[et_pb_section fb_built=”1″ theme_builder_area=”post_content” _builder_version=”4.27.7″ _module_preset=”default”][et_pb_row column_structure=”1_3,2_3″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”40px|||||” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”1_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_image src=”http:\/\/cvsc.upcebu.edu.ph\/wp-content\/uploads\/2026\/10\/Abellana-IJADS.jpg” title_text=”Abellana-IJADS” _builder_version=”4.27.7″ _module_preset=”default” hover_enabled=”0″ box_shadow_style=”preset2″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″][\/et_pb_image][\/et_pb_column][et_pb_column type=”2_3″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.7″ _module_preset=”default” text_font_size=”15px” text_line_height=”1.2em” custom_padding=”0px|||||” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” sticky_enabled=”0″]<\/p>\n
Lead Researcher(s): <\/strong>Kyle Adrian A. del Castillo, Dharyll Prince M. Abellana<\/strong>, and Miriam F. Bongo<\/p>\n Status:<\/strong> Published<\/p>\n Abstract\/summary:<\/strong> This study explores the application of neural controlled differential equations (NCDEs) for forecasting multivariate travel times within a road network in Metro Cebu (Philippines). The research uses a network comprising 20 routes in Cebu City, Philippines, with five key locations chosen as origin-destination pairs. The findings indicate that NCDEs significantly improve the accuracy of travel-time forecasting compared to traditional models. The promising results of this study suggest that hybrid models that combine NCDE with other methodologies could be developed to harness its robust performance, ensuring effectiveness when applied to irregular time-series data. Further validation and improvement of the applicability of these models in real-world traffic management systems could lead to more advanced AI-driven traffic management and control.<\/span><\/span><\/p>\n Keywords:<\/b><\/p>\n