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Analysing improvements to on-street public transport systems: a mesoscopic model approach

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Abstract

Light rail transit and bus rapid transit have shown to be efficient and cost-effective in improving public transport systems in cities around the world. As these systems comprise various elements, which can be tailored to any given setting, e.g. pre-board fare-collection, holding strategies and other advanced public transport systems (APTS), the attractiveness of such systems depends heavily on their implementation. In the early planning stage it is advantageous to deploy simple and transparent models to evaluate possible ways of implementation. For this purpose, the present study develops a mesoscopic model which makes it possible to evaluate public transport operations in details, including dwell times, intelligent traffic signal timings and holding strategies while modelling impacts from other traffic using statistical distributional data thereby ensuring simplicity in use and fast computational times. This makes it appropriate for analysing the impacts of improvements to public transport operations, individually or in combination, in early planning stages. The paper presents a joint measure of reliability for such evaluations based on passengers’ perceived travel time by considering headway time regularity and running time variability, i.e. taking into account waiting time and in-vehicle time. The approach was applied on a case study by assessing the effects of implementing segregated infrastructure and APTS elements, individually and in combination. The results showed that the reliability of on-street public transport operations mainly depends on APTS elements, and especially holding strategies, whereas pure infrastructure improvements induced travel time reductions. The results further suggested that synergy effects can be obtained by planning on-street public transport coherently in terms of reduced travel times and increased reliability.

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Notes

  1. Stamp cards are 10-fare cards that must be stamped in a machine when entering the vehicle.

References

  • Andersson P-Å, Scalia-Tomba G-P (1981) A mathematical model of an urban bus route. Transp Res Part B Methodol 15:249–266. doi:10.1016/0191-2615(81)90011-4

    Article  Google Scholar 

  • Avizienis A, Laprie J-C, Randell B (2001) Fundamental concepts of dependability. University of Newcastle upon Tyne, Computing Science

  • Balcombe R, Mackett R, Paulley N et al (2004) The demand for public transport: a practical guide. Transportation Research Laboratory, London

    Google Scholar 

  • Balmer M, Meister K, Rieser M et al (2008) Agent-based simulation of travel demand: Structure and computational performance of MATSim-T. In: 2nd TRB conference on innovations in travel modeling Portland, Portland, pp 1–33

  • Cats O (2011) Dynamic modelling of transit operations and passenger decisions. KTH-Royal Institute of Technology

  • Cats O (2013) Multi-agent transit operations and assignment model. Proc Comput Sci 19:809–814. doi:10.1016/j.procs.2013.06.107

    Article  Google Scholar 

  • Cats O (2016) The robustness value of public transport development plans. J Transp Geogr 51:236–246. doi:10.1016/j.jtrangeo.2016.01.011

    Article  Google Scholar 

  • Cats O, Jenelius E (2014) Dynamic vulnerability analysis of public transport networks: mitigation effects of real-time information. Netw Spat Econ 14:435–463. doi:10.1007/s11067-014-9237-7

    Article  Google Scholar 

  • Cats O, Burghout W, Toledo T, Koutsopoulos HN (2010) Mesoscopic modeling of bus public transportation. Transp Res Rec J Transp Res Board 2188:9–18. doi:10.3141/2188-02

    Article  Google Scholar 

  • Cats O, Larijani AN, Koutsopoulos HN, Burghout W (2012) Impacts of holding control strategies on transit performance. Transp Res Rec J Transp Res Board 2216:51–58. doi:10.3141/2216-06

    Article  Google Scholar 

  • Cats O, West J, Eliasson J (2016) A dynamic stochastic model for evaluating congestion and crowding effects in transit systems. Transp Res Part B Methodol 89:43–57. doi:10.1016/j.trb.2016.04.001

    Article  Google Scholar 

  • Ceder A (2007) Public transit planning and operation, 1st edn. Elsevier, Oxford

    Google Scholar 

  • Cortés CE, Fernandez R, Burgos V (2007) Modeling passengers, buses, and stops in traffic microsimulators: MISTRANSIT approach on PARAMICS platform

