A Traffic Capacity Model of Lane Occupation

2014 ◽  
Vol 599-601 ◽  
pp. 2083-2087
Author(s):  
Yi Xuan He
Keyword(s):  
Traffic Flow ◽  
Queue Length ◽  
Production Condition ◽  
Modern Society ◽  
Traffic Jam ◽  
Traffic Flow Theory ◽  
Related Data ◽  
Traffic Capacity ◽  
Capacity Model ◽  
Big City

In modern society, traffic jam has already become a major problem which curbs the development of big city. And lane occupation is an important reason why traffic jam happens. After studying on the production condition, time and queue length of traffic jam after lane occupation happens, we propose a model based on famous traffic flow theory and we use related data to verify the rightness of our model. Result shows that our model can predict the development of traffic jam caused by lane occupation

From Particle Hopping Models to Traffic Flow Theory

1998 ◽  
Vol 1644 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Kai Nagel
Keyword(s):  
Traffic Flow ◽  
Flow Theory ◽  
Traffic Jam ◽  
Traffic Flow Theory ◽  
Hopping Models ◽  
Simple Models

Very simple models of particles hopping on a grid appear too simple to have much similarity to traffic. Yet, some of these models can be proved to generate, in the so-called fluid-dynamical limit, variations of the Lighthill-Whitham theory. For more realistic particle hopping models, the fluid-dynamical limit is not known, but insight can be obtained by observing traffic jam dynamics.

Effects of Lanes Occupied on Urban Road Traffic Capacity

2014 ◽  
Vol 926-930 ◽  
pp. 3798-3801
Author(s):  
Zhi Wei Yang
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Flow Speed ◽  
Urban Traffic ◽  
Correction Coefficient ◽  
Dynamic Changes ◽  
Traffic Jam ◽  
The Road ◽  
Discharge Capacitance ◽  
Traffic Capacity

The article is research on the influence of urban lane occupied for the road traffic capacity. Under the condition that the density of urban traffic flow is big, and it‘s successional, we consider the quantity of vehicle is continuous. Through analyzing the dynamic changes of the road traffic capacity and its influencing factors after accidents, we can get reasonable suggestions of reducing the length of traffic jam. First we establish a flow-speed-density model to describe the dynamic changes of the road traffic capacity. Then we can compare the traffic flow to the electric current according to its continuity. So the upstream traffic flow and the traffic capacity of the accident cross section are equal to the charging current and the discharging current. And the vehicle queue is translated to the voltage of the charge-discharge capacitance. We can get the length of the vehicle queue by the formula of the capacitance voltage approximately. Finally the correction coefficient is introduced. In conclusion, the road traffic capacity is depended on the distance from the upstream intersection and the lane that the accident happened on and so on. Meanwhile, if we don’t solve the accident timely, the length will rise sharply. It will cause serious traffic jam. So we suggest relevant departments timely deal with the accident, evacuate the traffic, and prompt drivers to change lanes in advance.

Capacity Modeling of Permitted Left-Turn Signalized Intersections with Probabilistic Priority

2020 ◽  
Vol 2674 (10) ◽  
pp. 310-323 ◽  
Author(s):  
Daobin Wang ◽  
Guangchuan Yang ◽  
Zong Tian ◽  
Wei Liu ◽  
Dali Wei
Keyword(s):  
Traffic Flow ◽  
Queuing Theory ◽  
Signalized Intersections ◽  
Stochastic Simulations ◽  
Left Turn ◽  
Absolute Priority ◽  
Traffic Capacity ◽  
Capacity Model ◽  
Capacity Modeling ◽  
Traffic Operation

Probabilistic yielding behavior is commonly observed at permitted left-turn signalized intersections in China. Nevertheless, its impact on traffic capacity has not been explored in existing research. The uniqueness of this traffic operation is that neither left-turn traffic nor through traffic holds an absolute priority. Based on queuing theory, this research developed an analytical capacity model that took into account the probabilistic priority phenomenon at permitted left-turn signals. To validate the developed capacity model, stochastic simulations with different combinations of left-turn traffic yielding rates and through-traffic flow rates were performed. It was found that modeling results from the analytical model precisely matched the stochastic simulation results. In comparison with traditional capacity estimation models that assumed through traffic holds an absolute priority, this research revealed that when through-traffic flow rate is around 1000 vehicles per hour (vph), the capacity of left-turn traffic increased from 233 vph to 536 vph when left-turn traffic yielding rates decrease from 1 to 0.2; whereas when left-turn traffic yielding rates decreased from 0.2 to 0, left-turn capacity increased sharply to 1200 vehicles per hour (vph). In this regard, it is critical to take into account the probabilistic left-turn yield behavior when developing permitted left-turn signal warrants.

Research on the Traffic Control Optimization at Weigong Village Intersection in Beijing

2014 ◽  
Vol 644-650 ◽  
pp. 2619-2622
Author(s):  
Zi Qing Li ◽  
Rui Song
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Field Survey ◽  
Service Level ◽  
Signal Timing ◽  
Related Data ◽  
Traffic Conditions ◽  
Control Optimization ◽  
Traffic Capacity ◽  
Control Scheme

The traffic flow, existing signal timing, lane arrangement and other field survey data were analyzed and computed in the paper first. Next, the traffic capacity and service level of Weigong village intersection were evaluated. Finally, Using Webster method to calculate the related data of signal timing, and Vissim are used to simulate the traffic conditions of the intersection. The result of simulation indicated that the optimized traffic control scheme reached the goal improving the operational condition of Weigong village intersection and unobstructed degree of road.

Traffic Jam Prediction through Elman Neural Network Based on Monte Carlo Simulation

2014 ◽  
Vol 551 ◽  
pp. 675-678 ◽  
Author(s):  
Ze Yuan Yu
Keyword(s):  
Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Traffic Flow ◽  
Cross Section ◽  
Queue Length ◽  
Elman Neural Network ◽  
Traffic Jam ◽  
Quick Recovery

This paper simulates the traffic flow at upper stream intersection and the actual traffic flow at cross section of accident, and calculates the time for the queue to reach the upper stream intersection through Elman neural network. The result of 500 simulations shows that the probability for the time of the queue length being 140m: in [2.5min~3.5min] is 39.6% and in [3.5min~4min] is 52.0%. The total is 91.6%, which is highly precise. The prediction of queue length of post-accident traffic jam is of great importance to a quick recovery.

Traffic Flow Data Collection and Signal Control System Based on Internet of Things and Cloud Computing

2013 ◽  
Vol 846-847 ◽  
pp. 1608-1611 ◽  
Author(s):  
Hui Jie Ding
Keyword(s):  
Cloud Computing ◽  
Control System ◽  
Data Collection ◽  
Internet Of Things ◽  
Traffic Flow ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Flow Data ◽  
Traffic Jam ◽  
Sensing Data

As more and more cars are in service, the traffic jam becomes a serious problem in our society. At the same time, more and more sensors make the cars more and more intelligent, and this promotes the development of Internet of things. Real time monitoring the cars will produce massive sensing data, the Cloud computing gives us a good manner to solve this problem. In this paper, we propose a traffic flow data collection and traffic signal control system based on Internet of things and the Cloud computing. The proposed system contains two main parts, sensing data collection and traffic status control subsystem.

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