Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling
Until recently, a majority of modeling tasks of transportation planning, especially in discrete choice modeling, is conducted with the help of commercial software and only concerned about the result of parameter estimates to get a policy-sensitive interpretation. This common practice prevents resear...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Modelling and Simulation in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/8169036 |
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| author | Hyuk-Jae Roh Satish Sharma Prasanta K. Sahu Babak Mehran |
| author_facet | Hyuk-Jae Roh Satish Sharma Prasanta K. Sahu Babak Mehran |
| author_sort | Hyuk-Jae Roh |
| collection | DOAJ |
| description | Until recently, a majority of modeling tasks of transportation planning, especially in discrete choice modeling, is conducted with the help of commercial software and only concerned about the result of parameter estimates to get a policy-sensitive interpretation. This common practice prevents researchers from gaining a systematic knowledge involved in estimation mechanism. In this research, to shed a light on these limited modeling practices, a standard discrete choice model’s parameter is estimated using Quasi-Newton method, DFP, and BFGS. Two extended algorithms, called DFP-GSM and BFGS-GSM, are proposed for the first time to overcome the weakness of the Quasi-Newton method. The golden section method (GSM) incorporates a nonlinear programming technique to choose an optimal step size automatically. Partial derivatives of log-likelihood function are derived and coded using Visual Basic Application (VBA). Through extensive numerical evaluation, estimation capability of each proposed estimation algorithms is compared in terms of performance measures. The proposed algorithms show a stable estimation performance and the reasons were studied and discussed. Furthermore, useful insights educated in custom-built modeling are present. |
| format | Article |
| id | doaj-art-73135a173baa4b4b9758ba0fc0c230ba |
| institution | Kabale University |
| issn | 1687-5591 1687-5605 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Modelling and Simulation in Engineering |
| spelling | doaj-art-73135a173baa4b4b9758ba0fc0c230ba2025-02-03T07:25:10ZengWileyModelling and Simulation in Engineering1687-55911687-56052018-01-01201810.1155/2018/81690368169036Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice ModelingHyuk-Jae Roh0Satish Sharma1Prasanta K. Sahu2Babak Mehran3Faculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, Regina, SK, S4S 0A2, CanadaFaculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, Regina, SK, S4S 0A2, CanadaDepartment of Civil Engineering, Birla Institute of Technology and Science Pilani, Rajasthan 333031, IndiaFaculty of Engineering and Applied Science, Environmental Systems Engineering, University of Regina, Regina, SK, S4S 0A2, CanadaUntil recently, a majority of modeling tasks of transportation planning, especially in discrete choice modeling, is conducted with the help of commercial software and only concerned about the result of parameter estimates to get a policy-sensitive interpretation. This common practice prevents researchers from gaining a systematic knowledge involved in estimation mechanism. In this research, to shed a light on these limited modeling practices, a standard discrete choice model’s parameter is estimated using Quasi-Newton method, DFP, and BFGS. Two extended algorithms, called DFP-GSM and BFGS-GSM, are proposed for the first time to overcome the weakness of the Quasi-Newton method. The golden section method (GSM) incorporates a nonlinear programming technique to choose an optimal step size automatically. Partial derivatives of log-likelihood function are derived and coded using Visual Basic Application (VBA). Through extensive numerical evaluation, estimation capability of each proposed estimation algorithms is compared in terms of performance measures. The proposed algorithms show a stable estimation performance and the reasons were studied and discussed. Furthermore, useful insights educated in custom-built modeling are present.http://dx.doi.org/10.1155/2018/8169036 |
| spellingShingle | Hyuk-Jae Roh Satish Sharma Prasanta K. Sahu Babak Mehran Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling Modelling and Simulation in Engineering |
| title | Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling |
| title_full | Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling |
| title_fullStr | Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling |
| title_full_unstemmed | Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling |
| title_short | Performance Comparison of Mode Choice Optimization Algorithm with Simulated Discrete Choice Modeling |
| title_sort | performance comparison of mode choice optimization algorithm with simulated discrete choice modeling |
| url | http://dx.doi.org/10.1155/2018/8169036 |
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