scholarly journals Multi-factor Comprehensive Prediction of Delay Time through Congested Road Sections

2020 ◽  
Vol 4 (2) ◽  
pp. 1
Author(s):  
Yuhang Wu ◽  
Tong Jiao ◽  
Binggang Li
Keyword(s):  
Traffic Flow ◽  
Influencing Factors ◽  
Delay Time ◽  
Correlation Coefficients ◽  
Impact Factors ◽  
Volume Number ◽  
Average Speed ◽  
The Road ◽  
Prediction Function ◽  
Gray Correlation

The navigation software uses the positioning system to determine the traffic conditions of the road sections in advance, so as to predict the travel time of the road sections. However, in the case of traffic congestion, the accuracy of its prediction time is low. After empirical analysis, this paper establishes a multi-factor synthesis by studying 7 factors: traffic flow, number of stops, traffic light duration, road network density, average speed, road area, and number of intersections the prediction function achieves the purpose of accurately predicting the transit time of congested road sections. The gray correlation coefficients of the seven factors obtained from the gray correlation analysis are: 0.9827, 0.9679, 0.6747, 0.8030, 0.9445, 0.8759, 0.4328. The correlation coefficients of traffic volume, number of stops, average speed, and road congestion delay time were all about 95%, which were the main influencing factors of the study. The prediction needs to be based on functions. This paper fits the main influencing factors to the delay time of congested roads. It is found that the delay time varies parabolically with the traffic flow and the number of stops, and linearly with the average speed. Because the three impact factors have different weights on the delay time of congested roads, demand takes the weight of each factor. Therefore, the gray correlation coefficients occupied by the main influencing factors are normalized to obtain the weights of three of 0.340, 0.334, and 0.326. The weighted fitting function is subjected to nonlinear summation processing to obtain a multi-factor comprehensive prediction function. By comparing the original data with the fitting data and calculating the accuracy of the fitting function, it is found that the accuracy of each fitting function is close to 0, the residual error, the relative error is small, and the accuracy is high.

scholarly journals Improving the Level of Service of a Portion of 60-M Ring-Road in Erbil City

2019 ◽  
Vol 3 (1) ◽  
pp. 12-17 ◽  
Author(s):  
Dlzar B. Qadr ◽  
Shorsh A. Mohammed ◽  
Ibrahim A. Hasan ◽  
Rawand F. Mohammed Ali
Keyword(s):  
Traffic Flow ◽  
Urban Area ◽  
Video Camera ◽  
Traffic Volume ◽  
Level Of Service ◽  
Kurdistan Region ◽  
Average Speed ◽  
Ring Road ◽  
The Road ◽  
New City

This study aims to implement the factors that can improve the level of service of 60-M Ring-Road in Erbil, Kurdistan Region of Iraq. These factors are minimizing the traffic volume, providing a higher quality pavement, widening the road carriageway, and constructing bridges. The main objective of this paper is to increase the LOS of a specific road known as 60-m ring road from New City Mall to PAR hospital in Erbil city. For this purpose, the data on speed and traffic flow are collected using video camera to collect data to be reasonably analyzed with respect to the speed of the vehicles and set the LOS for the proposed road. The result shows that average speed in this study is 19 km/h, and this confirms that the LOS in this particular road segment is at F category. In addition, the calculated peak hour factor is 0.97 which is more than a typical value in urban area. Finally, the study revealed that the traffic volume on this specified road is too high.

scholarly journals ANALYZING IMPACT FACTORS OF AIRPORT TAXIING DELAY BASED ON ADS-B DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1251-1255 ◽  
Author(s):  
J. Li ◽  
X. Wang ◽  
Y. Xu ◽  
Q. Li ◽  
C. He ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Delay Time ◽  
Environmental Data ◽  
Impact Factors ◽  
The Impact ◽  
The Relationship

