discovering temporal and spatial patterns and characteristics of pavement distress condition data on...

Journal of Transport Geography 38 (2014) 148–158

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Journal of Transport Geography

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Discovering temporal and spatial patterns and characteristicsof pavement distress condition data on major corridors in New Mexico

http://dx.doi.org/10.1016/j.jtrangeo.2014.06.0050966-6923/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +1 505 277 0767; fax: +1 505 277 1988.E-mail addresses: [email protected] (C. Chen), [email protected] (S. Zhang),

[email protected] (G. Zhang), [email protected] (S.M. Bogus), [email protected](V. Valentin).

1 Tel.: +1 505 550 0746.2 Tel.: +1 505 615 9896.3 Tel.: +1 505 277 1395.4 Tel.: +1 505 277 0811.

Cong Chen a,1, Su Zhang b,2, Guohui Zhang a,⇑, Susan M. Bogus a,3, Vanessa Valentin a,4

a Department of Civil Engineering, University of New Mexico, Albuquerque, NM 87131, USAb Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, NM 87131, USA

a r t i c l e i n f o

Keywords:Pavement distress conditionsGIS-based temporal and spatial analysisRegression data

a b s t r a c t

Roadway networks, as part of transportation infrastructure, play an indispensable role in regional econ-omies and community development. The high-quality pavement serviceability of these networks isessential to ensure safe, cost-effective daily traffic operations. In-depth analyses of network-wide pave-ment surface condition data are necessary inputs for optimal pavement design and maintenance, trafficsafety enhancement, and sustainable traffic infrastructure system development. This study aims to inves-tigate various pavement distress condition performance measurements and their correlations to betterunderstand temporal–spatial characteristics of roadway distress based on pavement distress conditiondata collected in New Mexico from 2006 to 2009. Eight major corridors across various urban and ruralareas were selected for analyzing pavement surface-distress conditions and discovering their intrinsiccharacteristics and patterns across both temporal and spatial domains. The results show that there arenot strong correlations among different distress measurements, implying the rationality of the currentpavement performance measurement protocol used by the state transportation agencies. Regressionmodels were established and GIS-based spatial analyses were performed to extract temporal and spatialpatterns of Distress Rate (DR) data. The model results illustrate significant correlations of the DR data onthe same route between two consecutive years, which can be partially characterized by a Markov process.GIS-based spatial investigations also show unique features of pavement condition deterioration attrib-uted to diverse geometric characteristics and traffic conditions, such as vehicle compositions and vol-umes and urban and rural areas. The research findings are helpful to understand the characteristics ofpavement distress conditions more clearly and to optimize traffic infrastructure design and maintenance.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Infrastructure management is a systematic process for manag-ing effectively the entire life cycle of infrastructure and combiningengineering principles and economic theories for optimal infra-structure design and maintenance. An Infrastructure ManagementSystem (IMS) is indispensable in providing comprehensive infor-mation on infrastructure conditions and deploying appropriatemeasurements to maintain infrastructure serviceability, which isa dominant factor in determining traffic system operation effi-

ciency (Cordova, 2010). Pavement condition evaluation is an indis-pensable IMS component and high-quality pavement serviceabilityis essential to ensure safe, cost-effective traffic operations. Pave-ment distress and roughness are the major elements in the quan-titative evaluation of pavement conditions (Bogus et al., 2010a;Prakash and Sharma, 1994), and they have a direct impact onaccessibility and mobility conditions. Different transportationagencies apply various standards to quantify these criteria and,therefore, generate some composite indices as pavement condi-tion performance measurements (GDOT (Georgia Departmentof Transportation), 1996, NMDOT (New Mexico Departmentof Transportation), 2009, WDOT (Washington Department ofTransportation), 1999). Numerous studies have focused on theeffect of pavement conditions on traffic safety and operational effi-ciency by investigating the relationship between Pavement Condi-tion Indices (PCI) and traffic operation and crash data fromdifferent regions (Buddhavarapu et al., 2013; Buddhavarapuet al., 2012; Chan et al., 2010; Ihs, 2004; Kruntcheva et al., 2005;

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C. Chen et al. / Journal of Transport Geography 38 (2014) 148–158 149

Tighe et al., 2000). Buddhavarapu et al. (2012) developed anordered Probit injury-severity model to investigate the relation-ship between PCI and crash severity on two-lane highways. Theyconcluded that pavement condition is significantly correlated withcrash severity on two-lane highways (Buddhavarapu et al., 2013).Chan et al. (2010) investigated the connection between pavementdistress and traffic-crash frequency and reported that the Interna-tional Roughness Index (IRI) and present serviceability index weresignificant inputs for traffic-crash prediction. Ihs (2004) investi-gated traffic safety and travel comfort levels based on pavementconditions. Results indicated that increasing roughness obviouslyled to higher crash rates, and road surface condition was the mostimportant factor for determining driving comfort levels.

