{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/taxi-demand-supply-forecasting-impact-of","title":"Taxi Demand-Supply Forecasting: Impact of Spatial Partitioning on the Performance of Neural Networks","arxiv_id":"1812.03699","date":"2018-12-10","proceeding":null,"authors":["Neema Davis","Gaurav Raina","Krishna Jagannathan"],"abstract":"In this paper, we investigate the significance of choosing an appropriate\ntessellation strategy for a spatio-temporal taxi demand-supply modeling\nframework. Our study compares (i) the variable-sized polygon based Voronoi\ntessellation, and (ii) the fixed-sized grid based Geohash tessellation, using\ntaxi demand-supply GPS data for the cities of Bengaluru, India and New York,\nUSA. Long Short-Term Memory (LSTM) networks are used for modeling and\nincorporating information from spatial neighbors into the model. We find that\nthe LSTM model based on input features extracted from a variable-sized polygon\ntessellation yields superior performance over the LSTM model based on\nfixed-sized grid tessellation. Our study highlights the need to explore\nmultiple spatial partitioning techniques for improving the prediction\nperformance in neural network models.","url_abs":"http://arxiv.org/abs/1812.03699v1","url_pdf":"http://arxiv.org/pdf/1812.03699v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"taxi-demand-supply-forecasting-impact-of","repo_url":"https://github.com/R4h4/AIforSEA_Traffic_Management","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}