Traffic Guidance Oriented Model of Traffic State Probability Forecast

Dou, Huili; Wang, Guohua; Guo, Min

doi:10.1016/s1570-6672(10)60110-9

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some related terms used in the remainder of this paper; in order to avoid any ambiguity or confusion between them, they are clearly defined here based on existing research [27]. (Traffic) state: the state in which traffic is at any given time can be described by a number of parameters such as flow, density, and speed; as these are continuous variables, there can be an infinite number of traffic states.…”

Section: Traffic State Estimationmentioning

confidence: 99%

“…In China, several approaches have been proposed based on the development of social economy and the technology: Jia et al [26] used the floating-car data in Beijing to identify the traffic state based on average vehicle speed. Dou et al [27] proposed an algorithm of logistic regression for traffic state probability forecast to obtain the accurate and objective traffic state information to meet the demand of traffic guidance. Wang and Guo [28] present the heterogeneous data fusion methods and road traffic code model in Beijing.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of Urban Road Network Traffic State Dynamic Estimation Method

Wang

Yun

et al. 2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Traffic state estimation is a key problem with considerable implications in modern traffic management. A simple, general, and complete approach to the design of urban network traffic state and phase estimator has been developed in this paper. A uniform traffic state dynamic estimation method structure is designed which consists of three steps. (1) Floating-car data and radio frequency identification data preprocessing method is proposed to remove the abnormal data and finish the map matching process. (2) Section speed estimation method is proposed based on the degree of confidence. (3) Traffic phase identification method is proposed based on the estimated section speed. A number of simulation and field investigations have been conducted to test the estimator performance. The investigation results indicate that the proposed approach is of high accuracy and smoothness on the section speed estimation and effectively eliminates the influence of abnormal data fluctuations and insufficient data. And the traffic phase identification method can effectively filter out the abnormal distortion of estimated section speed around the threshold value and modify the phase step of traffic status caused by abnormal data.

show abstract

Section: Traffic State Estimationmentioning

confidence: 99%