How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data
2.2. Bayesian Network (BN) Model
3. Results
3.1. Models’ Results and Performance
3.2. Variable Importance
3.3. Relationships with Travel Mode to Reach Public Transit Stations in California
3.4. Interaction Impacts on Mode Choice to Reach Public Transit Stationsr
4. Discussions
5. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Martin, A.; Panter, J.; Suhrcke, M.; Ogilvie, D. Impact of changes in mode of travel to work on changes in body mass index: Evidence from the British Household Panel Survey. J. Epidemiol. Community Health 2015, 69, 753–761. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chinnam, R.B.; Murat, A.E. Community-Aware Charging Station Network Design for Electrified Vehicles in Urban Areas: Reducing Congestion, Emissions, Improving Accessibility, and Promoting Walking, Bicycling, and Use of Public Transportation. 2016. Available online: https://rosap.ntl.bts.gov/view/dot/31590 (accessed on 1 January 2022).
- McKenzie, B. Modes Less Traveled: Bicycling and Walking to Work in the United States, 2008–2012; US Department of Commerce, Economics and Statistics Administration: Washington, DC, USA, 2014. [Google Scholar]
- Macioszek, E.; Kurek, A. The analysis of the factors determining the choice of park and ride facility using a multinomial logit model. Energies 2021, 14, 203. [Google Scholar] [CrossRef]
- Henry, E.; Furno, A.; El Faouzi, N.-E.; Rey, D. Locating park-and-ride facilities for resilient on-demand urban mobility. Transp. Res. Part E Logist. Transp. Rev. 2022, 158, 102557. [Google Scholar] [CrossRef]
- Kumar, P.; Khani, A. Adaptive park-and-ride choice on time-dependent stochastic multimodal transportation network. Netw. Spat. Econ. 2021, 21, 771–800. [Google Scholar] [CrossRef]
- Rezaei, S.; Khojandi, A.; Haque, A.M.; Brakewood, C.; Jin, M.; Cherry, C.R. Park-and-ride facility location optimization: A case study for Nashville, Tennessee. Transp. Res. Interdiscip. Perspect. 2022, 13, 100578. [Google Scholar] [CrossRef]
- Switkes, F. Getting There is Half the Problem: Removing Obstacles to Accessing Rail Transit; Massachusetts Institute of Technology: Cambridge, MA, USA, 2003. [Google Scholar]
- Kanthavel, R.; Sangeetha, S.; Keerthana, K. Design of smart public transport assist system for metropolitan city Chennai. Int. J. Intell. Netw. 2021, 2, 57–63. [Google Scholar] [CrossRef]
- Rietveld, P. Non-motorised modes in transport systems: A multimodal chain perspective for The Netherlands. Transp. Res. Part D Transp. Environ. 2000, 5, 31–36. [Google Scholar] [CrossRef]
- Weinstein Agrawal, A.; Schlossberg, M.; Irvin, K. How far, by which route and why? A spatial analysis of pedestrian preference. J. Urban Des. 2008, 13, 81–98. [Google Scholar] [CrossRef]
- Tight, M.; Rajé, F.; Timms, P. Car-free urban areas: A radical solution to the last mile problem or a step too far? Built Environ. 2016, 42, 603–616. [Google Scholar] [CrossRef]
- Ali, M.; de Azevedo, A.R.G.; Marvila, M.T.; Khan, M.I.; Memon, A.M.; Masood, F.; Almahbashi, N.M.; Shad, M.K.; Khan, M.A.; Fediuk, R.; et al. The Influence of COVID-19-Induced Daily Activities on Health Parameters—A Case Study in Malaysia. Sustainability 2021, 13, 7465. [Google Scholar] [CrossRef]
- Ali, M.; Dharmowijoyo, D.B.; Harahap, I.S.; Puri, A.; Tanjung, L.E. Travel behaviour and health: Interaction of Activity-Travel Pattern, Travel Parameter and Physical Intensity. Solid State Technol. 2020, 63, 4026–4039. [Google Scholar]
- Zhao, T.-H.; Shi, L.; Chu, Y.-M. Convexity and concavity of the modified Bessel functions of the first kind with respect to Hölder means. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas 2020, 114, 96. [Google Scholar] [CrossRef]
