A Double-Level Calculation Model for the Construction Schedule Planning of Urban Rail Transit Network
Source abstract
Abstract
The construction of urban rail transit (URT) guides and promotes urban development. Different URT line construction schedule, including construction sequence (priority order of line construction) and construction timing (when to build), will have different effects on urban traffic and development. Therefore, the planning of construction schedule is an important part of URT network planning. At present, the determination of construction schedule is mainly based on qualitative analysis methods (i.e., experience, comparisons with other cities, and expert opinion) in engineering practice. In this study, based on an analysis of the main factors affecting the construction sequence and the construction timing data of existing URT lines, a quantitative double-level model of a construction schedule is proposed. The model consists of construction sequence and construction timing sub-models. The construction sequence sub-model employs an improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) with Rough Set method; the construction timing sub-model takes the results of the construction sequence model and the factors associated with urban development characteristics into account and presents an improved Logistic-β method. The model is verified using the Chengdu rail transit network as the case study. The results of the study show that the double-level calculation model could provide quantitative theoretical support for the construction schedule planning of URT network.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Transportation Planning and Optimization · Traffic Prediction and Management Techniques · Rough Sets and Fuzzy Logic
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
China Railway Eryuan Engineering Group Co., Ltd., Chengdu 610031, China
References (41)
Show source-provided references
1. Song, P., and Yang, Q.F. (2008, January 12–14). Empirical Research on Construction Scheduling for Urban Rail Transit Network. Proceedings of the 4th International Conference on Wireless Communications, Networking and Mobile Computing, Dalian, China.10.1109/WiCom.2008.2092 ↗
2. Transit network design: A procedure and an application to a large urban area10.1016/j.trc.2010.09.003 ↗
3. Laporte, G., and Mesa, J.A. (2015). The Design of Rapid Transit Networks. Location Science, Springer.10.1007/978-3-319-13111-5_22 ↗
4. Improved rapid transit network design model: Considering transfer effects10.1007/s10479-015-1999-x ↗
5. Chai, S., Liang, Q., and Zhong, S. (2019). Design of Urban Rail Transit Network Constrained by Urban Road Network, Trips and Land-Use Characteristics. Sustainability, 11.10.3390/su11216128 ↗
6. Vulnerability Assessment of the Urban Rail Transit Network Based on Travel Behavior Analysis10.1109/ACCESS.2020.3047159 ↗
7. Yang, T.Y., Zhao, P., Qiao, K., Yao, X.M., and Wang, T. (2021). Vulnerability Analysis of Urban Rail Transit Network by Considering the Station Track Layout and Passenger Behavior. J. Adv. Transp., 2021.10.1155/2021/6378526 ↗
8. Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach
9. The Timing of Urban Rail Transit Construction Based on the Weighted Gray Correlation10.4028/www.scientific.net/AMM.97-98.1189 ↗
10. Development of Rail Transit Network over Multiple Time Periods10.1016/j.tra.2019.01.017 ↗
11. Method for Optimizing the Phased Development of Rail Transit Lines10.1007/s40864-015-0029-2 ↗
12. Evolution of public transit modes in a commuter corridor10.1016/j.trc.2016.12.007 ↗
13. Uncertainty and the Timing of an Urban Congestion Relief Investment: The No-Land Case10.1016/j.jue.2005.04.003 ↗
14. Integration of Selecting and Scheduling Urban Road Construction Projects as a Time-Dependent Discrete Network Design Problem10.1016/j.ejor.2015.05.039 ↗
15. The Implications of High-Speed Rail for Chinese Cities: Connectivity and Accessibility10.1016/j.tra.2018.06.023 ↗
16. Railway Network Design with Multiple Project Stages and Time Sequencing10.1007/PL00011465 ↗
17. Lagrangian Relaxation Heuristic for Selecting Interdependent Transportation Projects under Cost Uncertainty10.1177/0361198105193100110 ↗
18. Island Models for Stochastic Problem of Transportation Project Selection and Scheduling10.3141/2039-02 ↗
19. Sequencing and Scheduling Highway Network Expansion Using a Discrete Network Design Model10.1007/s00168-007-0170-2 ↗
20. Time–Dependent Transport Network Design under Cost-Recovery10.1016/j.trb.2008.06.005 ↗
21. The Optimization of Road Building Schedule Based on Budget Restriction10.1108/03684920910944146 ↗
22. Prioritizing Interrelated Road Projects Using Metaheuristics10.1061/(ASCE)IS.1943-555X.0000293 ↗
23. He, Y., Song, Z.Q., and Zhang, L.H. (2018). Time-Dependent Transportation Network Design Considering Construction Impact. J. Adv. Transp., 2018.10.1155/2018/2738930 ↗
24. Hwang, C.L., and Yoon, K. (1981). Multiple Attributes Decision Making Methods and Applications, Springer.10.1007/978-3-642-48318-9 ↗
25. Development of TOPSIS Method to Solve Complicated Decision-Making Problems–An Overview on Developments from 2000 to 201510.1142/S0219622016300019 ↗
26. Comprehensive Assessment on Sustainable Development of Highway Transportation Capacity Based on Entropy Weight and TOPSIS10.3390/su6074685 ↗
27. Project rankings for participatory budget based on the fuzzy TOPSIS method10.1016/j.ejor.2016.12.044 ↗
28. Integration of GIS, AHP and TOPSIS for earthquake hazard analysis10.1007/s11069-018-3262-7 ↗
29. Unbalanced double hierarchy linguistic term set: The TOPSIS method for multi-expert qualitative decision making involving green mine selection10.1016/j.inffus.2019.04.002 ↗
30. Ali, M.A.M., Kim, J.G., Awadallah, Z.H., Abdo, A.M., and Hassan, A.M. (2021). Multiple-Criteria Decision Analysis Using TOPSIS: Sustainable Approach to Technical and Economic Evaluation of Rocks for Lining Canals. Appl. Sci., 11.10.3390/app11209692 ↗
31. Improving Service Quality with the Fuzzy TOPSIS Method: A case study of the Beijing Rail Transit System10.1109/ACCESS.2019.2932779 ↗
32. Using entropy-TOPSIS method to evaluate urban rail transit system operation performance: The China case10.1016/j.tra.2018.03.025 ↗
33. A hybrid fuzzy methodology to evaluate customer satisfaction in a public transportation system for Istanbul10.1080/14783363.2013.809942 ↗
34. Meng, Y.Y., Tian, X.L., Li, Z.W., Zhou, Z.J., and Zhong, M.H. (2020). Exploring node importance evolution of weighted complex networks in urban rail transit. Physica A, 558.10.1016/j.physa.2020.124925 ↗
35. Rough sets10.1007/BF01001956 ↗
36. Rough sets10.1145/219717.219791 ↗
37. A fuzzy TOPSIS and Rough Set based approach for mechanism analysis of product infant failure10.1016/j.engappai.2015.06.002 ↗
38. A New Improved Attribute Weight Algorithm Based on Rough Sets Theory for One Command Information System10.4028/www.scientific.net/AMR.989-994.2029 ↗
39. Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China10.1007/s10661-011-2306-1 ↗
40. A Method to Select between Gompertz and Logistic Trend Curves10.1016/0040-1625(94)90016-7 ↗
41. Growth with seasonally varying temperatures: An expansion of the von Bertalanffy growth model10.1111/j.1095-8649.1996.tb01453.x ↗