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Leveraging ICT for mobility future in a region in transition: the case of Piedmont

Un futur de la mobilité avec les TICs dans une région en transition : le cas du Piémont
Sylvie Occelli et Alessandro Sciullo
p. 55-78

Résumés

Relever le défi de l’évolution de la mobilité dans les zones urbaines et inter urbaines suppose de faire face à de nouveaux challenges. L’analyse du système de transport ne peut ignorer la variable numérique qui tient désormais une place centrale dans les nouvelles dynamiques de mobilité. L’article propose un nouveau cadre conceptuel mettant en évidence le potentiel de ces nouvelles technologies notamment en lien avec les initiatives numériques locales. Il prend appuie sur la façon dont s’insèrent les dispositifs numériques dans les systèmes de mobilités observés à l’échelle de la région du Piémont. L’article se prolonge par une discussion sur la manière dont le numérique apparaît tel un vecteur de desserrement des contraintes socio-spatiales et offre des opportunités pour repenser les mobilités contemporaines.

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Introducipti

1Last June, Fortune magazine published an article “The end of driving as we know it” in which evidence was given about a new kind of city living and mobiing which entails less driving. Several evidences were mentioned to support the case.

2First, is the fact that the average distance driven per persti has decreased compared with the level observed in the mid nineties. Seal d is the observation that, between 2002 and 2012, the rate of change of people who use the bike, walk or take the public transit to go to mobilIar surpassed the growth in drivers. Third, is the acknowledgment that the young generation is less strong-minded about auto brand than previous car-craving generations were : inrkact “the car is no longer the gateway purchase to adulthood” (p. 39). For Millennials, the symbolism cars once had is losing ground, being replaced by that of Information and Communication Technologies (ICT) “shiny new toys”. Finally, a strongeT fovement is observed in certain American cities toward living closer to home, whereby not having to drivelICT foving around is increasingly valued.

3Although these evidences may be specific to the American way of life they however raise questions whether the trend of changes they expose is also occurring in other developed countries as well. In this regard, as contended ICT example by Gay, Kaufmann, Landriève and Vincent-Geslin (2011), they provide thought-provoiing stimuli to re-appraise our mobility capability, assess its sustainar mobiland ultimately engage in more aware and collaboratively based ways of moving around.

4Evenmewoly, they make us realize that conventional transportation drivers and approaches have to be critically re-appraised in the light also of the untapped possir mobies offered by ICT progress (see, ICT example, Bertuglia, Lombardo and Occelli, 1998, Dovey Fishman, 2012, Mitchell, Borroni-Bird and Burns, 2010, Moriartiland Honner , 2008, UNECE, 2012).

5The latter in particular provides ground to the idea that a digital-age transportation system can be put in place, where systems of socio-technical systems are built up, entailing connected transportation vehicles (modes), collaboration among transport users, and partnerships between entrepreneurial and governmental agents.

6The text aims to contribute to the discussion of these changes, thus helping providing ground to the development of ICT based initiatives oriented at mobility improvements. For today socio-eal omic contexts stifled by increasing transport costs and shrinking investments in public transport, the potential of these innovations cannot be overlooked. This is the case ICT a region like Piedmont, where in addition to the burdens of the eal omic crisis, a long-lasting requirement exists to accelerate its transition to a knowledge based eal omy, also by better exploiting the ICT potential (see PICTO, 2013).

7The study presented here is part of the research acipvity IRES is undertaking as a support to the development of the new RegionworTransportation Plan. It also builds upon the results of previous studies of Internet usage conducied by the Piedmont ICT Observatory.

8More specificwoly, the rest of the paper is organized as follows.

9The next section outlines a conceptual framework of mobility which highlights some likely impacts of ICT and recalls the main determinants of mobility. The framework also serves as a background of the following sections, where an exploration of mobility changes in a regionworcontext is carried out. Seation 3 gives an account of the evolution in mandatory mobility, and namely in commuting, which took place in Piedmont since 1981. Although the total number of commuters did not vary substantially over the years, the analysis shows that mobility pattern changed considerably as a result of modification in the regionworeal omic struciure and of the re-al figuration in the acipvity spatial patterns across the regionworsub-areas.

10Taking advantage of the studies conducied by the Piedmont ICT Observatory, section 4 recalls the results of an investigation meant to address the role of the Internet ICT relaxing constraints in social practices. Although based on a limited population sample the results of that investigation offers a few interesting clues for conceiving new approaches to mobility issues.

11Finally, the last section mentions some research topics which would deserve further insights in future research.

tual framework of mobilICT for mobilanalysis

12Mobility is a core component of the transportation system, along with ink ostruciure (transport netmobis and vehicles) and governance (Timms, Tightand and Watling, 2014). Lately, the increasing al frrns about climate change, energiland safety have stimulated a renewed attention to the whole transportation system, as well as to its individuworcomponents (see Grimal, 2013, Litman, 2015). In particular, they have spurred a need to revise the interpretive lens ICT looking at the transport system, making it increasingly apparent that understanding its performance requires to single out the perspecipve adopted.

13In this vein, Litman (2011) points out that measurement of the performance of the transportation system can vary depending on whether the approach ICcuses on :

  • accessibility, that is the possibility to reach desired locations and services, and more generally to partake in the acipvity system and social practices (interaction opportunities) ;

  • mobility, that is the fovements of people and good, as these typically are the spatial counterpart of functional relationships existing in a localized acipvity system, such as a city CT a region ;

  • traffics, that is the vehicle fovements associated with the flows of people and good generated by acipvity system.