- Næss, P.; Peters, S.; Stefansdottir, H.; Strand, A. Causality, not just correlation: Residential location, transport rationales and travel behavior across metropolitan contexts. J. Transp. Geogr. 2018, 69, 181–195. [Google Scholar] [CrossRef]
- Kim, K.W.; Lee, D.W.; Chun, Y.H. A comparative study on the service coverages of subways and buses. KSCE J. Civ. Eng. 2010, 14, 915–922. [Google Scholar] [CrossRef]
- El-Geneidy, A.; Grimsrud, M.; Wasfi, R.; Tétreault, P.; Surprenant-Legault, J. New evidence on walking distances to transit stops: Identifying redundancies and gaps using variable service areas. Transportation 2014, 41, 193–210. [Google Scholar] [CrossRef]
- Patterson, R.; Webb, E.; Millett, C.; Laverty, A. Physical activity accrued as part of public transport use in England. J. Public Health 2019, 41, 222–230. [Google Scholar] [CrossRef] [PubMed]
- Ali, M.; Dharmowijoyo, D.B.E.; de Azevedo, A.R.G.; Fediuk, R.; Ahmad, H.; Salah, B. Time-Use and Spatio-Temporal Variables Influence on Physical Activity Intensity, Physical and Social Health of Travelers. Sustainability 2021, 13, 12226. [Google Scholar] [CrossRef]
- Tabatabaee, S.; Aghaabbasi, M.; Mahdiyar, A.; Zainol, R.; Ismail, S. Measurement Quality Appraisal Instrument for Evaluation of Walkability Assessment Tools Based on Walking Needs. Sustainability 2021, 13, 11342. [Google Scholar] [CrossRef]
- Aghaabbasi, M.; Moeinaddini, M.; Shah, M.Z.; Asadi-Shekari, Z. A new assessment model to evaluate the microscale sidewalk design factors at the neighbourhood level. J. Transp. Health 2017, 5, 97–112. [Google Scholar] [CrossRef]
- Aghaabbasi, M.; Moeinaddini, M.; Zaly Shah, M.; Asadi-Shekari, Z.; Arjomand Kermani, M. Evaluating the capability of walkability audit tools for assessing sidewalks. Sustain. Cities Soc. 2018, 37, 475–484. [Google Scholar] [CrossRef]
- Qian, Y.; Aghaabbasi, M.; Ali, M.; Alqurashi, M.; Salah, B.; Zainol, R.; Moeinaddini, M.; Hussein, E.E. Classification of Imbalanced Travel Mode Choice to Work Data Using Adjustable SVM Model. Appl. Sci. 2021, 11, 11916. [Google Scholar] [CrossRef]
- Zhao, T.-H.; Shen, Z.-H.; Chu, Y.-M. Sharp power mean bounds for the lemniscate type means. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas 2021, 115, 175. [Google Scholar] [CrossRef]
- Morency, C.; Trépanier, M.; Demers, M. Walking to transit: An unexpected source of physical activity. Transp. Policy 2011, 18, 800–806. [Google Scholar] [CrossRef]
- Wasfi, R.A.; Ross, N.A.; El-Geneidy, A.M. Achieving recommended daily physical activity levels through commuting by public transportation: Unpacking individual and contextual influences. Health Place 2013, 23, 18–25. [Google Scholar] [CrossRef]
- Zhao, T.-H.; Wang, M.-K.; Zhang, W.; Chu, Y.-M. Quadratic transformation inequalities for Gaussian hypergeometric function. J. Inequal. Appl. 2018, 2018, 251. [Google Scholar] [CrossRef] [Green Version]
- García-Palomares, J.C.; Gutiérrez, J.; Cardozo, O.D. Walking accessibility to public transport: An analysis based on microdata and GIS. Environ. Plan. B Plan. Des. 2013, 40, 1087–1102. [Google Scholar] [CrossRef]
- Johar, A.; Jain, S.; Garg, P.; Gundaliya, P. A study for commuter walk distance from bus stops to different destination along routes in Delhi. Eur. Transp. 2015, 59, 1–15. [Google Scholar]
- Alshalalfah, B.; Shalaby, A.S. Case study: Relationship of walk access distance to transit with service, travel, and personal characteristics. J. Urban Plan. Dev. 2007, 133, 114–118. [Google Scholar] [CrossRef]
- Besser, L.M.; Dannenberg, A.L. Walking to public transit: Steps to help meet physical activity recommendations. Am. J. Prev. Med. 2005, 29, 273–280. [Google Scholar] [CrossRef] [Green Version]
