Modeling and Characterization of Traffic Flows in Urban Environments

. 2018 Jun 23;18(7):2020. doi: 10.3390/s18072020

Algorithm 2 Extraction of travel times vs. load samples.

Require:
Reunified segment file, Segment-info files
Ensure:
Statistical learning by segment files
1:
for segment in Reunified segment file do
2:
$s e g m e n t C o n n e c t e d [] \leftarrow$ vector that stores all edge ids connected
3:
for s=0 to length( $s e g m e n t C o n n e c t e d$ ) do
4:
$s e g m e n t_i n f o \leftarrow$ Read lines $s e g m e n t C o n n e c t e d [s]$ in Segment-info files
5:
$s e g m e n t S o r t e d [] [] \leftarrow$ sort_by_ $t_{i n}$ ( $s e g m e n t_i n f o$ )
6:
for $t_{i n}$ =0 to length( $s e g m e n t S o r t e d$ ) do
7:
$ν_{n} \leftarrow$ number vehicles per segment in each lane
8:
for $t_{o u t} = t_{i n}$ to $t_{o u t} > = 0$ step − 1 do
9:
if $(s e g m e n t S o r t e d [t_{i n}] [l_{n}] = s e g m e n t S o r t e d [t_{o u t}] [l_{n}])$ and $(s e g m e n t S o r t e d [t_{i n}] [t_{i n}^{ν}] < = s e g m e n t S o r t e d [t_{o u t}] [t_{o u t}^{ν}])$ then
10:
$ν_{n} = ν_{n} + 1$ increase the number of vehicles if the condition is met
11:
end if
12:
end for
13:
end for
14:
$\bar{Δ t} \leftarrow$ average of the travel times for different degrees of congestion
15:
$ν \leftarrow$ number of vehicles in the segment before a new vehicle enters the segment
16:
end for
17:
end for