Identifying the factors that cause taxiing delay on airports is a prerequisite for optimizing aircraft taxiing schemes, and helps improve the efficiency of taxiing system. Few of current studies had quantified the potential influencing factors and further investigated their intrinsic relationship. In view of these problems, this paper uses ADS-B data to calculate taxiing delay time by restoring taxiing route and identifying key status points, and further analyzes the impact factors of airport taxiing delay by investigating the relationship between delay time and environmental data such as weather, wind, visibility etc. The case study in Guangzhou Baiyun Airport validates the effectiveness of the proposed method.

scholarly journals Impact of Automated Truck Platooning on the Performance of Freeway Mixed Traffic Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Seolyoung Lee ◽  
Cheol Oh ◽  
Gunwoo Lee
Keyword(s):  
Traffic Flow ◽  
Energy Savings ◽  
Automated Driving ◽  
Average Speed ◽  
The Road ◽  
Mobility Performance ◽  
Vehicle Platooning ◽  
Negative Effect ◽  
The Difference ◽  
On The Road

Vehicle platooning service through wireless communication and automated driving technology has become a reality. Vehicle platooning means that several vehicles travel like a train on the road with a minimum safety distance, which leads to the enhancement of safety, mobility, and energy savings. This study proposed a framework for exploring traffic mobility and safety performance due to the market penetration rate (MPR) of truck platoons based on microscopic traffic simulations. A platoon formation algorithm was developed and run on the VISSIM platform to simulate automated truck maneuvering. As a result of the mobility analysis, it was found that the difference in network mobility performance was not significant up to MPR 80%. Regarding the mobility performance of the truck-designated lane, it was found that the average speed was lower than in other lanes. In the truck-designated lane of the on-ramp section, the average speed was identified to be approximately 33% lower. From the viewpoint of network safety, increasing the MPR of the truck platoon has a positive effect on longitudinal safety but has a negative effect on lateral safety. The safety analysis of the truck-designated lane indicated that the speed difference by lane of MPR 100% is 2.5 times higher than that of MPR 0%. This study is meaningful in that it explores traffic flow performance on mobility and safety in the process of platoon formation. The outcomes of this study are expected to be utilized as fundamentals to support the novel traffic operation strategy in platooning environments.

scholarly journals The Effect of Posted Speed Limit on the Dispersion of Traffic Flow Speed

2019 ◽  
Vol 11 (13) ◽  
pp. 3594 ◽  
Author(s):  
Chao Gao ◽  
Jinliang Xu ◽  
Qunshan Li ◽  
Jie Yang
Keyword(s):  
Traffic Flow ◽  
Flow Speed ◽  
Traffic Volume ◽  
Free Flow ◽  
Speed Limit ◽  
Flow State ◽  
Speed Limits ◽  
Limit Values ◽  
Average Speed ◽  
The Road

Speed dispersion is an important indicator to portray the quality of traffic flow and is closely related to the road safety operation level. In order to clarify the influence of posted speed limits on the dispersion of traffic flow speed, three sections with speed limits of 80 km/h, 100 km/h and 120 km/h on the same expressway were selected for observation, and traffic volume, speed and other parameters were collected. The characteristic speeds, such as average speed, V15 and V85, were evaluation indicators, where V15 and V85 are the speeds of the 15th and 85th percentiles measured at the feature points of the road when the traffic is in a free-flow state and the weather is good. The relationship between different posted speed limit values and the above indicators was analyzed using the statistical analysis software, SPSS. The results show that the speed limit has a high correlation with the average speed of traffic flow, V15 and V85 in free-flow state, with the coefficient of determination being as high as 0.84, 0.85 and 0.92, respectively. In the restricted flow state, the factors affecting the driver’s driving speed are mainly the decrease in driving freedom caused by the increase of traffic volume rather than the speed limit value. In a free-flow state, when the posted speed limit is increased and the average speed and the V85 also increased by approximately the same magnitude. The posted speed limit values of 80 km/h, 100 km/h and 120 km/h correspond to the 90, 88 and 97 percentile speeds of the traffic flow, respectively. The higher the speed limit is, the larger the speed difference between V15 and V85 becomes. The results of the study are very useful for rationally determining the speed limit scheme under different traffic flows.