- He, J.; Zhang, R.; Huang, X.; Xi, G. Walking access distance of metro passengers and relationship with demographic characteristics: A case study of Nanjing metro. Chin. Geogr. Sci. 2018, 28, 612–623. [Google Scholar] [CrossRef] [Green Version]
- Durand, C.P.; Gabriel, K.K.P.; Hoelscher, D.M.; Kohl, H.W. Transit use by children and adolescents: An overlooked source of and opportunity for physical activity? J. Phys. Act. Health 2016, 13, 861–866. [Google Scholar] [CrossRef] [PubMed]
- Zhao, T.-H.; He, Z.-Y.; Chu, Y.-M. Sharp bounds for the weighted Hölder mean of the zero-balanced generalized complete elliptic integrals. Comput. Methods Funct. Theory 2021, 21, 413–426. [Google Scholar] [CrossRef]
- Freeland, A.L.; Banerjee, S.N.; Dannenberg, A.L.; Wendel, A.M. Walking associated with public transit: Moving toward increased physical activity in the United States. Am. J. Public Health 2013, 103, 536–542. [Google Scholar] [CrossRef] [PubMed]
- Zhao, T.-H.; Zhou, B.-C.; Wang, M.-K.; Chu, Y.-M. On approximating the quasi-arithmetic mean. J. Inequal. Appl. 2019, 2019, 1–12. [Google Scholar] [CrossRef]
- Sun, G.; Zacharias, J.; Ma, B.; Oreskovic, N.M. How do metro stations integrate with walking environments? Results from walking access within three types of built environment in Beijing. Cities 2016, 56, 91–98. [Google Scholar] [CrossRef]
- Yu, C.-Y.; Lin, H.-C. Exploring factors regarding transit-related walking and walking duration. J. Phys. Act. Health 2016, 13, 1220–1229. [Google Scholar] [CrossRef]
- Zhao, T.-H.; Wang, M.-K.; Chu, Y.-M. Monotonicity and convexity involving generalized elliptic integral of the first kind. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas 2021, 115, 46. [Google Scholar] [CrossRef]
- Kim, T.; Sohn, D.-W.; Choo, S. An analysis of the relationship between pedestrian traffic volumes and built environment around metro stations in Seoul. KSCE J. Civ. Eng. 2017, 21, 1443–1452. [Google Scholar] [CrossRef]
- Paydar, M.; Fard, A.K.; Khaghani, M.M. Walking toward metro stations: The contribution of distance, attitudes, and perceived built environment. Sustainability 2020, 12, 10291. [Google Scholar] [CrossRef]
- Yu, C.-Y.; Lin, H.-C. Transit-related walking to work in promoting physical activity. J. Phys. Act. Health 2015, 12, 483–489. [Google Scholar] [CrossRef]
- Clark, A.F.; Scott, D.M.; Yiannakoulias, N. Examining the relationship between active travel, weather, and the built environment: A multilevel approach using a GPS-enhanced dataset. Transportation 2014, 41, 325–338. [Google Scholar] [CrossRef] [Green Version]
- Pan, H.; Wei, P. 11. Effects of rail transit proximity emissions in the suburbs of Shanghai on commuter CO2. In Handbook on Transport and Urban Transformation in China; Edward Elgar Publishing: Northampton, MA, USA, 2020; p. 182. [Google Scholar]
- Wei, P.; Pan, H. Research on individual carbon dioxide emissions of commuting in peri-urban area of metropolitan cities—An empirical study in Shanghai. Transp. Res. Procedia 2017, 25, 3459–3478. [Google Scholar] [CrossRef]
- Day, K.; Loh, L.; Ruff, R.R.; Rosenblum, R.; Fischer, S.; Lee, K.K. Does bus rapid transit promote walking? An examination of New York City’s select bus service. J. Phys. Act. Health 2014, 11, 1512–1516. [Google Scholar] [CrossRef]
- Zhao, T.-H.; Yang, Z.-H.; Chu, Y.-M. Monotonicity properties of a function involving the psi function with applications. J. Inequal. Appl. 2015, 2015, 193. [Google Scholar] [CrossRef] [Green Version]
- Sadeghiravesh, M.H.; Khosravi, H.; Abolhasani, A.; Ghodsi, M.; Mosavi, A. Fuzzy Logic Model to Assess Desertification Intensity Based on Vulnerability Indices. Acta Polytech. Hung. 2021, 18, 7–24. [Google Scholar] [CrossRef]
