scholarly journals Mixed Vehicular Traffic Flow Model on an Inclined Multilane Road

2020 ◽  
Vol 5 (7) ◽  
pp. 331-342
Author(s):  
Delina Mshai Mwalimo ◽  
Mary Wainaina ◽  
Winnie Kaluki
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Flow Model ◽  
Free Flow ◽  
Traffic Density ◽  
Fundamental Diagram ◽  
Traffic Flow Model ◽  
The Road ◽  
Slow Moving

This study outlines the Kerner’s 3 phase traffic flow theory, which states that traffic flow occurs in three phases and these are free flow, synchronized flow and wide moving jam phase. A macroscopic traffic model that is factoring road inclination is developed and its features discussed. By construction of the solution to the Rienmann problem, the model is written in conservative form and solved numerically. Using the Lax-Friedrichs method and going ahead to simulate traffic flow on an inclined multi lane road. The dynamics of traffic flow involving cars(fast moving) and trucks(slow moving) on a multi-lane inclined road is studied. Generally, trucks move slower than cars and their speed is significantly reduced when they are moving uphill on an in- clined road, which leads to emergence of a moving bottleneck. If the inclined road is multi-lane then the cars will tend to change lanes with the aim of overtaking the slow moving bottleneck to achieve free flow. The moving bottleneck and lanechange ma- noeuvres affect the dynamics of flow of traffic on the multi-lane road, leading to traffic phase transitions between free flow (F) and synchronised flow(S). Therefore, in order to adequately describe this kind of traffic flow, a model should incorporate the effect of road inclination. This study proposes to account for the road inclination through the fundamental diagram, which relates traffic flow rate to traffic density and ultimately through the anticipation term in the velocity dynamics equation of macroscopic traffic flow model. The features of this model shows how the moving bottleneck and an incline multilane road affects traffic transistions from Free flow(F) to Synchronised flow(S). For a better traffic management and control, proper understanding of traffic congestion is needed. This will help road designers and traffic engineers to verify whether traffic properties and characteristics such as speed(velocity), density and flow among others determines the effectiveness of traffic flow.

scholarly journals A Cellular Automaton Traffic Flow Model with Advanced Decelerations

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Yingdong Liu
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Flow Model ◽  
Stable State ◽  
Practical Significance ◽  
Fundamental Diagram ◽  
Traffic Flow Model ◽  
Short Time ◽  
Vehicle Deceleration ◽  
Real Traffic

A one-dimensional cellular automaton traffic flow model, which considers the deceleration in advance, is addressed in this paper. The model reflects the situation in the real traffic that drivers usually adjust the current velocity by forecasting its velocities in a short time of future, in order to avoid the sharp deceleration. The fundamental diagram obtained by simulation shows the ability of this model to capture the essential features of traffic flow, for example, synchronized flow, meta-stable state, and phase separation at the high density. Contrasting with the simulation results of the VE model, this model shows a higher maximum flux closer to the measured data, more stability, more efficient dissolving blockage, lower vehicle deceleration, and more reasonable distribution of vehicles. The results indicate that advanced deceleration has an important impact on traffic flow, and this model has some practical significance as the result matching to the actual situation.

scholarly journals Evaluating Impacts of Overloaded Heavy Vehicles on Freeway Traffic Condition by a Novel Multi-Class Traffic Flow Model

2018 ◽  
Vol 10 (12) ◽  
pp. 4694 ◽  
Author(s):  
Xiang Wang ◽  
Po Zhao ◽  
Yanyun Tao
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Management Strategies ◽  
Maximum Speed ◽  
Heavy Vehicles ◽  
Fundamental Diagram ◽  
Traffic Condition ◽  
Traffic Flow Model ◽  
Traffic Conditions ◽  
The Impact

Overloaded heavy vehicles (HVs) have significant negative impacts on traffic conditions due to their inferior driving performance. Highway authorities need to understand the impact of overloaded HVs to assess traffic conditions and set management strategies. We propose a multi-class traffic flow model based on Smulders fundamental diagram to analyze the influence of overloaded HVs on traffic conditions. The relationship between the overloading ratio and maximum speed is established by freeway toll collection data for different types of HVs. Dynamic passenger car equivalent factors are introduced to represent the various impacts of overloaded HVs in different traffic flow patterns. The model is solved analytically and discussed in detail in the appendices. The model validation results show that the proposed model can represent traffic conditions more accurately with consideration for overloaded HVs. The scenario tests indicate that the increase of overloaded HVs leads to both a higher congestion level and longer duration.

scholarly journals Self-Delayed Feedback Based Car Following Control With Velocity Uncertainty of Preceding Vehicle On Gradient Roads

2021 ◽  
Author(s):  
Cong Zhai ◽  
Weitiao Wu
Keyword(s):  
Traffic Flow ◽  
Traffic Management ◽  
Flow Model ◽  
Transfer Functions ◽  
Delayed Feedback ◽  
Time Step ◽  
Current Time ◽  
Traffic Flow Model ◽  
Car Following ◽  
V2v Communication

