Study Of Statistical Models For Route Prediction Algorithms In VANET

Vehicle-to-vehicle communication is a concept greatly studied during the past years. Vehicles equipped with devices capable of short-range wireless connectivity can form a particular mobile ad-hoc network, called a Vehicular Ad-hoc NETwork (or VANET). The users of a VANET, drivers or passengers, can be provided with useful information and with a wide range of interesting services. Route prediction is the missing piece in several proposed ideas for intelligent vehicles. In this paper, we are studying the algorithms that predict a vehicle's entire route as it is driven. Such predictions are useful for giving the driver warnings about upcoming traffic hazards or information about upcoming points of interest, including advertising. This paper describes the route Prediction algorithms using Markov Model, Hidden Markov Model (HMM), Variable order Markov model (VMM). Keywords: VANET, MANET, ITs, GPS, HMM, VMM, PST

TTDM: A Travel Time Difference Model for Next Location Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to business and governments. In existing studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Unfortunately, due to the time and space complexity, these methods (e.g., Markov models) only use the just passed locations to predict next locations, without considering all the passed locations in the trajectory. In this paper, we seek to enhance the prediction performance by considering the travel time from all the passed locations in the query trajectory to a candidate next location. In particular, we propose a novel method, called Travel Time Difference Model (TTDM), which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Further, we integrate the TTDM with a Markov model via a linear interpolation to yield a joint model, which computes the probability of reaching each possible next location and returns the top-rankings as results. We have conducted extensive experiments on two real datasets: the vehicle passage record (VPR) data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over existing solutions. For example, compared with the Markov model, the top-1 accuracy improves by 40% on the VPR data and by 15.6% on the Taxi data

Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study

This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies

Traffic-known urban vehicular route prediction based on partial mobility patterns

Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs). Yet it is quit challenging to make accuracy route prediction for general vehicles in urban settings due to several practical issues such as very complicated traffic networks, the highly dynamic real-time traffic conditions and their interaction with drivers' route selections. In this paper, we undertake a systematic study on the vehicular route prediction in urban environments where the traffic conditions on complicated road networks keep changing from time to time. Inspired by the observation that a vehicle often has its own route selection flavor when traversing between its sources and destinations, we define a mobility pattern as a consecutive series of road segment selections that exhibit frequent appearance along all the itineraries of the vehicle. We further leverage Variable-order Markov Models (VMMs) to mine mobility patterns from the real taxi GPS trace data collected in Shanghai. In addition, considering the tremendous impact of dynamic traffic conditions to the accuracy of route prediction, we deploy multiple VMMs differentiating different traffic conditions in daytime. Our extensive trace-driven simulation results show that notable patterns can be mined from routes of common vehicles though they usually have no constraints when selecting routes. Given a specific taxi, around 40% next road segments are predictable using our model with a confidence weight of 60%. With multiple VMMs a high route prediction accuracy is achievable from the real traffic trace. © 2009 IEEE

SHORT TERM TRAVEL BEHAVIOR PREDICTION THROUGH GPS AND LAND USE DATA

The short-term destination prediction problem consists of capturing vehicle Global Positioning System (GPS) traces and learning from historic locations and trajectories to predict a vehicle’s destination. Drivers have predictable trip destinations that can be estimated through probabilistic modeling of past trips. This dissertation has three main hypotheses; 1) Employing a tiered Markov model structure will permit a shorter learning period while achieving similar accuracy results, 2) The addition of derived trip purpose information will increase accuracy of the start of trip and in-route models as a whole, and 3) Similar methodologies of travel pattern inference can be used to accurately predict trip purpose and socio-economic factors. To study these concepts, a database of GPS driving traces (120 participants for 70 days) is collected. To model the user’s trip purpose, a new data source was explored: Point of Interest (POI)/land use data. An open source land use/POI dataset is merged with the GPS dataset. The resulting database includes over 20,000 trips with travel characteristics and land use/POI data. From land use/POI data, and travel patterns, trip purpose is calculated with machine learning methods. A new model structure is developed that uses trip purpose when it is available, yet falls back on traditional spatial temporal Markov models when it is not. The start of trip model has an overall increase of accuracy over other start of trip models of 2%. This comes quickly, needing only 30 days to reach this level of accuracy compared to nearly a year in many other models. When adding trip purpose and the start of trip model to in-route prediction methods, the accuracy of the destination prediction increases significantly: 15-30% improvement of accuracy over similar models between 0-50% of trip progression. Certain trips are predicted more accurately than others: work and home based trips average of 90% correct prediction, whereas shopping and social based trips hover around the 50% mark. In all, the greatest contribution of this dissertation is the trip purpose methodology addition and the tiered Markov model structure in gaining fast results in both the start of trip and in-route models