# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 4 Conclusions and Recommendations  
The artificial neural network (ANN), MLR, and PR techniques were employed to model ARA based on 22 years of data from the OECD and its partner countries. These data include annual passenger and freight movement, demographic and economic indicators, and road network maintenance and new development investments. The ANN model was found to be more accurate and general in predicting the ARAs. The ANN model was used to illustrate the variation in ARAs with road infrastructure investments and the other variables mentioned. Safe limits per capita NRII and ERMI and their relative proportions for diverse combinations of demography, economy, freight level, and passenger movement were estimated and documented. Some other important observations are highlighted below:  
* The ANN is, by far, more reliable than the MLR and PR in modeling the nonlinear relationship of ARAs with road investment variables, demographic indicators, GDP, etc. * The results of the MLR model showed that all of the independent variables have a significant effect on the ARA, except per capita GDP and changes in the amount of newly registered vehicles. * The PR model indicated that all predicting variables have a significant effect on the ARA. * On average, the variables listed in decreasing order of influence on ARA are total NRII (#1), population of work group (#2), total ERMI (#3), freight movement (#4), NRII/capita (#5), population (#6), ERMI/capita (#7), passenger (#8), GDP (#9), proportion of work group (#10), and CNRV (#11). * New road expansion and existing road maintenance investments were found to exhibit a nonlinear concave-up relationship with ARAs. This was considered to be an explanation for non-uniform conclusions on the influence of these parameters on road accident frequency, as reported by some past literature. * A certain amount of NRII and ERMI that corresponds to minimum ARAs for a given demography, economy, freight level, and passenger movement exists. * ARAs showed positive exponential growth with an increase in freight movement. * ARAs exhibit a positive linear relationship as passenger movement increases but there is a non-zero intercept on the passenger movement axis. * A positive logarithmic growth relationship between ARAs and CNRV was also observed. * Population and population of work group showed a positive S-curve relationship with the ARAs. The proportion of the population work group showed a general non-linearly increasing association with ARAs. * The ARAs decrease linearly as the per capita GDP increases. Based on these observations, more and proper attention should be given to road safety as the target criteria for the capital funding of road network maintenance and infrastructural development. As a suggestion for further studies, different models for ARA prediction that  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Freight (Million km)** & \begin{tabular}{c} **Passenger (Million km)** \\ \end{tabular}  & \begin{tabular}{c} **CNVR** \\ **Passenger-km)** \\ \end{tabular}  & \begin{tabular}{c} **Group** \\ **of Working** \\ **Group (\%)** \\ \end{tabular}  & \begin{tabular}{c} **GDP US** \\ **of Working** \\ **of Coupling** \\ \end{tabular}  & \begin{tabular}{c} **Minimum** \\ **ARA** \\ **(1000)** \\ \end{tabular}  & \begin{tabular}{c} **Opt. ERMI** \\ **(EUR/Capita)** \\ \end{tabular}  & \begin{tabular}{c} **Opt. NRII** \\ **(EUR/Capita)** \\ \end{tabular}  & \ \begin{tabular}{c} **ERMI** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **NRII \\ **(\%)** \\ \end{tabular}  \\ \hline
50,000 & 200,000 & 0 & 60 & 20,000 & 14.922 & 20 & 2 & 0.1000 & 91\% & 9\% \\
50,000 & 200,000 & 0 & 60 & 30,000 & 9.454 & 32 & 17 & 0.5313 & 65\% & 35\% \\
50,000 & 200,000 & 0 & 6  
will enable the estimations of safe road investment limits for specific road facilities could provide more effective alternatives for road infrastructure investments. Conceptualization, M.A.D. ; methodology and software, M.A.D. and A.A.M. ; validation and formal analysis, M.A.D., M.K.I. and A.A.M.