Abstract

Growth in travel demand exacerbates the road network vulnerability. This study aims to explore how the source of travel demand, that is, the land-use spatial layout, impacts road network vulnerability. Based on the interaction between land use and transportation, this study develops a new raster-based road network vulnerability assessment model and employs a logistic model to quantify the correlation between land use and vulnerability. To assess the raster-based vulnerability, this study uses the change in the total travel time for all O-D pairs before and after the disruption of all intersecting links and nodes within the geographical extent of a grid. The central city in Wuhan, China was selected as the case study which was divided into 2,096 grids of uniformly shaped and sized cells. Highly vulnerable areas showed a centripetal distribution trend and were mainly concentrated in the areas around the bridges, expressways, and arterial roads. The logistic model revealed the statistical results that the closer to residential land, public service land, and water area, the higher the vulnerability. Intensive density and land-use mix increase the vulnerability of road networks. High-risk road links were identified for adapting strategies by overlapping the congested road maps with the vulnerability results. Police implications were summarized to mitigate road vulnerability. This method provided technical support for prioritizing the improvement of road network resilience.

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Acknowledgments

This research was supported by National Science Founding of China (72131008) and National Social Science Founding of China (18BGL270).

References

Alexander, L., S. Jiang, M. Murga, and M. C. González. 2015. “Origin–destination trips by purpose and time of day inferred from mobile phone data.” Transp. Res. Part C Emerging Technol. 58 (B): 240–250. https://doi.org/10.1016/j.trc.2015.02.018.
Balijepalli, C., and O. Oppong. 2014. “Measuring vulnerability of road network considering the extent of serviceability of critical road links in urban areas.” J. Transp. Geogr. 39: 145–155. https://doi.org/10.1016/j.jtrangeo.2014.06.025.
Bell, M. G. H. 2000. “A game theory approach to measuring the performance reliability of transport networks.” Transp. Res. Part B Methodol. 34 (6): 533–545. https://doi.org/10.1016/S0191-2615(99)00042-9.
Berdica, K. 2002. “An introduction to road vulnerability: What has been done, is done and should be done.” Transp. Policy 9 (2): 117–127. https://doi.org/10.1016/S0967-070X(02)00011-2.
Calabrese, F., G. Di Lorenzon, L. Liu, and C. Ratti. 2011. “Estimating origin–destination flows using mobile phone location data.” IEEE Pervasive Comput. 10 (04): 36–44. https://doi.org/10.1109/MPRV.2011.41.
Chen, B. Y., W. H. K. Lam, A. Sumalee, Q. Li, and Z.-C. Li. 2012. “Vulnerability analysis for large-scale and congested road networks with demand uncertainty.” Transp. Res. Part A Policy Pract. 46 (3): 501–516. https://doi.org/10.1016/j.tra.2011.11.018.
Cube Voyager. 2021. “Transportation and land-use modeling.” Accessed January 17, 2021. https://www.bentley.com/en/products/brands/cube.
Demirel, H., M. Kompil, and F. Nemry. 2015. “A framework to analyze the vulnerability of European road networks due to Sea-Level Rise (SLR) and sea storm surges.” Transp. Res. Part A Policy Pract. 81: 62–76. https://doi.org/10.1016/j.tra.2015.05.002.
El-Rashidy, R. A., and S. M. Grant-Muller. 2014. “An assessment method for highway network vulnerability.” J. Transp. Geogr. 34: 34–43. https://doi.org/10.1016/j.jtrangeo.2013.10.017.
Feng, Y., Y. Liu, X. Tong, M. Liu, and S. Deng. 2011. “Modeling dynamic urban growth using cellular automata and particle swarm optimization rules.” Landscape Urban Plann. 102 (3): 188–196. https://doi.org/10.1016/j.landurbplan.2011.04.004.
