Chapter 06 Model application and simulation results
6.6 Policy implications
The making and reshaping of the built environment is closely related to the policies of urban planning and management. The results could help to inform policy debate and encourage more effective critical thinking about spatial processes and impacts, and alternative policy scenarios (Wong, Baker, Webb, Hincks, & Schulze-Baing, 2015). Many policy implications can be drawn from the simulation results. Basically, the results provide evidence for the effectiveness of planning measures in affecting the travel behaviour of city residents and the potential of various planning strategies in
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alleviating various transport-related urban problems. First of all, a toolkit of planning measures for various policy goals can be derived from the simulation results:
- For the goal of reducing total car use, effective measures include increasing the retail density, the mix of uses and the accessibility to sub-centres, enhancing the coverage of bus services and improving the quality and continuity of street facades (effective measures here refer to those with an effect size larger than 0.05).
- For the goal of reducing non-commute car use, effective measures include increasing the population density, employment density, retail density and the accessibility to sub-centres, enhancing the coverage of bus services, decreasing the parking space and improving the continuity of street facade.
- For the goal of reducing the total travel distance needed for non-commute purposes, effective measures include increasing the population density, enhancing the coverage of bus services and improving the continuity of street facade.
- For other policy goals, one can refer to Figure 6-2 to Figure 6-15.
It should be noted that there can be substantial differences in the effects of built environment measures that belong to a same type (e.g. increasing density), therefore policies need to be specific enough to be effective. For instance, the four density features all have different effects on travel behaviour. For another instance, bus coverage shows a larger effect in both reducing the car use and travel distance than the distance to subway station, though they are both indicators of public transit accessibility. Therefore, it is not enough to simply state ‘high density’ or ‘good accessibility to public transit’ as a planning measure. Instead, policies should fully refer to the detailed findings and be effectively specific.
Moreover, policies could also aim at the mediating factors that intervene the relationship between the built environment and travel. For instance, the comparison between the simulation results with the theoretical assumptions and meta-analysis results suggest that the preference towards driving and the culture of ‘car pride’ might
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be a reason for the positive correlation between road density and car use in Beijing. For another example, the quality of street facade is positively correlated with the travel distances of non-commute activities, which is supposed to be mediated by the fact that high quality areas are usually more private and gated and thus less convenient for conducting activities. Besides, employment density is also found to be positively associated with the travel distances of most non-commute activities, especially shopping, possibly explained by the mismatch between the types of goods and services at business areas and the everyday needs. Policies that aim at these mediating conditions include:
- Alter people’s preference towards driving and the thinking of ‘car pride’ by improving the pedestrian environment and improving the image of walking and cycling;
- Increase the space for street shops in the areas where the street facade is good in quality but does not provide many activity opportunities;
- Increase the number of facilities that serve everyday needs, probably in medium-to low-price, in business areas.
However, since the effect sizes are generally small, policy making should also consider a cost-benefit analysis to ascertain whether changes to the built environment are a cost- effective way to modify travel behaviour, given the opportunity costs of spending resources in another way (Mokhtarian & Cao, 2008). For instance, policy makers should consider the energy consumption or carbon emission in the process of the deconstruction and reconstruction of buildings and other structures in order to realise a built environment change, and compare with the amount of energy and carbon emission saving in a given period.
Last, the differences between the results from Beijing and other cities suggest that special care needs to be taken when transferring the above mentioned policies to elsewhere. One needs to closely scrutinise the urban and social contexts and evaluate
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whether there is any factor, such as the mediating factors mentioned above, that would distort the relationship between the built environment and travel in another city.
6.7 Chapter summary
This chapter sets out to simulate the changes of travel behaviour in response to various scenarios of built environment changes using the BEATIM model. Many of the conclusions drawn from the simulations can be identified through careful observation of the diagrams in Figure 6-2 to Figure 6-19. A key note is that the effects of the built environment on VMT, which is the subject of analysis of many existing research, are to a large extent accounted for by the effects on commute travel. As a result, the relationship between the built environment and non-commute and other aspects of daily travel would be masked if only this synthesised indicator is used. For some built environment features that show similar impacts on VMT, their impacts on detailed behavioural aspects can be very different, e.g. on the mode choices for commute and non-commute activities, the travel distances for various non-commute purposes, etc. Besides, both commute and non-commute travel is shown to be more sensitive to the built environment in the near neighbourhood of one’s home (in my experiment, 500 metre buffer zone), when the work place is taken as exogenous.
The simulation results are partly consistent with theoretical assumptions 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 in various urban contexts. Four major implications can be made from the inconsistent results: (1) 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; (2) 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; (3) in the context of Beijing, high (construction and
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maintenance) quality of street facade can related to lower utilitarian values, when that happens, utilitarian considerations tend to overweigh the psychic enjoyments, thus making a location less attractive; (4) the ‘compensation’ mechanism between travel distance and frequency does exist, but is not likely to be stronger than the original effect.
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