In recent decades, pavement surface-condition data have beencollected manually or automatically based on image processingtechniques. In manual surveys, an evaluator visually assessed boththe severity and extent of the distress. For automatic data collec-tion, transportation agencies deployed some automated devices,such as a pavement scanning van, to measure the distress or toscan the pavement to get distress images and conduct offsite dataanalysis (Chang et al., 2009; Jahanshahi et al., 2012; Koduru et al.,2010; Zhang and Elaksher, 2012). For both methods, data qualityand consistency is always a concern for transportation agencies.Significant research has been conducted to address data qualityissue (FHWA (Federal Highway Administration), 2000; NMDOT(New Mexico Department of Transportation), 2009; Bogus et al.,2010a; Underwood et al., 2011). For instance, the FHWA (FHWA,2000) has defined technical training standards to minimize datavariation in its Long-Term Pavement Performance (LTPP) program.Bogus et al. (2010a) developed data quality enhancement proce-dures by calculating data variance in both spatial and temporaldomains. Temporal and spatial data pattern mining and discoveryplay an important role in pavement distress condition analysis toassist transportation agencies in understanding more comprehen-sively pavement deterioration tendency and in optimizing road-way maintenance scheduling. Geography Information Systems(GIS) provide a powerful and flexible platform to explore tempo-ral–spatial data correlations and visualize data characteristics.GIS-based temporal–spatial analyses have been widely applied inmany transportation-related areas, such as land use, urban andtraffic growth, crash distribution, traffic congestion, transit opera-tions, and so on (Shaw and Xin, 2003; Stern, 2004; Li et al., 2007;Datla and Sharma, 2008; Pantha et al., 2010; Kingham et al.,2011; Bhattacharjee and Goetz, 2012; Dai, 2012; Wang et al.,2012). Many GIS-aided statistical tools and methodologies, includ-ing spatial correlation and cluster analysis, multivariate modelregression analysis, Bayesian inference, etc., have been utilizedfor better data exploration. GIS has also been used in traffic oper-ations, asset management, and pavement design and maintenancestudies extensively. For example, Shahin et al. (1998) developed asimplified procedure for interfacing GIS tools with local pavementmanagement systems. Pantha et al. (2010) proposed a GIS-basedhighway maintenance prioritization model for the Nepalesemountains.

Although these previous studies set up a solid foundation, in-depth analyses for further temporal–spatial pavement distress pat-tern discovery and knowledge mining are necessary as crucialinputs for pavement design and maintenance optimization, trafficsafety enhancement, and sustainable traffic infrastructure systemdevelopment (Capuruc and Tighe, 2006; Saitoh and Fukuda,2000; Tsunokawa et al., 2006). The major objectives of this studyare to investigate various pavement condition performance mea-surements and their temporal–spatial correlations to better under-stand roadway surface distress condition deterioration tendencies.In addition, the study aims to provide guidance for the NMDOT, aswell as for other state DOTs, to prioritize statewide infrastructure

design and maintenance with available resources, based on thepavement condition data collected in New Mexico from 2006 to2009. In this paper, the correlations among eight different typesof flexible pavement distress criteria were investigated using Pear-son product-moment correlation, Spearman’s rank correlation, andKendall tau rank correlation tests. Statistical analysis and regres-sion models were developed to quantify the temporal patterns ofpavement distress variations. Temporal-spatial distress changingpatterns were extracted and visualized through GIS techniques,and the methodology applicability to other states was also dis-cussed. The research findings are helpful to understand moreclearly the characteristics of pavement distress conditions and tooptimize traffic infrastructure design and maintenance. The paperis organized as follows: a comprehensive literature review is pro-vided in the next section; data collection, process, visualization,and interpretation are detailed in Section 3, followed by compre-hensive temporal–spatial data modeling and analysis in Section 4.The model results, research limitations and methodology applica-bility are provided in Section 5, with conclusions discussed in thefinal section.

2. The state of the art and conventional practice

Roadway networks are one of the most important componentsof transportation infrastructure and their serviceability relies sig-nificantly on pavement conditions. Since there are several typesof distress evaluated in pavement condition surveys, such as bleed-ing, alligator cracking and patching, a composite index is needed toquantify pavement serviceability. Various transportation agencieshave developed different assessment approaches and standardsto formulate such performance indices. The Alabama Departmentof Transportation (ALDOT) applied a Pavement Condition Rating(PCR) standard for manual pavement evaluation (Turner, 1985;McQueen and Timm, 2005). A Pavement Structural Condition(PSC) index is used by Washington Department of Transportation(WSDOT, 1999) to represent all levels of pavement cracking(McQueen and Timm, 2005). The Georgia Department of Transpor-tation (GDOT) uses a Surface Condition Rating (SCR), ranging from0 to 100, to show pavement serviceability (GDOT, 1996; McQueenand Timm, 2005), while the New Mexico Department of Transpor-tation (NMDOT, 2009) evaluates pavement conditions by calculat-ing a Pavement Serviceability Index (PSI) to indicate overallpavement conditions based on roughness and surface distressmeasurements (Bogus et al., 2010a,b).