Gecchele, G., R. Ceccato, and M. Gastaldi. 2019. “Road network vulnerability analysis: Case study considering travel demand and accessibility changes.” J. Transp. Eng. Part A. Syst. 145 (7): 05019004. https://doi.org/10.1061/JTEPBS.0000252.
Hardiansyah, I. Muthohar, C. Balijepalli, and S. Priyanto. 2020. “Analysing vulnerability of road network and guiding evacuees to sheltered areas: Case study of Mt Merapi, Central Java, Indonesia.” Case Stud. Transp. Policy 8 (4): 1329–1340. https://doi.org/10.1016/j.cstp.2020.09.004.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An introduction to statistical learning. New York: Springer.
Jenelius, E., and L.-G. Mattsson. 2012. “Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study.” Transp. Res. Part A Policy Pract. 46 (5): 746–760. https://doi.org/10.1016/j.tra.2012.02.003.
Jenelius, E., and L.-G. Mattsson. 2015. “Road network vulnerability analysis: Conceptualization, implementation and application.” Comput. Environ. Urban Syst. 49: 136–147. https://doi.org/10.1016/j.compenvurbsys.2014.02.003.
Jenelius, E., T. Petersen, and L.-G. Mattsson. 2006. “Importance and exposure in road network vulnerability analysis.” Transp. Res. Part A Policy Pract. 40 (7): 537–560. https://doi.org/10.1016/j.tra.2005.11.003.
Jiang, R., Q.-C. Lu, and Z.-R. Peng. 2018. “A station-based rail transit network vulnerability measure considering land use dependency.” J. Transp. Geogr. 66: 10–18. https://doi.org/10.1016/j.jtrangeo.2017.09.009.
Khademi, N., B. Balaei, M. Shahri, M. Mirzaei, B. Sarrafi, M. Zahabiun, and A. S. Mohaymany. 2015. “Transportation network vulnerability analysis for the case of a catastrophic earthquake.” Int. J. Disaster Risk Reduct. 12: 234–254. https://doi.org/10.1016/j.ijdrr.2015.01.009.
Knoop, V. L., M. Snelder, H. J. van Zuylen, and S. P. Hoogendoorn. 2012. “Link-level vulnerability indicators for real-world networks.” Transp. Res. Part A Policy Pract. 46 (5): 843–854. https://doi.org/10.1016/j.tra.2012.02.004.
Lee, Y., and S. D. Brody. 2018. “Examining the impact of land use on flood losses in Seoul, Korea.” Land Use Policy 70 (Suppl. C): 500–509. https://doi.org/10.1016/j.landusepol.2017.11.019.
López, F. A., A. Páez, J. A. Carrasco, and N. A. Ruminot. 2017. “Vulnerability of nodes under controlled network topology and flow autocorrelation conditions.” J. Transp. Geogr. 59: 77–87. https://doi.org/10.1016/j.jtrangeo.2017.02.002.
Li, X., Y. Liu, Z. Gao, and D. Liu. 2016. “Linkage between passenger demand and surrounding land-use patterns at urban rail transit stations: A canonical correlation analysis method and case study in Chongqing.” Int. J. Transp. Sci. Technol. 5 (1): 10–16. https://doi.org/10.1016/j.ijtst.2016.06.002.
Matisziw, T. C., and A. T. Murray. 2009. “Modeling s–t path availability to support disaster vulnerability assessment of network infrastructure.” Comput. Oper. Res. 36 (1): 16–26. https://doi.org/10.1016/j.cor.2007.09.004.
Morelli, A. B., and A. L. Cunha. 2021. “Measuring urban road network vulnerability to extreme events: An application for urban floods.” Transp. Res. Part D Transp. Environ. 93: 102770. https://doi.org/10.1016/j.trd.2021.102770.
Nogal, M., O. Morales Nápoles, and A. O’Connor. 2019. “Structured expert judgement to understand the intrinsic vulnerability of traffic networks.” Transp. Res. Part A Policy Pract. 127: 136–152. https://doi.org/10.1016/j.tra.2019.07.006.
Nogal, M., A. O’Connor, B. Martinez-Pastor, and B. Caulfield. 2017. “Novel probabilistic resilience assessment framework of transportation networks against extreme weather events.” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A: Civ. Eng. 3 (3): 04017004. https://doi.org/10.1061/AJRUA6.0000908.
Sun, Z., F. Wu, C. Shi, and J. Zhan. 2016. “The impact of land use change on water balance in Zhangye city, China.” Phys. Chem. Earth. A/B/C/ 96: 64–73. https://doi.org/10.1016/j.pce.2016.06.004.