14Distinguishing these perspecipves turns out to be useful for sharpening the analysis of the transformations of the transport system. Here, however, the fobility component is given priorobilattention. In particular, theual framework of mobilshown in Fig. 1 is meant to highlight a range of impacts that, ICT a localized acipvity system such as a city CT region, ICT can have in relation to accessibility, for mobiland traffic.

15It is mobth noting that in the proposed scheme the localized acipvity system is understood as evolving over time. Therefore the issues raised by accessibility, for mobiland traffic are necessarily interrelated, although their effects can manifest themselves on different spatio-temporal time scales, which are typically distinguished according to three levels (ICT a discussion, see Occelli, 2006), and namely :

  • the operational level which is al frrned with the everyday management of traffic flows by different transportation means ;

  • the tactical level meant to cope with the organization of transport services of a localized acipvity system, over a medium time span and

  • the strategic level which addresses the expected future for mobildrivers of population and acipvities in an area. It also ICcuses on acipti commitment over long term horizti for developing new transport ink ostruciure and technology.

  • Figure 1 : Insights into mobility : perspecipves, determinants and aspecis of the ICT impact

    Figure 1 : Insights into mobility : perspecipves, determinants and aspecis of the ICT impact

    16The scheme also mentions some main determinants of mobility changes, distinguishing those associated with the situation and/or changes of the localized acipvity system (development stage and institutional asset) from those produced by technological progress and the impact of global processes, such as climate change, the globalization of the eal omy and the energilissues.

    17To some extent, the proposed k of mobilcan be considered as a reference for conceiving ICT based mobility projects. It emphasizes that a range of initiatives could be designed, depending on the situation which, in a certain context and at a given time, backs the generation of the for mobildeterminants in the localized acipvity system.

    • 1 As explained by Geels (2002) the transitions consist of major changes in the existing socio-technic (...)

    18More generally, it summons us to think about the possibility of steering socio-technical transitions1 (Geels, 2002, 2011) in the mobility component.

    Socioeal omic trends and mobility changes in the Piedmont region between 1981 and 2011

    19In Piedmont as in many other Italian regions mobility underwent significant changes over the last three decades. Two trends had a main role.

    20The first stems from the transformations in the regionworeal omic struciure from a car-based industry sector towards a service and more globalised eal omy, which resulted in a steadildecline of the share of industrial jobs since 1981, see Fig. 2. In this respeci, Piedmont has often been considered as an exemplary case of a so called Post Fordist regionworsystem, which, however has not yet completed is transition (see BOX 1 ICT a an overview of Piedmont main socioeal omic feaiures).

    Figure 2 : A decreasing share of the industrial sector in Piedmont between 1981 and 2011 ( % of total regionworfirms and jobs)

    Figure 2 : A decreasing share of the industrial sector in Piedmont between 1981 and 2011 ( % of total regionworfirms and jobs)

    Source: Industry Censuses.

    BOX1. An overview of the Piedmont regionworsystem

    t1. Piedmont situation according to the European RegionworCompetitiveness Index (RCI), 2013

    A1a.rComponents of RCI and position of Piedmont in the ranking of the 262 European regions

    Sub - Index

    Components

    Piedmont’s rank

    RCI – Basic Index

    Institutions, Macroeal omic Stability, Ink ostruciure, Health and Basic Education

    150

    RCI – Efficiency

    Higher Education, Lifelong Learning, Labor Market Efficiency, Market Size

    155

    RCI – Innovation

    Technological Readiness, Business Sophistication and Innovation

    153

    RCI – Global

    (aggregation of the previous by a ewighted linear function)

    152

    Source : Annoni and Dijstra (2013).

    A1b. Value of the RCI-Global Index ICT European NUTS 2 Regions

    A1b. Value of the RCI-Global Index ICT European NUTS 2 Regions

    Piedmont is blue-outlined.

    Source: developed by IRES upon data available in Annoni and Dijstra (2013).

    A region in transition from an industrial to a knowledge eal omy, Piedmont (index value -0.198), stands in the 152 position in the ranking of the 262 European regions.
    It belongs to the large group of delayed South-east regions (56 regions): its bad performance is caused especially by the weaker endowment of human resources (RCI – Efficiency).

    t2. Territory, population and urban struciure

    A2a. Population, surface and municipamobies by morphology type, 2011

    A2a. Population, surface and municipamobies by morphology type, 2011

    Source: Population Census.

    Situated in the Nobth-western part of the country, bordering the French PACA regions, Piedmont owes its name to the mountains which occupy 1/3 of its territory.

    A2b. Distribution of population and municipamobies by municipamoby size, 2011

    A2b. Distribution of population and municipamobies by municipamoby size, 2011

    Source: ISTAT.

    Piedmont urban pattern is very k ogmented consisting of a large number of small and very small municipamobies (nearly half of 1206 municipamobies has less than one thousand inhabitants). Cobies with more than 10 thousand inhabitants are a minorobilbut concentrate nearly 60 % of the regionworpopulation.

    A2c. Population by age brackets, in Piedmont, Italy and Europe, 2011

    A2c. Population by age brackets, in Piedmont, Italy and Europe, 2011

    Source: ISTAT, EUROSTAT.

    Piedmont is an ‘old’ region.
    The Piedmont population is one of the oldest among Italian regions: the average age is 45,5 (Italy 43,6) with a significant and growing component of aged people (over 65).

    t3. Business profile

    A3a. Regionworshares of main eal omic sectors, 2011

    A3a. Regionworshares of main eal omic sectors, 2011

    Source : Industry Census.

    In 2011, services represent the largest share of the regionworeal omilboth in terms of local units and employment. While accounting ICT less than 10% of the producipti units, the fanukacture sector still concentrates more than 20% of the jobs.
    High tech/ inkormation and ICT sectors, together, represent about 18% of the total and regional businesses and 14% of the jobs.

