Environmental Engineering Reference
In-Depth Information
greater attention to the negative impacts of motorized traffic (pollution, noise and
greenhouse gas emissions). In the current context, better knowledge of urban goods
transport organization has become essential (Patier and Routhier
2009a
). The
impacts of urban goods movements (UGM) on the environment are not always well-
explained and decision-making tools fail are inadequate due to the lack of pertinent
information. This lack of information is due to the difficulty of collecting and pro-
ducing the data needed to support public and private decisions, both in quantity and
quality. According to Gonzalez-Feliu et al. (
2013
), two main categories of tools can
be used to obtain UGM data for diagnosis and planning. The first is that of urban
goods surveys (Ambrosini and Routhier
2004
), which are presented in Chaps. ''
Data
Demand Data Collection Framework for Large Urban Areas
'
'of the present topic
1
but which suffer from a major drawback: the cost of obtaining sufficient and accurate
data. The second is that of data estimation tools, which are currently rather scarce.
The following examples can be cited: Wiver (Sonntag
1985
), now part of VISUM-
VISEVA (Lohse
2004
), Nätra (Eriksson
1997
), whose use has decreased in recent
years, Freturb (Aubert and Routhier
1999
), and City Goods, the latter being non-
commercial software only available to public authorities and research institutes
(Gentile and Vigo
2006
).
In this chapter we present the methodology of the Freturb decision support
system, i.e. the urban goods surveys initiated by the French Ministry of Transport
and the Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME), and
the proxy data simulation tool derived from their results. First, we present the main
definitions and the methodological aspects of the Freturb model, introducing the
main modules of the decision support system. Then, the four main components are
presented (one for each category of urban goods movement and another for esti-
mating environmental impacts). Finally, the strengths and limits of the modelling
approach as well as a set of further improvements are proposed.
2 Context, Definitions and Motivation
Urban goods modelling is a very popular research subject (Ogden
1992
; Ambrosini
et al.
2008
; Anand et al.
2012
; Comi et al.
2012
; Gonzalez-Feliu and Routhier
2012
;
Taniguchi et al.
2012
; Holguin-Veras et al.
2013
). However, and contrary to per-
sonal trip modelling, no standard exists and most models are limited to academic
use, without direct application to operational tools (Gonzalez-Feliu et al.
2010a
,
b
).
Therefore, we observe that researchers and practitioners have considerable diffi-
culties in proposing a single vision and predominant theoretical frameworks applied
to specific calibration-based datasets, without real perspectives for application.
1
Allen et al. (
2013
) and Holguin-Veras and Jaller (
2013
).
Search WWH ::
Custom Search