Taylor, M. A. P. 2017a. “Chapter 2: Critical infrastructure, services, and locations.” In Vulnerability analysis for transportation networks, edited by M. A. P. Taylor, 19–48. Amsterdam, Netherlands: Elsevier.
Taylor, M. A. P. 2017b. “Chapter 3: Methods for vulnerability analysis.” In Vulnerability analysis for transportation networks, edited by M. A. P. Taylor, 49–85. Amsterdam, Netherlands: Elsevier.
Taylor, M. A. P., S. V. C. Sekhar, and G. M. D’Este. 2006. “Application of accessibility based methods for vulnerability analysis of strategic road networks.” Networks Spatial Econ. 6 (3–4): 267–291. https://doi.org/10.1007/s11067-006-9284-9.
Trucco, P., E. Cagno, and M. De Ambroggi. 2012. “Dynamic functional modelling of vulnerability and interoperability of critical infrastructures.” Reliab. Eng. Syst. Saf. 105: 51–63. https://doi.org/10.1016/j.ress.2011.12.003.
Walker, B., C. S. Holling, S. R. Carpenter, and A. Kinzig. 2004. “Resilience, adaptability and transformability in social–ecological systems.” Ecol. Soc. 9 (2): 5. http://www.ecologyandsociety.org/vol9/iss2/art5/.
Watling, D., and N. C. Balijepalli. 2012. “A method to assess demand growth vulnerability of travel times on road network links.” Transp. Res. Part A Policy Pract. 46 (5): 772–789. https://doi.org/10.1016/j.tra.2012.02.009.
Yang, X., X. Ban, and J. Mitchell. 2018. “Modeling multimodal transportation network emergency evacuation considering evacuees’ cooperative behavior.” Transp. Res. Part A Policy Pract. 114: 380–397. https://doi.org/10.1016/j.tra.2018.01.037.
Zhang, Q., H. Yu, Z. Li, G. Zhang, and D. T. Ma. 2020. “Assessing potential likelihood and impacts of landslides on transportation network vulnerability.” Transp. Res. Part D Transp. Environ. 82: 102304. https://doi.org/10.1016/j.trd.2020.102304.
Zhao, L., and Z.-R. Peng. 2015. “LandSys II: Agent-based land use–forecast model with artificial neural networks and multiagent model.” J. Urban Plann. Dev. 141 (4): 04014045. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000255.
Zhao, L., and L. Shen. 2019. “The impacts of rail transit on future urban land use development: A case study in Wuhan, China.” Transp. Policy 81: 396–405. https://doi.org/10.1016/j.tranpol.2018.05.004.
Zhao, P., D. Liu, Z. Yu, and H. Hu. 2020. “Long commutes and transport inequity in China’s growing megacity: New evidence from Beijing using mobile phone data.” Travel Behav. Soc. 20: 248–263. https://doi.org/10.1016/j.tbs.2020.04.007.
Zhong, H., J. Wang, T.-L. Yip, and Y. Gu. 2018. “An innovative gravity-based approach to assess vulnerability of a Hazmat road transportation network: A case study of Guangzhou, China.” Transp. Res. Part D Transp. Environ. 62: 659–671. https://doi.org/10.1016/j.trd.2018.03.003.

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Go to Journal of Urban Planning and Development
Journal of Urban Planning and Development
Volume 148Issue 3September 2022

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Received: Jul 13, 2021
Accepted: Mar 22, 2022
Published online: May 27, 2022
Published in print: Sep 1, 2022
Discussion open until: Oct 27, 2022

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Professor, School of Architecture and Urban Planning, Huazhong Univ. of Science and Technology, Wuhan 430074, P.R. China (corresponding author). ORCID: https://orcid.org/0000-0003-2549-9795. Email: [email protected]
Shuxian Wang [email protected]
Ph.D. Candidate, School of Architecture and Urban Planning, Huazhong Univ. of Science and Technology, Wuhan 430074, P.R. China. Email: [email protected]
Jialing Wei [email protected]
Staff, Division of Planning and Institutional Reform, Guangxi Office on Planning, Development and Management of the Beibu Gulf Economic Zone, Nanning 536000, China. Email: [email protected]
Master’s Student, School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, P.R. China. ORCID: https://orcid.org/0000-0001-5418-3971. Email: [email protected]

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