A new binary genetic programming approach to designing public transportation systems according to transit-oriented development criteria

Document Type : Article

Authors

1 Department of Landscape Architecture, Faculty of Architecture and Design, Ataturk University, Erzurum, Turkey

2 - Department of Landscape Architecture, Faculty of Architecture and Design, Ataturk University, Erzurum, Turkey - Faculty of Agriculture, Kirgizstan Turkiye Manas University, Bishkek, Kirgizstan

3 Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey

Abstract

This study introduces a new evolutionary approach called binary genetic programming (BGP) to design and assess public transportation systems from a sustainable development perspective. The BGP combines evolutionary system identification techniques with k-fold cross-validation to obtain an accurate model between the land use and transportation parameters from a sustainable urban development point of view. To assess the new model, two public transportation systems including the new tram line of Antalya (Turkey) and the bus rapid transit line of Bhopal (India) were considered. The model was applied to classify the transportation systems into transit-oriented development (TOD) and non-TOD. The solutions generated by the new model were compared with those of classic decision tree (DT) as well as the state-of-the-art random forest (RF) models evolved as the benchmarks in this study. The results showed that the BGP is highly efficient and may provide less than 5% classification error. It is superior to the DT and RF solutions, which typically require higher datasets to avoid overfitting. Furthermore, the explicit formulation of BGP in combination with the multicriteria evaluation method increases human insight on the factors affecting the design of public transportations from a sustainable urban development point of view.

