Proyecto de reforma de normas de funcionamiento  EXPOSICIÓN DE MOTIVOS

Human, social and management factors that influence non-motorised transport in developed cities are related to environmental features of the network including separation from motorised modes,

personal safety in the evenings and climate (European Commission, 1998; McCann et al., 2000; Transport and Travel Research, 1999; Fitzroy and Smith, 1998; Svensson; 2000; Zacharias, 2001). The quality of non-motorised mode infrastructure, such as footpaths and cycleways is considered important in changing personal attitudes away from automobiles to walking and bicycling. Based on work in Delhi, Tiwari (2001) suggests that appropriate infrastructure for non-motorised modes is badly lacking in developing cities, as these modes must share the road space with every other mode. In Asian cities, Barter (1998) concludes that public transport fare structure is of critical importance in the use of walking and bicycling, and slight changes have dramatic repercussions on modal choice amongst the urban poor. Halcrow Fox (2000) comments that, in less developed cities, informal mobility in the form of paratransit systems constitutes a portion of the average person’s daily mobility. Paratransit systems fall between the private automobile and a conventional bus network in terms of capacities and service. Profit-driven entrepreneurs control the paratransit systems in developing cities. Unencumbered by strict operating regulations, paratransit’s success lies in its flexibility, adaptability and low fare cost, as the operators aggressively seek out new and expanding markets (World Bank, 1995; Shimazaki and Rahman, 1996; Cervero, 2000; World Bank, 2000e). Where good paratransit systems exist, non-motorised mobility is often reduced because people are able to undertake short trips more easily, at an affordable price, and possibly more safely.

In summary, the key underlying measures that drive urban public transport and non-motorised mobility are: the level of automobile use and personal affluence, metropolitan demographics and population density, the extent of the road network and parking supply. These measures do not act alone but together. Nor are they the only drivers of public transport and non-motorised mobility. In this respect, the literature highlights the importance and influence of numerous human, social, management and other measures which have been briefly discussed throughout this chapter. These measures, many of which are measured at a micro-scale, form a complex web of influences on public transport and non-motorised mode use, and are difficult to quantify, compare, standardise and analyse, but are nevertheless operative in both more and less developed cities. Attempts to model urban public transport and non-motorised mobility using aggregate metropolitan scale data are necessarily made more difficult by the existence of these other influences for which quantitative data are almost impossible to develop for city level modelling.

3.6

Public transport and non-motorised urban mobility: a synthesis

This section draws together the findings from within the literature on the underlying measures that appear to drive public transport and non-motorised urban mobility. Although this synthesis

emphasises the city level, it is important to recognise in the overall context of the thesis that some of the factors operate at a national and household level. Evidence from the literature has been enhanced though the establishment of a series of new data sets from a number of recently published independent sources that allow a broader time scale examination of the measures that drive public transport and non-motorised mobility. These additional data sets from 1960 to 1995 are drawn together later in this section through a statistical analysis to examine the significance of the different variables as determinants of public transport and non-motorised urban mobility. In particular, the analysis examines the stability or relevance of the relationships over decades, which is very important in attempting to develop a predictive model of these forms of mobility.

Table 3.20 and Table 3.21 identify respectively physical and economic and human, social and management measures found in the literature that relate to public transport and non-motorised mobility at a national, city and household level.

Table 3.20 – Physical and economic measures associated with public transport and non-motorised mobility at a national, city and household level

Measure National

level level City Household level

1. Income/wealth

GDP per capita Yes Yes Yes

2. Population and urban area

Population structure Yes Yes Yes

Metropolitan urbanised area Yes Yes

Density of population Yes

Density of employment Yes

3. Urban transport infrastructure

Total road network length Yes

Road network speed Yes

Overall public transport speed Yes

Ratio of public transport to road network

speed Yes

Automobile parking availability Yes Yes

4. Transport characteristics

Automobile ownership and use Yes Yes Yes

Table 3.21 – Human social and management measures associated with public transport and non-motorised mobility at a national and city level

Measure National level City level

1. Preference and perception associated with culture and ethnic background

Ingrained life-style habits Yes Yes

Community attitudes Yes Yes

Male and female demographic structure Yes Yes

Regional and geographical differences Yes Yes

Personal security Yes Yes

2. Governance, strategy, policy and management

Government national, state, local levels Yes Yes

Service management and administration Yes Yes

3. Quality of service

Public transport vehicles Yes Yes

Public transport infrastructure, stops, stations Yes Yes

Service information to customers Yes Yes

Integrated service, ticketing, timetables Yes Yes

Availability, efficiency and capacity of service Yes Yes

3.6.1

Statistical analysis of the measures

This section examines the statistical relationships between the measures shown in Table 3.20 and public transport and non-motorised mobility over a span of some 35 years. Because of a lack of available data, there is no statistical analysis of the human, social and management measures given in Table 3.21. The statistical analysis uses the same procedures and techniques as described in Chapter 2. The following statistical variables are provided for each relationship in Table 3.22 to Table 3.29:

• number of degrees of freedom, that is, number of cases minus 2 • the optimal function of the relationship and its R2 _value

• significance of the optimal function at the ≤0.05 level.

From this analysis will be drawn the core components of a model that attempts to predict public transport and non-motorised mobility at the city level to a high degree of statistical reliability. The primary data for this statistical analysis and a graphical representation of each relationship are shown in Appendices 4, 6 and 6A.

GDP per capita and public transport vehicle kilometres of service /capita at a national and GDP per

In document Segunda Ronda de Talleres. Cumbre Judicial Iberoamericana (página 44-56)