Vehicle driving
speed characteristics in extralong freeway tunnels are analyzed
using radar networking technologybased realtime traffic parameter acquisition method. The highprecision timeseries speed data of all
samples from the entire tunnel are extracted. The tunnel is then divided into
three sections: entrance, driving, and exit, and a shortterm prediction model for detecting vehicle driving speed in extralong freeway tunnels is constructed using a timeseries Transformer framework. The main findings of the study are as
follows: (1) The lowest average vehicle driving speed is discretely distributed
at the entrance section. (2) The statistical value of 85% quantile vehicle
driving speed in each lane shows a decreasing trend along the same road
section. (3) The driving speed of a passenger car at different sections
exhibits a decreasing law, and there is a considerable difference between the
entrance and exit sections. (4) Although the freight cars slow down at the
entrance section, the actual driving speed of passenger cars is higher than the
maximum speed limit of the main lane. (5) The maximum accuracy of the speed
prediction model is 97.82%, and the average absolute error is 1.67 km/h. These
results demonstrate that dark adaptation has a significant impact on the driver′s speed control behavior. The speed limit of the main lane is not
fully applicable on the extralong tunnel environment,
and thus, guidance must be provided for different vehicles and lanes in advance
to avoid accidents. The timeseries Transformer model
established in this paper is suitable for shortterm
speed prediction of all lanes in extralong freeway
tunnels.
WU Ling
,
LIU Jianbei
,
SHAN Donghui
,
MA Xiaolong
,
WANG Yuanqing
. Characteristics and ShortTerm
Prediction Model for Detecting Vehicle Driving Speed
in ExtraLong Freeway Tunnels[J]. Tunnel Construction, 2023
, 43(6)
: 1035
-1044
.
DOI: 10.3973/j.issn.2096-4498.2023.06.014
