Daily Commute Time Prediction Based on Genetic Algorithm

Zong, Fang; Lin, Haiyun; Yu, Bo; Pan, Xiang

doi:10.1155/2012/321574

Cited by 11 publications

(7 citation statements)

References 34 publications

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“…The MAPE value of the fatality forecasting model is 0.0226, and the Hit ratio is 100%, which recommend that this model has a high accuracy [17,18].…”

Section: Parameter Learningmentioning

confidence: 87%

Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models

Zong

Zhang

2013

Mathematical Problems in Engineering

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The paper presents a comparison between two modeling techniques, Bayesian network and Regression models, by employing them in accident severity analysis. Three severity indicators, that is, number of fatalities, number of injuries and property damage, are investigated with the two methods, and the major contribution factors and their effects are identified. The results indicate that the goodness of fit of Bayesian network is higher than that of Regression models in accident severity modeling. This finding facilitates the improvement of accuracy for accident severity prediction. Study results can be applied to the prediction of accident severity, which is one of the essential steps in accident management process. By recognizing the key influences, this research also provides suggestions for government to take effective measures to reduce accident impacts and improve traffic safety.

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“…The MAPE value of the fatality forecasting model is 0.0226, and the Hit ratio is 100%, which recommend that this model has a high accuracy [17,18].…”

Section: Parameter Learningmentioning

confidence: 87%

Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models

Zong

Zhang

2013

Mathematical Problems in Engineering

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“…Hence, some other optimization methods should be utilized to assist the GA [25]. The penalty function has the advantages of considering the points out of the feasible regions when solving nonlinear constraints problems [26].…”

Section: Optimization Of the Accuracy Of Multistep Numerical Methods mentioning

confidence: 99%

An Accurate Method for Real-Time Aircraft Dynamics Simulation Based on Predictor-Corrector Scheme

Zhao

Wang

Zhang

2015

Mathematical Problems in Engineering

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Real-time aircraft dynamics simulation requires very high accuracy and stability in the numerical integration process. Nonetheless, traditional multistep numerical methods cannot effectively meet the new requirements. Therefore, a novel real-time multistep method based on Predict-Evaluate-Correct scheme of three-step fourth-order method (RTPEC-34) is proposed and developed in this research to address the gap. In addition to the development of a highly accurate algorithm based on predictor-corrector, the contribution of this work also includes the analysis of truncation error for real-time problems. Moreover, the parameters for the RTPEC-34 method are optimized using intelligent optimization algorithms. The application and comparison of the optimization algorithms also lead to general guidelines for their applications in the development of improved multistep methods. Last but not least, theoretical analysis is also conducted on the stability of the proposed RTPEC-34 method, which is corroborated in simulation experiments and thus provides general guidelines for the evaluation of real-time numerical methods. The RTPEC-34 method is compared with other multistep algorithms using both numerical experiments and a real engineering example. As shown in the comparison, it achieves improved performance in terms of accuracy and stability and it is also a viable and efficient algorithm for real-time aircraft dynamics simulation.

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“…The search continues until HitRn-HitRn-1 < 0.001% or the number of generation reaches the maximum number of generations Tmax, which is set to be 5000 [30].…”

Section: Terminationmentioning

confidence: 99%

Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

et al. 2015

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Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA's contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices.

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Daily Commute Time Prediction Based on Genetic Algorithm

Cited by 11 publications

References 34 publications

Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models

Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models

An Accurate Method for Real-Time Aircraft Dynamics Simulation Based on Predictor-Corrector Scheme

Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

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