  • Daganzo CF, Pilachowski J (2011) Reducing bunching with bus-to-bus cooperation. Transp Res Part B Methodol 45:267–277. doi:10.1016/j.trb.2010.06.005

    Article  Google Scholar 

  • de Ortúzar JD, Willumsen LG (2011) Modelling transport. Wiley, New York

    Book  Google Scholar 

  • Fadaei M, Cats O (2016) Evaluating the impacts and benefits of public transport design and operational measures. Transp Policy 48:105–116. doi:10.1016/j.tranpol.2016.02.015

    Article  Google Scholar 

  • Fernandez R, Cortes C, Burgos V (2010) Microscopic simulation of transit operations: policy studies with the MISTRANSIT application programming interface. Transp Plan Technol 33:157–176. doi:10.1080/03081061003643762

    Article  Google Scholar 

  • Fosgerau M, Hjort K, Lyk-Jensen SV (2007) The Danish value of time study, Kgs. Lyngby

  • Hensher DA, Golob TF (2008) Bus rapid transit systems: a comparative assessment. Transportation (Amst) 35:501–518. doi:10.1007/s11116-008-9163-y

    Article  Google Scholar 

  • Hidalgo D, Muñoz JC (2014) A review of technological improvements in bus rapid transit (BRT) and buses with high level of service (BHLS). Public Transp 6:185–213. doi:10.1007/s12469-014-0089-9

    Article  Google Scholar 

  • Highway Capacity Manual (2000) Highway Capacity Manual. Transportation Research Board, Washington, DC

    Google Scholar 

  • Hwang M, Kemp J, Lerner-Lam E et al (2006) Advanced public transportation systems: state of the art update 2006

  • Ingvardson JB, Jensen JK (2012a) Implementation of bus rapid transit in copenhagen based on international experiences. Technical University of Denmark

  • Ingvardson JB, Jensen JK (2012b) Implementation of bus rapid transit: a mesoscopic model approach. In: Selected proceedings from the annual transport conference at Aalborg University (in Danish). Aalborg University, Aalborg

  • Kim H, Kim C, Chun Y (2015) Network reliability and resilience of rapid transit systems. Prof Geogr. doi:10.1080/00330124.2015.1028299

    Google Scholar 

  • Kittelson and Associates, KFH Group, Parsons Brinckerhoff Quade and Douglass, Hunter-Zaworski K (2003) Transit Capacity and Quality of Service Manual (TCRP Report 100). Transportation Research Board

  • Liu G, Wirasinghe SC (2001) A simulation model of reliable schedule design for a fixed transit route. J Adv Transp 35:145–174. doi:10.1002/atr.5670350206

    Article  Google Scholar 

  • Movia (2014) Reporting on the future of bus line 5A (in Danish). In: Afrapporteringen på fremtidens 5A. http://www.moviatrafik.dk/omos/bagomos/bestyrelse/2014/130314/Documents/09.1Rapportomfremtidens5A-MINIRAPPORT-Version28-01-2014.pdf

  • Nagel K, Neumann A (2010) Transport systems planning and transport telematics transport systems planning and transport telematics, Berlin

  • Nakanishi Y (1997) Bus: bus performance indicators: on-time performance and service regularity. Transp Res Rec 1571:1–13. doi:10.3141/1571-01

    Article  Google Scholar 

  • Neumann A, Balmer M, Rieser M (2012) Converting a static macroscopic model into a dynamic activity-based model to analyze public transport demand in Berlin. Int Conf Travel Behav Res 25

  • Neumann A, Kaddoura I, Nagel K (2013) Mind the gap—passenger arrival patterns in multi-agent simulations. Proc Soc Behav Sci Conf Agent Based Model Transp Plan Oper 1–9

  • Neumann A, Kern S, Leich G (2014) Boarding and alighting time of passengers of the Berlin public transport system, Berlin

  • Nielsen OA (2000) A stochastic transit assignment model considering differences in passengers utility functions. Transp Res Part B Methodol 34:377–402

    Article  Google Scholar 

  • Nielsen OA (2004) Behavioral responses to road pricing schemes: description of the Danish AKTA experiment. J Intell Transp Syst 8:233–251. doi:10.1080/15472450490495579

