scholarly journals Road Impedance Model Study under the Control of Intersection Signal

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yunlin Luo ◽  
Na Wang ◽  
Huaikun Xiang ◽  
Jinhu Wang
Keyword(s):  
Road Traffic ◽  
Wave Model ◽  
Wave Theory ◽  
Urban Traffic ◽  
Percentage Error ◽  
Route Guidance ◽  
Absolute Deviation ◽  
Impedance Model ◽  
Proposed Model ◽  
Traffic Wave

Road traffic impedance model is a difficult and critical point in urban traffic assignment and route guidance. The paper takes a signalized intersection as the research object. On the basis of traditional traffic wave theory including the implementation of traffic wave model and the analysis of vehicles’ gathering and dissipating, the road traffic impedance model is researched by determining the basic travel time and waiting delay time. Numerical example results have proved that the proposed model in this paper has received better calculation performance compared to existing model, especially in flat hours. The values of mean absolute percentage error (MAPE) and mean absolute deviation (MAD) are separately reduced by 3.78% and 2.62 s. It shows that the proposed model has feasibility and availability in road traffic impedance under intersection signal.

scholarly journals The Departure Characteristics of Traffic Flow at the Signalized Intersection

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zhaowei Qu ◽  
Yan Xing ◽  
Hongyu Hu ◽  
Yuzhou Duan ◽  
Xianmin Song ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Wave Model ◽  
Wave Theory ◽  
Signalized Intersection ◽  
Departure Process ◽  
Dispersion Process ◽  
Spatiotemporal Model ◽  
Quantitative Calculation ◽  
Traffic Wave ◽  
Motion Characteristics

The motion characteristics of the leading vehicle and the following vehicles of the traffic flow at the typical urban intersections are qualitatively analyzed through the kinematical equation and the traffic wave theory. Then, the motion characteristic of the whole traffic flow during the dispersion process is also studied. Based on the spatiotemporal model of kinematics in the departure process and traffic wave model in the dispersion process proposed, the change of the leading vehicle of the departure process and the time of the following vehicles reaching to the stable speed as well as the relationship between the green time and the departure vehicle number at the intersection are acquired. Furthermore, according to the qualitative analysis and the quantitative calculation of the departure traffic flow at the signalized intersection, the dispersion characteristic of traffic flow at the signalized intersection was studied and analyzed, which provides reliable theoretical basis for traffic signal setting at the intersection.

Modification of Traffic Wave Theory and the Development of a New Stop-Wave Model

2012 ◽  
Vol 253-255 ◽  
pp. 1615-1618
Author(s):  
Yu Han Jia ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Wave Phenomenon ◽  
Wave Model ◽  
Wave Theory ◽  
Signalized Intersections ◽  
Microscopic Level ◽  
Peak Period ◽  
Traffic Flow Theory ◽  
Traffic State ◽  
Traffic Wave ◽  
Real Traffic

This paper studies the traffic wave phenomenon on the basis of traffic flow theory and finds that the classical theory disagreeing with the real traffic state. The existing theory is modified and a new traffic wave model is proposed based on the improved theory. The aim of the model is to better describe the stop-wave phenomenon in signalized intersections at a microscopic level. Then the model is calibrated and validated by data obtained from an intersection during morning-peak period. Results show this new model can represent the characteristics of traffic wave with a high accuracy and can demonstrate the improved traffic wave theory.

Study on the Interference Ratio of Right-Turning Vehicles at Signalized Intersection under Mixed Traffic Environment

2011 ◽  
Vol 267 ◽  
pp. 555-560
Author(s):  
Shan Shan Lee ◽  
Da Lin Qian ◽  
Dong Mei Lin ◽  
Zhao Yong Peng
Keyword(s):  
Flow Rate ◽  
Wave Theory ◽  
Signalized Intersection ◽  
Motor Vehicles ◽  
Delay Model ◽  
Two Phase ◽  
Proposed Model ◽  
Traffic Wave ◽  
The Right ◽  
Bicycle Lane