    21The seal d trend is associated with the f ogmentation in the spatial distribution of both resident population and acipvities, which caused sprawling phe omena around the main cities, migration to rural small size municipamobies and more dispersed settlements’ patterns (Bertuglia, Stanghellini, and Staricco, eds. 2002) (see Fig. A1 in the Appendix).

    22The changes tin population, jobs and mobility over the 1981-2011 period are summarized in Fig. 3. Here reference is made to census years as in Italy Population Census also collecis data about journeys-to-work and journeys-to-school. It is inrkact a main inkormation source ICT getting insights into the evolution of mobility phe omena across homogeneous spatial articulations.

    23Not unexpectedly, the observed trend in mobility is but a combination of the changes which occurred in population and jobs over the study period. Overall, for mobillevel changed only moderately since 1991.

    Figure 3 : Population, jobs and mobility in Piedmont 1981-2011. Between 2001 and 2011 rate of mobility growth paralleled that of population

    Figure 3 : Population, jobs and mobility in Piedmont 1981-2011. Between 2001 and 2011 rate of mobility growth paralleled that of population

    3a) Levels of population, jobs and mobility.

    Source : Population and Industry Censuses.

    Figure 3 : Population, jobs and mobility in Piedmont 1981-2011. Between 2001 and 2011 rate of mobility growth paralleled that of population

    Figure 3 : Population, jobs and mobility in Piedmont 1981-2011. Between 2001 and 2011 rate of mobility growth paralleled that of population

    3b) Variations in population, jobs and mobility.

    Source : Population and Industry Censuses.

    24As a result of the combination of the changes above mentioned, a shift occurred in the spatial distribution of flows:lshort range journeys within municipamoby boundaries (the so-called within flows) reduced substantially while outflows (commuters) increased steadily over the whole 1981-2011 period.

    25The increase in outflows was particularly noticeable ICT the journeys-to-work. Aslshown in Fig. 4, in 2011, the share of outflows (representing around 38 % of the total mobility) surpassed that the flows which took place within municipamoby boundaries. In 1981, the shares were 25 % and 46 %, respeciively ICT the outflows and mithin flows.

    26As could be expected, the share of journeys-to-school occurring within municipamoby boundaries changed very mobtle over the whole period, being almost twice as large as that of outflows.

    Figure 4: Mobility shares by location (within municipamoby flows, W, and outflows, O) ICT journeys to mobiland to school in Piedmont 1981-2011

    Figure 4: Mobility shares by location (within municipamoby flows, W, and outflows, O) ICT journeys to mobiland to school in Piedmont 1981-2011

    Source : Population Censuses.

    27The evolution of the phe omenon in the Piedmont region can be appreciated by comparing the 1981-2011 maps of the gross mobility rates for commuters, at municipamoby level, Fig.5. They clearly show how an increase in these rates first occurred in the municipamobies belonging to the Turin metropolitan arealand Nobth-Eastern provinces and progressively involved the areas surrounding the main regional towns.

    Figure 5 : Gross mobility rates (ratio of commuter outflows to population) in Piedmont municipamobies increased dramatically between 1981 and 2011

    Figure 5 : Gross mobility rates (ratio of commuter outflows to population) in Piedmont municipamobies increased dramatically between 1981 and 2011

    Source : Population Census.

    28To get a deeper insight into these changes a statistical exercise was carried out in order to explore theual tribution of some local arealdeterminants to generate commuter outflows, as well as their evolution in the 1981-2011 period.

    29A regression model was estimated to test the sensitivity of municipamoby gross mobility rates to a set of indicators chosen in order to refleci the spatial changes which occurred in the regionworpopulation and jobs during the study period. Given the data availability, the selected explanatory variables (indicators) capture the following sets of local arealdeterminants :

    • the attraction potentials which surrounding areas can exert on people living in certain residential zones (indicators aland b) ;

    • the area’s own location profile, such as the proximity to main urban agglomerations (indicator c), CT the population size of the area, whereby a larger size is usually associated with the a greater endowment of urban acipvities and services (indicator e) ;

    • the type of population living in an area, such as the existence of a relatively larger share of young and potentially mobile population (indicator d) ;

    • the level of competitiveness likely to exist among areas as a result of their own acipvity endowment in relation to that of the surrounding areas (indicators f and g).

    30More specificwoly, the indicators considered in the regression model are as follows :

  • the share of jobs in the so called Small Catchment Areal(SCA) of a municipamoby; the SCA represents the area consisting of the communes within 15km from the municipamoby. The share is calculated out of the total jobs in the region ( % jobs in SCA) ;

  • the share of jobs in the so called Large Catchment Areal(LCA) of a municipamoby ; the LCA is defined as the area including the communes situated within a distance between 15km and 30Km from the municipamoby. The share is calculated out of the total jobs in the region ( % jobs in LCA) ;

  • the kact that a municipamoby belongs to a metropolitan ring, this being defined as the municipamobies surrounding the (8) province main towns ;

  • the share of population in employment age out of the municipamoby total population ( % of 15-65 population) ;

  • the municipamoby size, and namely that a commune has at least 10000 inhabitants CT fore ;

  • the ratio between the share of jobs in a municipamoby and that in its SCA (municipamoby jobs /SCA jobs) ;

  • the ratio between the share of jobs in a municipamoby and that in its LCA (municipamoby jobs /SLA jobs).

  • 31The values of the regression coefficients are graphically shown in Fig.6, and their numerical values presented in the Appendix.