- Rafiei-Sardooi, E.; Azareh, A.; Choubin, B.; Mosavi, A.H.; Clague, J.J. Evaluating urban flood risk using hybrid method of TOPSIS and machine learning. Int. J. Disaster Risk Reduct. 2021, 66, 102614. [Google Scholar] [CrossRef]
- Pinter, G.; Mosavi, A.; Felde, I. Artificial intelligence for modeling real estate price using call detail records and hybrid machine learning approach. Entropy 2020, 22, 1421. [Google Scholar] [CrossRef]
- Holtzclaw, J.; Clear, R.; Dittmar, H.; Goldstein, D.; Haas, P. Location efficiency: Neighborhood and socio-economic characteristics determine auto ownership and use-studies in Chicago, Los Angeles and San Francisco. Transp. Plan. Technol. 2002, 25, 1–27. [Google Scholar] [CrossRef]
- Shay, E.; Khattak, A.J. Household travel decision chains: Residential environment, automobile ownership, trips and mode choice. Int. J. Sustain. Transp. 2012, 6, 88–110. [Google Scholar] [CrossRef]
- Zhang, W.; Zhao, Y.; Cao, X.J.; Lu, D.; Chai, Y. Nonlinear effect of accessibility on car ownership in Beijing: Pedestrian-scale neighborhood planning. Transp. Res. Part D Transp. Environ. 2020, 86, 102445. [Google Scholar] [CrossRef]
- Chen, E.; Ye, Z.; Wu, H. Nonlinear effects of built environment on intermodal transit trips considering spatial heterogeneity. Transp. Research Part D Transp. Environ. 2021, 90, 102677. [Google Scholar] [CrossRef]
- Zhao, T.H.; Khan, M.I.; Chu, Y.M. Artificial neural networking (ANN) analysis for heat and entropy generation in flow of non-Newtonian fluid between two rotating disks. Math. Methods Appl. Sci. 2021. [Google Scholar] [CrossRef]
- Zandi, P.; Rahmani, M.; Khanian, M.; Mosavi, A. Agricultural risk management using fuzzy TOPSIS analytical hierarchy process (AHP) and failure mode and effects analysis (FMEA). Agriculture 2020, 10, 504. [Google Scholar] [CrossRef]
- Tavoosi, J.; Zhang, C.; Mohammadzadeh, A.; Mobayen, S.; Mosavi, A.H. Medical image interpolation using recurrent type-2 fuzzy neural network. Front. Neuroinform. 2021, 15, 667375. [Google Scholar] [CrossRef] [PubMed]
- Ma, T.; Aghaabbasi, M.; Ali, M.; Zainol, R.; Jan, A.; Mohamed, A.M.; Mohamed, A. Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm. Sustainability 2022, 14, 3395. [Google Scholar] [CrossRef]
- Ding, C.; Cao, X.J.; Næss, P. Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo. Transp. Res. Part A Policy Pract. 2018, 110, 107–117. [Google Scholar] [CrossRef]
- Stylianou, K.; Dimitriou, L.; Abdel-Aty, M. Big data and road safety: A comprehensive review. In Mobility Patterns, Big Data and Transport Analytics; Elsevier: Amsterdam, The Netherlands, 2019; pp. 297–343. [Google Scholar]
- Mousavi, S.M.; Ghasemi, M.; Dehghan Manshadi, M.; Mosavi, A. Deep learning for wave energy converter modeling using long short-term memory. Mathematics 2021, 9, 871. [Google Scholar] [CrossRef]
- Meiabadi, M.S.; Moradi, M.; Karamimoghadam, M.; Ardabili, S.; Bodaghi, M.; Shokri, M.; Mosavi, A.H. Modeling the producibility of 3D printing in polylactic acid using artificial neural networks and fused filament fabrication. Polymers 2021, 13, 3219. [Google Scholar] [CrossRef]
- Khosravi, K.; Golkarian, A.; Booij, M.J.; Barzegar, R.; Sun, W.; Yaseen, Z.M.; Mosavi, A. Improving daily stochastic streamflow prediction: Comparison of novel hybrid data-mining algorithms. Hydrol. Sci. J. 2021, 66, 1457–1474. [Google Scholar] [CrossRef]
- Ghaemi, A.; Zhian, T.; Pirzadeh, B.; Hashemi Monfared, S.; Mosavi, A. Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers. Environ. Sci. Pollut. Res. 2021, 28, 35971–35990. [Google Scholar] [CrossRef]
