IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v38y2014icp148-158.html
   My bibliography  Save this article

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

Author

Listed:
  • Chen, Cong
  • Zhang, Su
  • Zhang, Guohui
  • Bogus, Susan M.
  • Valentin, Vanessa

Abstract

Roadway networks, as part of transportation infrastructure, play an indispensable role in regional economies and community development. The high-quality pavement serviceability of these networks is essential to ensure safe, cost-effective daily traffic operations. In-depth analyses of network-wide pavement surface condition data are necessary inputs for optimal pavement design and maintenance, traffic safety enhancement, and sustainable traffic infrastructure system development. This study aims to investigate various pavement distress condition performance measurements and their correlations to better understand temporal–spatial characteristics of roadway distress based on pavement distress condition data collected in New Mexico from 2006 to 2009. Eight major corridors across various urban and rural areas were selected for analyzing pavement surface-distress conditions and discovering their intrinsic characteristics and patterns across both temporal and spatial domains. The results show that there are not strong correlations among different distress measurements, implying the rationality of the current pavement performance measurement protocol used by the state transportation agencies. Regression models were established and GIS-based spatial analyses were performed to extract temporal and spatial patterns of Distress Rate (DR) data. The model results illustrate significant correlations of the DR data on the 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 attributed to diverse geometric characteristics and traffic conditions, such as vehicle compositions and volumes and urban and rural areas. The research findings are helpful to understand the characteristics of pavement distress conditions more clearly and to optimize traffic infrastructure design and maintenance.

