TY - JOUR
T1 - Novel Trip Agglomeration Methods for Efficient Extraction of Urban Mobility Patterns
AU - Kumar, Praveen
AU - Chakroborty, Partha
AU - Gehlot, Hemant
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Agglomeration methods
KW - Travel demand representation
KW - Trip based agglomeration
KW - Urban mobility patterns
UR - http://www.scopus.com/inward/record.url?scp=85201265044&partnerID=8YFLogxK
U2 - 10.1007/s11067-024-09641-3
DO - 10.1007/s11067-024-09641-3
M3 - Article
AN - SCOPUS:85201265044
SN - 1566-113X
VL - 24
SP - 897
EP - 926
JO - Networks and Spatial Economics
JF - Networks and Spatial Economics
IS - 4
M1 - 103037
ER -