- Ebrahimi-Khusfi, Z.; Taghizadeh-Mehrjardi, R.; Roustaei, F.; Ebrahimi-Khusfi, M.; Mosavi, A.H.; Heung, B.; Soleimani-Sardo, M.; Scholten, T. Determining the contribution of environmental factors in controlling dust pollution during cold and warm months of western Iran using different data mining algorithms and game theory. Ecol. Indic. 2021, 132, 108287. [Google Scholar] [CrossRef]
- Aghaabbasi, M.; Shekari, Z.A.; Shah, M.Z.; Olakunle, O.; Armaghani, D.J.; Moeinaddini, M. Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques. Transp. Res. Part A Policy Pract. 2020, 136, 262–281. [Google Scholar] [CrossRef]
- Peng, Y.; Ghahnaviyeh, M.B.; Ahmad, M.N.; Abdollahi, A.; Bagherzadeh, S.A.; Azimy, H.; Mosavi, A.; Karimipour, A. Analysis of the effect of roughness and concentration of Fe3O4/water nanofluid on the boiling heat transfer using the artificial neural network: An experimental and numerical study. Int. J. Therm. Sci. 2021, 163, 106863. [Google Scholar] [CrossRef]
- Panahi, F.; Ehteram, M.; Ahmed, A.N.; Huang, Y.F.; Mosavi, A.; El-Shafie, A. Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging. Ecol. Indic. 2021, 133, 108285. [Google Scholar] [CrossRef]
- Mousavi, S.P.; Atashrouz, S.; Nait Amar, M.; Hemmati-Sarapardeh, A.; Mohaddespour, A.; Mosavi, A. Viscosity of Ionic Liquids: Application of the Eyring’s Theory and a Committee Machine Intelligent System. Molecules 2021, 26, 156. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Aghaabbasi, M.; Ali, M.; Anciferov, S.; Sabitov, L.; Chebotarev, S.; Nabiullina, K.; Sychev, E.; Fediuk, R.; Zainol, R. Hybrid Bayesian Network Models to Investigate the Impact of Built Environment Experience before Adulthood on Students’ Tolerable Travel Time to Campus: Towards Sustainable Commute Behavior. Sustainability 2022, 14, 325. [Google Scholar] [CrossRef]
- Musarat, M.A.; Alaloul, W.S.; Rabbani, M.B.A.; Ali, M.; Altaf, M.; Fediuk, R.; Vatin, N.; Klyuev, S.; Bukhari, H.; Sadiq, A.; et al. Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach. Sustainability 2021, 13, 10720. [Google Scholar] [CrossRef]
- Nafees, A.; Amin, M.N.; Khan, K.; Nazir, K.; Ali, M.; Javed, M.F.; Aslam, F.; Musarat, M.A.; Vatin, N.I. Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Machine Learning Techniques. Polymers 2022, 14, 30. [Google Scholar] [CrossRef]
- Zhao, T.-H.; Wang, M.-K.; Chu, Y.-M. Concavity and bounds involving generalized elliptic integral of the first kind. J. Math. Inequal. 2021, 15, 701–724. [Google Scholar] [CrossRef]
- Tavoosi, J.; Suratgar, A.A.; Menhaj, M.B.; Mosavi, A.; Mohammadzadeh, A.; Ranjbar, E. Modeling renewable energy systems by a self-evolving nonlinear consequent part recurrent type-2 fuzzy system for power prediction. Sustainability 2021, 13, 3301. [Google Scholar] [CrossRef]
- Tareeq, S.M.; Inamura, T. A sample discarding strategy for rapid adaptation to new situation based on Bayesian behavior learning. In Proceedings of the 2008 IEEE International Conference on Robotics and Biomimetics, Bangkok, Thailand, 22–25 February 2009; pp. 1950–1955. [Google Scholar]
- Wu, Q.; Yang, C.; Gao, X.; He, P.; Chen, G. EPAB: Early pattern aware bayesian model for social content popularity prediction. In Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM), Singapore, 17–20 November 2018; pp. 1296–1301. [Google Scholar]
- Liu, T.; Zhang, Y.; Chen, J.; Shen, H. Discovery of association rule of learning action based on Bayesian network. In Proceedings of the 2018 9th International Conference on Information Technology in Medicine and Education (ITME), Hangzhou, China, 19–21 October 2018; pp. 466–470. [Google Scholar]
- Tao, W.; Aghaabbasi, M.; Ali, M.; Almaliki, A.H.; Zainol, R.; Almaliki, A.A.; Hussein, E.E. An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety. Sustainability 2022, 14, 2436. [Google Scholar] [CrossRef]
