcement Learning Model to Mitigate Congestion in Real-Time Traffic Light Networks

2021 ◽  
Vol 6 (10) ◽  
pp. 138
Author(s):  
Fábio de Souza Pereira Borges ◽  
Adelayda Pallavicini Fonseca ◽  
Reinaldo Crispiniano Garcia
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Traffic Control ◽  
High Volume ◽  
Urban Traffic ◽  
Traffic Data ◽  
Traffic Light ◽  
Urban Network ◽  
Traffic Demand ◽  
Traffic Lights

Urban traffic congestion has a significant detrimental impact on the environment, public health and the economy, with at a high cost to society worldwide. Moreover, it is not possible to continually modify urban road infrastructure in order to mitigate increasing traffic demand. Therefore, it is important to develop traffic control models that can handle high-volume traffic data and synchronize traffic lights in an urban network in real time, without interfering with other initiatives. Within this context, this study proposes a model, based on deep reinforcement learning, for synchronizing the traffic signals of an urban traffic network composed of two intersections. The calibration of this model, including training of its neural network, was performed using real traffic data collected at the approach to each intersection. The results achieved through simulations were very promising, yielding significant improvements in indicators measured in relation to the pre-existing conditions in the network. The model was able to deal with a broad spectrum of traffic flows and, in peak demand periods, reduced delays and queue lengths by more than 28% and 42%, respectively.

Design and Application of Real-Time Flight Information Acquisition System

2013 ◽  
Vol 842 ◽  
pp. 686-690
Author(s):  
Yan Li ◽  
Yan Feng Liu ◽  
Zhi Yuan Shi ◽  
Feng Yang
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Information Acquisition ◽  
Information Source ◽  
Radar System ◽  
Acquisition System ◽  
External Information ◽  
Radar Target ◽  
Target Validation ◽  
The Real

In the radar target validation systems, the flight information as an important external information source occupies the irreplaceable position. However, in the resent researches, how to obtain the flight information stays focused and unsolved. The Real-time Multi-task Flight Information Acquisition System we designed and realized in this paper could obtain the flight information in real time and provide the real-time, reliable, detailed and complete flight information for the detective area. This system has already successfully used in Airway-nonairway Classification in an ATCRS (Air Traffic Control Radar System).

Multivariate Optimization Strategies for Real-Time Traffic Control Signals

1996 ◽  
Vol 1554 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Ghulam Q. Memon ◽  
A.G. R. Bullen
Keyword(s):  
Genetic Algorithms ◽  
Real Time ◽  
Traffic Control ◽  
Gradient Search ◽  
Multivariate Optimization ◽  
The Real ◽  
Real Time Traffic ◽  
Efficiency And Effectiveness ◽  
The University ◽  
Quasi Newton

The application of modern heuristic techniques, neural networks, simulated annealing, tabu search, and genetic algorithms for multivariate optimization is receiving increased attention compared with traditional techniques like hill climbing and gradient search. In the present research the efficiency and effectiveness of genetic algorithms are investigated for their application to the real-time optimization of traffic control signal timings and are compared with the efficiency and effectiveness of the Quasi-Newton gradient search method. The development, testing, comparison, and evaluation of these two multivariate optimization techniques for inclusion in the real-time traffic adaptive control system LOCAL model developed at the University of Pittsburgh as a part of an FHWA contract to a consortium led by the University of Maryland are described. The measures of effectiveness used in the comparisons include optimum total stopped delay, percentage of improvement in total stopped delay, optimal phase timings, execution time, and code size. Testing and evaluation results indicate that genetic algorithms are more efficient and effective than the Quasi-Newton method for this real-time application.

Sign in / Sign up

Export Citation Format

Share Document