Direct Effects of City Logistics Measures and Urban Freight Demand Models - Sustainable Urban Logistics: Concepts, Methods and Information Systems - page 209

also depend on the accessibility of shopping areas. Thus if accessibility changes
(for example, due to shopping demand travel management), type of shop and/or
transport mode can also change. If there is a change in the characteristics of end
consumers, residential and commercial land-use distribution, and/or accessibility
to the commercial area, then the freight restocking characteristics may also
change. Similarly, some city logistics measures can reduce the restocking acces-
sibility of an area and induce re-allocation of retail businesses.
Thus, the analysis of urban freight transport, design of new city logistics sce-
narios and relative assessment methodology should consider all these components
and actors. Although several methods and models have been proposed (Comi et al.
2012 ), they sometimes fail to point out the relationships among restocking and
shopping mobility and to predict changes in actors' behaviors. The integrated
modeling framework, reported below, tries to overcome these limits.
3 Freight Transport Simulation System
As stated in the introduction, in this paper the short-term direct effects of city
logistics measures will be considered and the forecasting models of these effects
will be analyzed. The transport simulation system that can be used is reported in
Fig. 1 and consists of different sub-systems: road network, demand, path choice/
assignment, and road performance (impacts).
The Road Network Sub-system comprises the graph of the main road network
and relative link cost functions specific to both passenger and freight vehicles. The
Demand Sub-system simulates the relevant aspects of travel demand as a function
of the activity system and road travel costs. It includes the demand models that
give the O-D matrices which are the input for the subsequent assignment sub-
system.
The Assignment Sub-system includes path choice models and network loading
models for both passenger and freight vehicles. Truck-driver path choice within an
urban network is constrained by vehicle size but there are other factors that tend to
influence the behaviors of truck drivers including driver preferences, vehicle and
route performances (e.g. travel time, vehicle operating costs, gateway tolls;
Taniguchi et al. 2001 ; Russo et al. 2010 ). The network loading model simulates
how O-D vehicle flows load the paths and the links of the road network, and
estimates the link flows, i.e. the number of cars and freight vehicles loading each
link. For more details on assignment models refer to Cascetta ( 2009 ) and refer-
ences quoted therein.
The link flows (output of the assignment module) are used to estimate the
various scenario variables (performance and impact sub-system) that are, in turn,
used to compute the new scenario effects:
• network transportation costs, using time-flow functions, like BPR (BPR 1964 )
or Davidson function (Davidson 1966 );