Skip to main content
SpringerLink
Log in

Role of street patterns in zone-based traffic safety analysis

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Although extensive analyses of road segments and intersections located in urban road networks have examined the role of many factors that contribute to the frequency and severity of crashes, the explicit relationship between street pattern characteristics and traffic safety remains underexplored. Based on a zone-based Hong Kong database, the Space Syntax was used to quantify the topological characteristics of street patterns and investigate the role of street patterns and zone-related factors in zone-based traffic safety analysis. A joint probability model was adopted to analyze crash frequency and severity in an integrated modeling framework and the maximum likelihood estimation method was used to estimate the parameters. In addition to the characteristics of street patterns, speed, road geometry, land-use patterns, and temporal factors were considered. The vehicle hours was also included as an exposure proxy in the model to make crash frequency predictions. The results indicate that the joint probability model can reveal the relationship between zone-based traffic safety and various other factors, and that street pattern characteristics play an important role in crash frequency prediction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. WHO. Global status report on road safety: Time for action [R]. Geneva: WHO, 2013.

    Google Scholar 

  2. RIFAAT S M, TAY R, DE BARROS A G, CHOI J S, PARK D J. Logistic model of injury risks in single vehicle crashes in urban neighborhoods [J]. Journal of Advanced Transportation, 2011, 45(3): 186–195.

    Article  Google Scholar 

  3. RIFAAT S M, TAY R. Effects of street pattern on injury risks in two-vehicle crashes [J]. Transportation Research Record, 2009, 2102: 61–67.

    Article  Google Scholar 

  4. RIFAAT S M, TAY R, DE BARROS A. Effect of street pattern on the severity of crashes involving vulnerable road users [J]. Accident Analysis and Prevention, 2011, 43(1): 276–283.

    Article  Google Scholar 

  5. RIFAAT S M, TAY R, de BARROS A. Severity of motorcycle crashes in Calgary [J]. Accident Analysis and Prevention, 2012, 49: 44–49.

    Article  Google Scholar 

  6. LOVEGROVE G R, SUN J. Using community-based macrolevel collision prediction models to evaluate safety level of neighborhood road network patterns [C]//Transportation Research Board 89th Annual Meeting. Washington, DC: TRB, 2010, 10–0535.

    Google Scholar 

  7. HILLIER B, HANSON J. The social logic of space [M]. UK: Cambridge University Press, 1984.

    Book  Google Scholar 

  8. HILLIER B. Space is the machine: A configurational theory of architecture [M]. UK: Cambridge University Press, 1996.

    Google Scholar 

  9. TRANSPORT DEPARTMENT. Travel Characteristics Survey 2002 Final Report [R]. Hong Kong: Hong Kong SAR Government, China, 2003.

    Google Scholar 

  10. PEI Xin, WONG S C, SZE N N. A joint-probability approach to crash prediction models [J]. Accident Analysis and Prevention, 2011, 43(3): 1160–1166.

    Article  Google Scholar 

  11. WANG Xue-song, TREMONT P J, CHEN Xiao-hong, JIN Yu, ABDEL-ATY M. Macrolevel model development for safety assessment of road network structures [J]. Transportation Research Record, 2012, 2280: 100–109.

    Article  Google Scholar 

  12. PRIYANTHA WEDAGAMA D M, BIRD R N, METCALFE A V. The influence of urban land-use on non-motorised transport casualties [J]. Accident Analysis and Prevention, 2006, 38(6): 1049–1057.

    Article  Google Scholar 

  13. XU Peng-peng, HUANG He-lai. Modeling crash spatial heterogeneity: Random parameter versus geographically weighting [J]. Accident Analysis and Prevention, 2015, 75: 16–25.

    Article  MathSciNet  Google Scholar 

  14. GLADHILL K, MONSERE C M. Exploring traffic safety and urban form in Portland, Oregon [J]. Transportation Research Record, 2012, 2318: 63–74.

    Article  Google Scholar 

  15. HAUER E. Speed and safety [J]. Transportation Research Record, 2009, 2103: 10–17.

    Article  Google Scholar 

  16. KIM K, PANT P, YAMASHITA E Y. Accidents and accessibility: Measuring the influences of demographic and land use variables inHonolulu, Hawaii [J]. Transportation Research Record, 2010, 2147: 9–17.

    Article  Google Scholar 

  17. OUYANG Y, BEJLERI I. Geographic information system-based community-level method to evaluate the influence of built environment on traffic crashes [J]. Transportation Research Record, 2014, 2432: 124–132.

    Article  Google Scholar 

  18. LI Y C, SZE N N, WONG S C. Spatial-temporal analysis of drink driving patterns in Hong Kong [J]. Accident Analysis and Prevention, 2013, 59: 415–424.

    Article  Google Scholar 

  19. LI Jun. Model and analyze Hong Kong street network using Space Syntax theory [M]. Qingdao: Qingdao University, 2007. (In Chinese)

    Google Scholar 

  20. PEI Xin, WONG S C, SZE N N. The roles of exposure and speed in road safety analysis [J]. Accident Analysis and Prevention, 2012, 48: 464–471.

    Article  Google Scholar 

  21. LAM W H K, HUNG W T, LO H K, LO H P, TONG C O, WONG S C, YANG H. Advancement of the annual traffic census in Hong Kong [J]. Proceedings of the Institution of Civil Engineers, Transport. 2003, 156: 103–115.

    Google Scholar 

  22. TONG C O, HUNG W T, LAM W H K, LO H K, LO H P, WONG S C, YANG H. A new survey methodology for the annual traffic census in Hong Kong [J]. Traffic Engineering and Control, 2003, 44: 214–218.

    Google Scholar 

  23. WONG W, WONG S C. Systematic bias in transport model calibration arising from the variability of linear data projection [J]. Transportation Research: Part B, 2015, 75: 1–18.

    Article  Google Scholar 

  24. WONG W, WONG S C. The effect variability in linear data projection on transport model calibration [C]//The 19th International Conference of Hong Kong Society for Transportation Stuelies (HKSTS), Hong Kong, China: HKSTS, 2014.

    Google Scholar 

  25. GARBER N J, GADIRAU R. Speed variance and its influence on crashes [R]. AAA Foundation for Traffic Safety, 1988.

    Google Scholar 

  26. National Research Council. Highway safety manual [M]. USA: American Association of State Highway and Transportation Officials, Washington, DC, 2010.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation, Tsinghua University, Beijing, 100084, China

    Qiang Guo  (郭强), Xin Pei  (裴欣) & Dan-ya Yao  (姚丹亚)

  2. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing, 210096, China

    Xin Pei  (裴欣) & Dan-ya Yao  (姚丹亚)

  3. Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China

    Sze-chun Wong  (黄仕进)

Authors
  1. Qiang Guo  (郭强)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Pei  (裴欣)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan-ya Yao  (姚丹亚)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sze-chun Wong  (黄仕进)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Pei  (裴欣).

Additional information

Foundation item: Project(71301083) supported by the National Natural Science Foundation of China; Project(2012AA112305) supported by the National High-Tech Research and Development Program of China; Project(2012CB725405) supported by the National Basic Research Program of China; Project(17208614) supported by the Research Grants Council of the Hong Kong Special Administrative Region, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Q., Pei, X., Yao, Dy. et al. Role of street patterns in zone-based traffic safety analysis. J. Cent. South Univ. 22, 2416–2422 (2015). https://doi.org/10.1007/s11771-015-2768-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-015-2768-3

Key words

Search

Navigation