Accounting for uncertainty and variation in accessibility metrics for public transport sketch planning


  • Matthew Wigginton Conway Conveyal (current affiliation: School of Geographical Sciences and Urban Planning, Arizona State University)
  • Andrew Byrd Conveyal
  • Michael van Eggermond Future Cities Laboratory, Singapore-ETH Centre



accessibility, public transport, scenario planning, probabilistic scenario comparison


Accessibility is increasingly used as a metric when evaluating changes to public transport systems. Transit travel times contain variation depending on when one departs relative to when a transit vehicle arrives, and how well transfers are coordinated given a particular timetable. In addition, there is necessarily uncertainty in the value of the accessibility metric during sketch planning processes, due to scenarios which are underspecified because detailed schedule information is not yet available. This article presents a method to extend the concept of "reliable" accessibility to transit to address the first issue, and create confidence intervals and hypothesis tests to address the second.

Author Biography

Matthew Wigginton Conway, Conveyal (current affiliation: School of Geographical Sciences and Urban Planning, Arizona State University)

I'm a Project Manager for Analysis at Conveyal, where I develop software and work with cities on accessibility planning for their public transit systems.


Abdi, H. 2007. The Bonferonni and Šidák Corrections for Multiple Comparisons. In N. Salkind, ed., Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage. URL:

Anselin, L. 1995. Local indicators of spatial association—LISA. Geographical Analysis, 27(2):93–115.doi: 10.1111/j.1538-4632.1995.tb00338.x. URL:


APPM Management Consultants and Goudappel Coffeng. 2016. Toekomstbeeld OV: Pilot Zuidelijke Randstad. Technical report. URL:

Chakirov, A. and A. Erath. 2012. Activity identification and primary location modeling based on smart card payment data for public transport. In 13th International Conference on Travel Behaviour Research. Toronto.

Chen, B. Y., Q. Li, D. Wang, S.-L. Shaw, W. H. K. Lam, H. Yuan, and Z. Fang. 2013. Reliable space–time prisms under travel time uncertainty. Annals of the Association of American Geographers, 103(6):1502–1521. doi: 10.1080/00045608.2013.834236. URL:

Chen, B. Y., H. Yuan, Q. Li, D. Wang, and S. L. Shaw. 2017. Measuring place-based accessibility under

travel time uncertainty. International Journal of Geographical Information Science, 31(4):783–804. doi: 10.1080/13658816.2016.1238919. URL:


Conway, M. W., A. Byrd, and M. van der Linden. 2017. Evidence-based transit and land use sketch planning using interactive accessibility methods on combined schedule and headway-based networks. Transportation Research Record, 2653:45–53. doi: 10.3141/2653-06. URL: http:


da Silva, A. R. and A. S. Fotheringham. 2015. The multiple testing issue in geographically weighted regression. Geographical Analysis, 48(3):233–247. doi: 10.1111/gean.12084. URL

Davison, A. C. and D. V. Hinkley. 1997. Bootstrap methods and their application. Cambridge, England: Cambridge University Press.

Efron, B. 1979. Bootstrap methods: another look at the jackknife. The Annals of Statistics, 7(1):1–26.


Efron, B. and R. J. Tibshirani. 1993. An Introduction to the Bootstrap. Boca Raton, FL: Chapman and Hall/CRC.

El-Geneidy, A. M. and D. M. Levinson. 2006. Access to Destinations: Development of Accessibility Measures. Technical Report MN/RC-2006-16, University of Minnesota. URL:

Farber, S. and L. Fu. 2017. Dynamic public transit accessibility using travel time cubes: Comparing the effects of infrastructure (dis) investments over time. Computers, Environment and Urban Systems. doi: 10.1016/j.compenvurbsys.2016.10.005. URL:

Farber, S. and M. Grandez. 2017. Transit accessibility, land development and socioeconomic priority: A typology of planned station catchment areas in the Greater Toronto and Hamilton Area. Journal of Transport and Land Use, 10(1):1–24. doi: 10.5198/jtlu.2017.980. URL:

Farber, S., M. Z. Morang, and M. J. Widener. 2014. Temporal variability in transit-based accessibility to supermarkets. Applied Geography, 53(C):149–159. doi: 10.1016/j.apgeog.2014.06.012. URL:

Fourie, P. J., A. Erath, S. A. Ordóñez Medina, A. Chakirov, and K. W. Axhausen. 2016. Using smartcard data for agent-based transport simulation: the case of Singapore. In J.-D. Schmoecker and F. Kurauchi, eds., Public transport planning with smart card data. Boca Raton, FL: CRC Press.

Geurs, K. T. and B. van Wee. 2006. Accessibility measures: a literature review. In Accessibility, land use and transport: Accessibility evaluation of land-use and transport developments and policy strategies, chapter 2. Eburon. Reprinted from Journal of Transport Geography, 12(2), 2004, 127–140., URL:

Hall, R. W. 1983. Travel outcome and performance: The effect of uncertainty on accessibility. Transportation Research Part B: Methodological, 17(4):275–290. doi: 10.1016/0191-2615(83)90046-2. URL:

Horni, A., K. Nigel, and K. W. Axhausen. 2016. The multi-agent transport simulation MATSim. London: Ubiquity Press.

Lahiri, S. N. 2003. Resampling methods for dependent data. New York: Springer.

Ordóñez Medina, S. A. and A. Erath. 2013. Estimating dynamic workplace capacities by means of public transport smart card data and household travel survey in Singapore. Transportation Research Record, 2344:20–30. doi: 10.3141/2344-03. URL:

Owen, A. and H. Jiang. 2015. Temporal Sampling Intervals and Service Frequency Harmonics in Transit Accessibility Evaluation. Unpublished.

Owen, A. and D. Levinson. 2014. Access Across America: Transit 2014 Methodology.Technical report, University of Minnesota. URL:


Palmateer, C., A. Owen, and D. M. Levinson. 2016. Accessibility evaluation of the Metro Transit A-Line. Technical report, University of Minnesota. URL:

Spear, B. 2011. Improving employment data for transportation planning. Technical Report NCHRP

-36, Task 098, American Association of State Highway and Transportation Officials. URL:

Stewart, A. F. 2017. Advancing accessibility: public transport and urban space. Phd thesis, Massachusetts

Institute of Technology, Cambridge, MA. URL:

Stewart, A. F. and P. C. Zegras. 2016. CoAXs: A Collaborative Accessibility-based Stakeholder Engagement System for communicating transport impacts. Research in Transportation Economics, pp. 1–11. doi: 10.1016/j.retrec.2016.07.016. URL

Walker, J. 2010. Should we redesign our bus network? How? URL:


Wasserstein, R. L. and N. A. Lazar. 2016. Thee ASA’s statement on p-values: Context, process, and

purpose. The American Statistician, 70(2):129–133. doi: 10.1080/00031305.2016.1154108. URL:

Zhao, Y. and K. M. Kockelman. 2002. The propagation of uncertainty through travel demand models: An exploratory analysis. The Annals of Regional Science, 36(1):145–163. doi: 10.1007/s001680200072. URL:




How to Cite

Conway, M. W., Byrd, A., & van Eggermond, M. (2018). Accounting for uncertainty and variation in accessibility metrics for public transport sketch planning. Journal of Transport and Land Use, 11(1).