Evaluating accessibility of small communities via public transit.

Authors

  • Antonio Danesi University of Bologna, Bologna Author
  • Simone Tengattini Rete Ferroviaria Italiana SpA, Bologna Author

DOI:

https://doi.org/10.5604/01.3001.0014.5601

Keywords:

public transit, travel impedance, time-of-day preference curves, schedule delay

Abstract

Accessibility to and from urban centres allows small communities’ dwellers to participate in primary activities and use essential services that are not available on-site, such as educational, work and medical services. Public transport networks are supposed to enhance accessibility and pursue equity principles, overcoming socio-economical differences among people that can exacerbate during crisis. In this paper a methodology is proposed and implemented to assess small communities’ accessibility via public transit. A metric is defined based on the calculation of total travel time, taken as a proxy of travel impedance, with consideration of in-vehicle time, schedule delay and users’ arrival and departure preference curves (i.e. time-of-day functions). A “rooftops” model is specified and implemented under the assumption that travellers cannot accept (scheduled) late arrival or early departure time penalties before and after the participation in their activities in the main urban centre, as many activities rarely admit time-flexibility. Also, a public transport specific impedance factor (PTSIF) is proposed, in order to account for travel impedance determinants, which are a consequence of service scheduling and routing decisions and not due to inherent geographical and infrastructural disadvantages affecting car users too. An application of the methodology for the city of Cesena, Italy, and 90 surrounding small communities is presented. The city is served by train and bus services. Assessment of small communities' accessibility based on both total travel time and PTSIF is presented and discussed. This practice-ready quantitative method can help transport professionals to evaluate impacts on small communities’ accessibility in light of public transport service changes or reduction. Quantitative approach to support strategic decisions is needed, for example, both to assess public transport strengthening politics against depopulation of rural and marginal mountainous areas and to mitigate the effects of possible increasing concentration of services towards high-demand lines, which may follow as a consequence of budget cuts or contingencies, such as vehicle capacity reductions required by sanitary emergencies.

References

Alex, A.P., Manju, V.S., Isaac, K.P. (2019), Modelling of travel behaviour of students using artificial intelligence, Archives of Transport, 51(3), 7-19.

Babu, D., Balan, S., Anjaneyulu, M.V.L.R. (2018), Activity-travel patterns of workers and students: a study from Calicut city, India, Ar-chives of Transport, 46(2), 21-32.

Boisjoly, G., El Geneidy, A. (2016), Daily fluctuations in transit and job availability: a comparative assessment of time-sensitive accessibility measures, Journal of Transport Geography, 52, 73-81.

Cascetta, E. (2009), Transportation Systems Analysis: Models and Applications, Second Edition, Springer.

Cascetta, E., Cartenì, A., Montanino, M. (2016), A behavioral model of accessibility based on the number of available opportunities. Journal of Transport Geography, 51, 45-58.

Cheng, S., Xie, B., Bie, Y., Zhang, Y., Zhang, S. (2018), Measure dynamic individual spatial-temporal accessibility by public transit: integrating time-table and passenger departure time, Journal of Transport Geography, 66, 235-247.

Dalvi, M.Q., Martin, K.M. (1976), The measurement of accessibility: some preliminary results, Transportation, 5, 17-42.

Danesi, A., (2010), The integration of air and rail transport systems: creating a passenger comodal high-speed network in Italy, Proceedings of the 13th International Conference on Transport Science, Portoroz, Slovenia, 27-28th May 2010.

Douglas, N.J., Henn, L., Sloan, K. (2011), Modelling the ability of fare to spread AM peak passenger loads using rooftops, Australian Transport Research Forum, 2011 Proceedings

Fayyaz, S.K., Liu, X.C., Porter, R.J. (2017), Dynamic transit accessibility and transit gap causality analysis, Journal of Transport Geography, 59, 27-39.

Fosgerau, M. (2009), The marginal social cost of headway for a scheduled service, Transportation Research Part B, 43, 813-820.

Giraud, T., Cura, R., Viry, M. (2020). OSRM: Interface Between R and the OpenStreetMapBased Routing Service OSRM. R package version 3.3.3. https://CRAN.R-project.org/pack-age=osrm.

Grammig, J., Hujer, R., Scheidler, M., (2005), Discrete choice modelling in airline network management. Journal of Applied Econometrics, 20, 467-486.

Kim, J., Lee, B. (2019), More than travel time: new accessibility index capturing the connectivity of transit services, Journal of Transport Geography, 78, 8-18.

Koppelman, F.S., Coldren, G.M., Parker, R.A. (2008), Schedule delay impacts on air-travel itinerary demand, Transportation Research Part B: Methodological, 42, 263-273.

Kroes, E., Daly, A. (2018) The economic value of timetable changes, Transportation Research Procedia, 31, 3-17.

Langdon, N., McPherson, C. (2011), CLICSIM: Simulation of passenger crowding on trains and at stations, European Transport Conference 2011, Association for European Transport.

Mavoa, S., Witten, K., McCreanor, T., O’Sullivan, D. (2012), GIS based destination accessibility via public transit and walking in Auckland, New Zealand, Journal of Transport Geography, 20, 15-22.

Mueller, F., Aravazhi, A. (2020), A new generalized travel cost based connectivity metric applied to Scandinavian airports, Transportation Research Part D: Transport and Environment, 81, April 2020.

Munoz, C., Laniado, H., Córdoba, J. (2020), Airline choice model for an international round-trip flight considering outbound and return flight schedules, Archives of Transport, 54(2), 75-93.

Nassir, N., Hickman, M., Malekzadeh, A., Irannezhad, E. (2016), A utility-based travel impedance measure for public transit network accessibility, Transportation Research Part A: Policy and Practice, 88, 26-39.

Páez, A., Scott, D.M., Morency, C. (2012), Measuring accessibility: positive and normative implementations of various accessibility indicators, Journal of Transport Geography, 25, 141-153

Prior, M., Vickers, J. Segal, J. and Quill, J. (2011), Modelling open access train services, European Transport Conference 2011, Association for European Transport.

Rietveld, P., Brons, M. (2001), Quality of huband-spoke networks: the effects of timetable coordination on waiting time and rescheduling time, Journal of Air transport Management, 7, 241-24.

Small, K.A. (1982), The Scheduling of Consumer Activities: Work Trips, The American Economic Review, 72, 467-479.

Teodorović D., Janić M. (2017), Transportation Engineering: Theory, Practice and Modeling, Butterworth-Heinemann, Chapter 7 - Public Transportation Systems, 387-493.

Zeid, M.A., Rossi, T.F., Gardner, B. (2006), Modeling Time-of-Day Choice in Context of Tour- and Activity-Based Models, Transportation Research Record, 1981(1), 42-49.

Downloads

Published

2020-12-31

Issue

Section

Original articles

How to Cite

Danesi, A., & Tengattini, S. (2020). Evaluating accessibility of small communities via public transit. Archives of Transport, 56(4), 59-72. https://doi.org/10.5604/01.3001.0014.5601

Share

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

1-10 of 66

You may also start an advanced similarity search for this article.