Reliable planning of hinterland-port freight network against transfer disruption risks
Abstract
Many previous cases have shown that port operations are susceptible to disruptive events. This paper proposes 2-stage Stochastic Programming (SP) for port users to reliably plan the hinterland-port intermodal freight network with consideration of risk aversion in cost. Probabilistic disruptions of intermodal terminals are considered as scenario-specific. In the 1st stage, intermodal paths are selected to obtain proper network capacities. In the 2nd stage, cargo flows are assigned for each disruption scenario on the planed network. The 2-stage model is firstly formulated in a risk-neutral environment to achieve the minimum expectation of total cost. Then, the Mean-Risk (MR) framework is adopted by incorporating a risk measure tool called Conditional Value-at-Risk (CVaR) into the expectation model, so as to reduce the cost of worst-case disruption scenarios. Benders’ Decomposition (BD) is introduced to efficiently solve the exponential many problem. Some numerical experiments are performed under different risk aversion parameters. With this study, network planners can decide network capacities with reasonable redundancies to improve the freight reliability in a cost-effective way. The proposed method provides a simple approach for the planners to quantify their risk appetites in cost and to impose them in the planning process, hence to trade-off the Expected Cost (EC) and the worst-case cost.
Keyword : hinterland-port freight, reliable planning, disruption risks, 2-stage stochastic programming, conditional value-at-risk, Benders’ decomposition
How to Cite
Wang, L., & Liu, Q. (2022). Reliable planning of hinterland-port freight network against transfer disruption risks. Transport, 37(4), 291–309. https://doi.org/10.3846/transport.2022.17067
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Benders, J. F. 1962. Partitioning procedures for solving mixed-variables programming problems, Numerische Mathematik 4(1): 238–252. https://doi.org/10.1007/bf01386316
Blake, E. S.; Zelinsky, D. A. 2017. Tropical Cyclone Report: Hurricane Harvey (AL092017). National Hurricane Center, Miami, FL, US. 77 p. Available from Internet: https://www.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdf
Brunet, S.; De la Llera, J. C.; Jacobsen, A.; Miranda, E.; Meza, C. 2012. Performance of port facilities in Southern Chile during the 27 February 2010 Maule earthquake, Earthquake Spectra 28(S1): S553–S579. https://doi.org/10.1193/1.4000022
Carturan, F.; Pellegrino, C.; Rossi, R.; Gastaldi, M.; Modena, C. 2013. An integrated procedure for management of bridge networks in seismic areas, Bulletin of Earthquake Engineering 11(2): 543–559. https://doi.org/10.1007/s10518-012-9391-6
Chen, H.; Cullinane, K.; Liu, N. 2017. Developing a model for measuring the resilience of a port-hinterland container transportation network, Transportation Research Part E: Logistics and Transportation Review 97: 282–301. https://doi.org/10.1016/j.tre.2016.10.008
Chen, H.-H.; Yang, C.-B. 2017. Multiperiod portfolio investment using stochastic programming with conditional value at risk, Computers & Operations Research 81: 305–321. https://doi.org/10.1016/j.cor.2016.11.011
Chen, L.; Miller-Hooks, E. 2012. Resilience: an indicator of recovery capability in intermodal freight transport, Transportation Science 46(1): 109–123. https://doi.org/10.1287/trsc.1110.0376
Contreras, I.; Cordeau, J.-F.; Laporte, G. 2011. Benders decomposition for large-scale uncapacitated hub location, Operations Research 59(6): 1477–1490. https://doi.org/10.1287/opre.1110.0965
Cordeau, J.-F.; Stojković, G.; Soumis, F.; Desrosiers, J. 2001. Benders decomposition for simultaneous aircraft routing and crew scheduling, Transportation Science 35(4): 375–388. https://doi.org/10.1287/trsc.35.4.375.10432
Cui, T.; Ouyang, Y.; Shen, Z.-J. M. 2010. Reliable facility location design under the risk of disruptions, Operations Research 58(4): 998–1011. https://doi.org/10.1287/opre.1090.0801
