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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.

Intelligent Transport Systems (ITS) [

VANETs enable enhancing safety level and the ability to driver assistance for vehicles. Through wireless communication between the vehicles, VANETs can help drivers acquire driving information beyond the scope of their vision and perception and then timely handle these potential hazards to avoid traffic accidents. Also, VANETs can depend on road traffic state information to provide real-time traffic guidance services for drivers and then help them to choose a reasonable route to avoid traffic congestion. So, in VANETs, a vehicle can perceive shared information from other vehicles and communicate with surrounding vehicles so as to optimize the drivers’ upcoming routes, to shorten driving time to destinations, and to improve driving experience.

Vehicle route prediction is of great application significance in VANETs. It can be used to effectively inform drivers which of the upcoming road segments will be frequently congested and inform them about related business information that drivers will be interested in. For example, a driver can depend on road congestion from VANETs to timely optimize the upcoming route. As we all know, most vehicles are equipped with navigation software to help drivers select a better driving route. However, the software is to find several routes between given origins and destinations by combining some path algorithms based on historical traffic data, for example, Google Map and Baidu Map, and also lacks a real-time traffic situation. For the same beginning and end point, if one of recommendation routes with smoother road segments has been selected at same time, in this case, the original relatively smooth roads will become congested and the original congested roads will become smooth [

This paper is organized as follows. The next section describes related work. Section

Here we introduce previous work in the field of route prediction. Karbassi and Barth [

At first, the road network can be represented as a graph

Then we illustrate the meaning of the road network with social characteristics. Social network is a set of social actors and the relationships between them; that is, a social network is composed of social actors and the relationship between actors [

Next, we can transform the graph of road network into the format of a matrix. Assume that a matrix to represent a network model is expressed in

Finally, we introduce two concepts from social network analysis to help us predict vehicle routes—point centrality of roads and cohesive subgroup of roads.

Here we introduce how to measure point centrality of roads in a graph. Suppose that in the graph

An example of a graph.

In all vehicles’ driving routes, some are relatively regular. As shown in Figure

Different relationships between roads.

The purpose of studying cohesive subgroup is to discover some small groups with strong relationship in the road network. In the multivalued model, we firstly need to dichotomize the matrix corresponding to a road network, which means to convert all values

After transforming a road network into a dichotomization matrix, we describe how to get cohesive subgroups. In the directed graph, the shortest distance of any two points in a cohesive subgroup is less than

The representation of graphs based on road network map.

A driving route from starting point A to end point B.

After the road network graph correction, we can establish a matrix. So the matrix is the basis of our route prediction. We use relevant software (such as UCINET [

Here, we need to illustrate that GPS points themselves are often noisy and some contain invalid sensor data, so if we only transform the raw GPS data into trips without cleaning those, trips comprised of these GPS points will be errors in some extent. At present, there are many methods [

Steps for route prediction are as follows.

To clearly illustrate the process of our algorithm, we create the dichotomization matrix and describe the directed graph shown in Figure

The process of route prediction algorithm based on social network analysis.

The data used to test our prediction algorithm come from Microsoft Multiperson Location survey (MSMLS) [

We both assume that the vehicle has driven through two roads, and in the process of dichotomization, we regard the average weight of each directed edges in road network model as the threshold

Figure

Correct prediction by prediction distance.

Jon Froehlich’s method firstly analyzes route regularity from the collected GPS data; that is, a large portion of a typical driver’s trips are repeated. So they exploit this fact for prediction by matching the first part of a driver’s current trip with one of the set of previously observed trips. However, there are some problems in their method. First, if previously driving routes from a driver have been collected in the historical data but the driver’s current trip never occurs before when predicting upcoming route, prediction accuracy will drop due to the new route even though road segments of the new routes coincide with previous routes in the collected data. Additionally, if previously driving routes from a driver never existed in the historical data set, then Jon Froehlich’s algorithm will not find the driver’s route regularity, which will greatly reduce the accuracy of the prediction algorithm. In our algorithm, we find relationship between road segments from all historical data rather than consider each entire route. And we also use social network analysis theory to explore the potential relationship between road segments. Even though the problem of above new routes will also exist, we know the internal relationship between each road to improve the accuracy of prediction. In addition, from the beginning of

Figure

Integrity rate of prediction routes by prediction distance.

The prediction accuracy and prediction integrity are contradictory. The larger the prediction distance

Figures

Correct prediction by threshold value.

Integrity rate of prediction routes by threshold value.

This paper mainly defines the relationship between different roads based on the method of social network analysis so as to predict possible routes in the future. First of all, we introduce the method of road network modeling. Then we illustrate the concepts of point centrality of roads and cohesive subgroup of roads and, based on these concepts, we correct the existing road network model. Finally, we design a valid route prediction algorithm and verify the effectiveness by experiments.

In the following studies, we will pay attention to the impacts of road congestion and advertisement on the route choice. But we need to comprehensively consider the effect of different information for the driving routes. Therefore, we need to design a method of route prediction to think about the interconnection between predicted routes and the actual driving routes.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This research was performed in cooperation with the institution. The research is supported by National Natural Science Foundation of China (nos. 61170065 and 61003039), Peak of Six Major Talent in Jiangsu Province (no. 2010DZXX026), China Postdoctoral Science Foundation (no. 2014M560440), Jiangsu Planned Projects for Postdoctoral Research Funds (no. 1302055C), Jiangsu provincial research scheme of natural science for higher education institutions (no. 12KJB520009), and Science & Technology Innovation Fund for higher education institutions of Jiangsu Province (no. CXZZ11-0405).