Vehicle trajectory prediction based on hidden Markov model

In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases' trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.This work is support by National Natural Science Foundation of P. R. China (Grant No.61572260, 61373017 and 61572261), Peak of Six Major Talent in Jiangsu Province(Grant No.2010DZXX026), China Postdoctoral Science Foundation(Grant No.2014M560440), Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No.1302055C), Scientific & Technological Support Project of Jiangsu Province(Grant No. BE2015702), Natural Science Foundation of Jiangsu Province(Grant No. BK20130882).http://www.itiis.orgam2017Electrical, Electronic and Computer Engineerin

Smart vehicle navigation system using hidden Markov model and RFID technology

The road transport of dangerous goods has been the subject of research with increasing frequency in recent years. Global positioning system (GPS) based vehicle location devices are used to track vehicles in transit. However, this tracking technology suffers from inaccuracy and other limitations. In addition, real-time tracking of vehicles through areas shielded from GPS satellites is difficult. In this paper, the authors have addressed the implementation of a smart vehicle navigation system capable of using radio frequency identification based on information about navigation paths. For prediction of paths and accurate determination of navigation paths in advance, predictive algorithms have been used based on the hidden Markov model. At the core of the system there is an existing field programmable gate array board and hardware for collection of navigation data. A communication protocol and a database to store the driver’s habit data have been designed. From the experimental results obtained, an accurate navigation path prediction is consistently achieved by the system. In addition, once-off disturbances to the driver habits have been filtered out successfully.http://link.springer.com/journal/112772017-10-31hb2017Electrical, Electronic and Computer Engineerin

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

A Method of Vehicle Route Prediction Based on Social Network Analysis

A method of vehicle route prediction based on social network analysis is proposed in this paper. The difference from proposed work is that, according to our collected vehicles’ past trips, we build a relationship model between different road segments rather than find the driving regularity of vehicles to predict upcoming routes. In this paper, firstly we depend on graph theory to build an initial road network model and modify related model parameters based on the collected data set. Then we transform the model into a matrix. Secondly, two concepts from social network analysis are introduced to describe the meaning of the matrix and we process it by current software of social network analysis. Thirdly, we design the algorithm of vehicle route prediction based on the above processing results. Finally, we use the leave-one-out approach to verify the efficiency of our algorithm

A Method of Vehicle Route Prediction Based on Social Network Analysis

A method of vehicle route prediction based on social network analysis is proposed in this paper. The difference from proposed work is that, according to our collected vehicles' past trips, we build a relationship model between different road segments rather than find the driving regularity of vehicles to predict upcoming routes. In this paper, firstly we depend on graph theory to build an initial road network model and modify related model parameters based on the collected data set. Then we transform the model into a matrix. Secondly, two concepts from social network analysis are introduced to describe the meaning of the matrix and we process it by current software of social network analysis. Thirdly, we design the algorithm of vehicle route prediction based on the above processing results. Finally, we use the leave-one-out approach to verify the efficiency of our algorithm