Novel Trip Agglomeration Methods for Efficient Extraction of Urban Mobility Patterns

Praveen Kumar (), Partha Chakroborty () and Hemant Gehlot ()
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Praveen Kumar: Indian Institute of Technology Kanpur
Partha Chakroborty: Indian Institute of Technology Kanpur
Hemant Gehlot: Indian Institute of Technology Kanpur

Networks and Spatial Economics, 2024, vol. 24, issue 4, No 6, 897-926

Abstract: Abstract Mobility patterns in an urban area can be defined as the trip making behavior of an urban population. Traditionally, the origin-destination matrix representation of travel demand, where trip ends are agglomerated toward zone centroids that are decided a priori, has historically been used to identify trip making behavior. In this paper, different agglomeration methods are explored to extract the trip making behavior and their performances are analyzed. First, a variant of the zone-based agglomeration method is proposed, in which zones are optimally located rather than having their locations determined beforehand. Then a trip-based agglomeration method is proposed, where each trip is represented as an ordered pair of origin and destination in the form of a line segment and agglomeration of these line segments is performed. The proposed line-based agglomeration method serves a two-fold purpose, (a) the proposed trip-based agglomeration method helps in identifying the corridors carrying the majority of the flow in a single step, as opposed to trip-end based agglomeration methods where several post-processing steps may be required to identify the corridors, and (b) this method performs better than the existing trip-end based agglomeration methods in terms of the number of corridors that are required to cover the given trips. Efficient algorithms are also developed to solve the proposed trip-based agglomeration method, their performance on real-world trip datasets is tested and finally, the properties of the proposed algorithms are explored.

Keywords: Urban mobility patterns; Agglomeration methods; Travel demand representation; Trip based agglomeration (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11067-024-09641-3

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