The results of previous investigations into the connection between land use and transportation generally support that higher population and employment densities, mixed land uses, a good balance between jobs and housing, and good access to transit are associated with transportation benefits. Transportation outcomes may include a reduction in the number of trips, reduced vehicle miles traveled, increased transit use, reduced fatalities and reduced emissions, resulting in benefits to transportation system effectiveness and efficiency, as well as transportation-related community benefits.. These ideas map well to the concepts of transit oriented developments and are the basis of the definition Ewing et al (2002, 2003, 2014) used in developing their sprawl index.
The current work reaffirmed the value of the sprawl index as the anticipated relationships between the sprawl index and transportation outcomes were observed for the gathered 2010 data. The current work also reaffirmed the difficulties of modeling this relationship. Although the models developed using a small number of land use variables and not the sprawl index generally
produced expected results, they also resulted in some unexpected and unexplained results. The drawn conclusion is that additional variables to control for confounding factors, and more
advanced modeling approaches to better represent the complexities of the land use and
transportation connections, are needed. In particular, evaluation of external factors such as total transportation investment commitment, market and economic forces, interstate and inter-regional travel demand, size and shape of the MSA, and social and demographic factors, was beyond the scope of this study. They will need to be addressed through both the collection of new data, and development of complex models that control for these factors.
The scatterplots, combined with comparison of the characteristics of the MSAs, were a useful tool to examine the correlation of the tier designations for the 26 MSAs with the sprawl index and transportation outcome data. Generally speaking, the MSAs appeared to be randomly dispersed by Tier, however Tier 3 MSAs appeared to be well clustered in the majority of the plots. Although no definitive reasoning is possible based on this examination, this pattern suggests a connection between the policy characteristics of this tier designation and the relative consistency in the sprawl index and transportation outcomes. The relative strength of these policies, their longevity, and their impact on economic and market factors, could all be elements of the explanation. Many of the policies determining tier designations for the various MSAs are relatively new, less than 10 years old; furthermore, many states (including Ohio) have recently adopted Balanced- Growth-Type policy that could influence land use patterns, and transportation benefits, over time. This research examination did not identify possible relationships between policy longevity and results; however, it is likely that multiple decades are needed for land use policy to take effect and be reflected in land development patterns. This is an indication of the need for additional
research over time, and for a strategic approach that optimizes potential benefits before full causality is known.
The review of previous work also served to identify what aspects of the relationship remain unexplained. For instance, there has apparently been little work done in the areas of cost, other than commute time, which would be useful to understand. Further, the work on commute time
has not yet successfully isolated whether changes are related to changes in trip length or
congestion levels. If congestion is a contributing factor, then issues such as the utilization of the transportation network become relevant. The current work briefly examined the relationship between utilization of the freeway and arterial networks but the results were not significant, which is likely due to the simplistic nature of the variable definition and/or the use of a multiple
regression model with few control variables. If travel demands are increasing but the
transportation network is not expanding, then the congestion can spread in time. To capture this phenomenon would require a robust measure of utilization or perhaps a sophisticated time based modeling approach.
In terms of modeling the relationship between land use and transportation, the following areas for future investigations are recommended. First, the influence of external factors at the macro level need to be better understood. Do geographic, topographic, economic, and climactic
characteristics influence travel demand and mode choice behavior? If not, it may be fair to compare areas that are dissimilar in these characteristics. If so, perhaps these variables can be controlled for in such a way as to improve the current models.
Second, the influence of socio-demographic characteristics needs to extend beyond travel demand and mode choice into areas such as housing location choices. Dunphy and Fisher (1996) found consistent differences in travel demand between those living in more dense areas versus those living in less dense areas, which raises questions about who is expected to live in which areas and how transportation planners and others could better consider these preferences in their plans and/or models. Such investigation may lead into areas such as understanding the link between housing choice and quality of schooling, proximity to particular activity centers, community safety and security and other community characteristics. A “next step” study would involve a literature review of such factors and their influence on overall development patterns. Third, the balance of transportation supply and demand over time and its relationship to land use needs to be better understood. The majority of models use annual, daily or peak transportation measures which could be hiding the influence of land use on the utilization of the transportation network. This investigation could include looking at how travel demand management approaches change the utilization of the transportation networks and what transportation benefits are likely to result from such measures.
Finally, the role of total investment in transportation must be considered. It is possible that proportionally higher levels of commitment in some states may drive the amount of transportation infrastructure developed, independent of the land use characteristics of the areas served. More ample transportation infrastructure (especially roads) could have an impact on mode choice, vehicle miles traveled, congestion/delay, emissions, and safety, as well as total lane miles provided. The challenge will be to define a model for evaluation of this relationship that controls for confounding factors, such as interstate and intra-regional travel demand, market value of property, and socio-demographic characteristics of the population.