Improving the system of road safety indicators in the analysis of the quality of motor vehicle transportation

2021 ◽  
pp. 85-98
Author(s):  
O. V. Mayboroda ◽  
◽  
B. A. Sarymsakov ◽  
Keyword(s):  
Traffic Flow ◽  
Road Safety ◽  
Road Traffic ◽  
Road Accident ◽  
Social Risk ◽  
Road Accidents ◽  
Average Speed ◽  
Safety Coefficient ◽  
The Road ◽  
Probability Of Death

The article presents the results of the development of an integral indicator of road safety - the road safety coefficient. The value of the road safety coefficient shows how many times, at the same levels of motorization, the risk of death of a resident in road traffic (social risk) decreased/increased in comparison with the average social risk that existed in the world in the 40s of the last century, when there were no special programs on improving road safety. The use of the road safety coefficient allows you to compare the level of road safety in countries and regions with different populations and different levels of motorization. It is shown that social risk is a complex indicator, the value of which depends on the indicators of road safety at a lower level - the probability of participation of a resident in road traffic, which is equal to the value of motorization, the probability of death of a road user (transport risk), the probability of a road accident (active road safety), the intensity of road accidents (reliability of driving) and the average annual mileage of the car, the probability of death in a road accident (passive and post-accident safety). An equation is obtained that relates the value of social risk to the values of lower-level road safety indicators. The analysis of the level of road safety in Kyrgyzstan is carried out. It is shown how the condition of the traffic flow affects its average speed and the probability of road accidents. The influence of traffic flow density on its average speed and intensity of road accidents is considered. To assess the impact of the condition of a traffic flow on its average speed and road safety, the concept of the conventional density of a traffic flow is used. It is revealed that in the road conditions of Kyrgyzstan there is a critical conventional density, when exceeding it the transport risk, the probability of a road accident and the probability of death in a road accident begin to decrease rapidly with a further increase in the conventional density. The low efficiency of detection of exceeding a permitted speed when measuring the maximum speed of cars at certain points is shown. It is proposed to use the measurement of average speed on certain road sections as a means of reducing the number of drivers exceeding the speed limit.

scholarly journals Finding the Reasons for the Delay Time in a Highway by Analyzing the Travel Time, Delay Time and Traffic Flow Data

2020 ◽  
Vol 01 (03) ◽  
pp. 76-84
Author(s):  
T M Junaid Bashar ◽  
Md. Sabbir Hossain ◽  
Shah Istiaque
Keyword(s):  
Time Delay ◽  
Travel Time ◽  
Traffic Flow ◽  
Delay Time ◽  
Speed Limit ◽  
Flow Data ◽  
Running Time ◽  
Journey Time ◽  
The Road ◽  
Total Vehicle

The objectives of this study are to show a comparison among travel time, running time, delay in peak and off-peak hours on different days of a week, and reasons behind the delay time. Moving car observer method has been carried out to count the traffic flow, journey time, running time, and delay time. Total vehicle flow, and comparative vehicle flow during the peak hour and off-peak hour for workdays and weekend days were surveyed to show a relationship between delay time and traffic flow. As the traffic flow increases the delay time also will increase. To measure the reasons behind the delay time of Fulbarigate-Daulatpur road, spot speed study was done in two intersections of the road. The 15th percentile speed for Religate intersection road is 10 K.P.H. That means 85% of vehicles tends to go faster than 10 K.P.H. in this section. And hence vehicles with less than or equal 10 K.P.H. are responsible for increasing the delay time in this area. This speed limit should be prohibited in this section to reduce delay time and congestion. For the Mohosin More road intersection, the prohibited speed is also 10 K.P.H. Easy bike and Mahindra account for the congestion of these intersections and tend to stop in these intersections to collect passengers which creates unwanted queue in this study area. Controlling traffic flow at intersections can be a possible way to reduce the congestion rate of Fulbarigate-Daulatpur road.

scholarly journals Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network

2020 ◽  
Vol 16 (01) ◽  
pp. 18
Author(s):  
Bagus Priambodo ◽  
Azlina Ahmad ◽  
Rabiah Abdul Kadir
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Clustering Method ◽  
Traffic Condition ◽  
Average Speed ◽  
The Road ◽  
Road Congestion ◽  
Neighbouring Area ◽  
Road Segments

For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used k-means clustering method to cluster condition of traffic flow on road segments.  Then, we applied the k-Nearest Neighbour (k-NN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of k-means and k-NN method produced better results than using both, correlation method and using the k-means method only.