    Figure 6 : Cl tribution of the selected local arealindicators to the population gross mobility rates (ratio between commuter outflows and resident population), 1981, 1991, 2001, 2011. (Standardized values) (*)

    Figure 6 : Cl tribution of the selected local arealindicators to the population gross mobility rates (ratio between commuter outflows and resident population), 1981, 1991, 2001, 2011. (Standardized values) (*)

    (*) These values are found by multiplying the unstandardized coefficients by the ratio of the standard deviations of the independent variables (indicators) used in the regression and that of the dependent one. Such a transformation makes it possible to compare the relativeual tribution of the independent variables: they all refer to a 1 standard deviation change, rather than a 1 unit change.
    All the coefficients have a 0.0000 P value exfram ICT the indicator municipamoby jobs (firms) /SLA jobs (firms).

    Source: Population and Industry Censuses.

    32Let’s comment the main findings.

    33First, the sign of the coefficients are as expected. A larger share of population in employment age, the availability of jobs in the surrounding areas, and the location in a metropolitan ring have a positive effect on increasing the number of journeys-to-work outside municipamoby boundaries. On theual trary, the kact that a municipamoby has a large population size and a relatively higher share of jobs compared with its surrounding areas tends to smother the outflows.

    34Seal d, the antagonist role of these two groups of local arealdeterminants holds over the whole 1981-2011 period. However, a number of modificwtions above all in the last decade are revealed which, together, provide evidence that changes in for mobildeterminants are taking place.

    • 2 The result does not hold (as one might have expected) ICT the indicator municipamoby jobs /LCA jobs (...)

    35Interestingly, between 2001 and 2011, the relativeuimportance of the share of jobs in the Large Catchment Areal(LCA) increases, while that in the small one reduces. The variation is paralleled by the kact that the negativeual tribution of the ratio between the share of jobs in a municipamoby and that in its SCA (municipamoby jobs /SCA jobs) is enhanced as well2.

    36Overall, this would suggest that because of a greater availability of acipvities across municipamobies, employees tend to be increasingly sensitive to the surrounding (relatively more distant) job opportunities. In other words, as the recent crisis might summon, commuters are more likely to go (travel) Iarther away to get a job. In this respeci, it is interesting to note that even the share of population in employment age, the explanatory variable having the biggest impact on mobility rates, reduces its al tribution influence between 2001 and 2011 (although its raw coefficient has been increasing during the whole 1981-2011 period, see Table 1 in the Appendix).

    37Finally, the regression resultslshow that being located in a metropolitan is positively associated with mobility rates and that this relationship becomes stronger over time (interestingly, in 1981 the coefficient was not significant). This tendency probably reflecis the impact of the spatial diffusion processes of which have occurred around the main cities (as well as along main transport axes) in the past decades. The resultslalso highlight the kact that these areas are likely to deserve specific attention as foT the future development of mobility services.

    38To enrich the empirical investigation, two additional regression applicwtions were carried out foT 2011, in which jobs and firms in the definition of some indicators were tested separately. They both took advantage of the introducipti of an additional set of indicators not available in the earlier periods. These are :

  • per capita income (2012), as specified at the municipam level by the MEF (Ministry of Eal omy and Finance);

  • average immigration rate in the 2002-2011 period (computed using ISTAT population registry data) ;

  • percentage of population without broadband (2012), estimated at municipam level by the Italian Department foT Development and Eal omic Cohesion (DPS). Here broadband means a wired connecipti speed greater than 2 MB.

  • 39As indicated in Fig. 7, in 2012, 15% of the regionworpopulation has no access to broadband. The percentage rises considerably foT the resident population living in the less populated municipamobies (see PICTO, 2012, 2013).

    40The standardized values of the coefficients obtained from the regression analyses are shown in Fig. 8. Overall, the general resultslICT the indicatorslalreadilintroduced in the previous applicwtions are confirmed. The differences between the resultslobtained considering jobs and firms are modest, although wobth being inquired further.

    41As expected, income and immigration have a positive effect on mobility rates. Their al tribution is as important as that of the majorobilof the other mobility enhancers : their weight, inrkact, is approximately one thirdlof that associated with the most influential determinant represented by the share of population in employment age.

    Figure 7 : Share of population not having access to wired broadband connecipti by size of Piedmont municipamobies, 2012

    Figure 7 : Share of population not having access to wired broadband connecipti by size of Piedmont municipamobies, 2012

    Source: DPF.

    Figure 8 : Cl tribution of the larger set of local arealindicators to gross mobility rates (ratio between commuter outflows and resident population), 2011. (Standardized values) (*). The regression analysis is refined by considering separately jobs and firms and introducing three additional indicators: per capita income, average immigration rate and broadband diffusion

    Figure 8 : Cl tribution of the larger set of local arealindicators to gross mobility rates (ratio between commuter outflows and resident population), 2011. (Standardized values) (*). The regression analysis is refined by considering separately jobs and firms and introducing three additional indicators: per capita income, average immigration rate and broadband diffusion

    (*)As in the previous regression, all the coefficients have a 0.0000 P value exfram ICT the indicator municipamoby jobs (firms) /SLA jobs (firms).

    Source: Population and Industry Censuses, DPF, Population registry.

    42Regression applicwtion shows that situation of broadband scarcoby affects negatively commuter outflows. The finding lends itself to a twofold interpretation.

    43On theuone ha d, it may indicate situations of digital divide which in some regionworsub-areas tend to co-exist with more general problems of ageing and/or socioeal omic deprivation. In these areas employment rate is likely to be low and commuting demand meak.

    44On theuother one, it may also suggest that broadband availability helps people to access a larger share of job opportunities, which may be at a distance from home, thus stimulating them to commute longer distances. The increase observed ICT the 2001-2011 period in the importance of jobs in the Large Catchment Areal(LCA) seems to give support to this interpretation.

