Prof. N. Moussiopoulos

Urban Goods Distribution (UGD) plays an important role in the sustainable development of cities. It helps to support urban lifestyles, to serve and retain industrial and trading activities, and contributes to the competitiveness of industry in the region concerned (Anderson et al., 2005). Despite the relevant role of this activity, goods distribution also conflicts with other urban functions and thus generates negative (economic, environmental and social) impacts on the economic power, accessibility, quality of life and attractiveness of urban areas.  

Figure 1: Traffic load in the city of Thessaloniki. (Photo by LHTEE record)

The most common examples of such impacts at the three dimensions of sustainability are: air pollution (environmental sustainability); fatalities, noise disturbance, local traffic safety (social sustainability); and journey unreliability and delivery delays (economic sustainability). Furthermore, goods traffic reduces the accessibility of passenger transport in urban areas.

Until recently, even though cities were aware of the negative side of UGD, to some extent they tried to live with those problems. Each city has tried to find and implement its own solution, resulting in initiatives that were usually less than optimal from a societal, environmental or economic point of view.

However, society is now becoming more demanding than it was in the past, and cities are facing a difficult challenge that must be met without further delay. Cities need to maintain and promote their sustainability, mobility and quality of life, while ensuring that UGD systems efficiently serve their needs.

In recent years, Soft Policy Measures (SPM) are generating an increasing interest in the field. SPM are oriented to motivate people adopting good behavior with regard to urban mobility. SPM aim to achieve important goals in the transport sector (e.g. modal shift, greater safety, and CO2 reduction), influencing the choices of individual mobility, through tools such as gamification, and consequently altering the demand for mobility (Jones et al., 2011). The behavioral change campaigns lever the will of individuals, which take the decision they consider the best without being forced (nudging).

In recent decades public authorities have developed a growing awareness of the crucial role of the UGD in cities' traffic: coordinated measures of city logistics began to spread. To assist policy makers, a typology of four common city transport contexts within the land-use and travel framework has been framed (CIVITAS). The four contexts (developing, sprawled, congested and multi-modal cities) describe some of the general travel trends and transport system issues facing cities across the globe.

Figure 2: Four cities contexts from the travel trend and transport system aspect (CIVITAS)

Developing cities experience increasing demand for transport services and rapid growth in private motorization, have relatively low densities and often have inadequate travel infrastructure, especially for non-motorised transport modes (e.g. walking and bicycling), and weak public transit services (e.g. unregulated, poor quality bus operators).

Sprawling cities tend to have low densities and high urban and suburban sprawl, they often have poorly-defined urban cores with commercial and business hubs spread intermittently throughout the urban and metropolitan areas. Public transit use and non-motorised transport shares tend to be low, while private motorised transport tends to be the primary means of travel.
Congested cities are characterized by heavy roadway traffic and can have extensive transit systems and high public transport modal shares. They generally have medium to high densities and strong urban cores, although urban sprawl may exist in surrounding metropolitan areas.

Multi-modal cities typically have high densities, strong urban cores, and high public transit shares. They generally have strongly interconnected, well-developed travel networks, which facilitate and encourage more efficient travel.

Therefore, it is important to identify the city model and a clear cause-effect relationship between actors, environment and local governments, on one hand, and different kinds of measures, on the other, to determine the optimal combination of the best policies with respect to the peculiarities of that specific environment. The policy interventions must also take into account the behavioral aspects of the actors involved, and agent-specific approaches must be adopted.



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