Based on these pavement condition performance indices, con-siderable studies have been conducted to better understand pave-ment condition quantification and change tendencies. Eldin andSenouci (1995) developed a pavement rating model based onback-propagation neural network techniques. Mahler et al.(1991) analyzed crack-induced pavement distress conditions usingimage data collected via an Automatic Crack Monitor (ACM) basedon image processing techniques. Zhang and Elaksher (2012) devel-oped image processing-based algorithms to quantify three dimen-sional details of pavement distress using unmanned aerial vehicle(UAV)-based image data. Ying and Salari (2010) proposed a pave-ment crack detection and classification method for crack analysis,and Lajnef et al. (2011) introduced a pavement condition monitor-ing system to quantify traffic effects on road-surface conditionsbased on combined smart sensing and data interpretation tech-niques. Deshpande et al. (2010) proposed an optimization modelfor pavement maintenance scheduling based on the pavement reli-ability and rehabilitation priority.

On the other hand, spatial and temporal analysis has beenwidely applied in transportation research fields. Wang et al.(2012) developed a Dynamic Spatial Multinomial Probit (DSMNP)

150 C. Chen et al. / Journal of Transport Geography 38 (2014) 148–158

model to analyze the parcel-level change of land use in Austin,Texas. Shaw and Xin (2003) proposed a spatiotemporal frameworkfor investigating the interaction between land use and traffic oper-ations, and indicated that the framework could provide criticalinformation to enhance traffic operations. Stern (2004) exploredtemporal–spatial patterns of traffic congestion and concluded thatoccasional severe congestions are not stable and congestion fluctu-ations are correlated to individual travelers. Datla and Sharma(2008) assessed the temporal–spatial features of vehicle volumevariance under cold and snow conditions. Li et al. (2007) studiedtemporal–spatial patterns to analyze crash risks, while Shahinet al. (1998) proposed a simplified framework to link pavementdata with Computer Aided Design (CAD) maps. Pantha et al.(2010) developed a GIS-based highway maintenance optimizationmodel using IRI as the pavement condition index. Some temporaland spatial analyses on Pavement Management Systems (PMS)have been conducted. Sirvio and Hollmén (2008) employed theMarkov Chain and artificial neural networks to forecast pavementconditions from a spatio-temporal perspective and concluded thatthe Markov Chain is superior to artificial neural networks. Tsaiet al. (2011) developed an integrated PMS based on GIS andimage-detection, allowing spatially synthesized pavement life-cycle activities and enhancing pavement maintenance. Barba andDelagnes (2000) proposed a spatio-temporal defect detectionmethod via video image processing and proved that it producedbetter discrimination of distressed and regular textured pavementsthan ordinary spatial analysis. Lin et al. (2013) developed a spatio-temporal database updatable according to real time changes andapplied it to urban PMS to improve pavement managementeffectiveness.

These existing studies provide insightful and comprehensiveunderstanding of pavement serviceability performance indicesand pavement distress quantification. However, few previous stud-ies have been conducted for extracting and discovering pavementdistress patterns and data across both temporal and spatialdomains. The authors were motivated to conduct this study toaddress this gap and to elaborate temporal–spatial distresscharacteristics.

3. Data collection, process, and visualization

3.1. Corridor selection and data collection

As the fifth largest state in the United States (121,589 squaremiles), New Mexico is located in the southwest and has a popula-tion of just over two million. In order to obtain pavement distressand roughness condition data and to understand pavement deteri-oration more effectively, NMDOT (NMDOT, 2009) collected pave-ment distress data based on manual surveys and roughness datausing inertial profilers from 2006 to 2009. These data were usedto calculate PSI in order to illustrate pavement conditions anddetermine a time schedule for pavement maintenance throughthe allocation of funding and manpower resources (Bogus et al.,2010a,b). NMDOT collects distress data on over 15,000 miles ofroadway statewide based on visual surveys, although the evalua-tors only collect pavement distress data for the rightmost drivinglane. NMDOT’s current manual data collection protocol clarifiesroute directions for pavement distress data collection and record-ing (Bandini et al., 2012). For east–west running routes, the east-bound is defined as the positive direction and the westbound isregarded as the negative direction. For roads running north–south,the positive direction is the northbound and the negative directionis the southbound. For multiple-lane routes, rightmost drivinglanes in both the positive and negative directions are evaluated.For two-lane routes, only the positive direction is used for data

collection. The sample session for pavement distress evaluation,defined by NMDOT, is one-tenth of each mile in the designatedlane, either starting at each milepost marker for the positive direc-tion lane or ending at each milepost marker for the negative direc-tion lane, with its width equal to the width of the designated lane.During the evaluation, the evaluators visually inspect the pave-ment condition of each sample section and record the severityand extent of each type of distress. In this study, eight major cor-ridors are selected within the state to represent the statewideroadway network, including Interstate 10 (I-10), 25 (I-25), and 40(I-40), along with U.S. 60, 64, 285, 380, and 550 (see Fig. 1). Allthese roadways consist of flexible pavement. In order to keep dataconsistent and better represent pavement conditions, both thepositive and negative directions of interstate highways (I-10, I-25, I-40) are included in this study; for U.S. routes (60, 64, 285,380, and 550), only the positive direction is used in this study.