Abstract Uphill and downhill roads are prevalent in mountainous areas and freeways. Despite the advancement of vehicle-to-vehicle (V2V) communication technology, the driving field of vision is still largely limited under such a complex road environment, which hinders the sensors accurately perceiving the speed of the front vehicle. As such, a fundamental question for autonomous traffic management is how to control traffic flow associated with the velocity uncertainty of preceding vehicles? This paper seeks to develop a controlling framework for corporative car following control under such complex road environment. To this end, we first propose a traffic flow model accounting for the uncertainty effect of preceding vehicles velocity on gradient road. We further design a new self-delayed feedback controller based on the velocity and headway difference between the current time step and historical time step, in an aim to enhance the robustness of traffic flow. The sufficient condition where traffic jam does not occur is derived from the perspective of the frequency domain via Hurwitz criteria H∞ and norm of transfer functions. The bode diagram reveals that the robustness of closed-loop traffic flow model has been significantly enhanced. We also conduct simulations to verify the theoretical analysis.

A Macroscopic Multi-Lane Traffic Flow Model for ACC/CACC Traffic Dynamics

2018 ◽  
Vol 2672 (20) ◽  
pp. 178-192 ◽  
Author(s):  
Anargiros I. Delis ◽  
Ioannis K. Nikolos ◽  
Markos Papageorgiou
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Flow Model ◽  
Extended Model ◽  
Cruise Control ◽  
Traffic Dynamics ◽  
Traffic Flow Model ◽  
Lane Changes ◽  
Gap Principle ◽  
Model Equations

An extended second-order macroscopic traffic flow model is presented that describes multi-lane traffic dynamics and also incorporates the effects of adaptive cruise control (ACC) or cooperative ACC (CACC). The extended model equations stem from a recently proposed multi-lane gas-kinetic traffic flow (GKT) model that can simulate lane changes due to vehicle interactions as well as spontaneous ones. The proposed extension that models the effects of ACC/CACC satisfies the time-gap principle of such systems on each lane and allows for consideration of mixed traffic comprising both manual and ACC/CACC vehicles. Numerical simulations are performed for a particular three-lane motorway stretch in the United Kingdom, where recurrent traffic congestion is observed during the morning peak hours, so as to compare the effects of ACC/CACC in the traffic flow conditions with those resulting from manual driving.

Variable speed limit strategies analysis with mesoscopic traffic flow model based on complex networks

2018 ◽  
Vol 29 (02) ◽  
pp. 1850014 ◽  
Author(s):  
Shu-Bin Li ◽  
Dan-Ni Cao ◽  
Wen-Xiu Dang ◽  
Lin Zhang
Keyword(s):  
Complex Networks ◽  
Traffic Flow ◽  
Traffic Management ◽  
Flow Model ◽  
Complexity Science ◽  
Control Measures ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic Flow Model

As a new cross-discipline, the complexity science has penetrated into every field of economy and society. With the arrival of big data, the research of the complexity science has reached its summit again. In recent years, it offers a new perspective for traffic control by using complex networks theory. The interaction course of various kinds of information in traffic system forms a huge complex system. A new mesoscopic traffic flow model is improved with variable speed limit (VSL), and the simulation process is designed, which is based on the complex networks theory combined with the proposed model. This paper studies effect of VSL on the dynamic traffic flow, and then analyzes the optimal control strategy of VSL in different network topologies. The conclusion of this research is meaningful to put forward some reasonable transportation plan and develop effective traffic management and control measures to help the department of traffic management.

An original traffic flow model with signal effect for energy dissipation

2014 ◽  
Vol 25 (07) ◽  
pp. 1450018 ◽  
Author(s):  
Wen-Xing Zhu ◽  
Li-Dong Zhang
Keyword(s):  
Energy Dissipation ◽  
Traffic Flow ◽  
Flow Model ◽  
Mathematical Expression ◽  
Velocity Model ◽  
Energy Dissipation Rate ◽  
Traffic Density ◽  
Traffic Signal Control ◽  
Optimal Velocity ◽  
Traffic Flow Model

We proposed an original traffic flow model with a consideration of signal effect based on Bando's optimal velocity model. The optimal velocity function was improved more realistically in describing the motion process of vehicles moving on a road with signals. Based on the improved model, we derived the mathematical expression for energy dissipation. Simulations are conducted to verify the energy dissipation laws in traffic flow with signals. Numerical results show that energy dissipation (rate) can be affected not only by traffic density, but also traffic signal control parameters: split and cycle.

scholarly journals Adaptive Traffic Management System using CNN (YOLO)

2021 ◽  
Vol 9 (VI) ◽  
pp. 3726-3732
Author(s):  
B. Sowmya
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Object Identification ◽  
Traffic Signal ◽  
Traffic Density ◽  
Loop Control ◽  
Control Framework

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

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.

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