The objective is to describe the interference degree between motor vehicles and bicycles at signalized intersection. The interference degree was expressed by conflict delay. Via analyzing the microscopic actions of the motor vehicles crossing through the bicycle flow at a typical two-phase signalized intersection, a conflict delay model of the right-turn vehicle was proposed applying the gap acceptance theory and traffic wave theory. The model was verified and compared with the existing conflict delay models. The result showed that the proposed model is stable and suitable for the condition of unsaturated to calculate the conflict delay of right-turn vehicle. Sensitivity of the conflict delay model with respect to the flow rate of the bicycle and the width of the bicycle lane was analyzed. It showed that the increase of the width of the bicycle lane within limits could reduce right-turning vehicle’s conflict delay effectively when the motor vehicle’s flow rate was higher and the bicycle flow rate was varying in a certain range.

scholarly journals GPS Data-based Non-parametric Regression for Predicting Travel Times in Urban Traffic Networks

2012 ◽  
Vol 22 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Hrvoje Marković ◽  
Bojana Dalbelo Bašić ◽  
Hrvoje Gold ◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Urban Traffic ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Gps Data ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Parametric Regression ◽  
Absolute Percentage Error ◽  
Proposed Model ◽  
Non Parametric

A model for predicting travel times by mining spatio-temporal data acquired from vehicles equipped with Global Positioning System (GPS) receivers in urban traffic networks is presented. The proposed model, which uses k-nearest neighbour (kNN) non-parametric regression, is compared with models that use historical averages and the seasonal autoregressive integrated moving average (ARIMA) model. The main contribution is provision of a methodology for mining GPS data that involves examining areas that cannot be covered with conventional fixed sensors. The work confirms that the method that predicts traffic conditions most accurately on motorways and highways (namely seasonal ARIMA) is not optimal for travel time prediction in the context of GPS data from urban travel networks. In all the examined cases, kNN approach yields a mean absolute percentage error that is twice as good as ARIMA, while in some cases it even yields a mean absolute percentage error that is an order of magnitude better. The merit of the model is demonstrated using GPS data collected by vehicles travelling through the road network of the city of Zagreb. To evaluate the performance, the models mean absolute percentage error, mean error, and root mean square error are calculated. A non-parametric ranked Friedman ANOVA to test groups of three or more models, and the Wilcoxon matched pairs test to test significance between two models are used. The alpha levels are adjusted using the Bonferroni correction. Today’s commercial fastest-route guidance systems can readily incorporate the proposed model. Since the model yields travel times that are dependent on dynamic factors, these commercial systems can be made dynamic. Furthermore, the model can also be used to generate pre-trip information that will help users to save time. KEYWORDS: travel time prediction, urban traffic, GPS data, k-nearest neighbour, seasonal ARIMA, non-parametric regression

scholarly journals Impedance Modeling and Stability Analysis of DFIG-Based Wind Energy Conversion System Considering Frequency Coupling

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3243
Author(s):  
Shaojian Song ◽  
Peichen Guan ◽  
Bin Liu ◽  
Yimin Lu ◽  
HuiHwang Goh
Keyword(s):  
Stability Analysis ◽  
Coupling Effect ◽  
Impedance Model ◽  
Wind Energy Conversion ◽  
Proposed Model ◽  
New Type ◽  
Impedance Modeling ◽  
Frequency Coupling ◽  
Bus Voltage ◽  
Voltage Dynamics

Impedance-based stability analysis is an effective method for addressing a new type of SSO accidents that have occurred in recent years, especially those caused by the control interaction between a DFIG and the power grid. However, the existing impedance modeling of DFIGs is mostly focused on a single converter, such as the GSC or RSC, and the influence between the RSC and GSC, as well as the frequency coupling effect inside the converter are usually overlooked, reducing the accuracy of DFIG stability analysis. Hence, the entire impedance is proposed in this paper for the DFIG-based WECS, taking coupling factors into account (e.g., DC bus voltage dynamics, asymmetric current regulation in the dq frame, and PLL). Numerical calculations and HIL simulations on RT-Lab were used to validate the proposed model. The results indicate that the entire impedance model with frequency coupling is more accurate, and it is capable of accurately predicting the system’s possible resonance points.