3.2. PSI calculation

PSI has been widely utilized as a composite index to quantifypavement conditions, and is composed of two parts: pavementroughness and surface distress. The formula used by NMDOT tocalculate PSI values is as follows (Bandini et al., 2012):

PSI ¼0:0416666; if X � 600:0625ðX � 60Þ½ � þ 2:4999; if X > 60

�ð1Þ

where,

X ¼ 100� ð0:6 � ðIRI � 25Þ þ 0:4DRÞ=2:9 ð2Þ

where IRI is the International Roughness Index and DR denotes thedistress rate. DR is defined as follows:

DR ¼Xn

i¼1

DRi ¼Xn

i¼1

SiEiWi ð3Þ

where n represents the number of distress types for flexible pave-ment (n = 8 for flexible pavements), Si is the severity rating of dis-tress type i, Ei is the extent factor, Wi is the correspondingweighting factor, and DRi is the distress rate value for distress typei for a certain pavement section. Based on the NMDOT criteria, theratings for both severity and extent are provided as integer values:0 (None), 1 (Low), 2 (Medium), 3 (High). The extent factors are con-verted from extent ratings and are specified in Table 1, togetherwith the weighting factors (Bandini et al., 2012). In this study, thepavement distress data are concentrated and the range of DR is from0 to 243 based on Eq. (3) and Table 1. The lower the DR value, thebetter the pavement condition for a specific roadway section.

3.3. Distress data visualization

This study utilizes flexible pavement data collected in NewMexico from 2006 to 2009 to analyze spatial distress conditioncharacteristics of the statewide major corridors and their temporalcorrelations over years. The severity and extent ratings for eachtype of distress are provided for each milepost. The extent factoris calculated for each type of distress based on the specific valuesshown in Table 1 and, therefore, the single DRi value for each typeof distress and the comprehensive DR value also can be calculatedfor each milepost.

This research also employs a statewide GIS-based milepostdatabase as the basis for presenting and analyzing the distressdata. To facilitate the data processing, a single data file was createdfor each combination of directions and roadway names respec-tively, i.e. I-25 negative direction, I-40 positive direction, U.S. 380positive direction, etc. Eleven data files were created and the soft-ware package, ArcGIS 10.1, was utilized to visualize distress data

Fig. 1. Selected routes for pavement distress condition analysis.


by connecting the distress database with the GIS-based milepostdatabase. The distress data were imported into the GIS databasewith classification techniques to visualize the distress conditions

Table 1Extent and weight factors for flexible pavements.

Distress type Weightfactor

Extentlevel

Extentrating

Extentfactor

Raveling andweathering

3 Low 1 0.3Medium 2 0.6High 3 1

Bleeding 2 Low 1 0.3Medium 2 0.6High 3 1

Rutting and shoving 14 Low 1 0.5Medium 2 0.8High 3 1

Longitudinal cracking 20 Low 1 0.7Medium 2 0.9High 3 1

Transverse cracking 12 Low 1 0.7Medium 2 0.9High 3 1

Alligator cracking 25 Low 1 0.7Medium 2 0.9High 3 1

Edge cracking 3 Low 1 0.5Medium 2 0.8High 3 1

Patching 2 Low 1 0.3Medium 2 0.6High 3 1

for each corridor. GIS-based analyses were also conducted to pres-ent the distress variation trends over time. As an example, Fig. 2shows representative distress condition distribution along thepositive directions of interstate corridors selected for this studyin 2007.

4. Temporal–spatial distress data modeling and analysis

4.1. Quantify the correlations of pavement distress measurements

As determined by Eq. (3), the range of the comprehensive DRvalue is from 0 to 243. Such a continuous integer value may con-tain too much variance information, and can be difficult to classify,compare, and visualize at an appropriate scale. Therefore, in thisstudy the DR values are classified into four categories based onthe NMDOT protocol, as shown in Table 2. The descriptive statisticsof discrete DR category data from 2006 to 2009 are shown inTable 3 and Table 4. As shown in Tables 3 and 4, Distress Type Iaccounts for 75–90% of the entire pavement conditions, and TypeII occupies about 10–20%, which show favorable pavement condi-tions statewide. From 2006 to 2009, the overall pavement condi-tions had deteriorated slightly. The percentage of Distress Type Idecreased and that of Type II and Type III increased. U.S. 64, whichruns near the northern state boundary and crosses the entire state,has the worst pavement condition among the eight selected corri-dors. We can see the obvious roadway condition improvementsverified by the data, whereby the less-deteriorated condition pro-portion increased and the more severely damaged condition pro-portion declined due to significant pavement maintenance andrehabilitation on I-40 from 2008 to 2009.

Table 2Distress rate categories.

Type DR range

I 0 6 DR 6 61II 61 < DR 6 122III 122 < DR 6 183IV 183 < DR 6 243


In order to analyze the correlations among the eight specificdistress measurements – e.g., raveling and weathering, bleeding,rutting and shoving, longitudinal cracking, transverse cracking,alligator cracking, edge cracking, and patching – the correlationcoefficients are calculated for each pair of measurements for theirlinear associations during the entire four-year time period. The sta-tistical software tool, SAS 9.3, is used to identify these correlations.Tables 5–7 show the results of Pearson product-moment correla-tion, Spearman’s rank correlation, and Kendall tau rank tests.

