scholarly journals Proactive freeway crash prevention using real-time traffic control

2003 ◽  
Vol 30 (6) ◽  
pp. 1034-1041 ◽  
Author(s):  
Chris Lee ◽  
Bruce Hellinga ◽  
Frank Saccomanno
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Evaluation Framework ◽  
Real Time Control ◽  
Real Time Traffic ◽  
Time Control ◽  
Crash Prevention ◽  
Safety Intervention ◽  
Safety Benefit

This paper makes use of a probabilistic model that predicts the likelihood of crashes (crash potential) on freeways on the basis of traffic flow conditions, in real-time crash prevention. The model was developed using incident logs and loop detector data collected over a 13-month period on the Gardiner Expressway in Toronto. Previous work suggested that an increase in levels of traffic turbulence generally yields high crash potential. Traffic turbulence was defined in terms of a series of crash precursors that represent traffic conditions that were present prior to crash occurrence. To apply the model in crash prevention, the link needs to be established between crash potential and real-time safety intervention. The objective of this paper is to explore this link for different thresholds of crash potential. The paper discusses the guidelines for evaluating the safety benefit of one crash prevention strategy (variable speed limits) and suggests the risk-based evaluation framework for real-time traffic control.Key words: crash, accident, freeway, safety, traffic flow, real-time control.

scholarly journals The Dynamic Control of the Light Signalling Device in Real-Time

2019 ◽  
Vol 292 ◽  
pp. 03014
Author(s):  
Jan Mrazek ◽  
Lucia Duricova Mrazkova ◽  
Martin Hromada ◽  
Jana Reznickova
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Dynamic Control ◽  
Traffic Density ◽  
Light Signaling ◽  
Dynamic Traffic ◽  
Real Time Control ◽  
Real Time Traffic ◽  
Time Control ◽  
Control Dynamic

The article is focused on the issue of interval on a light signaling device. Light signaling devices operate on different systems by means of which they are controlled. The control problem is a very static setting that does not respond to real-time traffic. Important variables for dynamic real-time control are traffic density in a selected area along with average speed. These variables are interdependent and can be based on dynamic traffic control. Dynamic traffic control ensures smoother traffic through major turns. At the same time, the number of harmful CO2 emitted from the means of transport should be reduced to the air. When used in low operation, power consumption should be reduced.

A real-time control method-based simulation for high-speed trains on large-scale rail network

2017 ◽  
Vol 28 (10) ◽  
pp. 1750126 ◽  
Author(s):  
Yutong Liu ◽  
Chengxuan Cao ◽  
Yaling Zhou ◽  
Ziyan Feng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
High Speed ◽  
Large Scale ◽  
Simulation Method ◽  
Real Time Control ◽  
Rail Network ◽  
Time Control ◽  
High Speed Trains ◽  
Rail Traffic

In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.

Formulation of Real-Time Control Policy for Oversaturated Arterials

2000 ◽  
Vol 1727 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Edward B. Lieberman ◽  
Jinil Chang ◽  
Elena Shenk Prassas
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Storage Capacity ◽  
Control Policy ◽  
Mixed Integer ◽  
System Throughput ◽  
Real Time Control ◽  
Mixed Integer Linear Program ◽  
Real Time Traffic ◽  
Queue Lengths

The formulation of a real-time traffic control policy designed expressly for oversaturated arterials is presented, and the operating protocol is described. Its objectives are to ( a) maximize system throughput, ( b) fully use storage capacity, and ( c) provide equitable service. This control policy, known as RT/IMPOST (real-time/internal metering policy to optimize signal timing), is designed to control queue growth on every saturated approach by suitably metering traffic to maintain stable queues. Consistent with this approach, bounds on queue lengths and signal offsets are determined. A mixed-integer linear program (MILP) tableau is formulated to yield optimal values of signal offsets and queue length for each approach. A nonlinear (quadratic) programming formulation adjusts the arterial green-phase durations of each signal cycle so that the actual arterial queue lengths on each saturated approach will continually closely approximate the optimal queue lengths computed by the MILP formulation. The policy principles are as follows: ( a) the signal phase durations “meter” traffic at intersections servicing oversaturated approaches to control and stabilize queue lengths and to provide equitable service to competing traffic streams; and ( b) the signal coordination (i.e., offsets) controls the interaction between incoming platoons and standing queues in a way that fully uses the available storage capacity, keeps intersections clear of queue spillback, and maximizes throughput.

Research on the Real-Time Prediction Model of the Traffic Flow Based on Wavelet Neural Network

2012 ◽  
Vol 241-244 ◽  
pp. 2088-2094
Author(s):  
Hui Ying Wen ◽  
Gui Feng Yang ◽  
Wei Tiao Wu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Traffic Flow ◽  
Traffic Safety ◽  
Traffic Control ◽  
Wavelet Neural Network ◽  
Mountain Area ◽  
Analog Simulation ◽  
Traffic Flow Prediction ◽  
Real Time Traffic

Real-time traffic flow prediction is the core of traffic control and management, which is the basis of traffic safety in mountain area. Traffic flow, which is highly time-relevant, with the features of high non-linear and non-determinism, can be treated as the time sequence forecast. Considering these features, this paper deals specially with this issue based on Wavelet neural network. Besides, by taking a road in mountain area for example, the paper realizes the analog simulation through the Matlab software programming. And the simulation results show that the traffic flow can be precisely forecast using Wavelet neural network, and its value is close to the expectations. The MAE of the Wavelet neural network is 20.1074 and the MSE is 2.5254.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
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.

Modelling and Real-time Control of Traffic Flow on the Boulevard Peripherique in Paris

1990 ◽  
Vol 23 (2) ◽  
pp. 205-211
Author(s):  
M. Papageorgiou ◽  
H. Hadj-Salem ◽  
J.M. Blosseville ◽  
N. Bhouri
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Real Time Control ◽  
Time Control

A Real Time Control Architecture for Continuously Managing Patients in a Care Unit

1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice
Keyword(s):  
Real Time ◽  
Linear Time ◽  
Treatment Plan ◽  
Control Architecture ◽  
Real Time Control ◽  
Time Representation ◽  
Time Control ◽  
Continuous Support ◽  
Definition Of ◽  
Computerized System

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.

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