  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.