In these three correlation test methods, Pearson product-moment correlation is used to measure linear dependencebetween two variables (Rodgers and Nicewander, 1988); Spear-man’s rank correlation is a non-parametric measurement of statis-tic dependence between two variables, quantifying how well thetwo variables can be related by a monotonic function (Myers andWell, 2003); Kendall tau rank correlation assesses the rank correla-tion between two variables and their similarity of the ordering(Kruskal, 1958). Spearman’s rank correlation and Kendall tau rankcorrelation are both rank correlation analysis methods to measurethe concordance of two variables in their change of values, withouta linear relationship assumption. To apply Kendall tau rank corre-lation to this study, we can consider that the pavement distresscondition evaluation is a ranking procedure, during which theinspectors will grade (0–3) the sample pavement section for eachof eight distress measurements. Kendall tau rank correlation veri-fies the rank correlation between two distress measurements andthe similarity of their ordering. For example, a significant, positiveKendall tau rank correlation coefficient between two measure-ments (bleeding, and raveling and weathering) indicates a strongsimilarity in their rankings. Thus, if the bleeding severity increasesalong a certain roadway segment, the pavement condition is verylikely to have more problems in terms of raveling and weathering.In our study 14,984 samples are considered over four years. Tables5–7 illustrate the estimated correlation coefficients and their cor-responding significant levels, p-value, for the three criteria. Foreach pair of pavement distress measurements, the value in theupper cell is the correlation coefficient, and the value in the lowercell is the corresponding p-value.

The sign of correlation coefficients indicates whether the mea-surement is positively or negatively related, and the magnitudedetermines the correlation strength. The significance test is con-

Fig. 2. Pavement distress condition distribution visualization on interstates inpositive directions in 2007.

ducted and verifies that there exist significant linear relationshipsbetween any pair of the distress measurements at the p = 0.01level. However, all of the correlation coefficients are lower than0.6 and most of them are very close to zero. The interpretationsof correlation strength are usually conditional and arbitrary, eventhough several guidelines are provided (Buda and Jarynowski,2010; Cohen, 1988). This research applies generally accepted crite-ria: none/weak (0–0.30 or �0.30–0), medium (0.30–0.70 or �0.70to �0.30), and strong (0.70–1.00 or �1.00 to �0.70) (Buda andJarynowski, 2010) to assess the correlation strength between anypair of the distress measurements. There are no strong linear asso-ciations between any of the eight distress measurements, and mostof them are not linearly correlated, with the exceptions that thecorrelations between alligator cracking and transverse cracking,alligator cracking and longitudinal cracking, and transverse crack-ing and longitudinal cracking lie in the medium category. Similardistress measurement correlation examination results can beobtained using the Spearman’s rank correlation and the Kendalltau rank correlation test criteria, as illustrated in Tables 6 and 7.Table 6 shows that the relationships between any of the two mea-surements cannot be formulated using a monotonic function, andTable 7 indicates that no significant ordering similarities existbetween any of the two distress measurements.

These results illustrate that the eight specific distress measure-ments are not statistically strongly correlated based on the pro-posed interpretation criteria at the p = 0.01 level. It is necessaryto collect all eight distress measurements to reflect overall pave-ment conditions comprehensively due to their strongindependence.

4.2. Temporal linear regression model development

Based on the fundamental descriptive statistics of pavementdistress data across multiple years shown in Tables 3 and 4, overallpavement conditions have gradually deteriorated without pave-ment maintenance and rehabilitation. It is of practical importanceto extract the temporally changing patterns of distress data andbetter elaborate and understand their deterioration tendency foroptimal pavement design and maintenance. It is indisputable thattraffic and environmental factors, such as traffic composition,truck-axle loading and temperature, have significant impacts onpavement distress conditions over the years. However, the aim ofthis temporal analysis is to examine variation in pavement condi-tion over several years. Therefore, the distress data for the previousyear is the only explanatory variable in the model specification,and traffic and environmental factors are excluded from modeling.For some roadway segments along which pavement rehabilitationand improvement were conducted during the evaluation periodfrom 2006 to 2009, further analysis is performed to screen thesesegments out. Eventually, 585 roadway segments were selectedfor temporal pavement distress regression model development.Three linear regression models were established as follows:

DR2009 ¼ 12:41þ 1:07DR2008 ð4ÞDR2008 ¼ 9:49þ 1:05DR2007 ð5ÞDR2007 ¼ 7:02þ 1:11DR2006 ð6Þ

Table 3Descriptive statistics of DR data of four categories for 2006 and 2007.

