# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 5 Conclusions and Outlook  
Determining the optimal speed limits that facilitate efficient and safe travel is crucial for urban roads. According to the general speed limit determination method, acting officials determine speed limits by considering whether the speed limit based on the statutory speed limit is appropriate for the characteristics of a specific road section. To calculate the appropriate speed limit, it is necessary to take into account the road geometry [7; 36] and roadside development information [35]. In previous studies, only factors related to the speed limit were classified because it was impossible to diversify the analysis section [26; 27] and collect accurate driving speed information [7; 36] for the driver due to the problem of the available data collection technology. To address this, we propose an optimal speed limit estimation model for road sections based on big data regarding traffic flow statistics, environmental characteristics for urban roads, and the anticipated accident reduction effect of the developed model. The optimal speed limit estimation method for urban roads developed in this study reflects the effects of actual roadside variables on speeds set according to the statutory maximum speed limit for one-way roads with two or more lanes. Based on surveying and testing various influential variables, the key variables influencing the operating speed were determined as the function of the road, the existence of a median strip, the lane width, the number of roadside access points, and the number of traffic breaks. The speed limit estimation model equation was derived by integrating the adjustment factor calculation equations for each variable. These equations act as reduction coefficients with respect to the statutory maximum speed limit in the proposed model equation and can be changed independently depending on the optimal relationship between the variable conditions. Validation tests show that the linear relationship between the variables assumed at the time of selecting the coefficients of the model and the speed was proven. The fit of the model is also shown to be high. The improvement of safety aspects can be expected with the implementation of the developed model. However, the model also has some limitations. Only the safety of the road speed, which should be used for public interest, was considered, while the social benefit achieved by considering the effects of noise and pollution on the residents was not taken into account. Even at the same volume of traffic, the amounts of noise and pollution caused vary according to the speed of the traffic [11] and are also related to the generation of artificial heat [38; 39]. It is also necessary to prevent global warming and improve the quality of life of citizens. In this study, atmospheric environmental factors were not considered due to the absence of a collection device related to noise, pollution, and heat generation. It is necessary to develop a model that takes into account environmental information in the future; through this, safety and operational efficiency could also be improved. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Speed Limit (km/h)** & **FC** & **CD** & **PL** & **SD** & **AD** \\ \hline
70 & Main arterial road & with & 0 & 0 & 0 \\
60 & Main arterial road & none & 0 & 0 & 0 \\
50 & Main arterial road & none & 1 & 1 & 1 \\
50 & Auxiliary arterial road & none & 0 & 1 & 1 \\
40 & Main arterial road & none & 2 & 2 & 1 \\
40 & Auxiliary arterial road & none & 1 & 2 & 2 \\
40 & Collector road & none & 0 & 2 & 1 \\
30 & Collector road & none & 1 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Classification of speed limit groups according to the road environment. The application of immersive virtual environments (IVEs) is necessary in terms of the utilization of research.