Automated Traffic State Optimization in the Weaving Area of Urban Expressways by a Reinforcement Learning-Based Cooperative Method of Channelization and Ramp Metering
Table 1
Wiedemann-99 car-following model and SL2015 lane-changing model parameters.
Parameters
Description
Value
CC0
Standstill distance (m)
1.5
CC1
Spacing time (s)
1.1
CC2
Following variation (m)
4
CC3
The threshold for entering “following” (s)
−8
CC4/CC5
Negative/positive “following” threshold (m/s)
0.35
CC6
Speed dependency of oscillation (10−4 rad/s)
11.44
CC7
Oscillation acceleration (m/s2)
0.25
CC8
Standstill acceleration (m/s2)
3.50
CC9
Acceleration at 80 km/h (m/s2)
1.50
lcStrategic
The eagerness for performing strategic lane changing
2
lcCooperative
The willingness for performing cooperative lane changing
1
minGapLat
The desired minimum lateral gap
0.6
lcSpeedGain
The eagerness for performing lane changing to gain speed
1.2
lcKeepRight
The eagerness for following the obligation to keep right
1.5
lcSublane
The eagerness for using the configured lateral alignment within the lane