    Article  Google Scholar 

  • Nielsen OA, Frederiksen RD (2006) Optimisation of timetable-based, stochastic transit assignment models based on MSA. Ann Oper Res 144:263–285. doi:10.1007/s10479-006-0012-0

    Article  Google Scholar 

  • Nuzzolo A, Russo F, Crisalli U (2001) A doubly dynamic schedule-based assignment model for transit networks. Transp Sci 35:268–285. doi:10.1287/trsc.35.3.268.10149

    Article  Google Scholar 

  • Nuzzolo A, Crisalli U, Rosati L, Comi A (2015) DYBUS2: a real-time mesoscopic transit modeling framework. In: IEEE conference on intelligent transportation systems, proceedings, ITSC, pp 303–308

  • Nuzzolo A, Crisalli U, Comi A, Rosati L (2016) A mesoscopic transit assignment model including real-time predictive information on crowding. J Intell Transp Syst. doi:10.1080/15472450.2016.1164047

    Google Scholar 

  • Parbo J, Nielsen OA, Prato CG (2014) User perspectives in public transport timetable optimisation. Transp Res Part C Emerg Technol 48:269–284. doi:10.1016/j.trc.2014.09.005

    Article  Google Scholar 

  • Parbo J, Nielsen OA, Prato CG (2016) Passenger perspectives in railway timetabling: a literature review. Transp Rev 36:500–526. doi:10.1080/01441647.2015.1113574

    Article  Google Scholar 

  • Pettersen KA, Schulman PR (2016) Drift, adaptation, resilience and reliability: toward an empirical clarification. Saf Sci. doi:10.1016/j.ssci.2016.03.004

    Google Scholar 

  • Prato C, Rasmussen T, Nielsen O (2014) Estimating value of congestion and of reliability from observation of route choice behavior of car drivers. Transp Res Rec J Transp Res Board 2412:20–27. doi:10.3141/2412-03

    Article  Google Scholar 

  • Razali NM, Wah YB (2011) Power comparisons of Shapiro–Wilk, Kolmogorov–Smirnov, Lilliefors and Anderson–Darling tests. J Stat Model Anal 2:21–33. doi:10.1515/bile-2015-0008

    Google Scholar 

  • Reggiani A, Nijkamp P, Lanzi D (2015) Transport resilience and vulnerability: the role of connectivity. Transp Res Part A Policy Pract 81:4–15. doi:10.1016/j.tra.2014.12.012

    Article  Google Scholar 

  • Shapiro ASS, Wilk MB (1965) Biometrika trust an analysis of variance test for normality (complete samples). Biometrika 52:591–611

    Article  Google Scholar 

  • Stewart C, El-Geneidy A (2014) All aboard at all doors. Transp Res Rec J Transp Res Board 2418:39–48. doi:10.3141/2418-05

    Article  Google Scholar 

  • Strathman JG, Kimpel TJ, Dueker KJ (2001) Bus transit operations control: review and an experiment involving Tri-Met’s automated bus dispatching system

  • Toledo T, Cats O, Burghout W, Koutsopoulos HN (2010) Mesoscopic simulation for transit operations. Transp Res Part C Emerg Technol 18:896–908. doi:10.1016/j.trc.2010.02.008

    Article  Google Scholar 

  • Viegas J, Lu B (2001) Widening the scope for bus priority with intermittent bus lanes. Transp Plan Technol 24:87–110. doi:10.1080/03081060108717662

    Article  Google Scholar 

  • Wahba M, Shalaby A (2006) MILATRAS: a microsimulation platform for testing transit-ITS policies and technologies. In: 2006 IEEE intelligent transportation systems conference, Toronto, pp 1495–1500

  • Wahba M, Shalaby A (2011) Large-scale application of MILATRAS: case study of the Toronto transit network. Transportation (Amst) 38:889–908. doi:10.1007/s11116-011-9358-5

    Article  Google Scholar 

  • Werf JV (2005) California partners for advanced transportation technology. University of California, Berkeley

    Google Scholar 

  • Xuan Y, Argote J, Daganzo CF (2011) