assessing the impact of network resolution and origin ...docs.trb.org/prp/17-05624.pdfdowds,...

Assessing the Impact of Network Resolution and Origin-Destination Aggregation on the Stability of 1

Transportation Network Criticality Rankings 2 3

Jonathan Dowds* 4

Tel: 802-656-1433; Email: [email protected] 5

6

Karen Sentoff 7 Tel: 802-656-1433; Email: [email protected] 8

9

James L. Sullivan 10 Tel: 802-656-1433; Email: [email protected] 11

12

Lisa Aultman-Hall 13 Tel: 802-656-1245; Email: [email protected] 14

15

University of Vermont Transportation Research Center 16

210 Colchester Ave, Burlington VT 05401 17

18

* Corresponding Author 19

20

Word Count: 4,498 + 12 tables/figures = 7,498 21

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Revised: November 15, 2016 23

Dowds, Sentoff, Sullivan and Aultman-Hall 2

2

ABSTRACT 1 Objective rankings of the criticality of transportation network infrastructure are essential for efficiently 2

allocating limited adaptation resources and must account for network connectivity and travel demand. 3

Road link criticality can be quantified by the total travel delay caused when the capacity of a road 4

segment or link is disrupted or removed. These methods can be applied using standard travel-demand 5

models but the exclusion of lower-volume roads and the aggregate nature of traffic analysis zones may 6

distort resulting criticality rankings. To test the impact of link exclusion and demand aggregation we 7

applied the Network Robustness Index, a well-established link criticality measure, to a hypothetical 8

network with varying levels of network resolution and demand aggregation. Our results show a 9

statistically significant change in criticality rankings when demand is aggregated and especially when 10

links are excluded from the network, suggesting that criticality rankings may be distorted when estimated 11

with typical demand models. Application to a real world road network in Vermont supports the finding on 12

the impact of network resolution on criticality rankings. 13


3

INTRODUCTION 1 A number of industry, governmental, and academic groups have produced guidelines to facilitate 2

adaptation planning to reduce the transportation system’s vulnerability to climate change and extreme 3

weather events (1–3). These efforts reflect the reality that an increasing share of transportation dollars are 4

spent repairing infrastructure damaged by extreme weather events (4). The cost of preparing for and 5

recovering from these events threatens agency budgets, increases travel times, reduces reliability, and 6

reduces level of service. 7

Adaptation planning guidelines, including the Federal Highway Administration’s (FHWA) 8

influential “Vulnerability Assessment Framework,” frequently include a step for assessing how important 9

specific assets are to the overall effectiveness of the transportation system (1–3). As resources are limited, 10

importance, or criticality, ratings are valuable for prioritizing adaptation projects. Unfortunately, objective 11

criticality assessment methods have not yet been widely adopted within the transportation community. In 12

a survey of 149 transportation practitioners at 137 transportation agencies, respondents rated the 13

availability and technical adequacy of tools for rating asset criticality at only 5.2 on a 1-10 scale (5). 14

Refining objective methods for assessing criticality and disseminating these methods to transportation 15

agencies will be crucial to supporting effective adaptation planning. 16

Simple traffic volume-based metrics are insufficient for assessing criticality; network 17

connectivity as well as traffic volumes must be accounted for in order to meaningfully understand the 18

importance of individual network links (6–9). Assessing the interaction between the volume and spatial 19

location of travel demand and network connectivity is difficult and requires the use of network demand 20

modeling. Several approaches to measuring criticality involve modeling the total traffic delay caused 21

when the capacity of a road segment or link is disrupted or removed (6, 7, 10, 11). These methods can be 22

applied to existing regional or statewide travel models regularly used by transportation agencies. Travel-23

demand models, however, generally use a simplified road network that includes major roadways (in some 24

cases only those that are the responsibility of the modeling agency), but not the complete road network. 25

The minor roads that are often omitted from these models are not necessarily important for congestion 26

management and infrastructure planning but can be important for criticality and reliability planning if 27

they provide functional redundancy. Failure to consider the connectivity provided by both major and 28

minor roadways could result in erroneous criticality rankings. Similarly, the size and locations of traffic 29

analysis zones (TAZs), which spatially define the origins and destinations of demand, are generally driven 30

by data availability and the level of demand aggregation in the resulting origin and destination (O/D) 31

matrices may not provide sufficient locational accuracy to capture the real impact of disruptions to 32

specific network elements. 33

Consequently, reassment of the spatial resolution of travel-demand models is needed if they are to 34

be applied to criticality assessment and resiliency planning in addition to congestion management and 35

level of service projections. Determining the required spatial resolution (in terms of road network 36

completeness and the spatial disaggregation of TAZs) for criticality assessment has important 37

implications for adaptation practice. We used the Network Robustness Index (NRI) (6), a well-38

established, link-specific criticality metric that measures the impact of individual road links on system 39

performance, in a series of applications to assess link criticality on a hypothetical road network at varying 40

network resolutions and with differing levels of O/D aggregation and on the road network in Chittenden 41

County, Vermont at two different network resolutions. These analyses were intended to address three 42

research questions: 43

How does reducing the network resolution from a complete network to a network of major roads 44

affect the assessed criticality of major roadways? 45

How does increasing O/D spatial aggregation affect the assessed criticality of major roadways? 46

Are there differences in these effects when network resolution and O/D aggregation are adjusted 47

in unison? 48


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LITERATURE REVIEW 1 In the absence of objective criticality measures, political factors may influence prioritization of adaptation 2

projects (1). During pilot testing of the FHWA’s “Vulnerability Assessment Framework,” many agencies 3

either skipped criticality assessment (12), assessed the criticality of only a small number of links of each 4

functional class (13), relied on qualitative assessments, or on metrics such as traffic volume that do not 5

account for network connectivity (1). 6

The growing array of methods for identifying critical links or nodes within the transportation 7

system have been documented in recent reviews by Mattsson and Jenelius (9) and Faturechi and Miller-8

Hooks (14). Mattsson and Jenelius categorize these methods as either topologically-based or system-9

based. The former methods assess the topological properties of highly abstracted networks and 10

consequently are computationally efficient. System-based methods, in contrast, tend to use networks that 11

capture more of the physical realities of transportation systems and to include travel-demand modeling 12

(9). Several system-based approaches to quantifying criticality account for traffic volumes and 13

redundancy in the network by calculating the total travel delay caused when the capacity of a road 14

segment or link is disrupted or removed. This approach is the basis for a number of studies that look at 15

single-link disruptions as a means for assessing link criticality and network robustness (6, 7, 10, 11). One 16

possible shortcoming of this approach is that they typically rely on practice-ready travel-demand models 17

which include only major network links even though other local roads may provide important functional 18

redundancy. 19

A few studies have examined the effect of network resolution on criticality assessment (10, 15, 20

16). Sullivan et al. (15) found that previously omitted links could change the criticality ranking of major 21

roadways when they are included in the assessment, although the number of omitted links that had a 22

major influence was limited in their case study. These findings are similar to those of Erath and Axhausen 23

who found that excluding local roads in a Swiss network impacted detour length significantly but only for 24

a comparatively small number of trips (10). None of these studies mirrored increasing the resolution of 25

the road network with increasing O/D disaggregation. 26

The problem of bias introduced by O/D aggregation has also been recognized (17). O/D 27

disaggregation is generally explored in applications where some data is available at a more disaggregated 28

level than the rest of the data, and increased disaggregation is needed to answer policy questions. 29

Attributes of the network itself, land-use characteristics, or person-level behaviors guide the estimation of 30

characteristics of the traveling population at a more disaggregate level (18, 19). Since disaggregation 31

requires considerable effort, multiple studies test the effects of O/D disaggregation on model outputs (20, 32

21). O/D disaggregation is also at the core of population synthesis methods being explored earnestly in 33

the travel modeling community in support of activity-based models (22). 34

METHODS 35 The NRI is a link-specific metric for assessing the criticality of individual road links on system 36

performance (6). A higher link-specific NRI value indicates greater link criticality. To test the network 37

resolution and level of O/D disaggregation required to obtain accurate criticality rankings, NRI values 38

were calculated for the major roads in a hypothetical travel-demand model for varying levels of network 39

resolution and O/D aggregation. In keeping with the findings in (11), NRI values were calculated using a 40

75% reduction in link capacity. 41

Changes in NRI rankings based on model (network and O/D) representation were assessed to 42

determine whether model structure impacts the consistency of criticality rankings. The complete network 43

and disaggregate O/D matrix were used to calculate baseline criticality rankings. Since the baseline 44

rankings account for the full connectivity provided by all network links and do not aggregate demand, 45

they are assumed to be accurate rankings of criticality as measured by the NRI. Scenarios with lower 46

network resolution or higher O/D aggregation mimic the network and demand aggregations used in many 47

travel-demand models as most agencies lack the disaggregate data for higher resolution model 48

development. Changes in criticality rankings as network resolution is reduced or as O/D aggregation is 49

increased would indicate the modeling resolution typically used in DOT and MPO travel-demand models 50


5

may not be appropriate for criticality assessment. Comparing the rankings produced under the baseline 1

scenario to the lower resolution scenarios enables us to test whether or not these simplifying assumptions 2

impact the results of criticality rankings. Particular attention is paid to the most critical links since, if 3

criticality is used as a project prioritization/selection tool, lower-ranked links are unlikely to be 4

considered and their precise ranking is relatively unimportant. For ease of comparison across scenarios, 5

the NRI is only calculated for the major-road links, which are included in the network for all modeling 6

scenarios. 7

In addition, NRI values were calculated for major roads in Chittenden County, Vermont at two 8

levels of network resolution. Since this “real-world” network is considerably larger than the hypothetical 9

network (8,000 links versus 90), our analysis of the results examines discrepancies in criticality rankings 10

for the top 10, 25, 50, 100 and 200 links included in both the full and reduced resolution networks. 11

Hypothetical Travel-Demand Model 12 The baseline hypothetical travel-demand model used in this analysis was created by Scott et al. (6). The 13

baseline model representation is shown in FIGURE 1A and consists of 54 major roads and 36 minor 14

roads. As described in (6), node placement is based on Central Place Theory (23) and node populations 15

are distributed according to the rank-size rule with highest population in the central node. The baseline 16

O/D matrix for trips between the 37 nodes in the model was created using a production-constrained 17

gravity model. The major road network, the lowest resolution representation of the road network, is 18

shown in FIGURE 1B. The reduced number of nodes (19) in FIGURE 1B represents the type of 19

aggregated O/D used in most agency models. To facilitate presentation of our results, we have divided the 20

major roads into rings and radials (blue and green respectively) and divided the minor roads into inner 21

and outer roads (solid and dashed, respectively). Similarly, we have labeled minor nodes as inner and 22

outer (gray and red, respectively). Major nodes are shown in black in Figure 1. 23

FIGURE 1 A. Complete network and B. Major roads network


6

Network Resolution Reduction Scenarios 1 To test the impact of network resolution on the NRI criticality rankings of the major links, we compared 2

the baseline NRI rankings calculated using the complete network of 90 links with five scenarios that 3

excluded various combinations of minor roads (TABLE 1). The most disaggregate, 37x37, O/D matrix 4

was used for each of these scenario runs. The link exclusion scenarios, LE1 through LE5, excluded an 5

increasing number of minor links, intended to represent the type of link omissions that occur in regional 6

or statewide model networks. Three scenarios (LE1, LE2, and LE4) excluded random selections of minor 7

roads. In each of these cases, we ran five iterations of the scenario, excluding a different random selection 8

of minor roads in each iteration, and used the averaged NRI values across all five iterations for the final 9

criticality rankings. 10

TABLE 1 Network resolution reduction scenarios 11

Scenario Excluded Links No. of Links Used

Baseline None – NRI calculated using complete network 90

LE1 12 randomly selected minor links 78

LE2 18 randomly selected minor links 72

LE3 All 24 outer minor links 66

LE4 All 24 outer minor links plus six randomly selected inner minor links 60

LE5 All minor links – NRI calculated using major road network only 54

O/D Aggregation Scenarios 12 To test the impact of increasing O/D aggregation on NRI rankings, we compared four scenarios of 13

increasingly aggregated demand to the baseline NRI rankings calculated with the original 37-node O/D 14

matrix. The complete 90-link network was used for each of these model runs. TABLE 2 summarizes the 15

scenarios tested. For the scenarios DA1 – DA4, demand from a subset of minor nodes was aggregated to 16

remaining adjacent nodes. DA3 aggregated demand from a random selection of minor nodes and in this 17

case, as with the random link selections in the network resolution reduction scenarios, we ran five 18

iterations of the scenario and used the average NRI values in the final criticality rankings. 19

A two-step demand aggregation process was used to aggregate demand from minor nodes to 20

proximal nodes in the network. First, the sum of the demand at each of the selected nodes was attributed 21

to the proximal nodes in proportion to the demand originating at those nodes. Second, the demand 22

between all remaining nodes was updated proportionally based on summation of demand at each node. 23

Therefore, each resulting matrix had the same total demand distributed across a decreasing number of 24

nodes. 25

TABLE 2 O/D aggregation scenarios 26

Scenario Demand Aggregation No. of Nodes Used

Baseline None – NRI calculated using disaggregate O/D matrix 37

DA1 Demand from 12 outer nodes aggregated to adjacent zones 25

DA2 Demand from 6 interior nodes aggregated to adjacent nodes 31

DA3 Demand from 9 randomly selected outer and interior nodes

aggregated to adjacent nodes 28

DA4 Demand from 18 interior and exterior nodes aggregated to adjacent

nodes 19

27

28


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Combined Network Reduction and O/D Aggregation Scenarios 1 Altering network resolution and demand aggregation simultaneously more closely replicates the 2

development of a real-world travel-demand model in which resolution is constrained by data availability. 3

Three scenarios that simultaneously excluded combinations of minor links and nodes were compared to 4

the baseline NRI rankings calculated using the complete 90-link network and 37-node disaggregated O/D 5

matrix. TABLE 3 summarizes the scenarios tested. The combined network reduction and demand 6

aggregation scenarios, CRA1 through CRA3, exclude an increasing number of minor links from the 7

modeling process while simultaneously aggregating the O/D matrix. The first scenario, CRA1, excluded a 8

random selection of minor links and aggregated demand from 9 randomly selected nodes. Five iterations 9

of this were run, removing a different random selection of minor links and nodes for each iteration, and 10

the average NRI across these iteration was used in final criticality rankings. 11

TABLE 3 Combined network resolution reduction and O/D aggregation scenarios 12

Scenario Excluded Links and Demand Aggregation No. of Links & Nodes

Baseline None 90 & 37

CRA1

18 randomly selected minor links excluded

Demand from 9 randomly selected minor nodes aggregated to

adjacent nodes

72 & 28

CRA2 All 24 outer minor links excluded

Demand from 12 outer nodes aggregated to adjacent nodes 66 & 25

CRA3

All 36 minor links excluded

Demand from 18 inner and outer nodes aggregated to adjacent

nodes

54 & 19

Chittenden County Network Resolution Reduction Scenario 13 Chittenden County covers 618 square miles in northern Vermont and has a population of 158,000 (24). It 14

includes a micropolitan area centered on the city of Burlington as well as numerous surrounding rural 15

communities. Baseline NRI rankings were calculated using a complete network, consisting of almost 16

8,000 links and 1,660 road-miles. The network resolution reduction scenario used the network in the 17

Chittenden County Regional Planning Commission’s (CCRPC) 2010 Regional Transportation Model. 18

The regional model includes only 591 road-miles represented by 2,322 links. Both scenarios used the 19

same O/D matrix based on the 352 traffic analysis zones in the CCRPC regional model. 20

RESULTS 21 The results of the NRI calculations for the hypothetical road network reveal that criticality rankings are 22

highly sensitive to the exclusion of minor links. The criticality rankings are also sensitive to the 23

aggregation of O/D nodes, though this impact is more moderate. In all of the hypothetical network 24

scenarios tested, the Wilcoxon Signed-Rank Test reveals significant differences in NRI rankings at the p 25

= 0.01 level. Because criticality assessment is used as a prioritization tool and lower-ranked links are 26

unlikely to be considered in project selection, properly ranking the most critical links is particularly 27

important. The discussion of the hypothetical network results focuses on the 10 highest ranked links for 28

each scenario since the baseline NRI values calculated with the complete network and the most 29

disaggregate O/D matrix are substantially higher for the top 10 ranked links than for lower ranked links, 30

as shown in Figure 2. The average added vehicle hours of travel (VHT) caused by disrupting one of the 31

top 10 ranked links is 75,923 hours while the average added VHT for disrupting one of the links ranked 32

11 to 20 is 47,541 hours. Thus, criticality rankings that fail to accurately identify these top 10 links are 33

likely to identify roads that have significantly less impact on overall system functioning. Similarly the 34

discussion of the results for the Chittenden County network considers criticality ranking for the top 200 35

links after which there is little difference in the NRI of the remaining links. 36