Short-term forecast model of taxi demand based on time and space heterogeneity

2021 ◽  
pp. 1-12
Author(s):  
Zhiyu Yan ◽  
Shuang Lv
Keyword(s):  
New York ◽  
New York City ◽  
York City ◽  
Urban Traffic ◽  
Percentage Error ◽  
Density Level ◽  
Traffic Data ◽  
Short Term ◽  
Time And Space ◽  
Density Data

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.

scholarly journals Research on the impact of ship traffic flow on the restricted channel segment of the middle Yangtze River based on traffic wave theory

2021 ◽  
Vol 3 (8) ◽  
Author(s):  
Ting Liu ◽  
Gabriel Lodewijks
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Flow Simulation ◽  
Wave Theory ◽  
Flow Density ◽  
List Type ◽  
Ship Traffic ◽  
Traffic Wave ◽  
The Impact ◽  
Channel Segment

Abstract Abstract On the basis of the influence of dry season on ship traffic flow, the gathering and dissipating process of ship traffic flow was researched with Greenshields linear flow—density relationship model, the intrinsic relationship between the ship traffic congestion state and traffic wave in the unclosed restricted channel segment was emphatically explored when the ship traffic flow in a tributary channel inflows, and the influence law of multiple traffic waves on the ship traffic flow characteristics in unclosed restricted segment is revealed. On this basis, the expressions of traffic wave speed and direction, dissipation time of queued ships and the number of ships affected were provided, and combined with Monte Carlo method, the ship traffic flow simulation model in the restricted channel segment was built. The simulation results show that in closed restricted channel segment the dissipation time of ships queued is mainly related to the ship traffic flow rate of segments A and C, and the total number of ships affected to the ship traffic flow rate of segment A. And in unclosed restricted channel segment, the dissipation time and the total number of ships affected are also determined by the meeting time of the traffic waves in addition to the ship traffic flow rate of segments. The research results can provide the theoretical support for further studying the ship traffic flow in unclosed restricted channel segment with multiple tributaries Article Highlights The inflow of tributaries' ship traffic flows has an obvious impact on the traffic conditions in the unenclosed restricted channel segment. The interaction and influence between multiple ship traffic waves and the mechanism of generating new traffic waves are explained. The expression of both dissipation time of queued ships and the total number of ships affected in the closed and unclosed restricted channel segment are given.

Travel mode recognition of urban residents using mobile phone data and MapAPI

2021 ◽  
pp. 239980832098300
Author(s):  
Zhenghong Peng ◽  
Guikai Bai ◽  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu
Keyword(s):  
Urban Planning ◽  
Mobile Phone ◽  
Traffic Flow ◽  
Road Traffic ◽  
Urban Traffic ◽  
Phone Call ◽  
Traffic Information ◽  
Mobile Phone Data ◽  
Urban Residents ◽  
Travel Mode

Obtaining the time and space features of the travel of urban residents can facilitate urban traffic optimization and urban planning. As traditional methods often have limited sample coverage and lack timeliness, the application of big data such as mobile phone data in urban studies makes it possible to rapidly acquire the features of residents’ travel. However, few studies have attempted to use them to recognize the travel modes of residents. Based on mobile phone call detail records and the Web MapAPI, the present study proposes a method to recognize the travel mode of urban residents. The main processes include: (a) using DBSCAN clustering to analyze each user’s important location points and identify their main travel trajectories; (b) using an online map API to analyze user’s means of travel; (c) comparing the two to recognize the travel mode of residents. Applying this method in a GIS platform can further help obtain the traffic flow of various means, such as walking, driving, and public transit, on different roads during peak hours on weekdays. Results are cross-checked with other data sources and are proven effective. Besides recognizing travel modes of residents, the proposed method can also be applied for studies such as travel costs, housing–job balance, and road traffic pressure. The study acquires about 6 million residents’ travel modes, working place and residence information, and analyzes the means of travel and traffic flow in the commuting of 3 million residents using the proposed method. The findings not only provide new ideas for the collection and application of urban traffic information, but also provide data support for urban planning and traffic management.

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

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