This chapter concludes with a discussion of the primary findings from the quantitative analysis of MPO freight planning capacity. One central finding is that the federal policy of designating MPOs as TMAs with associated increased levels of autonomy, authority, and responsibility has no effect on MPO freight planning capacity. The regression discontinuity models demonstrate no discontinuity at the cutoff of 200,000 people for the overall ability of an MPO to conduct freight planning as well as any of the four freight planning capacity subtypes. The use of urbanized area population thresholds forms a centerpiece of federal transportation policy. A minimum threshold of 50,000 is used as the determination of whether an MPO must exist. The 200,000 threshold determines whether an MPO is also designated as a
Transportation Management Area. In the debate and negotiations that occurred in 2012 during the reauthorization of the surface transportation bill, draft legislation at one point included provisions for potentially eliminating MPOs with populations under 200,000 (see Fogel 2012; Frazee 2012; Snyder 2011). Federal policymakers consistently attribute importance to the 200,000 population threshold. The results from this research show that even with some policy distinctions on either side of threshold, at least in terms of an MPO's ability to conduct freight planning, nothing at all changes at a population of 200,000.
The transportation policy literature that emerged in the years and decades subsequent to the passage of ISTEA in 1991 routinely concluded that ISTEA-era surface transportation policy represented a significant advance in terms of providing more transportation planning authority to local and regional levels in contrast to the traditional dominance of state departments of transportation (e.g. Dilger 1992; Gage and McDowell 1995; Goetz, Dempsey, and Larson 2002; McDowell 2003). The literature specifically pointed to the increased authority and
autonomy provided to "large MPOs," the much easier to understand term for MPOs with TMA designations. Although the results show no connection between TMA designation and elevated ability to conduct freight planning, it is possible this policy mechanism has effects on other forms of transportation planning. Freight planning remains uncommon and is a relatively new province of public sector urban and regional planning. MPOs are generally small organizations with limited resources. Perhaps the increased authority, autonomy, and responsibility that comes with TMA designation does positively affect an MPO's ability to conduct transit
planning or bicycle and pedestrian planning. While these transportation planning specialties are less common than the traditional focus on highway planning, they are much more prominent than freight transportation planning. The possibility, however, should also be considered that one of the centerpieces of federal policy for urban and metropolitan transportation has no discernable effect on the everyday practice of transportation planning at metropolitan planning organizations.
If federal policy in the form of elevated levels of authority, autonomy, and
responsibility provided by TMA designation does not help explain variations in MPO freight planning capacity, then what accounts for high, average, and low levels of metropolitan freight planning capacity? The main results from the assessment of alternative explanations are less clear than the failure to reject the null hypothesis that emerged from the RD analyses. The multiple regression models suggest that the central variables to the RD models, TMA
designation and regional population, are not important determinants of MPO freight planning capacity. The relative unimportance of regional population is somewhat surprising as
population measures assume such primacy in federal transportation policy. The link between higher levels of regional population and greater need to conduct freight planning is intuitive in
that more people require more goods and transportation services. Variables representing measures of regional population, however, seem to be proxies for other variables such as geographic area and the number of MPO full-time staff.
Several variables obtained from sources external to the survey are interesting and warrant further investigation in the case study analysis. The binary indicator for presence of a federally-mandated intermodal connector is pertinent to this dissertation’s research questions and focus on the efficacy of federal transportation policy in metropolitan freight planning. A sizable number of survey respondents also cited intermodal connections as one of the main freight transportation issues facing their MPO’s region. Whether the binary indicator measures some level of freight activity, is a more direct impact of federal policy, or has some other relationship with MPO freight planning capacity is unclear.