37


8

1

Figure 2. NRI values calculated for the baseline scenario on the hypothetical network 2 3

Network Resolution Reduction Scenarios 4 The 10 highest-ranked links from the baseline case and from each link exclusion scenario are shown in 5

TABLE 4. For the link exclusion scenarios, LE1 – LE5, the initial NRI rank from the baseline case is 6

given in parenthesis. In the baseline scenario the most critical links are all radials (see Figure 1) and the 7

six centermost radials occupied the top six slots. As minor links were removed from the network, 8

however, the composition of the 10 highest ranked links changed substantially. In scenario LE1, which 9

excluded outer minor links, radial links extending outward from the innermost ring constitute the majority 10

of the high criticality road links. A similar pattern is apparent in LE2, which excludes 18 randomly 11

selected minor links. In scenarios LE3, LE4, and LE5, which exclude 24, 30 and 36 minor links 12

respectively, the outer most radials hold the top six rankings while 4 outer ring links are also included in 13

the top ten rankings. Wilcox Signed-Rank Tests show that the NRI rankings of the major links are 14

statistically different from the baseline scenario for all link exclusion scenarios. 15

16

RAD6

RAD4

RAD5

RAD3

RAD1

RAD2

RAD10

RAD12

RAD18

RAD11

0

20,000

40,000

60,000

80,000

100,000

120,000

0 10 20 30 40 50

NR

I (H

ou

rs o

f A

dd

itio

nal

VH

T)

Link Rankings


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TABLE 4 Top 10 highest-ranked links by NRI for network resolution reduction scenarios 1

Rank Baseline

Links

LE1 Links

(Initial Rank)

LE2 Links

(Initial Rank)

LE3 Links

(Initial Rank)

LE4 Links

(Initial Rank)

LE5 Links

(Initial Rank)

1 RAD6 RNG25 (52) RAD18 (9) RAD17 (29) RAD17 (29) RAD17 (29)

2 RAD4 RNG27 (38) RNG31 (37) RAD18 (9) RAD18 (9) RAD18 (9)

3 RAD5 RAD16 (32) RAD17 (29) RAD16 (32) RAD16 (32) RAD16 (32)

4 RAD3 RAD13 (13) RNG33 (34) RAD13 (13) RAD13 (13) RAD13 (13)

5 RAD1 RAD18 (9) RNG32 (28) RAD15 (50) RAD15 (50) RAD15 (50)

6 RAD2 RNG29 (26) RAD13 (13) RAD14 (43) RAD14 (43) RAD14 (43)

7 RAD10 RAD15 (50) RAD16 (32) RNG33 (34) RNG33 (34) RNG33 (34)

8 RAD12 RNG26 (36) RNG29 (26) RNG28 (31) RNG25 (52) RNG28 (31)

9 RAD18 RNG30 (33) RAD6 (1) RNG25 (52) RNG18 (31) RNG25 (52)

10 RAD11 RAD14 (43) RNG30 (33) RNG31 (37) RNG31 (37) RNG30 (33)

The Wilcoxon Signed-Rank Test reveals significant differences in NRI rankings at the p = 0.01 level for all

scenarios relative to the baseline scenario

2 TABLE 5 provides scenario-specific rankings of the top 10 links from the baseline scenario for 3

each link exclusion scenario as well as the average change in ranking for these links. In all cases, the 4

average change in rank is greater than 10 spots. In the LE3 – LE5 scenarios more than half of the links in 5

the original top 10 baseline ranking fall in the bottom half of the rankings. 6

TABLE 5 Ranking of top 10 baseline links by network resolution reduction scenario 7

Link Baseline Rank LE1 Rank LE2 Rank LE3 Rank LE4 Rank LE5 Rank

RAD6 1 12 9 25 25 31

RAD4 2 13 13 27 29 40

RAD5 3 16 12 35 28 33

RAD3 4 14 19 26 26 38

RAD1 5 18 26 28 36 36

RAD2 6 22 29 37 34 41

RAD10 7 26 11 38 30 23

RAD12 8 29 14 33 31 24

RAD18 9 5 1 2 2 2

RAD11 10 39 25 34 35 25

Average Change in Rank1 14.7 12 24.4 23.5 25.2 1 Calculated as the mean of absolute value of the difference between the scenario rank and the baseline rank