De Camargo, R. S.; Miranda, G.; Luna, H. P. 2008. Benders decomposition for the uncapacitated multiple allocation hub location problem, Computers & Operations Research 35(4): 1047–1064. https://doi.org/10.1016/j.cor.2006.07.002
Dixit, V.; Seshadrinath, N.; Tiwari, M. K. 2016. Performance measures based optimization of supply chain network resilience: a NSGA-II + co-kriging approach, Computers & Industrial Engineering 93: 205–214. https://doi.org/10.1016/j.cie.2015.12.029
Drezner, Z. 1987. Heuristic solution methods for two location problems with unreliable facilities, Journal of the Operational Research Society 38(6): 509–514. https://doi.org/10.2307/2582764
Emecen Kara, E. G. 2016. Risk assessment in the Istanbul strait using Black sea MOU port state control inspections, Sustainability 8(4): 390. https://doi.org/10.3390/su8040390
Faghih-Roohi, S.; Ong, Y.-S.; Asian, S.; Zhang, A. N. 2016. Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks, Annals of Operations Research 247(2): 715–734. https://doi.org/10.1007/s10479-015-1909-2
Fan, Y.; Liu, C. 2010. Solving stochastic transportation network protection problems using the progressive hedging-based method, Networks and Spatial Economics 10(2): 193–208. https://doi.org/10.1007/s11067-008-9062-y
Fan, Y.; Liu, C.; Lee, R.; Kiremidjian, A. S. 2010. Highway network retrofit under seismic hazard, Journal of Infrastructure Systems 16(3): 181–187. https://doi.org/10.1061/(asce)is.1943-555x.0000024
Filippi, C.; Mansini, R.; Stevanato, E. 2017. Mixed integer linear programming models for optimal crop selection, Computers & Operations Research 81: 26–39. https://doi.org/10.1016/j.cor.2016.12.004
Freund, R. M. 2004. Benders’ Decomposition Methods for Structured Optimization, Including Stochastic Optimization. Massachusetts Institute of Technology, Cambridge, MA, US. 23 p.
Gotoh, J.-Y.; Takano, Y. 2007. Newsvendor solutions via conditional value-at-risk minimization, European Journal of Operational Research 179(1): 80–96. https://doi.org/10.1016/j.ejor.2006.03.022
Huang, Y.; Fan, Y.; Cheu, R. L. 2007. Optimal allocation of multiple emergency service resources for protection of critical transportation infrastructure, Transportation Research Record: Journal of the Transportation Research Board 2022: 1–8. https://doi.org/10.3141/2022-01
ISSConline. 2014. A Wave of Strikes Springs the Ports All Over the World. ISSConline, Xiamen, China. Available from Internet: https://www.issconline.com
John, A.; Paraskevadakis, D.; Bury, A.; Yang, Z.; Riahi, R.; Wang, J. 2014. An integrated fuzzy risk assessment for seaport operations, Safety Science 68: 180–194. https://doi.org/10.1016/j.ssci.2014.04.001
Jünger, M.; Liebling, T. M.; Naddef, D.; Nemhauser, G. L.; Pulleyblank, W. R.; Reinelt, G.; Wolsey, L. A. 2010. 50 Years of Integer Programming 1958–2008: from the Early Years to the State-of-the-Art. Springer. 804 p. https://doi.org/10.1007/978-3-540-68279-0
Lei, X.; Shen, S.; Song, Y. 2018. Stochastic maximum flow interdiction problems under heterogeneous risk preferences, Computers & Operations Research 90: 97–109. https://doi.org/10.1016/j.cor.2017.09.004
Lewis, B. M.; Erera, A. L.; Nowak, M. A.; Chelsea, W. 2013. Managing inventory in global supply chains facing port-of-entry disruption risks, Transportation Science 47(2): 162–180. https://doi.org/10.1287/trsc.1120.0406
Li, Q.; Zeng, B.; Savachkin, A. 2013. Reliable facility location design under disruptions, Computers & Operations Research 40(4): 901–909. https://doi.org/10.1016/j.cor.2012.11.012
Li, X.; Ouyang, Y. 2010. A continuum approximation approach to reliable facility location design under correlated probabilistic disruptions, Transportation Research Part B: Methodological 44(4): 535–548. https://doi.org/10.1016/j.trb.2009.09.004
Liu, C.; Fan, Y.; Ordóñez, F. 2009. A two-stage stochastic programming model for transportation network protection, Computers & Operations Research 36(5): 1582–1590. https://doi.org/10.1016/j.cor.2008.03.001
