Environmental Engineering Reference
In-Depth Information
Concerning the direct trips, the most significant variables are vehicle type and
city size. The relations for the direct trip lengths are the following:
l
dt
ðÞ¼
a
v
wr
ðÞþ
b
v
where: l
dt
corresponds to the direct trip length and wr to the radius of the city
T weighted by the number of d
t
trips.
Concerning starting/ending trips, the main significant variable is the distance of
the generator from the centre, the mode of transport management and the type of
activity. A linear function was defined to adjust the length of the starting trip and
can be defined by the following relation:
L
z
¼
a
dc
ðÞþ
b
where L
z
is the length of a starting/ending trip in z and dc(z) corresponds to the ''as
the crow flies'' distance of zone z from the city centre.
Regarding the connecting trips, the main significant variables are the number of
stops s in the round, the vehicle type v and the mode of management m. In order to
take into account spatial effects, two other significant variables have to be added:
the size of the city T (measured by the radius weighted by the number of move-
ments wr(T)) and the density of movements d
z
carried out in zones z (the denser
the zones, the shorter the round-trips are, because of congestion and more trading
opportunities):
l
¼
u v
;
m
;
s
;
wr
ðÞ;
d
i
;
j
Three classes of density d were identified. The general function is given by:
l
v
;
m
;
d
s
; ðÞ¼
a
v
;
m
;
d
log
ðÞþ
b
v
;
m
;
d
wr
ðÞþ
c
v
;
m
;
d
where a\ 0.
A comprehensive description of the calculus including all the relations can be
found in Routhier and Toilier (
2007
).
4.2.4 Trips generation and distribution
Although the main results of the FRETURB model are road occupancy issues
(Bonnafous et al.
2013
), the spatial distribution of freight transport flows can be
useful, nevertheless. Without this step, it is not possible to define the main paths
assigned to the road network, or model the impact of freight routes on traffic.
Consequently, it is necessary to formulate a flow distribution leading to an origin-
destination (O-D) matrix that first allows ''feeding'' urban traffic assignment
models. Therefore it is useful to estimate the impact on congestion, energy con-
sumption and local pollution.
According to Toilier et al. (
2005
), only TL transport flows can be represented
by an oriented O-D matrix using a gravity model. Because of the predominance of
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