O/D Aggregation Scenarios 8 The 10 highest ranked links from the baseline case and from each demand aggregation scenario, DA1-9

DA3, are shown in TABLE 6. The initial rank for each link from the baseline case is given in parenthesis. 10

Overall, the rankings are much more stable than in the link exclusions scenarios. Nonetheless, the 11

Wilcoxon Signed-Rank Tests show that the ranking of the major links are statistically different from the 12

baseline scenario for all demand aggregation scenarios. 13


10

TABLE 6 Top 10 highest-ranked links by NRI for demand aggregation scenarios 1

Rank Baseline

Links

DA1

(Initial Rank)

DA2

(Initial Rank)

DA3

(Initial Rank)

DA4

(Initial Rank)

1 RAD6 RAD6 (1) RAD6 (1) RAD6 (1) RAD6 (1)










The Wilcoxon Signed-Rank Test reveals significant differences in NRI rankings at the p = 0.01

level for all scenarios relative to the baseline scenario

2

The scenario-specific rankings for each of the baseline top 10 links for scenarios DA1 – DA4 are 3

provided in TABLE 7. The most notable change is the reduced criticality of the radial links extending 4

outward from the innermost ring as the level of aggregation increases. 5

TABLE 7 Ranking of top 10 baseline links by demand aggregation scenario 6

Link Baseline

Rank DA1 Rank DA2 Rank DA3 Rank DA4 Rank

RAD6 1 1 1 3 4

RAD4 2 2 2 1 1

RAD5 3 5 3 4 13

RAD3 4 4 4 2 2

RAD1 5 3 5 8 17

RAD2 6 8 10 5 3

RAD10 7 6 6 11 31

RAD12 8 7 7 16 40

RAD18 9 18 15 10 50

RAD11 10 11 9 17 42

Average Change 1 1.8 1.3 1.2 1.6 1 Calculated as the mean of absolute value of the difference between the scenario rank and the baseline rank

Combined Network Reduction and O/D Aggregation Scenarios 7 The 10 highest ranked links for each network resolution/demand aggregation interaction scenario are 8

shown in TABLE 8. As discussed previously, the most critical links in the baseline scenario using the 9

complete network and disaggregate O/D matrix are the centermost six radial links. As minor links are 10

excluded from the network and demand is aggregated, the highest ranked links include an increasing 11

number of outer radial links. Unlike the link exclusion scenarios, however, a significant number of links 12

in the top 10 of the baseline rankings are also included in the top 10 rankings in scenarios CRA1 – CRA3. 13


11

In scenario CRA1, which aggregates demand from 9 randomly selected nodes and excludes 18 randomly 1

selected connector links, five links that are in the baseline top 10 ranking remain in the top 10 ranking. 2

This is also true for scenarios CRA2 and CRA3. Nonetheless, the Wilcox Signed-Rank Tests show that 3

the NRI rankings for all 54 major links are statistically different from the baseline scenario for all link 4

interaction effect scenarios. 5

TABLE 8 Top 10 highest-ranked links by NRI by for combined reduction/aggregation scenarios 6

Rank Baseline CRA1

(Initial Rank)

CRA2

(Initial Rank)

CRA3

(Initial Rank)

1 RAD6 RAD18 (9) RAD18 (9) RAD18 (9)

2 RAD4 RNG32 (28) RAD13 (13) RAD13 (13)

3 RAD5 RAD13 (13) RAD14 (43) RAD14 (43)

4 RAD3 RAD6 (1) RAD17 (29) RAD17 (29)

5 RAD1 RNG31 (37) RAD6 (1) RAD10 (7)

6 RAD2 RAD17 (29) RAD4 (2) RAD6 (1)

7 RAD10 RAD7 (12) RAD16 (32) RAD12 (8)

8 RAD12 RAD4 (2) RAD15 (50) RAD7 (12)

9 RAD18 RAD10 (7) RAD3 (4) RAD4 (2)

10 RAD11 RAD12 (8) RAD1 (5) RAD11 (10) The Wilcoxon Signed-Rank Test reveals significant differences in NRI rankings at the p = 0.01 level for all

scenarios relative to the baseline scenario rankings at the p = 0.01 level for all scenarios relative to the

baseline scenario

7

As shown in TABLE 9, the average change in rank for the interaction effect scenarios, CRA1 – 8