Lu, J.; Gupte, A.; Huang, Y. 2018. A mean-risk mixed integer nonlinear program for transportation network protection, European Journal of Operational Research 265(1): 277–289. https://doi.org/10.1016/j.ejor.2017.07.025
Marufuzzaman, M.; Eksioglu, S. D.; Li, X.; Wang, J. 2014. Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain, Transportation Research Part E: Logistics and Transportation Review 69: 122–145. https://doi.org/10.1016/j.tre.2014.06.008
Miller-Hooks, E.; Chen, L.; Nair, R.; Mahmassani, H. S. 2009. Security and mobility of intermodal freight networks: evaluation framework for simulation and assignment, Transportation Research Record: Journal of the Transportation Research Board 2137: 109–117. https://doi.org/10.3141/2137-12
Miller-Hooks, E.; Zhang, X.; Faturechi, R. 2012. Measuring and maximizing resilience of freight transportation networks, Computers & Operations Research 39(7): 1633–1643. https://doi.org/10.1016/j.cor.2011.09.017
Mohaymany, A. S.; Pirnazar, N. 2007. Critical routes determination for emergency transportation network aftermath earthquake, in 2007 IEEE International Conference on Industrial Engineering and Engineering Management, 2–5 December 2007, Singapore, 817–821. https://doi.org/10.1109/IEEM.2007.4419304
MoT. 2017. Statistical Bulletin on Transportation Industry Development. Ministry of Transport (MoT) of the People’s Republic of China. Available from Internet: https://www.mot.gov.cn
Novati, M.; Achurra-Gonzalez, P.; Foulser-Piggott, R.; Bowman, G.; Bell, M. G. H.; Angeloudis, P. 2015. Modelling the effects of port disruptions: assessment of disaster impacts using a cost-based container flow assignment in liner shipping networks, in Transportation Research Board 94th Annual Meeting, 11–15 January 2015, Washington, DC, US, 1–16.
Noyan, N. 2012. Risk-averse two-stage stochastic programming with an application to disaster management, Computers & Operations Research 39(3): 541–559. https://doi.org/10.1016/j.cor.2011.03.017
Rockafellar, R. T.; Uryasev, S. 2000. Optimization of conditional value-at-risk, Journal of Risk 2(3): 21–41. https://doi.org/10.21314/jor.2000.038
Sawik, T. 2013a. Selection of optimal countermeasure portfolio in IT security planning, Decision Support Systems 55(1): 156–164. https://doi.org/10.1016/j.dss.2013.01.001
Sawik, T. 2013b. Selection of resilient supply portfolio under disruption risks, Omega 41(2): 259–269. https://doi.org/10.1016/j.omega.2012.05.003
Sawik, T. 2011. Selection of supply portfolio under disruption risks, Omega 39(2): 194–208. https://doi.org/10.1016/j.omega.2010.06.007
Shen, Z.-J. M.; Zhan, R. L.; Zhang, J. 2011. The reliable facility location problem: formulations, heuristics, and approximation algorithms, INFORMS Journal on Computing 23(3): 470–482. https://doi.org/10.1287/ijoc.1100.0414
Snyder, L. V.; Daskin, M. S. 2005. Reliability models for facility location: the expected failure cost case, Transportation Science 39(3): 400–416. https://doi.org/10.1287/trsc.1040.0107
Tan, Z.; Wang, G.; Ju, L.; Tan, Q.; Yang, W. 2017. Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response, Energy 124: 198–213. https://doi.org/10.1016/j.energy.2017.02.063
Uryasev, S. 2000. Conditional value-at-risk: optimization algorithms and applications, in Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr), 26–28 March 2000, New York, NY, US, 49–57. https://doi.org/10.1109/cifer.2000.844598
Wang, X.; Meng, Q.; Miao, L. 2016. Delimiting port hinterlands based on intermodal network flows: Model and algorithm, Transportation Research Part E: Logistics and Transportation Review 88: 32–51. https://doi.org/10.1016/j.tre.2016.02.004
Xu, Q.; Zhou, Y.; Jiang, C.; Yu, K.; Niu, X. 2016. A large CVaR-based portfolio selection model with weight constraints, Economic Modelling 59: 436–447. https://doi.org/10.1016/j.econmod.2016.08.014
Yang, Z.; Ng, A. K. Y.; Wang, J. 2014. A new risk quantification approach in port facility security assessment, Transportation Research Part A: Policy and Practice 59: 72–90. https://doi.org/10.1016/j.tra.2013.10.025
