EL CONTENIDO DEL INFORME DE AUDITORÍA EN LA RESOLUCIÓN DE 19 DE ENERO DE 1991

7.1 Summary of findings

This work sets out to investigate how people’s daily travel behaviour would be influenced by the built environment conditions. Travel utility maximisation is used as the theoretical base for the possible influences and a series of assumptions on the relationship between various built environment features and travel behaviour. It is argued in the first chapter that despite of a large number of studies on this topic, there still exist many research gaps. The gaps include:

- First of all, a major gap lies in that a large proportion of existing research focus on the synthesised outcomes of the complex process of travel decision making, while the behavioural processes that give rise to these outcomes have received much less attention. It is related to the gap in methodology that many existing research use regressions between the synthesised outcomes and a set of socioeconomic and built environment explanatory variables, which usually cannot probe into the detailed behavioural processes.

- Second, there is a lot of inconsistency in existing findings in terms of the directions and sizes of the influences, which undermines the reliability and generalisability of the findings.

- Third, the built environment features that have been studied are mainly two- dimensional and land use-related. The features related to the dimension of street facade have received much less attention.

- Last, most existing studies are based on American and European (plus a few Oceanian) cities, while evidences from Asian cities are relatively scarce.

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In order to address the first gap, an overarching methodology is designed by linking the activity-based modelling approach, which is mainly developed in the field of transport simulation, with the analysis of the built environment-travel relationship. Activity- based models simulate the full process of decision making in daily activity participation and travel, including which activities are conducted when, where, for how long, and the transport mode involved. Although the development of activity-based models has progressed substantially since the 1990s, the built environment factors are seldom sufficiently account for in the model systems. Therefore, this research is novel in developing an activity-based model that fully takes into account the built environment contexts and using this model to scrutinise the impacts of the built environment on travel behaviour at much greater detail.

After the introduction, Chapter 2 provides a review of related theoretical and empirical works and partly addresses the second gap with a meta-analysis of existing works. A comprehensive conceptual framework is developed on the relationship between the built environment and the travel costs and gains. Based on this, a series of assumptions are made regarding to the influences of various built environment changes on the integrated outcomes of activity-travel (e.g. total travel distance, total car use) based on utility maximisation. The review of empirical studies and activity-based model developments provides evidences for the gaps mentioned above. Besides, the meta- analysis shows that the effect sizes of the built environment are more consistent across studies on VMT than on walking and transit use. Therefore, the results on VMT are used to compare with my own findings in Beijing, from which the second gap can be further addressed.

Chapter 3 and Chapter 4 describe the study area and the process of data collection and pre-processing. Particularly, Chapter 4 deals with the third gap that features related to the dimension of street facade are seldom studied. I proposed a novel method that automatically evaluate the street facade in a large-scale by leveraging state-of-the-art

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machine learning techniques and online street view images. Two specific features are selected based on architecture and urban design theories, which are the construction and maintenance quality of building facade and the continuity of street wall. The performance indicators show that the machine learning models are able to produce acceptably good approximation to the expert ratings.

The data pre-processed in Chapter 3 and 4 are fed to Chapter 5, which develops the BEATIM model. The model generally takes the paradigm of utility-maximising econometric models, coupled with weak features of computational process models. Special care is taken to keep close focus on the daily travel behaviour and the influence of the built environment when building the model. The model system contains four major components: namely the sub-models for the activity participation and organisation, the location choice for primary activities, the time of travel and mode choice, and the location choice for intermediate stops. The validation shows that the model is able to provide a reasonably good prediction of people’s daily travel. It is acknowledged that there can be many prediction errors at the individual level due to the complex, stochastic nature of activity-travel behaviour (Kulkarni & McNally, 2000). However, the correlation between simulation results and observed travel behaviour at more aggregate levels, such as by ring roads, can be high (R2=0.8-1). To the best of the author’s knowledge, it is the most comprehensive model that explicitly links the activity-based modelling approach from the field of transport simulation with the analysis of the built environment-travel relationship in the field of urban planning and design.

Scenario analysis is conducted in Chapter 6 with the BEATIM model. Two types of scenarios are designed, namely local scenarios and regional scenarios. The former analyse the impacts of the built environment in the immediate neighbourhood of one’s home (the home TAZ). The latter explore the impacts of the built environment in the 0- to-2000-metre buffer zones from one’ home. The findings from the scenario analysis

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include

- In the case of Beijing, total VMT is prominently affected by land use mix, bus coverage, facade quality and facade continuity.

- The influences of the built environment on total VMT are to a large extent accounted for by the influences on commute VMT. Therefore, a sole focus on this indicator, which is the case in many existing research, could mask the understanding of the influence on many other aspects of daily travel.

- Both commute and non-commute travel are more sensitive to the built environment in proximity to home place (in my experiment, 500 metre buffer zone), if the work place is taken as exogenous.

- For a full description of the effects of the built environment on detailed aspects of activity-travel, please refer to Figure 6-2 to Figure 6-15.

The simulation results are partly consistent with the theoretical assumptions put forward in Chapter 2 and partly not. The comparison with the meta-analysis also shows that the impacts of the built environment are neither perfectly consistent nor completely different between Beijing and European and American cities. The implications include - Whether higher density relates to enhanced travel gains and thus shorter travel distance could depend on the matchness between the types of density and people’s needs.

- Social cultural factors (in the case of Beijing, the ‘car pride’) can play a non- negligible role in shaping the (dis)utility of travel choices and distort the relationship between the built environment and travel.

- In the context of Beijing, high street facade quality can related to lower utilitarian value, when that happens, utilitarian considerations tend to overweigh the psychic enjoyments, thus making a location less attractive.

- The ‘compensation’ mechanism between travel distance and frequency does exist, but is not likely to be stronger than the original effect.