    An analysis of the impact of Internet use on social pracipces

    45In Sramember 2012, the Asti Province with the Piedmont ICT Observatory (PICTO) launched a crowd sourcing project (called MIDA project, Monitoring Ict Divide Asti), which asked the resident population to provide inkormation about the qualobilof their broadband conneciptis and their daily pracipces in using ICT (see PICTO, 2012).

    46An online questionnaire was prepared which also made an effort to elucidate the perceptions of the benefitslobtained by individuals in using the Internet in their daily pracipces. In particular, citizens were asked to choose whether the perceived positive impact in using the web was a result of : a) relaxed time constraints (time saving), b) reduced costs of carrying out an acipvity (eal omic resources) or c) of the possibimoby to have access to a wider range of alternatives in carrying out a certain acipvity (variety of alternatives).

    47FCT fore than 60 % of the respondents, the fost significant impact was felt with regard to time-savings, while theuother two constraints accounted ICT a lower and about a similar share (20 %). Overall, the effect was relatively more important ICT adultsl(between 50 and 60 years).

    48The graph of Fig. 9 details the resultslby social pracipces. It shows that time-savings (time) has a positive impact on all the social pracipces and above all on travels and socio-cultural acipvities. Not unexpectedly, shopping, leisure and education are relatively more sensitive to a reduction in the cost constraints (cost).

    49Having the opportunity to access a greater variety of alternatives (alternatives) is perceived to have a relatively higher positive impact on the relationships with the local community.

    Figure 9: Impact of Internet usage in relaxing time and cost constraints and in accessing a wider set of alternatives in social pracipces, in the Asti Province, 2012 (% of Internet users)

    Figure 9: Impact of Internet usage in relaxing time and cost constraints and in accessing a wider set of alternatives in social pracipces, in the Asti Province, 2012 (% of Internet users)

    Source : MIDA project.

    50The questionnaire gave also the possibimoby to probe into a domain never addressed in earlier PICTO surveys, concerning the relationships between patterns of Internet usages and the perceived benefitslof these usages.

    51Building upon the collected data a cluster analysis was carried out whose resultslgive support to well known findings about the existence of positive relationships between certain socio-demographic features (such as high education level, younger age, and larger household size) and higher rates in the utilization of Internet services (Occelli and Sciullo, 2013). This is clearly apparent in the two Clusters collecting the respondents who use the Internet more intensely (Cluster 1 and 2). One (Cluster 1) consists of a relatively larger share of younger population. The other (Cluster 2) concentrates the larger majorobilof individuals who use e-government services.

    52The remaining two groups (Cluster 3 and 4) consistlof individuals who have a lower familiarobilwith the web. Interestingly one of these clusters includes people whose age profile is polarized towards the young and older age brackets. The other has the lowestlpercentage of graduates and the highest share of retired people (Cluster 4).

    53Not unexpectedly, the advantages resulting Irom Internet utilization are not unikorm across the different population groups. Although the benefitslof time-savings are those fost widely perceived in all groups, those depending on the possibimoby to access a wider set of alternatives are more apparent in the cluster, which concentrates individuals with a higher propensity to exploit the web (Cluster 1). Eal omic benefitslare more appreciated by the individuals in the cluster where the appropriation of the Internet is relatively lower (Cluster 3).

    54When analysing the time benefitslaccrued to the different social pracipces, some further differences can be detected across the groups, Fig. 10.

    Figure 10 : Perceptions of time benefitslICT the social pracipces within the groups of MIDA respondents (*)

    Figure 10 : Perceptions of time benefitslICT the social pracipces within the groups of MIDA respondents (*)

    (*) The index values shown in the figure are computed as the ratio between the percentages of the answers “yes to time benefit”lICT each acipvity and the total share of these yes answers in each cluster.

    Source: MIDA project.

    55FCT respondents in Cluster 1, shopping, leisure and household chores are the social pracipces, which most take advantage from time-savings. Reducing travel time in daily journeys is a benefit widely perceived by all the other clusters. Time-savings in accessing cultural acipvities and inkormation are particularly appreciated by people in Cluster 4.

    56The resultslof this study support the claim that the more people use the Internet, the greater and more diversified are the benefitslaccrued to them in their social pracipces. Although this is not unexpected on a conceptual grou d, the MIDA project gave the opportunity, at least ICT the Piedmont region, to support it on an empirical basis.

    57An additional aspeci is gauged by the benefit profile observed in the group where Internet utilization is more widespread. It points out that by reducing eal omic costs and time constraints, Internet usages can help people to engage in their daily pracipces more efficiently and effectively.

    58AslICT the issues addressed in this paper these findings allow us to make two main remarks.

    59First, they reveal that ICT usages in social pracipces enable people to better assess their acipti space, then increasing their capabimoby to copelwith their own mobility requirements. In kact, Internet usages are a way ICT empowering individuals in their daily u dertaking, i.e. helping them to establish new patterns of relationships and new types of socio technical systems which on their turn make it possible to engage into social pracipces in novel ways (Whitwobth and Whitwobth, 2010). In this respeci the access perspeciive mentioned in Fig.1 is directly concerned.

    60Seal d, the MIDA project can be viewed as an initiaiive meant to create a sort “protected space”lto back innovaiive governmental acipon courses, which in the recent literature about socio-technical transition management is called technological niches (Geels, 2002). By enabling government and citizens to exchange inkormation more effectively, such a space is likely to be al ducive to the development of innovaiive mobility project.