- Zhao, T.-H.; Wang, M.-K.; Chu, Y.-M. A sharp double inequality involving generalized complete elliptic integral of the first kind. AIMS Math. 2020, 5, 4512–4528. [Google Scholar] [CrossRef]
- Taghizadeh-Mehrjardi, R.; Emadi, M.; Cherati, A.; Heung, B.; Mosavi, A.; Scholten, T. Bio-inspired hybridization of artificial neural networks: An application for mapping the spatial distribution of soil texture fractions. Remote Sens. 2021, 13, 1025. [Google Scholar] [CrossRef]
- Ratanawaraha, A.; Chalermpong, S.; Chullabodhi, C. Walking distance of commuters after modal shift to rail transit in Bangkok. J. East. Asia Soc. Transp. Stud. 2015, 11, 1467–1478. [Google Scholar]
- Rodriguez-Gonzalez, M.; Aguero-Valverde, J. Walking distances from home to bus stops in San José, Costa Rica: Real, perceived, and stated-preference distances. Adv. Transp. Stud. 2017, 43, 63–74. [Google Scholar]
- Townsend, C.; Zacharias, J. Built environment and pedestrian behavior at rail rapid transit stations in Bangkok. Transportation 2010, 37, 317–330. [Google Scholar] [CrossRef]
- Vandebona, U.; Tsukaguchi, H. Impact of urbanization on user expectations related to public transport accessibility. Int. J. Urban Sci. 2013, 17, 199–211. [Google Scholar] [CrossRef]
- Voss, C.; Winters, M.; Frazer, A.; McKay, H. School-travel by public transit: Rethinking active transportation. Prev. Med. Rep. 2015, 2, 65–70. [Google Scholar] [CrossRef] [Green Version]
- Wang, J.; Cao, X. Exploring built environment correlates of walking distance of transit egress in the Twin Cities. J. Transp. Geogr. 2017, 64, 132–138. [Google Scholar] [CrossRef]
- Xi, Y.; Saxe, S.; Miller, E. Accessing the subway in Toronto, Canada: Access mode and catchment areas. Transp. Res. Rec. 2016, 2543, 52–61. [Google Scholar] [CrossRef]
- Zacharias, J.; Zhao, Q. Local environmental factors in walking distance at metro stations. Public Transp. 2018, 10, 91–106. [Google Scholar] [CrossRef]
- Zhao, J.; Deng, W.; Song, Y.; Zhu, Y. What influences Metro station ridership in China? Insights from Nanjing. Cities 2013, 35, 114–124. [Google Scholar] [CrossRef]
- Zuo, T.; Wei, H.; Rohne, A. Determining transit service coverage by non-motorized accessibility to transit: Case study of applying GPS data in Cincinnati metropolitan area. J. Transp. Geogr. 2018, 67, 1–11. [Google Scholar] [CrossRef]
- Friedman, N.; Geiger, D.; Goldszmidt, M. Bayesian network classifiers. Mach. Learn. 1997, 29, 131–163. [Google Scholar] [CrossRef] [Green Version]
- Murphy, P.M. UCI repository of machine learning databases. 1994. Available online: https://archive.ics.uci.edu/ml/datasets.php (accessed on 1 January 2022).
- Chow, C.; Liu, C. Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory 1968, 14, 462–467. [Google Scholar] [CrossRef] [Green Version]
- Cervero, R.; Kockelman, K. Travel demand and the 3Ds: Density, diversity, and design. Transp. Res. Part D: Transp. Environ. 1997, 2, 199–219. [Google Scholar] [CrossRef]
- Targa, F.; Clifton, K.J. Built environment and nonmotorized travel: Evidence from Baltimore city using the NHTS. J. Transp. Stat. 2005, 8, 55–70. [Google Scholar]
- Schneider, R.J. Local environment characteristics associated with walking and taking transit to shopping districts. J. Transp. Land Use 2015, 8, 125–147. [Google Scholar] [CrossRef] [Green Version]
- Chia, J.; Lee, J.; Kamruzzaman, M. Walking to public transit: Exploring variations by socioeconomic status. Int. J. Sustain. Transp. 2016, 10, 805–814. [Google Scholar] [CrossRef] [Green Version]
- Nigro, A.; Bertolini, L.; Moccia, F.D. Land use and public transport integration in small cities and towns: Assessment methodology and application. J. Transp. Geogr. 2019, 74, 110–124. [Google Scholar] [CrossRef] [Green Version]
