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Table 3 Traffic incident duration prediction studies
From: Overview of traffic incident duration analysis and prediction
Method Category | Methodology | Â | Data source | Duration time phase | Accuracy |
|---|---|---|---|---|---|
Regression model | Time sequential method (truncated regression model) | Khattak et al. [5] | 109 larger incidents | Duration time | Not test without available dataset |
Regression model | Garib et al. [6] | 205 incidents | Incident duration | 81% (adjusted R2) | |
Linear regression (LR) | Peeta et al. [7] | 835 crashes and 1176 debris | Clearance time | R2: 0.234 for crashes; 0.362 for debris | |
OLS regression models | Khattak et al. [32] | 59,804 incidents | Incident duration | Best MAPE: 37%a | |
A linear model with a stepwise regression | Yu, Xia [66] | 503 records | Incident duration | Acceptable (77.8% predictions have an error within 60Â min) | |
Cluster-based log-normal distribution model | Weng et al. [67] | 2512 accidents | Accident duration | Best MAPE: 34.1% | |
Quantile Regression | Khattak et al. [68] | 85,000 incidents | Incident duration | RSME: 57.49Â min | |
Fuzzy system | Fuzzy system model | Kim, Choi [69] | 2457 incidents | Incident service time | Average error: 0.3Â min |
Fuzzy logic (FL) model | Wang et al. [70] | 457 records | Incident duration | Average performance | |
Fuzzy duration model | Dimitriou, Vlahogianni [71] | 1449 accidents | Accident duration | Best MAPE: 36%. | |
Classification Tree Method (CTM) | Decision tree | Ozbay, Kachroo [22] | 650 incidents | Clearance time | 60% less than 10Â min |
Non-parametric regression and CTM | Smith, Smith [43] | 6828 accidents | Clearance time | Not good (correct rate 58%) | |
CTM | Knibbe et al. [72] | 1853 incidents | Incident duration time | Theoretical reliability: 65% | |
Hybrid tree-based quantile regression | He et al. [40] | 1245 incidents | Incident duration | MAPE: 49.1%. | |
M5P tree algorithm | Zhan et al. [15] | 2585 incidents | Lane clearance time | MAPE: 42.7%. | |
CTM | Chang, Chang [73] | 4697 cases | Incident duration | Accuracy of classification: 75.1%. | |
Artificial neural networks | FL and ANNs | Wang et al. [74] | 695 vehicle breakdowns | Incident duration | RMSE: about 20% |
ANNs | Wei, Lee [33] | 39 accidents | Accident duration | MAPE: 20%–30% | |
ANN-based models | Wei, Lee [16] | 24 incidents | Incident duration | MAPE mostly under 40%. | |
A sequential forecast based on two ANN-based models | Lee, Wei [17] | 39 accidents | Accident duration | The MAPE value at each time point is mostly under 29%. | |
Multiple LR; DT; ANN; SVM/RVM; K nearest neighbour (KNN) | Valenti et al. [19] | 237 incidents | Incident duration | MAPE of the five models: 34%–44%. | |
Four adaptive ANN-based models | Lopes et al. [56] | 10,762 incidents | Clearance time | Model 4: 72% incidents: <10Â min error; 92%: <20Â min error | |
Topic modelling and ANN-based models | Pereira et al. [45] | 10,139 accidents | Incident duration | A median error of 9.9Â min in the best model | |
ANN models | Vlahogianni, Karlaftis [18] | 1449 accidents | Accident duration | Accuracy defined in the paper is about 10% | |
Bayesian ANNs | Park et al. [57] | 13,987 incidents | Incident duration | MAPE: 0.18–0.29. | |
Bayesian networks | Bayesian networks | Ozbay, Noyan [75] | 700 incidents | Incident clearance times | Accuracy of approximately 80% |
Probabilistic model based on a naïve Bayesian classifier (NBC) | Boyles et al. [8] | 2970 incidents | Incident duration | Classification is correct half of the time. | |
Bayesian decision model | Ji et al. [76] | 1853 incidents | Incident duration | Theoretical reliability of 74% | |
Tree-augmented NBC and a continuous model based on latent Gaussian NBC | Li, Cheng [77] | 2973 incidents | Incident duration | The frequency of the correct classification is below 0.5. | |
Bayesian network | Shen, Huang [78] | 2629 incidents | Incident duration | overall classification accuracy is 72.6% | |
hazard-based duration model | Time sequential procedure with HBDM | Qi, Teng [55] | 1660 incidents | Remaining incident duration | Accuracy increases with more information |
Log-logistic AFT model | Chung [58] | 4869 accidents | Accident duration | MAPE: 47%. | |
Log-logistic AFT model | Hu et al. [35] | 5362 incidents | Incident duration | MAPE: 43.7%. | |
Weibull AFT model | Kang, Fang [79] | 1327 incidents | Incident duration | MAPE: 43%. | |
KNN and Log-logistic AFT model | Araghi et al. [34] | 5362 incidents | Incident duration | MAPE: KNN: 41.1%; AFT: 43.7% | |
HBDM | Ji et al. [38] | 24,604 incidents | Clearance and arrival time | 39.68% of incident: <10Â min error | |
Competing risk mixture HBDM | Li et al. [52] | 12,093 incidents | Incident duration | MAPE: 45% for >15 mins | |
G-component mixture model | Zou et al. [44] | 2584 incidents | Clearance time | MAPE: 39% | |
SVM | Ordered probit model and SVM | Zong et al. [80] | 3914 cases | Accident duration | MAPE: 22% |
SVM | Wu et al. [81] | 1853 incidents | Incident duration | Total accuracy: 70% | |
Combined/hybrid | Ordered probit model and a rule-based supplemental module | Lin et al. [10] | 22,495 incidents | Incident duration | Duration less than 60Â min is 82.25% (within 10-min error) |
CTM and Rule-Based Tree Model (RBTM), DCM | Kim et al. [14] | 4 years’ worth of data | Incident duration | The overall confidence is more than 80%. | |
A hybrid model that consists of a RBTM, MultiNomial Logit model (MNL), and NBC | Kim, Chang [20] | 6765 records | Incident duration | Performed satisfactorily for incidents that last from 120 to 240Â min | |
Combined M5P tree and HBDM | Lin et al. [54] | 602 accident records | Accident duration | MAPE: 36.2% for I-64 and 31.87% for I-190. |