CRA3, relative to the baseline is considerably smaller than in the link exclusion scenarios but larger than 9

in the demand aggregation scenarios. Across all three scenarios, the top 10 baseline links are only ranked 10

outside of the top 20 in one instance (RAD2 in scenario CRA2). 11

TABLE 9 Ranking of top 10 baseline links by combine reduction/aggregation scenario 12

Link Baseline Rank CRA1 Rank CRA2 Rank CRA3 Rank

RAD6 1 4 5 6

RAD4 2 8 6 9

RAD5 3 11 11 14

RAD3 4 15 9 12

RAD1 5 12 10 15

RAD2 6 21 13 18

RAD10 7 9 12 5

RAD12 8 10 14 7

RAD18 9 1 1 1

RAD11 10 16 17 10

Average change1 6.8 5.9 6.4

1 Calculated as the mean of absolute value of the difference between the scenario rank and the baseline rank


12

Chittenden County Network Resolution Reduction 1 Comparing the baseline NRI ranking of major roads in the real-world network, calculated using the 2

complete, 8,000-link network, to rankings calculated using the 2,322-link, travel-model network also 3

demonstrates that reducing network resolution reduces the accuracy of criticality rankings. TABLE 10 4

shows the percentage of links that were erroneously attributed to various rank categories and the average 5

baseline NRI ranks of these links. The low average rank of the misidentified links reflects the fact that 6

roads that appear highly critical within the travel-model network may actually have low criticality when 7

accounting for the redundancy of the complete road network. 8

TABLE 10 Ranking disparities using complete and travel-model networks for Chittenden County 9

Criticality Rank

Category

% of Links within Category Misidentified

Using Travel-Model Network

Average Baseline NRI Rank of

Misidentified Links

Top 10 Links 50% 720.4 (n=5)

Top 25 Links 40% 971.1 (n=10)

Top 50 Links 30% 971.9 (n=15)

Top 100 Links 26% 755.7 (n=26)

Top 200 Links 27% 1198.7 (n=54)

10

CONCLUSIONS 11 Most agency travel-demand models have been developed with limited numbers of network links and 12

aggregate TAZs that locate the spatial origins and destinations of travel demand. These simplifying 13

assumptions reduce the burden of collecting data and maintaining travel-demand models and are 14

appropriate for modeling network operations and level of service under different scenarios. These model 15

structures are not necessarily appropriate for the criticality modeling needed for the adaptation planning. 16

In this research, reducing network resolution or increasing demand aggregation was shown to have a 17

statistically significant impact on link criticality rankings relative to rankings from a high resolution 18

complete baseline model structure. These differences in criticality ranking could have profound impacts 19

on adaptation and resilience planning if they lead to misallocation of limited resources to links that appear 20

critical when assessed with aggregate models but that are less critical in the context of the complete 21

network or demand that is more disaggregated spatially. 22

The exclusion of minor road links in particular, had a significant impact on criticality rankings for 23

the hypothetical network used here. Removal of minor links representing 15% of the total links in the 24

hypothetical network (12 links in scenario LE1), resulted in an average change of rank of almost 15 25

places for the top 10 baseline links. Distortions to the criticality rankings are more moderate for the 26

demand aggregation scenarios. Given that improving network resolution is easier than O/D 27

disaggregation, these results suggest that travel-demand models may be able to be expanded to improve 28

their efficacy in criticality assessment. 29

Results from the hypothetical network are consistent with a limited analysis of the Chittenden 30

County road network. Considerably more research is needed to explore the impact of network resolution 31

on real world transportation networks, however. Incorporating alternate modes into this modeling 32

approach will also improve the accuracy of criticality ratings since in some situations vehicle, transit, and 33

bicycle and pedestrian networks all provide varying degrees of redundancy for one another. 34

ACKNOWLEDGEMENTS 35 This work was funded by the USDOT with UTC funding through the University of California Davis’ 36

National Center for Sustainable Transportation at the University of Vermont. The authors are grateful to 37

Dr. Darren Scott of McMaster University for providing original network for the hypothetical travel-38


13

demand model. The ongoing guidance of Dr. David Novak University of Vermont is gratefully 1

acknowledged. 2

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