Traffic congestion prediction based on Hidden Markov Models and contrast measure

Predicting Traffic Flow Propagation Based on Congestion at Neighbouring Roads Using Hidden Markov Model

“…In our study, statistical Hidden Markov approach is used for predicting propagation of road congestion on other roads in the neighboring area. Many work has been done which applied HMM to predict traffic flow [2], [6], [19], [20], [21], [22]. A study which is similar to our work is by Wang et al [2].…”

“…Other studies used various method to determine spatial relationship between roads. Linear regression [37] [24] [25], and clustering method, k-Means [21], [40], fuzzy clustering [41], fuzzy C-means [42], and spectral clustering [2] have been widely used to group road segments. There is also a study using probabilistic Markov chain [43] to find similarity pattern between roads in spatial and time.…”

“…For predicting the impact of road congestion, we extended from [2] by considering road congestion and time window as emission. Furthermore, we used similarity clustering of traffic state based on statistical feature from [19], [21]. We proposed traffic state clustering based on frequency of congestion occurrence at certain time interval.…”

Smart traffic management with cyber-physical systems for scenic areas

“…According to the preference of tourists for travelling routes and accommodation data from tourism websites, daily traffic flow transfer was estimated. The Markov state transition matrix [19] among "the eastern routethe northern route -the western route" was set as follow (1) According to the historical traffic data of busy seasons, the vector of initial vehicles number on "the eastern route -the northern route the western route" was set as [21500, 10800, 5000]. Two simulation cased were performed in this paper: (1) With information flow, which means tourist vehicles could receive real-time traffic information; (2) Without information flow, which means tourist vehicles could not receive real-time traffic information.…”

Generalized Cauchy difference iterative forecasting model for wind speed based on fractal time series

“…In recent years, the trend forecasting of stochastic sequences with LRD characteristics has become a hot topic that has attracted the interest of many scholars [1][2][3][4]. A large number of experiments prove that the forecasting methods based on regression analysis [5,6], Gray system [7][8][9], Wiener process [10,11], Markov process [12][13][14], support vector machine [15,16], fuzzy analysis [17,18], and neural network [19,20] cannot describe the LRD characteristics in the forecasting process of the actual stochastic sequences, which leads in low accuracy of forecasting results. Therefore, a stochastic model with LRD characteristics is proposed, such as fractional Gaussian processes and fBm, etc.…”