# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 1 Introduction  
In recent years, the usage and improvement of mobile techniques have been growing for a variety of real-life applications. For instance, many intelligent transportation systems (ITSs) and services have been designed and developed by several enterprises [1; 2; 3; 4]. For the development of ITSs, studying how to collect the traffic information efficiently is a really important topic. The case studies of cooperative intelligent transportation systems (C-ITS) [2; 3] and connected-vehicle technology [4] developed real-time traffic information collection methods and evaluated traffic status. Furthermore,Gao _et al._[1] considered traffic status to predict travel time for the design of a navigation system and the avoidance of traffic jams. ITSs have three kinds of popular position collection approaches, which are the vehicle detector (VD), the reports of global positioning system (GPS)-equipped probe cars and cellular floating vehicle data (CFVD). VD is a sensor based on the techniques of active infrared/laser, magnetic, radar or video to regularly detect vehicles on a road for the analysis of time mean speed and traffic flow [5; 6] GPS-equipped probe cars can periodically report their location information to a server for computing the space mean speed [7]. CFVD, which is obtained by tracking the network signals of the mobile station (MS) in the car, can be analyzed to estimate the traffic information (e.g., traffic flow, vehicle speed and traffic density) [5]. The advantages and limitations of these three methods are illustrated in the following paragraphs. The establishment cost and maintenance fees of VDs were discussed and analyzed in some studies from different countries [8]. For instance, the Texas Transportation Institute spent 43,500 U.S. dollars to install video image vehicle detection systems (VIVD) for the detection of real-time traffic information in 2002. The Ministry of Transportation and Communications in Taiwan spent 75,873 U.S. dollars to establish one VD per each kilometer on Taiwan Highway No. 1 for the collection of real-time road information [9]. However, the maintenance fees of VD solutions are really high because the damage of VD usually result by the environmental factors. Some studies discussed traffic collection and estimation methods based on the reports of GPS-equipped probe cars, which were compared to the traffic information from VDs for the evaluation of traffic estimation accuracies. For instance, [PERSON] _et al._[10] showed that the lower error rate of space mean speed estimation (less than 3%) can be obtained with enough active probe vehicles. [PERSON] _et al._[10] showed that GPS-equipped probe vehicles periodically recorded their speeds and locations in practical environments for traffic information estimation. Furthermore, their study suggested that a 2%-3% penetration of GPS-equipped probe vehicles is enough to provide accurate measurements of space mean speed. However, the cost of deployment of GPS-equipped probe cars is very expensive for reaching a penetration rate of 2%-3% [10; 11]. CFVD can be obtained by tracking MS signals (call arrival (CA), handover (HO), double handover (DHO), normal location update (NLU) and period location update (PLU)), and it can be used for the analysis of traffic status [12; 13]. Furthermore, the International Telecommunication Union (ITU) indicated that the handphone penetration rate is more than 100% in many countries [14], and the penetration rate is high enough to assume that everyone has a handphone. Therefore, some studies assumed one handphone in one vehicle. For instance, [PERSON] _et al._[9] analyzed two HO locations based on the location service (LCS) as CFVD to estimate the real-time travel time and real-time vehicle speed between HO locations. Their experimental results indicated that the accuracy of the proposed traffic information estimation method was high. However, due to tracing the routes of each MS, a higher computation power is required, and privacy threats may exist.