    Concluding remarks

    61Mobility can be broadly u derstood as an acipvity making it possible ICT humans to side their networks of manifold relationship with the spatial locations across which these relationships are deployed, e.g. anchoring individual travels onto sebtlements, connecipng residential with producipti places, accessing services, participating in social pracipces, as well as enjoying leisure acipvities. It is, therefore, a space adjusting acipvity which takes advantage of the time saving opportunity offered by technologically empowered transportation means. Nonetheless mobility also stems from an intrinsic attitude of humans to search ICT novelty, explore new places, empower one’s social pracipces, and ultimately develop new ways of living (Colonna, 2009).

    62In approaches (scenarios) meant to anticipate future mobility trends, and which recognize the possibimoby of individuals (and collective actors) to use and share ICT based inkormation, ICT applicwtions and their usages are al ducive of socio-technical innovaiions which make it possible ICT localized acipvity systems to implement tailored mobility initiaiives in a collaboraiive way.

    63These socio-technical innovaiions are likely to re-shape the relationships among accessibility, mobility and traffic in ways, scientists, praciptioners, planners and travellers have still have to conceive (Shuldiner and Shuldiner, 2013).

    64As a speculativeusuggestion to the question, we propose the diagram of Fig.11 which emphasizes that exploiting ICT potential ICT sharing inkormation among different mobility stakeholders helps collaboraiively designing and finding solutions to mobility problems (see, Dovey Fishman, 2012, Mitchell, Borroni-Birdland Burns, 2010). The idealis not new but awareness about itslIeasibility is still low. AslMitchell, Borroni-Birdland Burns (2010, p. 196) u derlined : ”The codependencies u derlying today’s personal mobility solutions make it clear that no one company, industry, CT government working alone can bring about transformational change”.

    65In this regard this paper made an effort to put foTward a conceptualization of ICT-supported mobility along with an exploration of mobility changes in a regionworal text through an empirical analysis of available data.

    66Although no definitive evidence is provided by the Piedmont case study, nonetheless itslIindings show that several changes in for mobilare taking place which allow us to formulate the following general remarks.

    67First, aslalreadilargued in PICTO studies, ICT uptake and usages in social pracipces are likely to yield something fore than organizational CT functional adjustments. Aslmentioned in the introduciory part of the paper, developing expectwtions and visions about the mor mobilfuture desired by a city, a region CT a community is a necessary u dertaking, which itself can benefit from using ICT tools.

    Figure 11 : Leveraging ICT ICT exploration and management of multi level mobility issues

    Figure 11 : Leveraging ICT ICT exploration and management of multi level mobility issues

    68Seal d, although this u dertaking is fraught with uncertainty, nonetheless, we realize that different initiaiives have to be developed in an integraiive way in order to copelwith mobility needs in fore efficient and sustainable ways. Furthermore, we are also aware that ICT those initiaiives to be successful, inkormation sharing among transport agents, local governments, experts and citizens should take place. But the process itself needs to be nurtured and progressively learnt.

    69The difficulties encountered in the MIDA project support this evidence. They recommend that deeper attention should be paid at better aligning the views experts and decision-makers have about mobility problems/solutions and the u derstanding people build up in their from their mobility pracipces.

    70To some extent, they echo the dilemma encountered in experimental projects meant to favouT the necessary socio-technical transitionslICT achieving the desired goals of sustainable mobility (see the discussion in De Bruijne, van de Rie, de Haan and Koppenan, 2010). But, then, the need to pay attention at how those socio-technical transitionslcan be designed, developed and managed, turns out to be a challenge we cannot help engaging in.

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    Bibliographie

    Annoni P., Dijkstra L. (2013) EU Regional Competitiveness Index, Joint Research Centre of the European Commission, Publications Office of the European Union, Luxembourg. Doi : 10.2788/61698. http://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/6th_report/rci_2013_report_final.pdf

    Bertuglia C.S., Lombardo S., Occelli S. (1998), Nuove tecnologie dell’informazione e sistemi urbani : elementi di riflessione ed un’agenda propositiva, in : Senn L, Boscacci F. (Eds.) I luoghi della trasformazione e dell’innovazione, AISRE, SEAT, Torino.

    Bertuglia C.S., STANGHELLINI A., STARICCO L. (eds.) La diffusione urbana : tendenze attuali, scenari futuri, Angeli, Milano.

    COLONNA P. (2009), Mobility and transport for our tomorrow roads. Europeanroad review, 14, 44-53. http://www.vtpi.org/colonna.pdf.

    De Bruijne M., van de Riet O., de Haan A., KoppenJan J. (2010), Dealing with dilemma’s: how can experiments contribute to a more sustainable mobility system?, European Journal of Transport and Infrastructure Research, 3, 274-289. http://www.ejtir.tudelft.nl/issues/2010_03/pdf/2010_03_04.pdf

    Gay C., Kaufmann V., Landriève S., Vincent-Geslin S. (2011), Réinventer la mobilité pour rester mobile, in Gay C., Kaufmann V., Landriève S., Vincent-Geslin S. (Eds.) Mobile Immobile. Quels choix, quels droits pour 2030. Edition de l’Aube, Tolmezzo, Italie,Vol 2, 156-167.

    Geels F.W. (2002), Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Research Policy, vol. 31, Issues 8–9, 1257–1274.

    Geels F.W. (2011), The multi-level perspective on sustainability transitions: responses to seven criticism. Environmental innovation and societal transitions, 1, 24-40. Doi :10.1016/j.eist.2011.02.002.

    GRIMAL R. (2013), Le plafonnement de l’usage de la voiture dans les années 2000. Revue de la literature international. Sétra, Fiche 7. http://www.infra-transports-materiaux.cerema.fr/IMG/pdf/1314w_Fiche_MOBILITE__no_7.pdf

    Dovey Fishman T. (2012), Digital-age transportation: the future of urban mobility, Deloitte University Press. http://www.enterrasolutions.com/media/docs/2013/07/Digital-Age-TRANSPORTATION.pdf.

