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Abstract

The integration of crash data with vehicle probe speed segment data represents a potential advancement in traffic management and road safety. Traditional crash analysis often relies on aggregated or outdated information, limiting the ability to proactively identify and mitigate risk. This study presents a comprehensive and replicable framework for integrating crash data with segment-based vehicle probe speed data. The methodology employs spatial analysis techniques, using customized buffer distances based on functional roadway classification and lane count, to accurately link crashes with corresponding speed segments, achieving an integration success rate of 77% which demonstrating the approach’s reliability and its suitability for large-scale applications. The integrated dataset shows the potential for more precise identification of high-risk areas and may enhance an agency’s ability to develop proactive safety strategies. By addressing spatial and directional alignment challenges, this framework provides a practical pathway for multi-source data integration, offering valuable insights for regions seeking to strengthen traffic safety through real-time data use.

Keywords

data integration traffic management road safety speed crash data spatial analysis

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References

Elvik

Peter

Astrid

H. A.

Speed and Road Accidents: An Evaluation of the Power Model, 2024. https://www.toi.no/publications/speed-and-road-accidents-an-evaluation-of-the-power-model-article17882-29.html.

Kloeden

C. N.

McLean

Glonek

G. F. V.

Reanalysis of Travelling Speed and the Risk of Crash Involvement in Adelaide South Australia. Report. Australian Transport Safety Bureau, 2002. http://www.atsb.gov.au/publications/2002/pdf/Speed_Risk_3.pdf; https://digital.library.adelaide.edu.au/dspace/handle/2440/33715.

Martin

J.-L.

Relationship Between Crash Rate and Hourly Traffic Flow on Interurban Motorways. Accident Analysis & Prevention, Vol. 34, No. 5, 2002, pp. 619–629. https://doi.org/10.1016/S0001-4575(01)00061-6.

Nilsson

Traffic Safety Dimensions and the Power Model to Describe the Effect of Speed on Safety. Doctoral Thesis (monograph). Traffic Engineering, 2004. https://www.semanticscholar.org/paper/Traffic-Safety-Dimensions-and-the-Power-Model-to-of-Nilsson/5d9b35acb6fac92a7fa995c1849fd304ef0a4cca.

World Health Organization (WHO). Global Status Report on Road Safety 2018. World Health Organization, Geneva, Switzerland, 2019. https://www.who.int/publications/i/item/9789241565684.

Aarts

van Schagen

Driving Speed and the Risk of Road Crashes: A Review. Accident Analysis & Prevention, Vol. 38, No. 2, 2006, pp. 215–224. https://doi.org/10.1016/j.aap.2005.07.004.

Cui

Henriksson

Biancardo

S. A.

Wang

Establishing Multisource Data-Integration Framework for Transportation Data Analytics, 2020. https://doi.org/10.1061/JTEPBS.0000331.

Kadadi

Agrawal

Nyamful

Atiq

Challenges of Data Integration and Interoperability in Big Data. 2014 IEEE International Conference on Big Data (Big Data), 2014, pp. 38–40. https://doi.org/10.1109/BigData.2014.7004486.

Haroon

S. M.

Ryan

Understanding Key Factors in Automated Vehicle Collisions: Automating Data Extraction and Analyzing Key Insights Using Explainable AI. Journal of Transportation Safety & Security, 2024. https://www.tandfonline.com/doi/abs/10.1080/19439962.2024.2320630.

10.

Hosseinzadeh

Karimpour

Kluger

Orthober

Data Linkage for Crash Outcome Assessment: Linking Police-Reported Crashes, Emergency Response Data, and Trauma Registry Records. Journal of Safety Research, Vol. 81, 2022, pp. 21–35. https://doi.org/10.1016/j.jsr.2022.01.003.

11.

Valadkhani

A. H.

Hong

Ramezani

Integration of Loop and Probe Data for Traffic State Estimation on Freeway and Signalized Arterial Links. 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017, pp. 1–6. https://doi.org/10.1109/ITSC.2017.8317822.

12.

Kang

Scully

J. Y.

Stewart

Hurvitz

P. M.

Moudon

A. V.

Split-Match-Aggregate (SMA) Algorithm: Integrating Sidewalk Data with Transportation Network Data in GIS. International Journal of Geographical Information Science, Vol. 29, No. 3, 2015, pp. 440–453. https://doi.org/10.1080/13658816.2014.981191.

