Traffic State Estimation with Big Data
| Title | Traffic State Estimation with Big Data |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Xing, Han, Zhang, Ke, Yang, Zifan, Sun, Lianying, Liu, Yi |
| Conference Name | Proceedings of the 4th ACM SIGSPATIAL International Workshop on Safety and Resilience |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6044-9 |
| Keywords | control theory, Cyber physical system, cyber physical systems, HMM, Human Behavior, pubcrawl, resilience, Resiliency, Scalability, SEA, System State Network, Traffic State Estimation |
| Abstract | Traffic state estimation helps urban traffic control and management. In this paper, a traffic state estimation model based on the fusion of Hidden Markov model and SEA algorithm is proposed considering the randomness and volatility of traffic systems. Traffic data of average travel speed in selected city were collected, and the mean and fluctuation values of average travel speed in adjacent time windows were calculated. With Hidden Markov model, the system state network is defined according to mean values and fluctuation values. The operation efficiency of traffic system, as well as stability and trend values, were calculated with System Effectiveness Analysis (SEA) algorithm based on system state network. Calculation results show that the method perform well and can be applied to both traffic state assessment of certain road sections and large scale road networks. |
| URL | https://dl.acm.org/citation.cfm?doid=3284103.3284112 |
| DOI | 10.1145/3284103.3284112 |
| Citation Key | xing_traffic_2018 |