Yu, G.; Haskell, W. B.; Liu, Y. 2017. Resilient facility location against the risk of disruptions, Transportation Research Part B: Methodological 104: 82–105. https://doi.org/10.1016/j.trb.2017.06.014
Blake, E. S.; Zelinsky, D. A. 2017. Tropical Cyclone Report: Hurricane Harvey (AL092017). National Hurricane Center, Miami, FL, US. 77 p. Available from Internet: https://www.nhc.noaa.gov/data/tcr/AL092017_Harvey.pdf
Brunet, S.; De la Llera, J. C.; Jacobsen, A.; Miranda, E.; Meza, C. 2012. Performance of port facilities in Southern Chile during the 27 February 2010 Maule earthquake, Earthquake Spectra 28(S1): S553–S579. https://doi.org/10.1193/1.4000022
Carturan, F.; Pellegrino, C.; Rossi, R.; Gastaldi, M.; Modena, C. 2013. An integrated procedure for management of bridge networks in seismic areas, Bulletin of Earthquake Engineering 11(2): 543–559. https://doi.org/10.1007/s10518-012-9391-6
Chen, H.; Cullinane, K.; Liu, N. 2017. Developing a model for measuring the resilience of a port-hinterland container transportation network, Transportation Research Part E: Logistics and Transportation Review 97: 282–301. https://doi.org/10.1016/j.tre.2016.10.008
Chen, H.-H.; Yang, C.-B. 2017. Multiperiod portfolio investment using stochastic programming with conditional value at risk, Computers & Operations Research 81: 305–321. https://doi.org/10.1016/j.cor.2016.11.011
Chen, L.; Miller-Hooks, E. 2012. Resilience: an indicator of recovery capability in intermodal freight transport, Transportation Science 46(1): 109–123. https://doi.org/10.1287/trsc.1110.0376
Contreras, I.; Cordeau, J.-F.; Laporte, G. 2011. Benders decomposition for large-scale uncapacitated hub location, Operations Research 59(6): 1477–1490. https://doi.org/10.1287/opre.1110.0965
Cordeau, J.-F.; Stojković, G.; Soumis, F.; Desrosiers, J. 2001. Benders decomposition for simultaneous aircraft routing and crew scheduling, Transportation Science 35(4): 375–388. https://doi.org/10.1287/trsc.35.4.375.10432
Cui, T.; Ouyang, Y.; Shen, Z.-J. M. 2010. Reliable facility location design under the risk of disruptions, Operations Research 58(4): 998–1011. https://doi.org/10.1287/opre.1090.0801
De Camargo, R. S.; Miranda, G.; Luna, H. P. 2008. Benders decomposition for the uncapacitated multiple allocation hub location problem, Computers & Operations Research 35(4): 1047–1064. https://doi.org/10.1016/j.cor.2006.07.002
Dixit, V.; Seshadrinath, N.; Tiwari, M. K. 2016. Performance measures based optimization of supply chain network resilience: a NSGA-II + co-kriging approach, Computers & Industrial Engineering 93: 205–214. https://doi.org/10.1016/j.cie.2015.12.029
Drezner, Z. 1987. Heuristic solution methods for two location problems with unreliable facilities, Journal of the Operational Research Society 38(6): 509–514. https://doi.org/10.2307/2582764
Emecen Kara, E. G. 2016. Risk assessment in the Istanbul strait using Black sea MOU port state control inspections, Sustainability 8(4): 390. https://doi.org/10.3390/su8040390
Faghih-Roohi, S.; Ong, Y.-S.; Asian, S.; Zhang, A. N. 2016. Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks, Annals of Operations Research 247(2): 715–734. https://doi.org/10.1007/s10479-015-1909-2
Fan, Y.; Liu, C. 2010. Solving stochastic transportation network protection problems using the progressive hedging-based method, Networks and Spatial Economics 10(2): 193–208. https://doi.org/10.1007/s11067-008-9062-y
Fan, Y.; Liu, C.; Lee, R.; Kiremidjian, A. S. 2010. Highway network retrofit under seismic hazard, Journal of Infrastructure Systems 16(3): 181–187. https://doi.org/10.1061/(asce)is.1943-555x.0000024
Filippi, C.; Mansini, R.; Stevanato, E. 2017. Mixed integer linear programming models for optimal crop selection, Computers & Operations Research 81: 26–39. https://doi.org/10.1016/j.cor.2016.12.004
Freund, R. M. 2004. Benders’ Decomposition Methods for Structured Optimization, Including Stochastic Optimization. Massachusetts Institute of Technology, Cambridge, MA, US. 23 p.
