Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

Abstract

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Based on mobility patterns, propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. It also leverages the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips. GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Ola, Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. Now, study a new kind of query – Modified k-Nearest Neighbor Search with Hill Climbing (MkNNHC), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, an MkNNHC query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RkNNT query. Then introduce a filter refinement framework for processing MkNNHC queries using the proposed indexes. Experiments on real datasets demonstrate the efficiency and scalability of our approaches.