Modeling the accuracy of traffic crash prediction models
Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111222000115 |
| _version_ | 1797996958785208320 |
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| author | Mohammad Hesam Rashidi Soheil Keshavarz Parham Pazari Navid Safahieh Amir Samimi |
| author_facet | Mohammad Hesam Rashidi Soheil Keshavarz Parham Pazari Navid Safahieh Amir Samimi |
| author_sort | Mohammad Hesam Rashidi |
| collection | DOAJ |
| description | Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant average monthly crashes. On the contrary, the accuracy of crash prediction improved in provinces with higher per capita GDP, and higher traffic exposure. A 1% increase in crash variability, average historical crash count, GDP per capita, and traffic exposure, respectively, resulted in a 0.65%, 0.52%, −0.38%, and −0.13% change in the RMSE of forecasting. The addition of traffic exposure and macroeconomic factors significantly enhanced the model fit and improved the adjusted R-squared by 14% compared to the reduced model that only used the historical average and variability of crash count as the independent variables. The findings of this research suggest planners and policymakers should consider the notable influence of macroeconomic factors and traffic indicators on the crash forecasting accuracy. |
| first_indexed | 2024-04-11T10:24:31Z |
| format | Article |
| id | doaj.art-1390b8797a2542339a42dbe17d85f8be |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-04-11T10:24:31Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-1390b8797a2542339a42dbe17d85f8be2022-12-22T04:29:37ZengElsevierIATSS Research0386-11122022-10-01463345352Modeling the accuracy of traffic crash prediction modelsMohammad Hesam Rashidi0Soheil Keshavarz1Parham Pazari2Navid Safahieh3Amir Samimi4Department of Civil Engineering, Sharif University of Technology, Tehran, IranDepartment of Civil Engineering, Sharif University of Technology, Tehran, IranDepartment of Civil Engineering, Sharif University of Technology, Tehran, IranFaculty of Economics, University of Tehran, Tehran, IranDepartment of Civil Engineering, Sharif University of Technology, Tehran, Iran; Corresponding author.Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant average monthly crashes. On the contrary, the accuracy of crash prediction improved in provinces with higher per capita GDP, and higher traffic exposure. A 1% increase in crash variability, average historical crash count, GDP per capita, and traffic exposure, respectively, resulted in a 0.65%, 0.52%, −0.38%, and −0.13% change in the RMSE of forecasting. The addition of traffic exposure and macroeconomic factors significantly enhanced the model fit and improved the adjusted R-squared by 14% compared to the reduced model that only used the historical average and variability of crash count as the independent variables. The findings of this research suggest planners and policymakers should consider the notable influence of macroeconomic factors and traffic indicators on the crash forecasting accuracy.http://www.sciencedirect.com/science/article/pii/S0386111222000115Traffic safetyCrash frequencyForecast accuracyHolt-Winters modelIranTime series analysis |
| spellingShingle | Mohammad Hesam Rashidi Soheil Keshavarz Parham Pazari Navid Safahieh Amir Samimi Modeling the accuracy of traffic crash prediction models IATSS Research Traffic safety Crash frequency Forecast accuracy Holt-Winters model Iran Time series analysis |
| title | Modeling the accuracy of traffic crash prediction models |
| title_full | Modeling the accuracy of traffic crash prediction models |
| title_fullStr | Modeling the accuracy of traffic crash prediction models |
| title_full_unstemmed | Modeling the accuracy of traffic crash prediction models |
| title_short | Modeling the accuracy of traffic crash prediction models |
| title_sort | modeling the accuracy of traffic crash prediction models |
| topic | Traffic safety Crash frequency Forecast accuracy Holt-Winters model Iran Time series analysis |
| url | http://www.sciencedirect.com/science/article/pii/S0386111222000115 |
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