Meso-Scale Analysis of Journey-to-Work Patterns The micro-scale analysis discussed in the previous section provides an

insightful evidence base for understanding the factors that influence travel behaviour. In the context of strategic urban planning, the key factors identified, such as regional accessibility and socio-economic variables, need to be

measured and analysed in the context of specific cities to be used for planning policy. As there is widespread variation within cities in socio-economic geography and accessibility, it follows that intra-urban variation in travel patterns will also be high. This is of importance to urban planners but is not directly tackled in the micro and macro scale approaches. There is a strong case for intermediate „meso-scale‟ analyses to allow the study of direct relationships between travel patterns and the intra-metropolitan geography of city-regions.

Several studies have taken this approach (e.g. Cervero and Wu 1997; Wang 2000) and there is considerable scope for more research in this area, particularly in a UK context. The meso-scale of analysis is directly relevant to debates over the efficiency of monocentric and polycentric structures.

The characteristics of meso-scale intra-urban city-region analyses are distinct from the micro and macro approaches. There is the potential to achieve more comprehensive sample sizes compared to micro-studies using national survey data such as censuses and travel surveys. The trade-off is that a level of spatial aggregation is necessary (discussed in detail in Section 4.1). The inclusion of socio-economic factors is more practical than macro-scale studies, though remains problematic as there is the risk of ecological fallacy errors¹. The detailed data requirements of the meso-scale approach have overwhelmingly restricted studies to the analysis of journey-to-work. This is the most data-rich trip type due to its inclusion in national censuses. While commuting remains a significant journey purpose in terms of its economic importance and

contribution to congestion (see Sub-Section 3.2.4), overlooking other trip

1 Where aggregate neighbourhood level characteristics are assumed to apply to all individuals within that neighbourhood- see Sub-Section 4.1.3.

purposes is a limitation, as all trip types are relevant to sustainability and transportation efficiency issues.

We focus here on research that has considered how the efficiency and sustainability of commuting patterns has changed over time, particularly in relation to employment and population decentralisation. The main question is whether the decentralisation leads to a better integration of residential and workplace locations, minimising trip distances and facilitating more sustainable modes; or whether decentralisation leads to a disintegration of journey-to-work patterns with less sustainable patterns. A useful diagram of conceptual trip patterns in relation to decentralisation is shown in Figure 3.25 from research by Ma and Banister (2007). In this framework the monocentric city can

decentralise into a spectrum of polycentric and hybrid forms. The two opposing polycentric forms are the „city village‟ structure with local travel patterns to dispersed centres, and the „random movement‟ city with long distance cross-commuting between dispersed centres.

Notes:

In the figure, four different trip patterns within a metropolitan area are taken from Bertaud (2002). Bertaud described:

city (a) as the monocentric model;

city (b) as the urban-village polycentric model;

city (c) as the random-movement polycentric model;

and city (d) as the radial and random movement hybrid model.

Figure 3.25: Conceptual Models of Urban Spatial Structure and Travel Patterns.

Source: Ma and Banister (2007).

An early strand of research into regional travel variation comes from Thomas (1969) who studied London‟s New Towns, developing a self-containment indicator which is still used in current research. An example from the Polynet study (Hall and Pain, 2006) is shown in Figure 3.26. This indicator summarises

the proportion of live-work residents increases with distance from Greater London. Whilst it is useful to highlight self-containment relationships, this indicator cannot provide detailed sustainability analysis as it omits mode-choice data and simplifies trip distance information. Instead we need to look at data relevant to the dependent variables from the previous micro and macro scale travel studies, such as trip distance, mode-choice and energy use.

Figure 3.26: Self-Containment Measure for Urban Settlements in the Greater South East.

Source: Hall and Pain (2006).

Frost and Spence (2008) researched changes in commuting energy use in the major UK cities of London, Birmingham and Manchester using 1981-2001 census data. There have been a number of studies calculating transportation energy using trip length and mode coefficient data (Banister et al., 1997). The Frost and Spence results identified a 25.8% increase in journey-to-work per-capita energy use in London during the 1981-2001, related to increasing distances and greater car use. This figure of 25% is, in the context of twenty years of major socio-economic change, not overly high, and this result is likely connected to the growth of London‟s urban core during this period (explored in Chapter 5). Birmingham and Manchester, which experienced population decline and counter-urbanisation during this period, were identified as having much

higher per-capita commuting energy increases of 66.2% and 67.2% respectively (Frost and Spence, 2008). This research thesis seeks to extend the Frost and Spence work to consider the underlying intra-urban patterns that generate these city-wide trends. Many more specific land use planning questions (such as how Inner London compares to Outer London; how new employment centres compare to older centres; or what is occurring in wider region beyond the Greater London boundary) cannot be answered using a city-scale methodology, as they require intra-urban scales of analysis. A relevant study incorporating elements of the intra-urban approach comes from Titheridge and Hall (2006), which focuses on journey-to-work patterns for two rail corridors in South East England connected to Greater London. Whilst not including the entire city-region as advocated here, the study is notable for analysing socio-economic variables at an intra-urban city-region scale, and connecting occupational class to mode-choice and travel distance behaviours.

