Urban Traffic Flow Guidance System Based on Data Driven

2009 ◽  
Author(s):  
Ming Zhao
Keyword(s):  
Traffic Flow ◽  
Urban Traffic ◽  
Guidance System ◽  
Data Driven

Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests

2017 ◽  
Vol 2645 (1) ◽  
pp. 157-167 ◽  
Author(s):  
Jishun Ou ◽  
Jingxin Xia ◽  
Yao-Jan Wu ◽  
Wenming Rao
Keyword(s):  
Feature Selection ◽  
Traffic Flow ◽  
Random Forests ◽  
Urban Traffic ◽  
Data Driven ◽  
Series Data ◽  
Selection Strategy ◽  
Percentage Error ◽  
Short Term ◽  
Traffic Flow Forecasting

Urban traffic flow forecasting is essential to proactive traffic control and management. Most existing forecasting methods depend on proper and reliable input features, for example, weather conditions and spatiotemporal lagged variables of traffic flow. However, the feature selection process is often done manually without comprehensive evaluation and leads to inaccurate results. For that challenge, this paper presents an approach combining the bias-corrected random forests algorithm with a data-driven feature selection strategy for short-term urban traffic flow forecasting. First, several input features were extracted from traffic flow time series data. Then the importance of these features was quantified with the permutation importance measure. Next, a data-driven feature selection strategy was introduced to identify the most important features. Finally, the forecasting model was built on the bias-corrected random forests algorithm and the selected features. The proposed approach was validated with data collected from three types of urban roads (expressway, major arterial, and minor arterial) in Kunshan City, China. The proposed approach was also compared with 10 existing approaches to verify its effectiveness. The results of the validation and comparison show that even without further model tuning, the proposed approach achieves the lowest average mean absolute error and root mean square error on six stations while it achieves the second-best average performance in mean absolute percentage error. Meanwhile, the training efficiency is improved compared with the original random forests method owing to the use of the feature selection strategy.

Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

2012 ◽  
Vol 238 ◽  
pp. 503-506 ◽  
Author(s):  
Zhi Cheng Li
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Guidance System ◽  
Traffic Network ◽  
Dynamic Traffic ◽  
Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

Study on Path Optimization Method of Urban Traffic Flow Guidance System Based on Internet of Things

2014 ◽  
Vol 624 ◽  
pp. 557-560
Author(s):  
Dan Ping Wang ◽  
Kun Yuan Hu
Keyword(s):  
Traffic Flow ◽  
Optimization Method ◽  
Urban Traffic ◽  
Route Optimization ◽  
Path Optimization ◽  
Guidance System ◽  
Traffic Information ◽  
Advantages And Disadvantages ◽  
Traffic Guidance ◽  
Service Application

This paper analyzes the existing traffic guidance systems and conducts a detailed explanation of things based on traffic flow guidance system and describes its characteristics and advantages and disadvantages. After a comparison beteween them, this paper designs the optimal way and introduce its features detaily. On this basis, this paper studies the structure and composition traffic information system, including the service application platform designed to induce traffic information systems and traffic information collection system and vehicle location system to provide basic information on traffic flow guidance system design. This paper analyzes the general method of route optimization to build a theoretical model based route optimization and this lay a foundation for things urban traffic guidance system path optimization.

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