Limits of Predictability and Patterns of Vehicular Mobility
Brief Introduction
The newly emerged vehicular communication net-work is seen as a key technology for solving the increasingly serious vehicular traffic congestion as well as improving road safety, and applications of vehicular networks are also emerging at the same time. The capability of predicting the time and locations of a vehicle’s next movement can play a significant role in lots of communication and networking functions from bandwidth reservation to service provisioning. It is an open and unsolved question that to what degree is the vehicle mobility predictable in large-scale cities and are there regularities existing to govern the vehicular mobility. This project aims at exploring the limits of predictability characterizing vehicular mobility in large-scale cities and search certain mobility patterns across the whole datasets.
The newly emerged vehicular communication net-work is seen as a key technology for solving the increasingly serious vehicular traffic congestion as well as improving road safety, and applications of vehicular networks are also emerging at the same time. The capability of predicting the time and locations of a vehicle’s next movement can play a significant role in lots of communication and networking functions from bandwidth reservation to service provisioning. It is an open and unsolved question that to what degree is the vehicle mobility predictable in large-scale cities and are there regularities existing to govern the vehicular mobility. This project aims at exploring the limits of predictability characterizing vehicular mobility in large-scale cities and search certain mobility patterns across the whole datasets.
My Contribution
- I use linear interpolation to preprocess real vehicular mobility traces from two large-scale vehicular datasets in Shanghai and Beijing on which our work is based, and propose a model by selecting hundreds of intersections in Beijing and Shanghai and partitioning the two cities by these intersections’ Voronoi cells.
- I calculate the entropy and limits of predictability of vehicles’ staying duration in each area for both datasets and explore the effect of precision and slot time on the predictability. I conclude that reducing the precision and using proper time slot are both effective ways of raising predictability, and I get the highest potential predictability of 76.3% in Beijing and 82.5% in Shanghai when we use smallest slot time and lowest requirement for precision.
- I also calculate the entropy and limits of predictability of vehicles’ location selection in the two datasets and find that there are two types of vehicles in the traces: one with their limits of predictability near 98.6% and the other around 60% 80%, both of which are highly predictable.
- I analyze the datasets and demonstrate that the visit frequency to an area follows the same pattern every day and the area transition probability are relatively stable over days. Also, I find that traffic volume inward and outward the city in different time of the day accords with our expectation.
