3 research outputs found
On the Temporal-spatial Analysis of Estimating Urban Traffic Patterns Via GPS Trace Data of Car-hailing Vehicles
Car-hailing services have become a prominent data source for urban traffic
studies. Extracting useful information from car-hailing trace data is essential
for effective traffic management, while discrepancies between car-hailing
vehicles and urban traffic should be considered. This paper proposes a generic
framework for estimating and analyzing urban traffic patterns using car-hailing
trace data. The framework consists of three layers: the data layer, the
interactive software layer, and the processing method layer. By pre-processing
car-hailing GPS trace data with operations such as data cutting, map matching,
and trace correction, the framework generates tensor matrices that estimate
traffic patterns for car-hailing vehicle flow and average road speed. An
analysis block based on these matrices examines the relationships and
differences between car-hailing vehicles and urban traffic patterns, which have
been overlooked in previous research. Experimental results demonstrate the
effectiveness of the proposed framework in examining temporal-spatial patterns
of car-hailing vehicles and urban traffic. For temporal analysis, urban road
traffic displays a bimodal characteristic while car-hailing flow exhibits a
'multi-peak' pattern, fluctuating significantly during holidays and thus
generating a hierarchical structure. For spatial analysis, the heat maps
generated from the matrices exhibit certain discrepancies, but the spatial
distribution of hotspots and vehicle aggregation areas remains similar
Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data
The unprecedented urbanization in China has dramatically changed the urban
spatial structure of cities. With the proliferation of individual-level
geospatial big data, previous studies have widely used the network abstraction
model to reveal the underlying urban spatial structure. However, the
construction of network abstraction models primarily focuses on the topology of
the road network without considering individual travel flows along with the
road networks. Individual travel flows reflect the urban dynamics, which can
further help understand the underlying spatial structure. This study therefore
aims to reveal the intra-urban spatial structure by integrating the road
network abstraction model and individual travel flows. To achieve this goal, we
1) quantify the spatial interaction relatedness of road segments based on the
Word2Vec model using large volumes of taxi trip data, then 2) characterize the
road abstraction network model according to the identified spatial interaction
relatedness, and 3) implement a community detection algorithm to reveal
sub-regions of a city. Our results reveal three levels of hierarchical spatial
structures in the Wuhan metropolitan area. This study provides a data-driven
approach to the investigation of urban spatial structure via identifying
traffic interaction patterns on the road network, offering insights to urban
planning practice and transportation management