Studies from the US regarding decentralisation and commuting efficiency are highly mixed in their results. Several studies have identified quicker journey times associated with greater decentralisation (Cervero and Wu, 1997; Gordon et al., 1991). This travel time gain does not however necessarily mean

sustainability gains, as decentralisation has been linked to mode-shifts away from public transport towards private vehicles on less congested routes (Cervero and Wu, 1997). This is consistent with the conclusions emphasising

accessibility in the micro and micro travel pattern analysis, as public transport accessibility will decline outside of city centres. The importance of accessibility was also highlighted by Wang (2000) who identified strong relationships between regional employment accessibility and commuting distances in

Chicago. These studies are also significant in considering variation within cities, with intra-urban employment centres in San Francisco (Cervero and Wu, 1997) and Chicago (Wang, 2000) distinct in terms of trip distances and mode-choice.

Cervero and Wu also used employment class data to disaggregate their model, illustrating how socio-economic data can be included at meso-scales.

In summary, intra-urban meso-scale analysis provides a useful city-specific

and macro scale approaches. Similar accessibility, built-environment and socio-economic relationships are likely to hold at these intermediate scales, although the number of studies is relatively limited and there is significant scope for expanding the intra-urban evidence base. There is great potential to add improved socio-economic and accessibility analysis into the study of

commuting efficiency, and advance the analysis of environmental indictors such as energy use and carbon emissions.

3.3.6 Summary

The varied scales of analysis in sustainable urban travel research provide different perspectives on relationships with urban form and are ultimately complementary in building a more complete picture of this complex topic.

International comparison studies reveal massive variation in the performance of cities across the world, whilst micro-studies provide evidence on the factors affecting individual travel behaviour. There are significant connections in the research evidence, with cross correlations between socio-economic, urban form/land use and accessibility/infrastructure variables present in both micro and macro scale studies.

Socio-economic factors have strong connections to trip distances and mode-choice. At macro city scales this is this expressed through income and fuel taxation variables, with fuel price being amongst the most strongly correlated variables in the Newman and Kenworthy dataset. At micro-scales the socio-economic variables of car ownership, income and household structure are connected to trip patterns, with car ownership typically the most strongly correlated variable in predicting transport energy use. The influence of socio-economic factors does not negate the importance of planning- there is considerable variation beyond these socio-economic variables. Furthermore planners can influence key factors that affect car ownership. Yet the presence of multi-collinearities with socio-economic factors greatly complicates the

identification of relationships between travel patterns and urban form.

We have supported the theoretical argument in this section that accessibility is the key geographical factor on travel patterns and that built-environment

measures, such as density, are essentially accessibility proxies (Handy, 1996).

This argument has largely been confirmed in the research review. Whilst accessibility was not modelled directly in the macro-scale analysis, the multiple correlations with density and transportation infrastructure variables support the accessibility perspective, as to an extent does the influence of fuel taxation. In micro-scale studies accessibility variables were modelled explicitly, and

regional accessibility was found to be the most significant variable in predicting total vehicle miles travelled in multivariate models, and local accessibility variables were also significant. Whilst density is likely not to have a causal role in determining travel patterns, it is strongly connected to accessibility and relatively high built-environment densities are likely to be a necessary, but not sufficient, condition of greater non-motorised and transit travel. Furthermore some researchers have argued for an additional role of density beyond its influence on accessibility (Chen et al., 2008), and this is likely connected to car parking availability.

Finally we have considered studies at an intra-urban „meso-scale‟, which provides an intermediate city-region analysis for strategic planning most relevant to the polycentric focus of this research. The conclusions of the micro-scale analysis- that both local and regional accessibility need to be considered and that trip-end factors are influential- can be further explored at city-region scales of analysis. Existing research at this scale points to similar accessibility and socio-economic relationships, but the number of studies is relatively limited, particularly for the UK, and there is significant scope for expanding the intra-urban evidence base. There is great potential to add improved socio-economic and accessibility analysis into the study of commuting efficiency, and advance the analysis of environmental indictors such as energy use and carbon emissions.