2006 2007

Type I Type II Type III Type IV Type I Type II Type III Type IV

I-10-M Number of miles 134 18 1 0 140 9 0 0Percentage 87.58% 11.76% 0.65% 0.00% 93.96% 6.04% 0.00% 0.00%

I-10-P Number of miles 124 28 1 0 143 6 0 0Percentage 81.05% 18.30% 0.65% 0.00% 95.97% 4.03% 0.00% 0.00%





U.S. 60 Number of miles 317 30 2 0 246 99 21 0Percentage 90.83% 8.60% 0.57% 0.00% 67.21% 27.05% 5.74% 0.00%


U.S. 285 Number of miles 286 29 1 0 258 35 17 0Percentage 90.51% 9.18% 0.32% 0 83.23% 11.29% 5.48% 0.00%




where DR2009, DR2008, and DR2007 are the continuous distress ratesfor 2009, 2008 and 2007, respectively. In Eq. (4), DR2009 can be esti-mated only based on DR2008, whose coefficient is 1.07 at the signif-icant level of p = 0.01. The constant is 12.41 and its p-value is 0.01.Similar patterns can be observed for Eqs. (5) and (6), and indicatethe distress rate is only significantly related to the data of theimmediately previous year. The R-Squares are 0.7272, 0.7511 and0.6776 for the 2009, 2008 and 2007 models, respectively. All theindependent variables’ coefficients are close to 1, and the constantsincrease slightly from 7.02 to 12.41. These results indicate that thepavement deterioration rate has accelerated due to the potentialimpact factors, such as increasing vehicle miles traveled, increasingtruck operations and unpredictable weather conditions. Addition-ally, since the regression model estimated, based on the multiple-year distress data, has only one significant explanatory variable,the distress data from the previous year, this illustrates that thepavement condition deterioration is a Markov process. A Markovprocess is characterized as a random process of memoryless, show-ing that the current state is only dependent on the immediately pre-vious state of the process (Ross, 2014). The specific property ofmemorylessness in a Markov process is called the Markov property(Ross, 2014). In this research, the pavement distress condition interms of the DR value is only significantly related to that of itsimmediately previous year, which possesses the Markov property.

4.3. GIS-based spatial data visualization and analysis

The distress data characteristics are also visualized using ArcGIS10.1. The yearly DR differences are color-coded in Fig. 3. The red5

color represents a significant DR increase from the previous year,which means that pavement conditions deteriorated considerably.A yellow color means that pavement conditions maintained at thesame level, and a green color denotes pavement improvement andrehabilitation occurred. The magnitude of DR difference is reflectedby color depth. Some distinguishable roadway segments with signif-icant pavement deterioration and rehabilitation can be clearlyobserved in Fig. 3. Some segments along northern I-25 are coded

5 For interpretation of color in Fig. 3, the reader is referred to the web version othis article.

f

with green in Fig. 3(a), showing the corresponding minor pavementimprovement efforts undertaken in 2007, but pavement conditionsfor major routes deteriorated during 2006–2007 and 2007–2008,indicated by the widespread red color. Many roadway segmentsalong U.S. 64 are coded orange or red, illustrating substantial pave-ment deterioration over the first two-year time period. Using I-40as the boundary to separate the entire state into two parts, we canroughly observe that the roadway pavement conditions in the north-ern part change more severely than those in the southern part,where the pavement conditions were well maintained and no signif-icant deterioration occurred. In the Albuquerque metropolitan area,the pavement conditions continuously deteriorated from 2006 to2008. Substantial pavement rehabilitation was conducted in 2009,especially on U.S. 64 and I-40 in and to the west of the Albuquerquearea, which is visualized by the green in Fig. 3(c).

Pavement distress conditions are also studied and their spatialdistributions are visualized by GIS techniques. As indicated byFig. 3, the pavement conditions of major routes continued to dete-riorate between 2006 and 2008, although significant progress wasmade to improve the pavement condition in 2009. Therefore, pave-ment distress conditions in 2006 and 2008, shown in Fig. 4, wereselected for the discussion of pavement distress spatial distributionbefore and after deterioration. If we still use I-40 as the boundary,some DR spatial distribution patterns can be extracted. Overall, thecorridors in the northern part show more yellow–red than ones inthe southern part, which demonstrates that relatively favorablepavement conditions are observed in the southern part. For urbanareas, such as Albuquerque, Santa Fe, Farmington, Raton, Roswelland Tucumcari, pavement conditions are worse than those in ruralareas, where less vehicle miles are traveled and fewer truckingoperations occur as shown in Fig. 4(b).

Interstate highways I-10, I-25 and I-40 are major corridors car-rying a significant amount of traffic and we found that there areobvious similarities in DR spatial distributions on interstate high-ways in the positive and negative directions. Fig. 4 also showsthe pavement conditions of the positive direction of the threeinterstates in New Mexico. I-25 is the major corridor connectingColorado to Mexico. Its northern sections had undesirable pave-ment conditions in 2008, illustrated frequently by yellow or red,compared to the southern part. Some severe pavement conditions

Table 4Descriptive statistics of DR data of four categories for 2008 and 2009.

2008 2009

Type I Type II Type III Type IV Type I Type II Type III Type IV












Table 5Pearson product-moment correlation coefficients.