Suggested Citation

  • Chen, Cong & Zhang, Su & Zhang, Guohui & Bogus, Susan M. & Valentin, Vanessa, 2014. "Discovering temporal and spatial patterns and characteristics of pavement distress condition data on major corridors in New Mexico," Journal of Transport Geography, Elsevier, vol. 38(C), pages 148-158.
    • Handle: RePEc:eee:jotrge:v:38:y:2014:i:c:p:148-158
    DOI: 10.1016/j.jtrangeo.2014.06.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692314001197
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2014.06.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Toong Khuan Chan, 2009. "Measuring performance of the Malaysian construction industry," Construction Management and Economics, Taylor & Francis Journals, vol. 27(12), pages 1231-1244.
    2. Datla, Sandeep & Sharma, Satish, 2008. "Impact of cold and snow on temporal and spatial variations of highway traffic volumes," Journal of Transport Geography, Elsevier, vol. 16(5), pages 358-372.
    3. Kingham, Simon & Sabel, Clive E. & Bartie, Phil, 2011. "The impact of the ‘school run’ on road traffic accidents: A spatio-temporal analysis," Journal of Transport Geography, Elsevier, vol. 19(4), pages 705-711.
    4. Kim, Joseph H.T. & Hardy, Mary R., 2009. "Estimating the Variance of Bootstrapped Risk Measures," ASTIN Bulletin, Cambridge University Press, vol. 39(1), pages 199-223, May.
    5. Bo-Young Chang & Peter Christoffersen & Kris Jacobs & Gregory Vainberg, 2011. "Option-Implied Measures of Equity Risk," Review of Finance, European Finance Association, vol. 16(2), pages 385-428.
    6. Pantha, Bhoj Raj & Yatabe, Ryuichi & Bhandary, Netra Prakash, 2010. "GIS-based highway maintenance prioritization model: an integrated approach for highway maintenance in Nepal mountains," Journal of Transport Geography, Elsevier, vol. 18(3), pages 426-433.
    7. Bhattacharjee, Sutapa & Goetz, Andrew R., 2012. "Impact of light rail on traffic congestion in Denver," Journal of Transport Geography, Elsevier, vol. 22(C), pages 262-270.
    8. Frank Leung & Kevin Chow & Simon Chan, 2010. "Measures of trend inflation in Hong Kong," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy and the measurement of inflation: prices, wages and expectations, volume 49, pages 177-200, Bank for International Settlements.
    9. Dai, Dajun, 2012. "Identifying clusters and risk factors of injuries in pedestrian–vehicle crashes in a GIS environment," Journal of Transport Geography, Elsevier, vol. 24(C), pages 206-214.
    10. Wang, Xiaokun (Cara) & Kockelman, Kara M. & Lemp, Jason D., 2012. "The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data," Journal of Transport Geography, Elsevier, vol. 24(C), pages 77-88.
    11. Huang, Songshan (Sam), 2009. "Measuring Tourism motivation: Do Scales matter?," MPRA Paper 25198, University Library of Munich, Germany, revised 20 Apr 2009.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Song & Wei, Xiaoyan & Xia, Nan & Yan, Zhaojin & Yuan, Yi & Zhang, H. Michael & Li, Manchun & Cheng, Liang, 2019. "Understanding road performance using online traffic condition data," Journal of Transport Geography, Elsevier, vol. 74(C), pages 382-394.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amin, Shohel & Tamima, Umma & Amador-Jiménez, Luis E., 2019. "Optimal pavement management: Resilient roads in support of emergency response of cyclone affected coastal areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 45-61.
    2. Xilong Chen & Eric Ghysels, 2011. "News--Good or Bad--and Its Impact on Volatility Predictions over Multiple Horizons," The Review of Financial Studies, Society for Financial Studies, vol. 24(1), pages 46-81, October.
    3. Takumi Asada & Tran Vinh Ha & Mikiharu Arimura & Shuichi Kameyama, 2022. "A Novel Approach for Urban Road Network Maintenance Plans Using Spatial Autocorrelation Analysis and Roadside Conditions: A Case Study of Muroran City, Japan," Sustainability, MDPI, vol. 14(23), pages 1-17, December.
    4. Christoffersen, Peter & Jacobs, Kris & Chang, Bo Young, 2013. "Forecasting with Option-Implied Information," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 581-656, Elsevier.
    5. Lee, Shin S. & Senior, Martyn L., 2013. "Do light rail services discourage car ownership and use? Evidence from Census data for four English cities," Journal of Transport Geography, Elsevier, vol. 29(C), pages 11-23.
    6. Ali Enes Dingil & Federico Rupi & Domokos Esztergár-Kiss, 2021. "An Integrative Review of Socio-Technical Factors Influencing Travel Decision-Making and Urban Transport Performance," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    7. Kelly, J. Andrew & Fu, Miao, 2014. "Sustainable school commuting – understanding choices and identifying opportunities," Journal of Transport Geography, Elsevier, vol. 34(C), pages 221-230.
    8. Svetlana BAČKALIĆ & Dragan JOVANOVIĆ & Todor BAČKALIĆ & Boško MATOVIĆ & Miloš PLJAKIĆ, 2019. "The Application Of Reliability Reallocation Model In Traffic Safety Analysis On Rural Roads," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 14(1), pages 115-125, April.
    9. Prato, Carlo G. & Kaplan, Sigal & Patrier, Alexandre & Rasmussen, Thomas K., 2019. "Integrating police reports with geographic information system resources for uncovering patterns of pedestrian crashes in Denmark," Journal of Transport Geography, Elsevier, vol. 74(C), pages 10-23.
    10. Andrey, Jean & Hambly, Derrick & Mills, Brian & Afrin, Sadia, 2013. "Insights into driver adaptation to inclement weather in Canada," Journal of Transport Geography, Elsevier, vol. 28(C), pages 192-203.
    11. Vo Dang Khoa & Thanwadee Chinda, 2023. "Assessment of Construction Competitiveness through Knowledge Management Process Implementation," Sustainability, MDPI, vol. 15(22), pages 1-33, November.
    12. Chaogui Kang & Dongwan Fan & Hongzan Jiao, 2021. "Validating activity, time, and space diversity as essential components of urban vitality," Environment and Planning B, , vol. 48(5), pages 1180-1197, June.
    13. Bernales, Alejandro & Guidolin, Massimo, 2015. "Learning to smile: Can rational learning explain predictable dynamics in the implied volatility surface?," Journal of Financial Markets, Elsevier, vol. 26(C), pages 1-37.
    14. Ian W. R. Martin & Christian Wagner, 2019. "What Is the Expected Return on a Stock?," Journal of Finance, American Finance Association, vol. 74(4), pages 1887-1929, August.
    15. Jung, Soyoung & Qin, Xiao & Oh, Cheol, 2016. "Improving strategic policies for pedestrian safety enhancement using classification tree modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 53-64.
    16. DeMiguel, Victor & Plyakha, Yuliya & Uppal, Raman & Vilkov, Grigory, 2013. "Improving Portfolio Selection Using Option-Implied Volatility and Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(6), pages 1813-1845, December.
    17. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    18. Schadner, Wolfgang, 2021. "Ex-Ante Risk Factors and Required Structures of the Implied Correlation Matrix," Finance Research Letters, Elsevier, vol. 41(C).
    19. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    20. Yanmin Qi & Zuduo Zheng & Dongyao Jia, 2020. "Exploring the Spatial-Temporal Relationship between Rainfall and Traffic Flow: A Case Study of Brisbane, Australia," Sustainability, MDPI, vol. 12(14), pages 1-24, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jotrge:v:38:y:2014:i:c:p:148-158. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.