- Sun, B.; Yin, C. Impacts of a multi-scale built environment and its corresponding moderating effects on commute duration in China. Urban Stud. 2019, 57, 2115–2130. [Google Scholar] [CrossRef]
- Calthorpe, P. The Next American Metropolis: Ecology, Community, and the American Dream; Princeton Architectural Press: New York, NY, USA, 1993. [Google Scholar]
- Marquet, O.; Miralles-Guasch, C. The Walkable city and the importance of the proximity environments for Barcelona’s everyday mobility. Cities 2015, 42, 258–266. [Google Scholar] [CrossRef]
- Forsyth, A.; Oakes, J.M.; Schmitz, K.H.; Hearst, M. Does residential density increase walking and other physical activity? Urban Stud. 2007, 44, 679–697. [Google Scholar] [CrossRef]
- Saelens, B.E.; Handy, S.L. Built environment correlates of walking: A review. Med. Sci. Sports Exerc. 2008, 40, S550. [Google Scholar] [CrossRef] [Green Version]
- Van Holle, V.; Deforche, B.; Van Cauwenberg, J.; Goubert, L.; Maes, L.; Van de Weghe, N.; De Bourdeaudhuij, I. Relationship between the physical environment and different domains of physical activity in European adults: A systematic review. BMC Public Health 2012, 12, 1–17. [Google Scholar] [CrossRef] [Green Version]
- Jiang, Y.; Zegras, P.C.; Mehndiratta, S. Walk the line: Station context, corridor type and bus rapid transit walk access in Jinan, China. J. Transp. Geogr. 2012, 20, 1–14. [Google Scholar] [CrossRef]
- Asadi-Shekari, Z.; Moeinaddini, M.; Aghaabbasi, M.; Cools, M.; Zaly Shah, M. Exploring effective micro-level items for evaluating inclusive walking facilities on urban streets (applied in Johor Bahru, Malaysia). Sustain. Cities Soc. 2019, 49, 101563. [Google Scholar] [CrossRef]
- Aghaabbasi, M.; Shah, M.Z.; Zainol, R. Investigating the Use of Active Transportation Modes Among University Employees Through an Advanced Decision Tree Algorithm. Civ. Sustain. Urban Eng. 2021, 1, 26–49. [Google Scholar] [CrossRef]
- Aghaabbasi, M.; Moeinaddini, M.; Asadi-Shekari, Z.; Shah, M.Z. The equitable use concept in sidewalk design. Cities 2019, 88, 181–190. [Google Scholar] [CrossRef]
Study | Location | Sample Size (Unit) | Analysis Method |
---|---|---|---|
Patterson, Webb, Millett and Laverty [19] | USA | 2448 (passengers) | LRM |
Ratanawaraha et al. [82] | Thailand | 1020 (travelers) | LRM |
Rodriguez-Gonzalez and Aguero-Valverde [83] | Costa Rica | N/A | PDF and CDF |
Sun, Zacharias, Ma and Oreskovic [38] | China | 495 (adults) | LRM |
Townsend and Zacharias [84] | Thailand | 1489 (travelers) | LRM |
Vandebona and Tsukaguchi [85] | Japan | 3560 (persons) | DE |
Voss et al. [86] | Canada | 42 (students) | LRM |
Wang and Cao [87] | USA | 7077 (travelers) | LRM |
Wasfi, Ross and El-Geneidy [27] | Canada | 6913 (trips) | LRM |
Xi et al. [88] | Canada | 21,470 (trips) | DE |
Yu and Lin [39] | USA | 18,180 (trips) | LRM |
Zacharias and Zhao [89] | China | 2409 (passengers) | LRM |
Zhao et al. [90] | China | 1544 (travelers) | LRM |
Zuo et al. [91] | USA | 1330 (trips) | DE |
Variable | Acronym | Category/Data Range |
---|---|---|
Health condition attributes (HCA) | ||
Medical condition results in using bus or subway less frequently | CONDPUB | yes (1) and no (2) |
Medical condition results in giving up driving | CONDRIVE | yes (1) and no (2) |
Opinion of health | HEALTH | excellent (1), very good (2), good (3), fair (4), poor (5) |
Medical condition, length of time | MEDCOND6 | 6 months or less (1), more than 6 months (2), and all life (3) |
Built environment characteristics (BEC) | ||
Category of population density (persons per square mile) in the census block group of the household’s home location | HBPPOPDN | 0–99 (50), 100–499 (300), 500–999 (750), 1000–1999 (1500), 2000–3999 (3000), 4000–9999 (7000), 10,000–24,999 (17,000), and 25,000–999,999 (30,000) |
