Nature of Modeling Boundary Pedestrian Crashes at Zones
Abstract
Traffic analysis zones are often delineated by the existing street network. This practice may result in a considerable number of crashes on or near zonal boundaries. Although the traditional macrolevel approach to crash modeling assigns zonal attributes to all crashes that occur within the zonal boundary, this paper acknowledges the inaccuracy resulting from relating crashes on or near the boundary of the zone to merely the attributes of that zone. This paper proposes a novel approach to account for the spatial influence of neighboring zones on crashes that occur specifically on or near the zonal boundaries. Predictive models for pedestrian crashes per zone were developed with a hierarchical Bayesian framework and with separate predictor sets for boundary and interior (nonboundary) crashes. The hierarchical Bayesian model that accounted for spatial autocorrelation was found to have better goodness-of-fit measures than did models that had no specific consideration for crashes located on or near the boundaries. In addition, the models were able to capture some unique predictors associated explicitly with interior and boundary-related crashes. For example, two variables, total roadway length with a posted speed of 35 mph and long-term parking cost, were not statistically significant from zero in the interior crash model but were significantly different from zero at the 95% level in the boundary crash model.
Get full access to this article
View all access and purchase options for this article.
References
1. Levine N., Kim K.E., and Nitz L. H. Spatial Analysis of Honolulu Motor Vehicle Crashes: II. Zonal Generators. Accident Analysis and Prevention, Vol. 27, No. 5, 1995, pp. 675–685.
2. Hadayeghi A., Shalaby A.S., Persaud B.N., and Cheung C. Temporal Transferability and Updating of Zonal Level Accident Prediction Models. Accident Analysis and Prevention, Vol. 38, No. 3, 2006, pp. 579–589.
3. Hadayeghi A., Shalaby A.S., and Persaud B. N. Development of Planning-Level Transportation Safety Models Using Full Bayesian Semiparametric Additive Techniques. Journal of Transportation Safety and Security, Vol. 2, No. 1, 2010, pp. 45–68.
4. Washington S., Schalkwyk I.V., You D., Shin K., and Samuelson J. P. PLANSAFE User Manual: Forecasting the Safety Impacts of Socio-Demographic Changes and Safety Countermeasures (CD-ROM). CRP-CD-78. NCHRP, Transportation Research Board of the National Academies, Washington, D.C., 2010. http://www.trb.org/Main/Blurbs/PLANSAFE_Forecasting_the_Safety_Impacts_of_SocioDe_163790. aspx. Accessed April 11, 2011.
5. Lovegrove G., and Sayed T. Macro-Level Collision Prediction Models for Evaluating Neighborhood Traffic Safety. Canadian Journal of Civil Engineering, Vol. 33, No. 5, 2006, pp. 609–621.
6. Lovegrove G.R., and Sayed T. A. Macrolevel Collision Prediction Models to Enhance Traditional Reactive Road Safety Improvement Programs. In Transportation Research Record: Journal of the Transportation Research Board, No. 2019, Transportation Research Board of the National Academies, Washington, D.C., 2007, pp. 65–73.
7. Lovegrove G., Lim C., and Sayed T. Community-Based, Macrolevel Collision Prediction Model Use with a Regional Transportation Plan. Journal of Transportation Engineering, Vol. 136, No. 2, 2010, pp. 120–128.
8. Khondakar B., Sayed T., and Lovegrove G. Transferability of Community-Based Collision Prediction Models for Use in Road Safety Planning Applications. Journal of Transportation Engineering, Vol. 136, No. 10, 2010, pp. 871–880.
9. Quddus M. A. Modelling Area-Wide Count Outcomes with Spatial Correlation and Heterogeneity: An Analysis of London Crash Data. Accident Analysis & Prevention, Vol. 40, No. 4, 2008, pp. 1486–1497.
10. Noland R.B., and Quddus M. A. Analysis of Pedestrian and Bicycle Casualties with Regional Panel Data. In Transportation Research Record: Journal of the Transportation Research Board, No. 1897, Transportation Research Board of the National Academies, Washington, D.C., 2004, pp. 28–33.
11. LaScala E.A., Gerber D., and Gruenewald P. J. Demographic and Environmental Correlates of Pedestrian Injury Collisions: A Spatial Analysis. Accident Analysis & Prevention, Vol. 32, No. 5, 2000, pp. 651–658.
12. Loukaitou-Sideris A., Liggett R., and Sung H.-G. Death on the Crosswalk: A Study of Pedestrian–Automobile Collisions in Los Angeles. Journal of Planning Education and Research, Vol. 26, No. 3, 2007, pp. 338–351.
13. Wier M., Weintraub J., Humphreys E.H., Seto E., and Bhatia R. An Area-Level Model of Vehicle–Pedestrian Injury Collisions with Implications for Land Use and Transportation Planning. Accident Analysis & Prevention, No. 41, No. 1, 2009, pp. 137–145.
14. Cottrill C.D., and Thakuriah P. Evaluating Pedestrian Crashes in Areas with High Low-Income or Minority Populations. Accident Analysis & Prevention, Vol. 42, No. 6, 2010, pp. 1718–1728.
