IEDC-2010 Conference Proceedings (Download ... - NED University

v f
(km/h)
p cr
(veh/km/lane)
a
model parameters. We choose the set of feasible values for A as A [ 1: .01: 2.5] . The initial values and
limits for these parallel estimators were chosen to be:
v (0) 180 km / h, (0) 10 veh / km / lane, 80 km / h v 200 km / h
f cr f
5 veh / km / lane 50 veh / km / lane, P (0)
cr
6
10 0
0 10
Off-line estimation of three model parameters are shown in Figures 2, 3 and 4. It took about 1.5 hours for
the estimates to settle at the steady state values. The real values of critical density, free flow speed and
exponent are 29 veh/km/lane, 120 km/h and 1.63 respectively.
6
55
50
45
40
35
30
25
20
15
10
5
0
0 0.5 1 1.5 2 2.5 3 3.5 4
time(hour)
Figure 2. Off-line estimation of critical density based on RLS method
200
180
160
140
120
100
80
0 0.5 1 1.5 2 2.5 3 3.5 4
time(hour)
Figure 3. Off-line estimation of free speed based on RLS method
2.5
2
1.5
1
0.5
0
0 0.5 1 1.5 2 2.5 3 3.5 4
time(hour)
Figure 4. Off-line estimation of exponent based on RLS method
Now we use these offline estimated parameters for traffic state estimation based on extended Kalman filter
algorithm. The simulation result for speed estimation at segment 3 is shown in Fig. 5. The corresponding
performance index is 0.0761. For evaluating the performance of non-adaptive state estimator, different types of
changes in the model parameters were tested but only the case of change in free speed is reported here. it is
Proceedings of IEDC 2010, 1-3 July, 2010
321
IEDC-333-073