Bleeding Rutting andshoving

Alligatorcracking

Transversecracking

Longitudinalcracking

Edgecracking

Patching


Correlationcoefficient: q

1.00 0.04 0.14 0.21 0.16 0.14 0.17 0.05

Significant Level:p-value

<.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Bleeding q 0.04 1.00 0.30 0.05 0.04 0.10 0.01 0.07p-value <.0001 <.0001 <.0001 <.0001 <.0001 0.4022 <.0001

Rutting andshoving

q 0.14 0.30 1.00 0.20 0.21 0.35 0.16 0.26p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Alligator cracking q 0.21 0.05 0.20 1.00 0.52 0.49 0.46 0.15p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Transversecracking

q 0.16 0.04 0.21 0.52 1.00 0.53 0.38 0.16p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


q 0.14 0.10 0.35 0.49 0.53 1.00 0.34 0.27p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Edge cracking q 0.17 0.01 0.16 0.46 0.38 0.34 1.00 0.16p-value <.0001 0.4022 <.0001 <.0001 <.0001 <.0001 <.0001

Patching q 0.05 0.07 0.26 0.15 0.16 0.27 0.16 1.00p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


are observed on I-10, especially around the urban area in Deming,Luna County. Segments of I-40 around urban areas such as Gallup,Grants, Albuquerque, and Tucumcari had more severely damagedpavement surfaces in 2008, although they were substantiallyimproved by NMDOT pavement maintenance and rehabilitationefforts in 2009.

5. Result discussions and research limitations

Based on the modeling results for pavement distress data col-lected on eight major corridors in New Mexico from 2006 to2009, specific correlation relationships between different types of

distress measurements were quantified and regression modelswere established to characterize their spatio-temporal changingtendency. The correlation analysis results provided by three differ-ent correlation criteria indicate that there are not strong associa-tions between any pair of distress measurements based on theproposed criteria. Therefore, it is not appropriate to estimate anyspecific distress measurement through the others based on linearmodels. The analysis verifies that the current distress data collec-tion protocol is proper and necessary to measure all eight typesof distress data to evaluate comprehensively pavement conditions.The correlation analysis also illustrates that implicit relationshipsshould be further investigated between any two of alligator crack-ing, transverse cracking, and longitudinal cracking based on the

Table 6Spearman’s rank correlation coefficients.



Alligatorcracking

Transversecracking


Edgecracking

Patching



1.00 �0.02 0.05 0.13 0.08 0.06 0.09 �0.01

Significant level:p-value

0.0390 <.0001 <.0001 <.0001 <.0001 <.0001 0.2033

Bleeding q �0.02 1.00 0.25 0.05 0.06 0.08 0.03 0.09p-value 0.0390 <.0001 <.0001 <.0001 <.0001 0.0013 <.0001

Rutting andshoving

q 0.05 0.25 1.00 0.18 0.20 0.32 0.16 0.22p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


Transversecracking

q 0.08 0.06 0.20 0.48 1.00 0.50 0.37 0.14p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


q 0.06 0.08 0.32 0.46 0.50 1.00 0.31 0.21p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


Patching q �0.01 0.09 0.22 0.13 0.14 0.21 0.12 1.00p-value 0.2033 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001

Table 7Kendall tau rank correlation coefficients.



Alligatorcracking

Transversecracking


Edgecracking

Patching



1.00 �0.02 0.05 0.11 0.07 0.06 0.08 �0.01

Significant level:p-value

0.0365 <.0001 <.0001 <.0001 <.0001 <.0001 0.2010

Bleeding q �0.02 1.00 0.23 0.04 0.05 0.08 0.02 0.08p-value 0.0365 <.0001 <.0001 <.0001 <.0001 0.0012 <.0001

Rutting andshoving

q 0.05 0.23 1.00 0.15 0.17 0.29 0.14 0.21p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


Transversecracking

q 0.07 0.05 0.17 0.41 1.00 0.45 0.32 0.13p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


q 0.06 0.08 0.29 0.40 0.45 1.00 0.28 0.20p-value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


Patching q �0.01 0.08 0.21 0.11 0.13 0.20 0.11 1.00p-value 0.2010 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001


relatively large absolute values of the corresponding correlationcoefficients shown in Tables 5–7.

Significant unique distress distribution patterns are alsoobserved in the temporal and spatial domains. The linear regres-sion models show that the distress condition for a specific roadwaysegment is only significantly related to its condition in the imme-diately previous year, which can be characterized with a Markovprocess if there is no external interference such as pavement main-tenance and rehabilitation. The coefficients of the explanatory vari-ables are identically around 1.00, but the constants increaseconsistently from 7.02 to 12.41 in three linear models. The modelresults indicate that one year’s pavement distress data share anapproximately constant weight in predicting next year’s pavementconditions, but annual pavement deterioration rates accelerate.This could be attributed to the fact that traffic volumes, vehiclemiles traveled, and trucking operations have increased continu-ously in recent years, which caused more severe pavement

damage. Such findings can be further confirmed by the spatial pat-terns for pavement distress condition distribution extracted byvisualized comparisons across multiple years as shown in Fig. 3,and in the same year as shown in Fig. 4. These visualized compar-isons highlight the pavement condition deterioration tendency aswell as the rehabilitation and maintenance efforts undertaken dur-ing a certain time period.