Keywords

References

References:
1. Khare, R., Villuri, V.G.K., Chaurasia, D., et al.  Measurement of transit-oriented development (TOD) using GIS technique: a case study", Arabian J. Geosci., 14(10), pp. 1-16 (2021b).
2. Cervero, R. "Accessible cities and regions: A framework for sustainable transport and urbanism in the 21st century", WP UCB-ITS-VWP-2005-3, UC Berkeley Center for Future Urban Transport, Berkeley, CA (2005).
3. Zhou, J., Yang, Y., Gu, P., et al. "Can TODness improve (expected) performances of TODs? An exploration facilitated by non-traditional data", Transp. Res. Part D Transp. Environ., 74, pp. 28-47 (2019).
4. Ewing, R. and Cervero, R. "Travel and the built environment: A meta-analysis", J. Am. Plan. Assoc., 76(3), pp. 265-294 (2010).
5. Ibraeva, A., de Almeida Correia, G.H., Silva, C., et al. "Transit-oriented development: A review of research achievements and challenges", Transp. Res. Part A Policy Pract., 132, pp. 110-130 (2020).
6. Jamme, H.T., Rodriguez, J., Bahl, D., et al. "A twenty-five-year biography of the TOD concept: From design to policy, planning, and implementation", J. Plan. Educ. Res., 39(4), pp. 409-428 (2019).
7. l'Hostis, A. and Darchen, S., Characterising Transit Oriented Development in the Paris Metropolitan Region: What Type of TOD Are They? ffhal-01138076f (2015).
8. Pojani, D. and Stead, D. "Past, present and future of transit-oriented development in three European capital city-regions", In Y. Shiftan & M. Kamargianni, Eds., Preparing for the new era of transport policies: Learning from experience, pp. 93-118, Amsterdam: Elsevier (2018).
9. Loo, B.P., Chen, C., and Chan, E.T. "Rail-based transit-oriented development: lessons from New York City and Hong Kong", Landscape Urban Plann., 97(3), pp. 202-212 (2010).
10. Singh, Y.J., Fard, P., Zuidgeest, M., et al. "Measuring transit-oriented development: a spatial multi criteria assessment approach for the City Region Arnhem and Nijmegen", J. Transp. Geogr., 35, pp. 130-143 (2014).
11. Joshi, R., Munshi, T., Joseph, Y., et al., Links Between Transit Riders and Built Form: The Case of Ahmedabad (West), CUE Working Paper Series, Centre for Urban Equity, Ahmedabad, India (2017).
12. Newman, P. "Transit oriented development: An Australian overview. Transit oriented development: making it happen", Australia (2005).
13. Loo, B.P.Y. and du Verle, F. "Transit-oriented development in future cities: towards a two-level sustainable mobility strategy", Int. J. Urban Sci., 21, pp. 54-67 (2017).
14. Yu, Z., Zhu, X., and Liu, X. "Characterizing metro stations via urban function: Thematic evidence from transit-oriented development (TOD) in Hong Kong", J. Transp. Geogr., 99, 103299 (2022).
15. Singh, Y.J., Lukman, A., Flacke, J., et al. "Measuring TOD around transit nodes-towards TOD policy", Transp. Policy, 56, pp. 96-111 (2017).
16. Curtis, C. "Delivering the'D'in transit-oriented development: Examining the town planning challenge", J. Transp. Land Use, 5(3), pp. 83-99 (2012).
17. Binglei, X.I.E. and Chuan, D.I.N.G. "An evaluation on coordinated relationship between urban rail transit and land-use under TOD mode", J. Transp. Syst. Eng. Inf. Technol., 13(2), pp. 9-13 (2013).
18. Khare, R., Villuri, V.G.K., and Chaurasia, D. "Urban sustainability assessment: The evaluation of coordinated relationship between BRTS and land use in transit-oriented development mode using DEA model", Ain Shams Eng. J., 12(1), pp. 107-117 (2021a).
19. Quan, S.J., Park, J., Economou, A., et al. "Artificial intelligence-aided design: Smart design for sustainable city development", Environ. Plan. B Urban Anal. City Sci., 46(8), pp. 1581-1599 (2019).
20. Kim, D., Shim, J., Park, J., et al. "Supervised machine learning approaches to modeling residential infill development in the city of Los Angeles", J. Urban Plan. Dev. Div., ASCE, 148(1), 04021060 (2022).
21. Yeh, A.G.O. and Li, X. "Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning", Photogramm. Eng. Remote Sens., 69(9), pp. 1043-1052 (2003).
22. Grekousis, G., Manetos, P., and Photis, Y.N. "Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area", Cities, 30, pp. 193-203 (2013).
23. Lu, S. and Liu, Y. "Evaluation system for the sustainable development of urban transportation and ecological environment based on SVM", J. Intell. Fuzzy Syst., 34(2), pp. 831-838 (2018).
24. Mirbagheri, B. and Alimohammadi, A. "Integration of local and global support vector machines to improve urban growth modelling", ISPRS Int. J. Geo-Inf., 7(9), p. 347 (2018).
25. Karimi, F., Sultana, S., Babakan, A.S., et al. "An enhanced support vector machine model for urban expansion prediction", Comput. Environ. Urban Syst., 75, pp. 61-75 (2019).
26. Kranjcic, N., Medak, D., Zupan, R., et al. "Support vector machine accuracy assessment for extracting green urban areas in towns", Remote Sens., 11(6), p. 655 (2019).