Research and Improvement of the Keep-Right-Except-to-Pass Rule

2014 ◽  
Vol 915-916 ◽  
pp. 459-463
Author(s):  
He Quan Zhang
Keyword(s):  
Fluid Mechanics ◽  
Traffic Flow ◽  
Influence Factors ◽  
Traffic Model ◽  
Light Traffic ◽  
The Road ◽  
Model Based ◽  
Light Passing ◽  
Improved Model ◽  
The Impact

In order to deal with the impact on traffic flow of the rule, we compare the influence factors of traffic flow (passing, etc.) into viscous resistance of fluid mechanics, and establish a traffic model based on fluid mechanics. First, in heavy and light traffic, we respectively use this model to simulate the actual segment of the road and find that when the traffic is heavy, the rule hinder the further increase in traffic. For this reason, we make further improvements to the model to obtain a fluid traffic model based on no passing and find that the improved model makes traffic flow increase significantly. Then, the improved model is applied to the light traffic, we find there are no significant changes in traffic flow .In this regard we propose a new rule: when the traffic is light, passing is allowed, but when the traffic is heavy, passing is not allowed.

On kinematic waves II. A theory of traffic flow on long crowded roads

1955 ◽  
Vol 229 (1178) ◽  
pp. 317-345 ◽  
Keyword(s):  
Traffic Flow ◽  
Functional Relationship ◽  
Vehicle Speed ◽  
Main Road ◽  
The Road ◽  
Traffic Distribution ◽  
Kinematic Waves ◽  
The Subject ◽  
The Mean ◽  
Starting Flow

This paper uses the method of kinematic waves, developed in part I, but may be read independently. A functional relationship between flow and concentration for traffic on crowded arterial roads has been postulated for some time, and has experimental backing (§2). From this a theory of the propagation of changes in traffic distribution along these roads may be deduced (§§2, 3). The theory is applied (§4) to the problem of estimating how a ‘hump’, or region of increased concentration, will move along a crowded main road. It is suggested that it will move slightly slower than the mean vehicle speed, and that vehicles passing through it will have to reduce speed rather suddenly (at a ‘shock wave’) on entering it, but can increase speed again only very gradually as they leave it. The hump gradually spreads out along the road, and the time scale of this process is estimated. The behaviour of such a hump on entering a bottleneck, which is too narrow to admit the increased flow, is studied (§5), and methods are obtained for estimating the extent and duration of the resulting hold-up. The theory is applicable principally to traffic behaviour over a long stretch of road, but the paper concludes (§6) with a discussion of its relevance to problems of flow near junctions, including a discussion of the starting flow at a controlled junction. In the introductory sections 1 and 2, we have included some elementary material on the quantitative study of traffic flow for the benefit of scientific readers unfamiliar with the subject.

Report on design of modules for the stochastic traffic simulation

2021 ◽  
Author(s):  
Christian Siebke ◽  
◽  
Maximilian Bäumler ◽  
Madlen Ringhand ◽  
Marcus Mai ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Main Part ◽  
Traffic Simulation ◽  
Cognitive Structure ◽  
Flow Simulation ◽  
Road User ◽  
The Road ◽  
Traffic Flow Simulation ◽  
Cognitive Behaviour ◽  
Traffic Simulations

As part of the AutoDrive project, OpenPASS is used to develop a cognitive-stochastic traffic flow simulation for urban intersection scenarios described in deliverable D1.14. The deliverable D4.20 is about the design of the modules for the stochastic traffic simulation. This initially includes an examination of the existing traffic simulations described in chapter 2. Subsequently, the underlying tasks of the driver when crossing an intersection are explained. The main part contains the design of the cognitive structure of the road user (chapter 4.2) and the development of the cognitive behaviour modules (chapter 4.3).

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