Gotoh, J.-Y.; Takano, Y. 2007. Newsvendor solutions via conditional value-at-risk minimization, European Journal of Operational Research 179(1): 80–96. https://doi.org/10.1016/j.ejor.2006.03.022
Huang, Y.; Fan, Y.; Cheu, R. L. 2007. Optimal allocation of multiple emergency service resources for protection of critical transportation infrastructure, Transportation Research Record: Journal of the Transportation Research Board 2022: 1–8. https://doi.org/10.3141/2022-01
ISSConline. 2014. A Wave of Strikes Springs the Ports All Over the World. ISSConline, Xiamen, China. Available from Internet: https://www.issconline.com
John, A.; Paraskevadakis, D.; Bury, A.; Yang, Z.; Riahi, R.; Wang, J. 2014. An integrated fuzzy risk assessment for seaport operations, Safety Science 68: 180–194. https://doi.org/10.1016/j.ssci.2014.04.001
Jünger, M.; Liebling, T. M.; Naddef, D.; Nemhauser, G. L.; Pulleyblank, W. R.; Reinelt, G.; Wolsey, L. A. 2010. 50 Years of Integer Programming 1958–2008: from the Early Years to the State-of-the-Art. Springer. 804 p. https://doi.org/10.1007/978-3-540-68279-0
Lei, X.; Shen, S.; Song, Y. 2018. Stochastic maximum flow interdiction problems under heterogeneous risk preferences, Computers & Operations Research 90: 97–109. https://doi.org/10.1016/j.cor.2017.09.004
Lewis, B. M.; Erera, A. L.; Nowak, M. A.; Chelsea, W. 2013. Managing inventory in global supply chains facing port-of-entry disruption risks, Transportation Science 47(2): 162–180. https://doi.org/10.1287/trsc.1120.0406
Li, Q.; Zeng, B.; Savachkin, A. 2013. Reliable facility location design under disruptions, Computers & Operations Research 40(4): 901–909. https://doi.org/10.1016/j.cor.2012.11.012
Li, X.; Ouyang, Y. 2010. A continuum approximation approach to reliable facility location design under correlated probabilistic disruptions, Transportation Research Part B: Methodological 44(4): 535–548. https://doi.org/10.1016/j.trb.2009.09.004
Liu, C.; Fan, Y.; Ordóñez, F. 2009. A two-stage stochastic programming model for transportation network protection, Computers & Operations Research 36(5): 1582–1590. https://doi.org/10.1016/j.cor.2008.03.001
Lu, J.; Gupte, A.; Huang, Y. 2018. A mean-risk mixed integer nonlinear program for transportation network protection, European Journal of Operational Research 265(1): 277–289. https://doi.org/10.1016/j.ejor.2017.07.025
Marufuzzaman, M.; Eksioglu, S. D.; Li, X.; Wang, J. 2014. Analyzing the impact of intermodal-related risk to the design and management of biofuel supply chain, Transportation Research Part E: Logistics and Transportation Review 69: 122–145. https://doi.org/10.1016/j.tre.2014.06.008
Miller-Hooks, E.; Chen, L.; Nair, R.; Mahmassani, H. S. 2009. Security and mobility of intermodal freight networks: evaluation framework for simulation and assignment, Transportation Research Record: Journal of the Transportation Research Board 2137: 109–117. https://doi.org/10.3141/2137-12
Miller-Hooks, E.; Zhang, X.; Faturechi, R. 2012. Measuring and maximizing resilience of freight transportation networks, Computers & Operations Research 39(7): 1633–1643. https://doi.org/10.1016/j.cor.2011.09.017