27. Safari, M.J.S. and Mehr, A.D. "Design of smart urban drainage systems using evolutionary decision tree model", In IOT Technol. Smart-Cities from Sensors to Big Data, Secur. Trust, F. Al-Turjman and M. Imran, Eds., pp. 131-149, IET (2020).
28. Ramirez, T., Hurtubia, R., Lobel, H., et al. "Measuring heterogeneous perception of urban space with massive data and machine learning: An application to safety", Landsc. Urban Plan., 208, 104002 (2021).
29. Cevik, A., Arslan, M.H., and Koroglu, M.A. "Geneticprogramming- based modeling of RC beam torsional strength", KSCE J. Civ. Eng., 14(3), pp. 371-384 (2010).
30. Shahnazari, H., Dehnavi, Y., and Alavi, A.H. "The next-generation constitutive correlations for simulation of cyclic stress-strain behaviour of sand", J. Civ. Eng. Manag., 21(1), pp. 31-44 (2015).
31. Tsai, H.C. and Liao, M.C. "Modeling torsional strength of reinforced concrete beams using genetic programming polynomials with building codes", KSCE J. Civ. Eng., 23(8), pp. 3464-3475 (2019).
32. Danandeh Mehr, A. "Seasonal rainfall hindcasting using ensemble multi-stage genetic programming", Theor. Appl. Climatol., 143(1), pp. 461-472 (2021).
33. Keshavarz, A. and Tofighi, H. "Gene expression programming models for liquefaction-induced lateral spreading", Sci. Iran., 27(6), pp. 2704-2718 (2020).
34. Zhang, Q., Barri, K., Jiao, P., et al. "Genetic programming in civil engineering: advent, applications, and future trends", Artif. Intell. Rev., 54(3), pp. 1863- 1885 (2021).
35. Aydemir, P.K., Yilmazsoy, B.K., Akyuz, B., et al. "Kentsel Ulasimda Yaya  Oncelikli Planlama/Tasarim ve Transit Odakli Gelisimin Metropol Kentlerdeki Deneyimi, _Istanbul  Ornegi", (In Turkish), Kent Akademisi, 11(4), pp. 523-544 (2018).
36. Pilatin, K. "The investigation of marmaray line stations for conformity to transit-oriented development", Tasarim Kuram, 17(32), pp. 150-159 (2021).
37. Ann, S., Yamamoto, T., and Jiang, M. "Reexamination of the standards for transit oriented development influence zones in India", J. Transp. Land Use, 12(1), pp. 679-700 (2019).
38. Jain, D. and Singh, E. "Transit oriented development in India: A critical review of policy measures", Int. J. Rec. Tech. Eng., 7, pp. 745-751 (2018).
39. Koza, J.R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT press (1992).
40. Abooali, D. and Khamehchi, E. "New predictive method for estimation of natural gas hydrate formation temperature using genetic programming", Neural Comput. Appl., 31(7), pp. 2485-2494 (2019).
41. Mehdizadeh, S., Ahmadi, F., Mehr, A.D., et al. "Drought modeling using classic time series and hybrid wavelet-gene expression programming models", J. Hydrol., 587, 125017 (2020).
42. Hrnjica, B. and Danandeh Mehr, A. "Optimized genetic programming applications: emerging research and opportunities: emerging research and opportunities", IGI Global, PA (2018).
43. Danandeh Mehr, A., Nourani, V., Hrnjica, B., et al. "A binary genetic programing model for teleconnection identification between global sea surface temperature and local maximum monthly rainfall events", J. Hydrol., 555, pp. 397-406 (2017).
44. Jijo, B.T. and Abdulazeez, A.M. "Classification based on decision tree algorithm for machine learning", J. Appl. Sci. Technol. Trends, 2(01), pp. 20-28 (2021).
45. Breiman, L., Random Forests. Mach. Learn., 45(1), pp. 5-32 (2001).
46. Morrison, G.A., Searson, D.P., and Willis, M.J. "Using genetic programming to evolve a team of data classifiers", World Acad. Sci. Eng. Technol., 72, pp. 261-264 (2010).
47. Tur, R., Uzunsakal, L., and Mehr, A.D. "Coastline change determination using UAV technology: a case study along the Konyaalti coast, Antalya, Turkey", In Drones in Smart-Cities, Elsevier, pp. 123-141 (2020).
48. Vilela, A.P.L., Reboita, M.S., Silva, L.F., et al. "Sustainability neighborhoods in Brazil: a comparison of concepts and applications", Environ. Dev. Sustain., 22(6), pp. 6001-6028 (2020).
49. Kamruzzaman, M., Baker, D., Washington, S., et al. "Advance transit oriented development typology: case study in brisbane", Australia. J. Transp. Geogr., 34, pp. 54-70 (2014).
50. Landis, J.R. and Koch, G.G. "An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers", Biometrics, 33, pp. 363-374 (1977).
51. Danandeh Mehr, A. "Drought classification using gradient boosting decision tree", Acta Geophys., 69, pp. 909-918 (2021).
52. Aram, A., Dalalian, M.R., Saedi, S., et al. "An assessment of data mining and bivariate statistical methods for landslide susceptibility mapping", Sci. Iran., 29(3), pp. 1077-1094 (2021). DOI: 10.24200/SCI.2021.57334.5240.
Scientia Iranica
Volume 30, Issue 3
Transactions on Civil Engineering (A)
May and June 2023
Pages 936-951

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