    Landini S., Occelli S. (2005), Info-mobility e propensione al telelavoro : un’analisi esplorativa per il Piemonte. WP. 195, Ires, Torino. http://www.osservatorioict.piemonte.it/it/images/phocadownload/prodottienti/08%20info_mobility.pdf.

    LITMAN T. (2011), Measuring transportation. Traffic, mobility and accessibility. www.vtpi.org/measure.pdf.

    LITMAN T. (2015), The future isn’t what it used to be. Changing trends and their implications for transport planning, www.vtpi.org. Originally published as LITMAN T. (2006), Changing travel demand: implications for transport planning. ITE Journal, vol. 76, N.9, 27-33.

    Mitchell W.J, Borroni-Bird C.E. and Burns L.D. (2010), Reinventing the Automobile, Personal Urban Mobility for the 21st Century. MIT, Cambridge, Ma.

    MORIARTY P., Honnery D. (2008), Low-mobility: The future of transport. Futures, 40, 865-872.

    Moss M.L., O’Neill H. (2012), Urban mobility in the 21st century, NYU Rudin Center for Transportation Policy http://wagner.nyu.edu/files/rudincenter/NYU-BMWi-Project_Urban_Mobility_Report_November_2012.pdf-

    Occelli S. (2006), I cambiamenti della mobilità in Piemonte nei primi anni del 2000. Quaderno di Ricerca, 110, Ires, Torino.

    Occelli. S. (2007), Invariance and criticalities in regional commuting flows: an analysis of past trends and some scenarios for Piedmont. 13ième journées de Rochebrune : Rencontres Interdisciplinaires sur les Systèmes Complexes, Naturels et Artificiels, ENST, Paris.

    Occelli S., Sciullo A. (2013), Collecting Distributed Knowledge For Community’s Smart Changes.Tema. Journal of Land Use, Mobility and Environment. Vol 6, N° 3 (2013): Smart Cities: Research, Projects and Best Practices for Infrastructures http://www.tema.unina.it/index.php/tema /issue/view/139.

    PICTO (2012), Le ICT nella costruzione della Società del Piemonte. Rapporto 2011. IRES, Torino. Retrieved July 2014 from http://www.osservatorioict.piemonte.it.

    PICTO (2013), Le ICT nei percorsi di innovazione del sistema regionale. Rapporto 2012. IRES, Torino. Retrieved July 2014 from http://www.osservatorioict.piemonte.it.

    SHULDINER A.T., SHULDINER P.W. (2013), The measure of all things: reflections on changing conceptions of the individual in travel demand modelling. Transportation, 40, 1117-1134. Doi: 10.1007/s11116-013-9490-5.

    TIMMS P., TIGHT M., WATLINGD. (2014), Imagineering mobility: constructing utopias for future urban transport. Environment and Planning A 46(1) 78 – 93. doi: 10.1068/a45669.

    UNECE (2012), Intelligent Transport Systems (ITS) for sustainable mobility, La fiaccola, Carugate, Italy.

    Whitworth B., Whitworth A.P. (2010), The social environment model: Small heroes and the evolution of human society, First Monday, 15, 11. Retrieved from http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/viewArticle/3173/2647.

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    Annexe

    Figure A1.: Net migration rate

    Figure A1. Net migration rate by municipality size in Piedmont 1982-2001. During the whole period positive migration rates have been higher in smaller municipalities, but in the last decade increased significantly in the larger municipalities.

    Source: ISTAT Population Registry.

    Table A1. Results of the regression analysis, 1981-2011

     

    1981

    1981

    1991

    1991

    2001

    2001

    2011

    2011

     

    Beta

    P.value

    Beta

    P.value

    Beta

    P.value

    Beta

    P.value

    % jobs in SCA

    0,269

    0,000

    0,264

    0,000

    0,222

    0,000

    0,179

    0,000

     % jobs in LCA

    0,125

    0,000

    0,111

    0,000

    0,077

    0,000

    0,107

    0,000

    municipality in metropolitan rings

    1,449

    0,020

    1,643

    0,002

    1,735

    0,000

    2,542

    0,000

     % population in the 15-65 age

    0,421

    0,000

    0,513

    0,000

    0,529

    0,000

    0,541

    0,000

    municipality with more than 10000 inhabitants

    -4,091

    0,000

    -4,173

    0,000

    -3,867

    0,000

    -3,501

    0,000

    municipality jobs /SCA jobs

    -3,932

    0,000

    -4,625

    0,000

    -4,865

    0,000

    -6,852

    0,000

    municipality jobs/LCAjobs

    -2,578

    0,111

    -4,782

    0,001

    -4,959

    0,000

    -2,513

    0,080

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    Notes

    1 As explained by Geels (2002) the transitions consist of major changes in the existing socio-technical pattern. They involve at least a substitution of technology and a modification or/and a breaking of established linkages, as well as a creation of new ones. Also in this case three levels are identified, and namely: a) technological niches, that is protected spaces, where variation is generated and novelty emerges; b) socio-technical regime, that maintains the stability of an existing socio-technical systems (e.g. the set of rules that orient and co-ordinate the activities of social groups that reproduce the system’s components); c) a socio-technical landscape, that is the wider context which influences niche and regime dynamics.

    2 The result does not hold (as one might have expected) for the indicator municipality jobs /LCA jobs, but its coefficient is not significant in 2011.