The two explanatory variables derived from the survey responses consistently returned statistically significant results in multiple regression models. The number of full-time
employees is a relatively concrete measure that may be less susceptible to respondent bias than more subjective questions. The binary indicator for the presence of a regional freight champion is a more problematic and, at the same time, potentially more interesting variable. The answer to the question of whether there is a regional freight champion is inherently subjective as what constitutes a regional freight champion is ill defined. One hypothesis is that because an MPO concurrently has limited resources and wide-ranging responsibilities the impetus for an MPO to allocate more resources for freight planning likely comes from an outside entity or coalition of stakeholders. Particularly, the literature review resulted in a conceptual framework
hypothesizing that a regional coalition of freight stakeholders, potentially described as a
relationship between regional freight champions, regional freight coalitions, and MPO freight planning becomes an organizing focus of the case study research and analysis in Chapter 6.
6 Case Studies of Metropolitan Freight Planning
This chapter presents the results of a cross-case analysis of freight planning in four metropolitan areas. The central aim of the chapter is to examine research questions pertaining to the relationship of MPOs to other regional organizations. Pattern matching constitutes the primary analytical method for the case studies. The chapter also directly builds on the results and findings from the quantitative analysis of the national survey on MPO freight planning. One of the main findings from Chapter 5 was the strong association between MPOs
identifying their region as having a freight champion and higher levels of MPO freight planning capacity. This finding provides an organizing focus for this chapter and becomes a point of entry to examining MPOs within a broader context of multisector stakeholder organizations.
The results from the quantitative analysis of the national survey data also establish the population from which the cases were selected. As detailed in Chapter 3, case study selection consisted of selecting four cases based on two criteria: the identification or not of a regional freight champion and high or low levels of MPO freight planning capacity. While identifying a regional freight champion is different than participating in a coalition, a relationship
between an MPO and a champion suggests the potential for interorganizational collaboration on freight transportation issues. The importance of champion organizations in regional regimes has also been highlighted in the urban regime theory literature, particularly in regards to leadership from high-level business organizations (e.g. Hamilton 2002; Hamilton
The case study selection process yields four cases with Greenville and Grand Rapids appearing to support the expected pattern of the association between the identification or not of a regional freight champion and corresponding levels of MPO freight planning capacity (the 2x2 selection matrix is depicted in Figure 6.1). For the cases of Greenville and Grand Rapids, this chapter focuses on the questions of if, how and why the presence (or absence) of a regional freight champion corresponds with high (or low) levels of MPO freight planning capacity.
The case study selection methodology also provides two cases in Honolulu and Fort Meyers that appear to offer rival patterns of the association between the identification of a regional freight champion and MPO freight planning capacity. The examination of rival explanations strengthens the pattern matching analytical method for this cross-case analysis (see Yin 2014, 140). Fort Myers and Greenville are similar by not identifying a regional freight champion but diverge in outcomes, so the central question in the case of Fort Myers is why does the MPO have a high level of freight planning capacity even though there is no regional freight champion? Honolulu and Grand Rapids both specified a regional freight champion while diverging in outcomes, so the central question in the case of Honolulu is why does the MPO have a low level of freight planning capacity despite the presence of a regional freight champion?
Figure 6.1 Pattern Matching and Case Study Selection Matrix Fort Myers, FL
(No champion / High capacity) Rival Pattern
Grand Rapids, MI (Yes champion / High capacity)
Expected pattern Greenville, SC
(No champion / Low capacity) Expected Pattern
Honolulu, HI
(Yes champion / Low capacity) Rival Pattern
This chapter begins with an overview of regional contexts for the case studies of metropolitan freight planning including geography, population, history, economy, and freight activity. The organizational structure of the MPOs is also described in this section. The chapter then transitions to the results of the cross-case analysis. Each case will first be presented individually starting with Greenville, South Carolina and Grand Rapids, Michigan where cases appear to match the pattern of expected relationships between a regional freight champion and MPO freight planning capacity. The chapter then proceeds to Honolulu, Hawaii and Fort Meyers, Florida where the cases appear to contradict the expected pattern of relationships. The chapter concludes with a cross-case synthesis and discussion of the case study findings.