Mohaymany, A. S.; Pirnazar, N. 2007. Critical routes determination for emergency transportation network aftermath earthquake, in 2007 IEEE International Conference on Industrial Engineering and Engineering Management, 2–5 December 2007, Singapore, 817–821. https://doi.org/10.1109/IEEM.2007.4419304
MoT. 2017. Statistical Bulletin on Transportation Industry Development. Ministry of Transport (MoT) of the People’s Republic of China. Available from Internet: https://www.mot.gov.cn
Novati, M.; Achurra-Gonzalez, P.; Foulser-Piggott, R.; Bowman, G.; Bell, M. G. H.; Angeloudis, P. 2015. Modelling the effects of port disruptions: assessment of disaster impacts using a cost-based container flow assignment in liner shipping networks, in Transportation Research Board 94th Annual Meeting, 11–15 January 2015, Washington, DC, US, 1–16.
Noyan, N. 2012. Risk-averse two-stage stochastic programming with an application to disaster management, Computers & Operations Research 39(3): 541–559. https://doi.org/10.1016/j.cor.2011.03.017
Rockafellar, R. T.; Uryasev, S. 2000. Optimization of conditional value-at-risk, Journal of Risk 2(3): 21–41. https://doi.org/10.21314/jor.2000.038
Sawik, T. 2013a. Selection of optimal countermeasure portfolio in IT security planning, Decision Support Systems 55(1): 156–164. https://doi.org/10.1016/j.dss.2013.01.001
Sawik, T. 2013b. Selection of resilient supply portfolio under disruption risks, Omega 41(2): 259–269. https://doi.org/10.1016/j.omega.2012.05.003
Sawik, T. 2011. Selection of supply portfolio under disruption risks, Omega 39(2): 194–208. https://doi.org/10.1016/j.omega.2010.06.007
Shen, Z.-J. M.; Zhan, R. L.; Zhang, J. 2011. The reliable facility location problem: formulations, heuristics, and approximation algorithms, INFORMS Journal on Computing 23(3): 470–482. https://doi.org/10.1287/ijoc.1100.0414
Snyder, L. V.; Daskin, M. S. 2005. Reliability models for facility location: the expected failure cost case, Transportation Science 39(3): 400–416. https://doi.org/10.1287/trsc.1040.0107
Tan, Z.; Wang, G.; Ju, L.; Tan, Q.; Yang, W. 2017. Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response, Energy 124: 198–213. https://doi.org/10.1016/j.energy.2017.02.063
Uryasev, S. 2000. Conditional value-at-risk: optimization algorithms and applications, in Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr), 26–28 March 2000, New York, NY, US, 49–57. https://doi.org/10.1109/cifer.2000.844598
Wang, X.; Meng, Q.; Miao, L. 2016. Delimiting port hinterlands based on intermodal network flows: Model and algorithm, Transportation Research Part E: Logistics and Transportation Review 88: 32–51. https://doi.org/10.1016/j.tre.2016.02.004
Xu, Q.; Zhou, Y.; Jiang, C.; Yu, K.; Niu, X. 2016. A large CVaR-based portfolio selection model with weight constraints, Economic Modelling 59: 436–447. https://doi.org/10.1016/j.econmod.2016.08.014
Yang, Z.; Ng, A. K. Y.; Wang, J. 2014. A new risk quantification approach in port facility security assessment, Transportation Research Part A: Policy and Practice 59: 72–90. https://doi.org/10.1016/j.tra.2013.10.025
Yu, G.; Haskell, W. B.; Liu, Y. 2017. Resilient facility location against the risk of disruptions, Transportation Research Part B: Methodological 104: 82–105. https://doi.org/10.1016/j.trb.2017.06.014