Overall, pavement distress condition distributions maintain thesame patterns over the four-year analysis time period, even thoughsurface condition had deteriorated gradually. If I-40 is used as theboundary, the corridors in the northern part of the state includingI-40 have worse pavement conditions than those in the southernpart. This can be explained by the fact that a more intensive citydensity generates more regular traffic and freight movements,leading to more severe damage to pavements in the northern part.There is significant difference in statewide distress conditionsbetween rural and urban areas, as shown in Fig. 4. It also illustrates

Fig. 3. DR change pattern visualization: (a) DR change pattern from 2006 to 2007; (b) DR change pattern from 2007 to 2008; (c) DR change pattern from 2008 to 2009.


the same distinctions for segments of specific corridors in rural ormetropolitan areas. These findings indicate that further investiga-tions are needed to identify the quantitative relationship betweenpavement conditions and traffic volumes, vehicle miles traveled,vehicle compositions, and daily traffic operations, especially fortruck activities. Furthermore, since there are no substantial direc-tional traffic movements generated by one-way trade corridorson the interstate highway network, it is reasonable to observe asimilar distress distribution pattern between positive and negativedirections along interstate highways.

This study has some limitations that should be considered whengeneralizing and applying the proposed research methodology toother situations. Our research utilized four years of data to formu-late and analyze temporal–spatial pavement distress characteris-tics in New Mexico. More data, especially from multiple years,would be desirable to extract reliable and steady distress patterns.Due to limited accessibility to the IRI data, this study only uses dis-tress rates partially to represent pavement conditions, and rough-ness characteristics are not included. More comprehensiveinvestigations are suggested to involve both distress and rough-

ness data for PSI-based pavement-condition assessments. The pro-posed research method is applicable and transferable to othersituations. Different state Departments of Transportation (DOTs)and transportation agencies have similar pavement performanceindices, which generally consist of two components, DR and IRIdata (Turner, 1985; GDOT, 1996; WSDOT, 1999; McQueen andTimm, 2005; NMDOT, 2009). Although there might be differentapproaches measuring DR and IRI data, the proposed correlationanalysis is applicable for identifying the correlations amongdifferent distress measurements. Besides, the spatial and temporalanalyses, represented by regression models and GIS-based inter-pretations, could be employed to investigate pavement conditionsand deterioration patterns in the other states.

6. Conclusions

Roadway infrastructure is one of the key components necessaryto support a sustainable economy and community development.High quality pavement service condition is crucial to ensure trafficsafety and cost-efficient traffic operations. A good understanding of

Fig. 4. DR spatial distribution: (a) 2006 distress condition distribution; (b) 2008distress condition distribution.


pavement distress characteristics in both spatial and temporaldomains is important for optimal pavement design and mainte-nance. In this study, descriptive statistical analyses and GIS-baseddata visualization and interpretation were performed to extracttemporal-spatial pavement distress evolution patterns on eightmajor corridors in the state of New Mexico. After classifying pave-ment distress conditions into four categories based on the NMDOTprotocol, we are able to qualitatively assess the pavement condi-tions along the selected corridors. The pavement conditions on70–82% of the corridors are identified as Distress Type I(DR 6 61), which illustrates that the majority of roadway networksare characterized by a desirable pavement service condition. High-way U.S. 64 has the worst condition and the Distress Type I seg-ments only account for 55–65% of the entire roadway. Thesefindings provide necessary information for pavement maintenanceand rehabilitation project prioritization. In addition, correlationsare investigated among eight different specific distress measure-ments for flexible pavements, with results indicating that it is nec-essary to collect all the measurements in order to quantifypavement distress conditions comprehensively.

Three regression models were also established to extract tem-poral distress condition change tendencies. The model results indi-cate the distress deterioration possesses a Markov property andthe deterioration rate accelerates over time. The distress character-istics are also examined spatially. Overall, pavement distress con-dition distributions maintained the same patterns over the four-year analysis time period, even though surface conditions haddeteriorated gradually. For the selected roadway network, thehighways in the northern part of the state are in worse pavementcondition than those in the southern part, due to more intensivecity density and, correspondingly, more regular traffic volumeand freight movements in the northern part. Similarly, route sec-tions located in metropolitan areas are associated with the worsepavement conditions compared to those in rural areas, indicatingthe significant impacts of traffic operations and compositions onpavement conditions, which should be explored further.

The approach proposed in this paper is also applicable to otherstate agencies, since they have similar pavement evaluationschemes, such as pavement condition indices and distress types.Although this research explored temporal-spatial pavement dis-tress characteristics, some further studies are desirable to enhanceour understanding of pavement distress deterioration patterns andthe data visualization and interpretation techniques needed toovercome limited access to external data sources. Furthermore,more investigations are needed to identify the quantitative rela-tionship between pavement conditions and traffic operations,especially for truck activities.

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