Effect of Smart Growth Policies on Travel Demand (2013) / Chapter Skim
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42 C h A P T e R 3 Background and Use The SmartGAP tool was developed from the background research described in Chapter 2 to evaluate the impact of various smart growth policies. The tool is designed to be a highlevel evaluation at a regional scale that can bridge the distance between evaluating smart growth policies during a regional visioning process and evaluating smart growth policies at a project or alternative level in a regional transportation plan.
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43 The tool does not provide specific spatial results beyond the built environment categories at the regional level, but does capture individual household and firm characteristics and the interactions between policies. The disaggregate nature of the model captures impacts that may be occurring for small portions of the population (say, 0-vehicle households)
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44 Increased car sharing in turn reduces household vehicle ownership, which also reduces household vehicle miles traveled. The following is an explanation of major steps in the model execution in Figure 3.1.
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45 Place Type Development Process One emerging school of thought in land use planning is to consider land uses in terms of place types instead of simply residential or commercial or high density compared to low density. A place type refers to all of the characteristics of a developed area such as the types of uses included, the mix of uses, and the density and intensity of uses.
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46 • The close-in community place type would be those areas located near the urban cores and would consist primarily of housing with scattered mixed-use centers and arterial corridors. Housing would be varied in terms of density and type.
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47 2. Employment data is employment by firm size and industry derived from County Business Pattern data by county.
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48 • Daily vehicle miles traveled; • Peak travel speeds by facility class; and • Vehicle hours of travel, delay. Environment and Energy Impacts • Greenhouse gas and criteria emissions; and • Fuel consumption.
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49 • Household income model, which identifies the mean household income for each household; and • Firm size model, which identifies how many firms of a particular size category reside in each industry. The output of these three models is the individual households and firms in a region with age and income characteristics for households and size and industry characteristics for firms: • The age categories for persons in households are 0–14 years old, 15–19 years old, 20–29 years old, 30–54 years old, 55–64 years old, and 65 years or older.
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50 the case of the transit supply, the level of accessibility is dependent on the transit revenue miles operated in the region. For automotive or vehicular facilities, the level of accessibility is dependent on the level of freeway lane miles.
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51 • Transit revenue miles interacted with urban areas; • Transit revenue miles per capita interacting with households in an urban mixed-use area; and • Urban mixed-use areas interacted with freeway lane miles. Travel Demand This component of the model calculates the average daily vehicle miles traveled (VMT)
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52 a regional basis as a function of the base year estimate of heavy truck VMT and the growth in the total regional income. As a default, the model grows heavy truck VMT at the rate of total regional income, but the user can apply a factor to change the relative rate of heavy truck growth.
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53 suburban development (CSD) and concluded that TND networks produced 57% less internal trip VMT, 400% less volume on local streets, 15% less on collectors, and 25% less on arterials.
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54 where the percentage change is calculated relative to the midpoint, and x x x x x( ) = − + % change in 2 2 1 2 1 y y y y y( )
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55 Induced Demand Induced demand is estimated as a result of changes to the transportation system supply. These changes are introduced as changes in freeway lane miles or transit revenue miles.
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56 parking costs into the calculation of other vehicle costs such as gas. The model represents both parking costs for employees who are charged to part at or near their place of work, and other parking costs.
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57 Carlo processes are used to identify which households participate in transit pass programs. The proportion of employees participating in this program is a policy input.
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58 two speeds: a lower speed for roads without ITS and other technology and service to manage incidents that cause nonrecurring congestion, and a higher speed for roads that do have such technology. The policy model interpolates between the two speeds based on the proportion of the highway network that is covered by the ITS and other incident management technologies and services to calculate an average speed for the region for each of the functional classes and vehicle types.
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59 in a parameter file (see example in Table 3.12) that can be edited by the model user.
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60 • Resurface and widen lanes. • Resurface pavement.
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61 It is interesting to note the comparative cost per trip of bus and heavy rail, while commuter rail and light rail are both considerably -- almost four times -- higher. Annual Traveler Cost This is fuel plus travel time.
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62 Sources The travel and environmental impacts are calculated from the models that were adapted from the Greenhouse Gas Statewide Transportation Emissions Planning (GreenSTEP) Model Documentation (November 2010)
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63 is network congestion. Better traffic management or real-time information can provide modest reductions in VMT or CO2 emissions.
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64 street deterioration from trucking (with an average ranking of 3.7, out of 5)
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65 centralized employment scenario, they removed half the basic, retail, and service jobs found in the region's rural-designated traffic analysis zones and 30% of such jobs in the suburbandesignated zones and placed these jobs in the urban- and central-designated zones (in proportion to existing jobs counts for those zones)
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66 roadway expansions, that is, a supply-side investment. Very little work has been done on the impacts of demand-side strategies, whether they be TDM (e.g., pricing, ITS)
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67 and travel and who perhaps have observed changes in travel behavior over time of smart growth projects. Regardless, a Delphi-like process of eliciting opinions about rebound effects would not be grounded in empiricism.
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68 LLC 2011) evaluated the potential energy benefits of conventional suburban development as compared to more compact and mixed-use communities.
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