13.

Graettinger

A. J.

Qin

Spea

Parker

S. T.

Forde

Combining State Route and Local Road Linear Referencing System Information, 2009. https://doi.org/10.3141/2121-17.

14.

Imprialou

M.-I. M.

Quddus

Pitfield

D. E.

Lord

Re-Visiting Crash–Speed Relationships: A New Perspective in Crash Modelling. Accident Analysis & Prevention, Vol. 86, 2016, pp. 173–185. https://doi.org/10.1016/j.aap.2015.10.001.

15.

Zeng

Hao

Lee

Chen

Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis. International Journal of Environmental Research and Public Health, Vol. 17, No. 8, 2020, Article 8. https://doi.org/10.3390/ijerph17082768.

16.

Quddus

Wang

Yang

Impact of Data Aggregation Approaches on the Relationships Between Operating Speed and Traffic Safety. Accident Analysis & Prevention, Vol. 120, 2018, pp. 304–310. https://doi.org/10.1016/j.aap.2018.06.007.

17.

Garber

N. J.

Ehrhart

A. A.

Effect of Speed, Flow, and Geometric Characteristics on Crash Frequency for Two-Lane Highways, 2000. https://journals.sagepub.com/doi/abs/10.3141/1717-10.

18.

K. M.

Kockelman,

(eds.). Freeway Speeds and Speed Variations Preceding Crashes, Within and Across Lanes. Journal of the Transportation Research Forum, Vol. 46, No. 1, 2007, pp. 43–61. https://doi.org/10.22004/ag.econ.206875.

19.

Rolison

J. J.

Regev

Moutari

Feeney

What Are the Factors That Contribute to Road Accidents? An Assessment of Law Enforcement Views, Ordinary Drivers’ Opinions, and Road Accident Records—ScienceDirect, 2018. https://www.sciencedirect.com/science/article/pii/S0001457518300873.

20.

Federal Highway Administration (FHWA). 3. Collecting/Analyzing Speed and Crash Data | FHWA, 2024. https://highways.dot.gov/safety/speed-management/speed-management-eprimer-rural-transition-zones-and-town-centers/3.

21.

Zhou

Exploring the Relationship between Interstate Crashes and Various Speed Metrics Based upon Probe Vehicle Data. MS thesis. Iowa State University, 2019. https://www.proquest.com/docview/2296635701/abstract/EE78795084524D3APQ/1.

22.

Wang

Abdel-Aty

Zhong

Quasi-Vehicle-Trajectory-Based Real-Time Safety Analysis for Expressways. Transportation Research Part C: Emerging Technologies, Vol. 103, 2019, pp. 30–38. https://doi.org/10.1016/j.trc.2019.04.003.

23.

Kononov

Bailey

Allery

B. K.

Relationships Between Safety and Both Congestion and Number of Lanes on Urban Freeways. Transportation Research Record, 2008. 2083(1): 26–39. https://doi.org/10.3141/2083-04.

24.

Stempfel

Guler

S. I.

Menéndez

Brucks

W. M.

Effects of Urban Congestion on Safety of Networks. Journal of Transportation Safety & Security, Vol. 8, No. 3, 2016, pp. 214–229. https://doi.org/10.1080/19439962.2015.1007193.

25.

Sun

Chen

Analysis of Safety Factors for Urban Expressways Considering the Effect of Congestion in Shanghai, China. Accident Analysis & Prevention, Vol. 95, 2016, pp. 503–511. https://doi.org/10.1016/j.aap.2015.12.011.

26.

Ahsani

Sharma

Hegde

Knickerbocker

Hawkins

Improving Probe-Based Congestion Performance Metrics Accuracy by Using Change Point Detection. Journal of Big Data Analytics in Transportation, Vol. 2, No. 1, 2020, pp. 61–74. https://doi.org/10.1007/s42421-020-00017-w.

27.

Zhang

Ryan

Y.-J.

Influential Factors of Pedestrian and Bicycle Crashes Near Pedestrian Hybrid Beacons: Observing Trends Through an Applied Analysis. Journal of Transportation Safety & Security, Vol. 16, No. 11, 2024, pp. 1–24. https://doi.org/10.1080/19439962.2024.2325399.

28.

