Identifying Temporal Instability in Factors Causing Work Zone Crash Occurrences Using Fast Causal Inference
Publication: Computing in Civil Engineering 2021
ABSTRACT
Finding effective countermeasures of work zone crashes requires identifying the causes of work zone crashes. Recently, several researchers have identified some evidence that the influence of factors determining crash severity change over time (year to year), which is referred as temporal instability. So far, this phenomenon has been identified for factors determining crash severity and not studied for occurrence of work zone crashes. This paper focuses on investigating possible temporal instability in factors causing work zone crash occurrences. Three research gaps are identified: (1) Current models focus on statistical associations rather than causal relations. But causal relations are required for implementing work zone countermeasures; (2) Crash records cannot cover all variables defining crash risk such as human behavior, which induces unobserved observation-specific variations (unobserved heterogeneity) on safety impacts of observed variables; and (3) Current studies usually use work zone crash records in low spatio-temporal granularity, which cannot capture effects of fast-changing factors (weather conditions, traffic speed) on crash risk. In this paper, the fast causal inference (FCI) model is applied to data including work zone crashes and environmental conditions (weather conditions, traffic speed, and roadway characteristics) in high granularity to identify temporal instability in factors causing work zone crash occurrence. The proposed method is tested on the Pennsylvania work zone crash data from 2015 to 2017. Among 45 pairs of factors, the proposed model identified four pairs of factors whose causal relations changed across 2015 and 2016, and 10 pairs of factors whose causal relations changed across 2016 and 2017.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Ahmed, M. M., and Abdel-Aty, M. A. (2012). “The Viability of Using Automatic Vehicle Identification Data for Real-Time Crash Prediction.” IEEE Transactions on Intelligent Transportation Systems, 13(2), 459–468.
Alnawmasi, N., and Mannering, F. (2019). “A statistical assessment of temporal instability in the factors determining motorcyclist injury severities.” Analytic Methods in Accident Research, 22, 100090.
Andrews, B., Ramsey, J., and Cooper, G. F. (2019). “Learning High-dimensional Directed Acyclic Graphs with Mixed Data-types.” The 2019 ACM SIGKDD Workshop on Causal Discovery, PMLR, 4–21.
ARTBA. (2021). “National Estimates of Total and Injury Work Zone Crashes.” The National Work Zone Safety Information Clearinghouse, <https://www.workzonesafety.org/crash-information/work-zone-injuries-injury-property-damage-crashes/>(Feb. 22, 2021).
Commonwealth Pennsylvania. (2018). “RCRS_Event_Data Service.” <http://www.penndot.gov/Doing-Business/OnlineServices/Pages/Developer-Resources-DocumentationAPI.aspx>(Jun. 10, 2018).
FHWA. (2017). “Work Zone Facts and Statistics.” <https://web.archive.org/web/20170828102601/https://ops.fhwa.dot.gov/wz/resources/facts_stats/safety.htm>(Oct. 2, 2018).
FHWA. (2019). “Work Zone Facts and Statistics.” <https://ops.fhwa.dot.gov/wz/resources/facts_stats.htm#ftn2>(Oct. 22, 2019).
FHWA. (2021). “CMF Clearinghouse.” <http://www.cmfclearinghouse.org/results.cfm>(Jan. 26, 2021).
Glymour, C., Zhang, K., and Spirtes, P. (2019). “Review of Causal Discovery Methods Based on Graphical Models.” Frontiers in Genetics, 10.
Hou, Q., Huo, X., and Leng, J. (2020). “A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates.” Accident Analysis & Prevention, 134, 105326.
Islam, M., Alnawmasi, N., and Mannering, F. (2020). “Unobserved heterogeneity and temporal instability in the analysis of work-zone crash-injury severities.” Analytic Methods in Accident Research, 28, 100130.
Islam, M., and Mannering, F. (2020). “A temporal analysis of driver-injury severities in crashes involving aggressive and non-aggressive driving.” Analytic Methods in Accident Research, 27, 100128.
Kim, S., and Coifman, B. (2014). “Comparing INRIX speed data against concurrent loop detector stations over several months.” Transportation Research Part C: Emerging Technologies, Pergamon, 49, 59–72.
Li, Y., Song, L., and Fan, W. D. (2021). “Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: A random parameters logit approach with heterogeneity in means and variances.” Analytic Methods in Accident Research, 29, 100152.
Mannering, F. (2018). “Temporal instability and the analysis of highway accident data.” Analytic Methods in Accident Research, 17, 1–13.
Mannering, F., Bhat, C. R., Shankar, V., and Abdel-Aty, M. (2020). “Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis.” Analytic Methods in Accident Research, 25, 100113.
Mannering, F. L., Shankar, V., and Bhat, C. R. (2016). “Unobserved heterogeneity and the statistical analysis of highway accident data.” Analytic Methods in Accident Research, 11, 1–16.
Milton, J. C., Shankar, V. N., and Mannering, F. L. (2008). “Highway accident severities and the mixed logit model: An exploratory empirical analysis.” Accident Analysis & Prevention, 40(1), 260–266.
Ozturk, O., Ozbay, K., and Yang, H. (2014). “Estimating the Impact of Work Zones on Highway Safety.”
Ozturk, O., Ozbay, K., Yang, H., and Bartin, B. (2013). “Crash Frequency Modeling for Highway Construction Zones.”
Pearl, J., and Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books, New York.
PennDOT. (2018). “Crash Download Map.” <https://pennshare.maps.arcgis.com/apps/webappviewer/index.html?id=8fdbf046e36e41649bbfd9d7dd7c7e7e>(Jun. 10, 2018).
Pennshare. (2018). “RMSSEG (State Roads) |.” <https://data-pennshare.opendata.arcgis.com/datasets/rmsseg-state-roads>(Jun. 10, 2018).
Pennsylvania State Climatologist. (2020). “Pennsylvania State Climatologist.” <http://climate.psu.edu/data/ida/index.php?t=3&x=faa_hourly&id=KABE>(Jan. 19, 2019).
Scheines, R., Spirtes, P., Glymour, C., Meek, C., and Richardson, T. (1998). “The TETRAD Project: Constraint Based Aids to Causal Model Specification.” Multivariate Behavioral Research, 33(1), 65–117.
Spirtes, P., Glymour, C. N., and Scheines, R. (2001). Causation, Prediction, and Search. MIT Press.
Theofilatos, A., Ziakopoulos, A., Papadimitriou, E., Yannis, G., and Diamandouros, K. (2017). “Meta-analysis of the effect of road work zones on crash occurrence.” Accident Analysis & Prevention, 108, 1–8.
TomTom. (2019). “TomTom MultiNet | ADCi | TomTom GIS Data.” <https://www.adci.com/tomtom/gis/>.
Yang, H., Ozbay, K., Xie, K., and Bartin, B. (2015). “Modeling Crash Risk of Highway Work Zones with Relatively Short Durations.”
Yu, M., Zheng, C., Ma, C., and Shen, J. (2020). “The temporal stability of factors affecting driver injury severity in run-off-road crashes: A random parameters ordered probit model with heterogeneity in the means approach.” Accident Analysis & Prevention, 144, 105677.
Yu, R., Abdel-Aty, M., and Ahmed, M. (2013). “Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors.” Accident Analysis & Prevention, 50, 371–376.
Information & Authors
Information
Published In
History
Published online: May 24, 2022
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.