15. Aguero-Valverde J., and Jovanis P. P. Spatial Analysis of Fatal and Injury Crashes in Pennsylvania. Accident Analysis and Prevention, Vol. 38, No. 3, 2006, pp. 618–625.
16. Huang H., Abdel-Aty M. A., and Darwiche A. L. County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling. In Transportation Research Record: Journal of the Transportation Research Board, No. 2148, Transportation Research Board of the National Academies, Washington, D.C., 2010, pp. 27–37.
17. Noland R. B. Traffic Fatalities and Injuries: The Effect of Changes in Infrastructure and Other Trends. Accident Analysis and Prevention, Vol. 35, No. 4, 2003, pp. 599–611.
18. Kim K., Brunner I.M., and Yamashita E. Y. Influence of Land Use, Population, Employment, and Economic Activity on Accidents. In Transportation Research Record: Journal of the Transportation Research Board, No. 1953, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 56–64.
19. Fotheringham A.S., and Wegner M. Spatial Models and GIS: New Potential and New Models. Taylor & Francis, London, 2000.
20. Huang H., and Abdel-Aty M. Multilevel Data and Bayesian Analysis in Traffic Safety. Accident Analysis & Prevention, Vol. 42, No. 6, 2010, pp. 1556–1565.
21. El-Basyouny K., and Sayed T. Collision Prediction Models Using Multivariate Poisson-Lognormal Regression. Accident Analysis and Prevention, Vol. 41, No. 4, 2009, pp. 820–828.
22. El-Basyouny K., and Sayed T. Application of Generalized Link Functions in Developing Accident Prediction Models. Safety Science, Vol. 48, No. 3, 2010, pp. 410–416.
23. Lawson A.B., Browne W.J., and Rodiero C. L. V. Disease Mapping with WinBUGS and MLwiN. John Wiley & Sons, Oxford, United Kingdom, 2003.
24. Besag J. Spatial Interaction and the Statistical Analysis of Lattice Systems. Journal of the Royal Statistical Society B, Vol. 36, No. 2, 1974, pp. 192–236.
25. Haleem K., Abdel-Aty M., and Mackie K. Using a Reliability Process to Reduce Uncertainty in Predicting Crashes at Unsignalized Intersections. Accident Analysis & Prevention, Vol. 42, No. 2, 2010, pp. 654–666.
26. Carter D.L., and Council F. M. Factors Contributing to Pedestrian and Bicycle Crashes on Rural Highways. Presented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.
27. Tignor S.C., and Warren D. Driver Speed Behavior on U.S. Streets and Highways. In 1990 Compendium of Technical Papers, ITE 60th Annual Meeting, Orlando, Fla., 1990.
Cite article
Cite article
Cite article
OR
Download to reference manager
If you have citation software installed, you can download article citation data to the citation manager of your choice
Information, rights and permissions
Information
Published In
Article first published online: January 1, 2012
Issue published: January 2012
Authors
Metrics and citations
Metrics
Journals metrics
This article was published in Transportation Research Record: Journal of the Transportation Research Board.
VIEW ALL JOURNAL METRICSArticle usage*
Total views and downloads: 89
*Article usage tracking started in December 2016
Altmetric
See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores
Articles citing this one
Receive email alerts when this article is cited
Web of Science: 0
Crossref: 27
- A crash feature-based allocation method for boundary crash problem in ...
- Assessing the effectiveness of built environment-based safety measures...
- Tabu-Search-Based Combinatorial Subset Selection Approach to Support I...
- A multiple membership multilevel negative binomial model for intersect...
- Use of a model-based gradient boosting framework to assess spatial and...
- Impact of road features on shared e-scooter trip volume: A study based...
- Safety impacts of the discrepancies and accesses between adjacent traf...
- Assessing the Effectiveness of Built Environment-based Safety Measures...
- Multiple membership multilevel model to estimate intersection crashes
- Insights from Integrated Geo-Location Data for Pedestrian Crashes, Dem...
- A review of spatial approaches in road safety
- Integrating macro- and micro-level safety analyses: a Bayesian approac...
- Investigation of the consequences of the modifiable areal unit problem...
- Boundary crash data assignment in zonal safety analysis: An iterative ...
- Investigating the safety impact of roadway network features of suburba...
- The modifiable areal unit problem in traffic safety: Basic issue, pote...
- Hierarchical ordered model for injury severity of pedestrian crashes i...
- Comparative analysis of zonal systems for macro-level crash modeling
- Revisiting crash spatial heterogeneity: A Bayesian spatially varying c...
- Macro and micro models for zonal crash prediction with application in ...
- Road network safety evaluation using Bayesian hierarchical joint model
- Evaluating Long-Range Regional Safety with Scenario Planning Analysis
- Geographical Boundary Dependency Versus Roadway Hierarchy in Macroscop...
- A framework of boundary collision data aggregation into neighbourhoods
- Multivariate crash modeling for motor vehicle and non-motorized modes ...
- Development of zone system for macro-level traffic safety analysis
- Planning-Level Model for Assessing Pedestrian Safety
Figures and tables
Figures & Media
Tables
View Options
Get access
Access options
If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:
loading institutional access options
Alternatively, view purchase options below:
Purchase 24 hour online access to view and download content.
Access journal content via a DeepDyve subscription or find out more about this option.