Individual and household attributes (IHA) | ||
Educational attainment | EDUC | Less than a high school graduate (1), high school graduate or GED (2), some college or associated degree (3), bachelor’s degree (4), graduate degree or professional degree (5) |
Household income | HHFAMINC | less than USD 10,000 (1), 10,000–14,999 (2), 15,000–24,999 (3), 25,000–34,999 (4), 35,000–49,999 (5), 50,000–74,999 (6), 75,000–99,999 (7), 100,000–124,999 (8), 125,000–149,999 (9), 150,000–199,999 (10) and 200,000 or more (11) |
Age | AGE | 16–75 |
Gender | SEX | male (1) and female (2) |
Owned vehicle longer than a year | VEHOWNED | yes (1) and no (2) |
Number of workers in household | WRKCOUNT | 1–4 |
Work attributes (WA) | ||
Flexibility in work arrival time | FLEXTIME | yes (1) and no (2) |
Count of days working from home in the last month | WKFMHMXX | 0–16 |
Full-time or part-time worker | WKFTPT | full time (1) and part time (2) |
Option of working from home | WKRMHM | yes (1) and no (2) |
Work trip attributes (WTA) | ||
Road network distance, in miles, between respondent’s home location and work location | DISTTOWK17 | 0–399.07 |
Minutes spent commuting to work | PUBTIME | 0–50 |
Trip time to work in minutes | TIMETOWK | 0–170 |
Trip time to transit station in minutes | TRACCTM | 0–70 |
Target variable | ||
Walk as mode used to reach public transit station | TRACC_WLK | yes (1) and no (2) |
POV as mode used to reach public transit station | TRACC_POV | yes (1) and no (2) |
Model | Train | Test | ||
---|---|---|---|---|
Correct (%) | Wrong (%) | Correct (%) | Wrong (%) | |
TRACC_WALK | 94.94 | 5.06 | 94.48 | 5.52 |
TRACC_POV | 96.52 | 3.48 | 96.93 | 3.07 |
Factor Type | Variable | Walk | POV |
---|---|---|---|
Health condition attributes (HCA) | |||
HCA | CONDPUB | 0.03 | 0.04 |
HCA | CONDRIVE | 0.02 | 0.04 |
HCA | HEALTH | 0.02 | 0.05 |
HCA | MEDCOND6 | 0.02 | 0.04 |
Built environment characteristics (BEC) | |||
BEC | HBPPOPDN | 0.03 | 0.12 |
Individual and household attributes (IHA) | |||
IHA | EDUC | 0.05 | 0.05 |
IHA | HHFAMINC | 0.07 | 0.05 |
IHA | AGE | 0.05 | 0.08 |
IHA | SEX | 0.02 | 0.05 |
IHA | VEHOWNED | 0.02 | 0.03 |
IHA | WRKCOUNT | 0.06 | 0.07 |
Work attributes (WA) | |||
WA | FLEXTIME | 0.02 | 0.07 |
WA | WKFMHMXX | 0.00 | 0.00 |
WA | WKFTPT | 0.00 | 0.06 |
Work trip attributes (WTA) | |||
WTA | DISTTOWK17 | 0.03 | 0.03 |
WTA | PUBTIME | 0.04 | 0.03 |
WTA | TIMETOWK | 0.05 | 0.09 |
WTA | TRACCTM | 0.42 | 0.07 |
WTA | WKRMHM | 0.04 | 0.03 |
Factor Type | Cumulative Importance | |
---|---|---|
Walk | POV | |
Health condition attributes (HCA) | 0.10 | 0.17 |
Built environment characteristics (BEC) | 0.03 | 0.12 |
Work trip attributes (WTA) | 0.58 | 0.25 |
Work attributes (WA) | 0.02 | 0.13 |
Individual and household attributes (IHA) | 0.27 | 0.33 |
Sum | 1.00 | 1.00 |
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Tang, P.; Aghaabbasi, M.; Ali, M.; Jan, A.; Mohamed, A.M.; Mohamed, A. How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships. Sustainability 2022, 14, 3989. https://doi.org/10.3390/su14073989
Tang P, Aghaabbasi M, Ali M, Jan A, Mohamed AM, Mohamed A. How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships. Sustainability. 2022; 14(7):3989. https://doi.org/10.3390/su14073989
Chicago/Turabian StyleTang, Panyu, Mahdi Aghaabbasi, Mujahid Ali, Amin Jan, Abdeliazim Mustafa Mohamed, and Abdullah Mohamed. 2022. "How Sustainable Is People’s Travel to Reach Public Transit Stations to Go to Work? A Machine Learning Approach to Reveal Complex Relationships" Sustainability 14, no. 7: 3989. https://doi.org/10.3390/su14073989