# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 5 Conclusions and Future Work  
Several studies have reviewed and analyzed how to obtain the traffic information from CFVD. However, they cannot be applied directly to predict the future traffic information in dynamical environments. Therefore, this study proposes analytic models to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Furthermore, the vehicle speeds can be estimated according to the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed via the current traffic information. In an experimental environment, this study adopted the practical traffic information from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the MS communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracies of vehicle speed estimation of road segments are 79%, 85%, 63% and 82% for the estimated vehicle speeds \(u_{i,ha}\), \(u_{i,na}\), \(u_{i,hp}\) and \(u_{i,np}\), respectively. Moreover, the average accuracy of the vehicle speed forecasting method based on BPNN is 95.72%. Therefore, the proposed methods can be used to estimate and predict vehicle speed from CFVD for ITS. However, this study assumes that each MS in the car can be filtered and tracked for the collection of cellular network signals (e.g., CAs, HOs, NLUs and PLUs). In the future, filtering out non-vehicle terminals and correctly predicting the routes of vehicle terminals can be investigated in the next study. Furthermore, this study focuses on analyzing the signals from GSM and UMTS networks to estimate the traffic information of the highway for the evaluation of the proposed methods. As the demand for \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{\(\mathbf{U}_{i}^{\Phi+1}\)} & **Forecasted Vehicle** & **Forecasted Vehicle** & **The Accuracy** & **The Accuracy** \\  & & **Speed of LR** & **Speed of BPNN** & **of LR** & **of BPNN** \\ \hline
8 & 70 & 76 & 69 & 92.32\% & 98.90\% \\
9 & 71 & 78 & 73 & 90.05\% & 96.32\% \\
10 & 83 & 79 & 77 & 95.95\% & 93.26\% \\
11 & 83 & 83 & 81 & 99.78\% & 97.30\% \\
12 & 83 & 85 & 86 & 98.40\% & 96.86\% \\
13 & 83 & 80 & 80 & 96.60\% & 96.74\% \\
14 & 82 & 80 & 80 & 97.89\% & 98.18\% \\
15 & 83 & 81 & 78 & 97.89\% & 93.95\% \\
16 & 70 & 82 & 79 & 83.16\% & 87.91\% \\
17 & 70 & 72 & 73 & 97.73\% & 96.19\% \\
18 & 71 & 74 & 75 & 95.30\% & 93.91\% \\
19 & 84 & 79 & 81 & 94.50\% & 96.52\% \\
20 & 84 & 79 & 80 & 94.54\% & 95.47\% \\
21 & 84 & 80 & 84 & 94.75\% & 99.93\% \\
22 & 85 & 79 & 86 & 93.47\% & 98.76\% \\
**Mean** & & & & **94.82\%** & **96.01\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The accuracies of vehicle speed forecasting of the road segment covered by Cell\({}_{1}\). LR, logistic regression. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cell** & **The Accuracy of LR** & **The Accuracy of BPNN** \\ \hline Cell\({}_{1}\) & 94.82\% & 96.01\% \\ Cell\({}_{2}\) & 93.76\% & 96.29\% \\ Cell\({}_{3}\) & 93.42\% & 95.24\% \\ Cell\({}_{4}\) & 92.39\% & 94.03\% \\ Cell\({}_{5}\) & 93.25\% & 94.72\% \\ Cell\({}_{6}\) & 93.59\% & 97.35\% \\ Cell\({}_{7}\) & 92.26\% & 95.74\% \\ Cell\({}_{8}\) & 93.73\% & 95.19\% \\ Cell\({}_{9}\) & 93.84\% & 96.88\% \\
**Mean** & **93.45\%** & **95.72\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The accuracies of vehicle speed forecasting of each road segment. real-time traffic information increases, the next goal is to estimate and analyze the traffic congestion and transportation delays of urban road segments. The research is supported by the Ministry of Science and Technology of Taiwan under the grant No. MOST 104-2221-E-110-041.