How Built Environments Influence Transportation Costs and Travel Behaviours

The relationship between how you travel and the environment you live in is well documented in academic literature. Typically, studies explore how different built environment characteristics are associated with various travel behaviours (i.e., mode choice, travel times, and car ownership). This line of research has thus far provided a valuable evidence base for policymakers looking to design land-use and transport policies which positively contribute to sustainability goals and missions. However, our recent research (co-authored with Dr. Kevin Credit takes a different approach by analyzing how local environments impact travel costs for commuters in the Dublin Metropolitan Area.

Understanding Travel Costs

We measure relative mobility costs when commuting to work. We interpret relative mobility costs to relate to the cost-effectiveness associated with using specific modes of transport (i.e., cars, walking, cycling, and public transport). Therefore, lower relative mobility costs correspond to greater “modal efficiency”, something which should correspond to increased mode use. We use a “cost-distance” framework to estimate these costs, which helps us infer expected behaviours (i.e., mode and route choice) by assessing the costs associated with travelling a given distance. We do this because our data consists of “flows,” representing trip origins (residential areas) and destinations (workplaces) of commuters. We then map these flows onto relevant transport networks (i.e., roads for car users and bus routes for transit users), meaning we can account for factors like congestion levels, infrastructure quality, and service frequency, when computing these cost measures.

The data primarily comes from the 2016 Irish Census, supplemented by sources like Google Maps, Transport For Ireland, The Irish Revenue Commissioners, OpenStreetMap, and The Irish Property Price Register. Using this data, we employ random forest modeling techniques to measure how mobility costs differ across modes based on built environment and transport network characteristics in both residential and workplace areas.

Random Forest models are a type of supervised machine-learning that offer (potentially) significant advantages over more traditional empirical approaches, such as Ordinary Least Squares Regression. For instance, they can explicitly model non-linear relationships between variables. But they are also robust to overfitting given that each “tree” in the forest is trained on a different subset of data, while the results are generated through the averaging of multiple trees.

Key Findings: Built Environments and Mobility Costs

Our analysis reveals that built environments significantly influence mobility costs. Specifically, we find that walkable areas reduce the relative mobility costs for walking and cycling, thereby making them relatively more efficient to use than alternatives. This is especially the case in Dublin’s city centre, while these costs are generally found to increase in suburban and peripheral areas (i.e., Kimmage, Clonskeagh, and Ballyfermot). This may reflect the sprawling characteristics of these areas and the segregated nature of land-uses, and therefore highlights the importance of urban design in making active travel more cost-effective, and therefore, attractive to commuters.

Car-use is generally more expensive in inner-city areas and along the M50 motorway relative to other modes, which is perhaps unsurprising given the high density and high traffic concentrations within these areas. Public transport becomes expensive in suburban areas (i.e., Palmerstown and Castleknock), something we relate to sprawling development patterns. But interestingly, public transport also becomes more expensive once population densities exceed a particular threshold. We link this to the fact that buses and cars share infrastructure across most of Ireland, and as a result “share” congestion effects. Shared congestion means that factors like comfort and convenience become crucial in determining mobility costs, making cars often appear more attractive for short-distance travel.

Non-Linear Relationships in Mobility Costs

The most interesting part of this study is arguably the non-linear relationships we uncover. You already got a taste of one (i.e., the relationship between population densities and public transport costs), but we also observe that, although active travel becomes cheaper as building densities increase, beyond a certain point, these costs start to rise. We believe that this reflects how, initially, higher intersection densities reduce the distance people need to travel, but excessive density may make areas too busy, thereby reducing the attractiveness of active travel relative to alternatives. Nonetheless, we see that the initial benefits associated with these measures generally outweigh the costs, suggesting that increasing density can produce a “net benefit” in increasing active travel.

Policy Implications

If the goal is to promote sustainable travel, we argue that measures must be in place to reduce the costs associated with this transition. This involves understanding and influencing “transport hierarchies,” or the range of viable travel options available to individuals. By altering mode-specific costs, policymakers can encourage shifts toward more sustainable travel behaviours, by altering these hierarchies. One key observation we make is that Dublin’s public transport and cycling networks may be lagging behind those of global cities. For public transport, this observation stems from the lack of segregated transport infrastructure, while for active transport, it stems from the fragmentation of networks.

To address these problems, we contend that measures like segregated bus and cycle lanes can reduce congestion effects and improve network connectivity, measures which reduce the costs associated with traveling by bus and bike. However, these initiatives must be spatially coherent to realize shifts in local transport hierarchies. Projects like the Bus Connects schemes in Irish cities are steps in the right direction. Yet, coordinated land-use policies are essential to complement these efforts and counteract decades of urban sprawl that have favoured car-dependent transport hierarchies.

Conclusion

Our research underscores the critical role of built environments in shaping transportation costs and commuter choices in Dublin. By using advanced modelling techniques and comprehensive data, we provide new insights into how urban design can promote more cost-effective and sustainable travel options. Policymakers can leverage these findings to design cities that support efficient, sustainable transportation modes, ultimately contributing to healthier and more livable urban environments.

For more detailed technical information about our analysis, please refer to the full paper.