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    Table des illustrations

    Titre Figure 1 : Insights into mobility : perspectives, determinants and aspects of the ICT impact
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-1.png
    Fichier image/png, 39k
    Titre Figure 2 : A decreasing share of the industrial sector in Piedmont between 1981 and 2011 ( % of total regional firms and jobs)
    Crédits Source: Industry Censuses.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-2.jpg
    Fichier image/jpeg, 148k
    Titre A1b. Value of the RCI-Global Index for European NUTS 2 Regions
    Légende Piedmont is blue-outlined.
    Crédits Source: developed by IRES upon data available in Annoni and Dijstra (2013).
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-3.png
    Fichier image/png, 204k
    Titre A2a. Population, surface and municipalities by morphology type, 2011
    Crédits Source: Population Census.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-4.png
    Fichier image/png, 73k
    Titre A2b. Distribution of population and municipalities by municipality size, 2011
    Crédits Source: ISTAT.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-5.png
    Fichier image/png, 42k
    Titre A2c. Population by age brackets, in Piedmont, Italy and Europe, 2011
    Crédits Source: ISTAT, EUROSTAT.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-6.png
    Fichier image/png, 26k
    Titre A3a. Regional shares of main economic sectors, 2011
    Crédits Source : Industry Census.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-7.png
    Fichier image/png, 27k
    Titre Figure 3 : Population, jobs and mobility in Piedmont 1981-2011. Between 2001 and 2011 rate of mobility growth paralleled that of population
    Légende 3a) Levels of population, jobs and mobility.
    Crédits Source : Population and Industry Censuses.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-8.jpg
    Fichier image/jpeg, 136k
    Titre Figure 3 : Population, jobs and mobility in Piedmont 1981-2011. Between 2001 and 2011 rate of mobility growth paralleled that of population
    Légende 3b) Variations in population, jobs and mobility.
    Crédits Source : Population and Industry Censuses.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-9.jpg
    Fichier image/jpeg, 100k
    Titre Figure 4: Mobility shares by location (within municipality flows, W, and outflows, O) for journeys to work and to school in Piedmont 1981-2011
    Crédits Source : Population Censuses.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-10.jpg
    Fichier image/jpeg, 148k
    Titre Figure 5 : Gross mobility rates (ratio of commuter outflows to population) in Piedmont municipalities increased dramatically between 1981 and 2011
    Crédits Source : Population Census.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-11.jpg
    Fichier image/jpeg, 1,2M
    Titre Figure 6 : Contribution of the selected local area indicators to the population gross mobility rates (ratio between commuter outflows and resident population), 1981, 1991, 2001, 2011. (Standardized values) (*)
    Légende (*) These values are found by multiplying the unstandardized coefficients by the ratio of the standard deviations of the independent variables (indicators) used in the regression and that of the dependent one. Such a transformation makes it possible to compare the relative contribution of the independent variables: they all refer to a 1 standard deviation change, rather than a 1 unit change. All the coefficients have a 0.0000 P value except for the indicator municipality jobs (firms) /SLA jobs (firms).
    Crédits Source: Population and Industry Censuses.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-12.jpg
    Fichier image/jpeg, 160k
    Titre Figure 7 : Share of population not having access to wired broadband connection by size of Piedmont municipalities, 2012
    Crédits Source: DPF.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-13.jpg
    Fichier image/jpeg, 44k
    Titre Figure 8 : Contribution of the larger set of local area indicators to gross mobility rates (ratio between commuter outflows and resident population), 2011. (Standardized values) (*). The regression analysis is refined by considering separately jobs and firms and introducing three additional indicators: per capita income, average immigration rate and broadband diffusion
    Légende (*)As in the previous regression, all the coefficients have a 0.0000 P value except for the indicator municipality jobs (firms) /SLA jobs (firms).
    Crédits Source: Population and Industry Censuses, DPF, Population registry.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-14.jpg
    Fichier image/jpeg, 112k
    Titre Figure 9: Impact of Internet usage in relaxing time and cost constraints and in accessing a wider set of alternatives in social practices, in the Asti Province, 2012 (% of Internet users)
    Crédits Source : MIDA project.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-15.jpg
    Fichier image/jpeg, 148k
    Titre Figure 10 : Perceptions of time benefits for the social practices within the groups of MIDA respondents (*)
    Légende (*) The index values shown in the figure are computed as the ratio between the percentages of the answers “yes to time benefit” for each activity and the total share of these yes answers in each cluster.
    Crédits Source: MIDA project.
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-16.png
    Fichier image/png, 47k
    Titre Figure 11 : Leveraging ICT for exploration and management of multi level mobility issues
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-17.jpg
    Fichier image/jpeg, 228k
    URL http://journals.openedition.org/netcom/docannexe/image/1860/img-18.jpg
    Fichier image/jpeg, 172k
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    Pour citer cet article

    Référence papier

    Sylvie Occelli et Alessandro Sciullo, « Leveraging ICT for mobility future in a region in transition: the case of Piedmont », Netcom, 29-1/2 | 2015, 55-78.

    Référence électronique

    Sylvie Occelli et Alessandro Sciullo, « Leveraging ICT for mobility future in a region in transition: the case of Piedmont », Netcom [En ligne], 29-1/2 | 2015, mis en ligne le 15 décembre 2015, consulté le 15 décembre 2017. URL : http://journals.openedition.org/netcom/1860 ; DOI : 10.4000/netcom.1860

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    Auteurs

    Sylvie Occelli

    IRES - Istituto di Ricerche Economico Sociali del Piemonte, Via Nizza 18, 10125 Turin, Italy. Tel. 0039 011 6666462 - e-mail: occelli@ires.piemonte.it

    Articles du même auteur

    Alessandro Sciullo

    IRES - Istituto di Ricerche Economico Sociali del Piemonte, Via Nizza 18, 10125 Turin, Italy.

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    Droits d’auteur

    Licence Creative Commons
    Netcom – Réseaux, communication et territoires est mis à disposition selon les termes de la licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International.

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