INRIX. Speed. Inrix, Bellevue, Washington, 2024. https://inrix.com/products/speed/.

29.

Raez

Segment Speed. INRIX Documentation, May 20, 2024. https://www.inrix.com/traffic/segmentspeed/.

30.

Cai

Abdel-Aty

Mahmoud

Ugan

Al-Omari

M. M. A.

Developing a Grouped Random Parameter Beta Model to Analyze Drivers’ Speeding Behavior on Urban and Suburban Arterials With Probe Speed Data. Accident Analysis & Prevention, Vol. 161, 2021, p. 106386. https://doi.org/10.1016/j.aap.2021.106386.

31.

Jordan

Measuring Quality of Traffic Information. INRIX, Bellevue, Washington, 2017. https://inrix.com/blog/measuring-quality/.

32.

INRIX. INRIX Floating Car Data Enhances Traffic Accuracy in Bavaria, 2015. https://inrix.com/press-releases/bavaria-quality-report/#:∼:text=The%20analysis%20revealed%20that%20INRIX,most%20congested%20motorways%20in%20Bavaria.

33.

Federal Highway Administration (FHWA). Utilizing Probe-Vehicle Data for Work Zone Mobility Performance Management—FHWA Office of Operations, 2019. https://ops.fhwa.dot.gov/publications/fhwahop19051/index.htm.

34.

Federal Highway Administration (FHWA). FHWA Office of Operations—Private Sector Data for Performance Management: Chapter 3. Private Sector Data Marketplace Review, 2011. https://ops.fhwa.dot.gov/publications/fhwahop11029/ch3.htm.

35.

Arizona Department of Transportation (ADOT). Crash Data | Department of Transportation, 2024. https://azdot.gov/tags/crash-data.

36.

ArcGIS Pro 3.0 (Version 3.0). (Computer software). Esri, 2022. https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview.

37.

esri. Buffer (Analysis)—ArcGIS Pro | Documentation, 2024. https://pro.arcgis.com/en/pro-app/latest/tool-reference/analysis/buffer.htm.

38.

American Association of State Highway and Transportation Officials. A Policy on Geometric Design of Highways and Streets, 2018. https://highways.dot.gov/safety/pedestrian-bicyclist/safety-tools/417-policy-geometric-design-highways-and-streets-6^th.

39.

Ahsani

Amin-Naseri

Knickerbocker

Sharma

Quantitative Analysis of Probe Data Characteristics: Coverage, Speed Bias and Congestion Detection Precision. Journal of Intelligent Transportation Systems, Vol. 23, No. 2, 2019, pp. 103–119. https://doi.org/10.1080/15472450.2018.1502667.

40.

Kassmeyer

Work Zone Crash Prediction Model and Characteristics Analysis. MS thesis. Iowa State University, 2020. https://www.proquest.com/docview/2445945001/abstract/93C31D69F7E24FFDPQ/1.

41.

National Operations Center of Excellence. Leveraging High-Resolution Event-Based Data for Ramp Metering Improvement Name of Organizations | National Operations Center of Excellence, December 28, 2023. https://transportationops.org/case-studies/leveraging-high-resolution-event-based-data-ramp-metering-improvement-name.

42.

Cottam

Y.-J.

Large-Scale Freeway Traffic Flow Estimation Using Crowdsourced Data: A Case Study in Arizona. Journal of Transportation Engineering, Part A: Systems, Vol. 150, No. 7, 2024, p. 04024030. https://doi.org/10.1061/JTEPBS.TEENG-8304.

43.

Berhanu

Schroeder

Teklu

Alemayehu

Spatial Analysis of Road Traffic Accidents: Identifying Hotspots for Improved Road Safety in Addis Ababa, Ethiopia. Cogent Engineering, Vol. 10, No. 2, 2023, p. 2269655. https://doi.org/10.1080/23311916.2023.2269655(42.

44.

Ryan

Fitzpatrick

Knodler

Developing a Geospatial Safety Analysis Tool: A Systematic Approach to Identify Safety-Critical Horizontal Curve Segments and Hazardous Contributing Factors. Journal of Transportation Engineering, Part A: Systems, Vol. 149, No. 7, 2023, p. 04023051. https://doi.org/10.1061/JTEPBS.TEENG-7258.

Integrating Vehicle Probe Speed and Crash Data for Statewide Traffic and Road Safety Enhancement

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