Predicting subjective measures of walkability index from objective measures using artificial neural networks

Increasing urbanization has been one of the most significant concerns of urban managers. The role of non-motorized transportation in sustainable urban development is vitally important for reducing overweight and obesity among citizens. These issues have led to numerous studies of the association between environmental characteristics, walkability index and levels of human health. The encouragement of public walking and cycling requires measures of walkability indices. One of the common challenges in measuring a walkability index is the complexity of the connection between the subjective indices resulting from public opinion and objective measures of geographic data. The scientific novelty of this paper lies in two aspects: First we developed and evaluated several artificial neural network (ANN) configurations for predicting subjective measures of walkability index from objective measures. Second, we introduced an index for two distinctive modes of walkability: daily shopping and recreation purposes that ranges from 1 to 10. The parameters of land-use diversity, population density, intersection density, network density, access to public transportation, green spaces and commercial places were utilized to calculate the objective value of the walkability index. The determination of subjective value of the walkability index was achieved using fieldwork reports. The resulting index was tested in districts 1 and 3 of Region 18 in the city of Tehran. The quantities used to evaluate the results included RMSE, MAE, MBE, and R. Network training was performed using the Levenberg-Marquardt algorithm. A 10-fold cross validation was used to evaluate and compare the performance of different network configurations. Our findings indicated that the best walkability index for purchasing can be estimated using Levenberg-Marquardt algorithm with one hidden layer and seven neurons. This configuration resulted in a correlation coefficient and an RMSE of 93.79% and 0.1368 respectively. To predict the walkability index for recreational purpose, the best result was obtained using Levenberg-Marquardt algorithm, representing a combination of one layer with four neurons for which the correlation coefficient and RMSE are 90.71% and 0.1602 respectively.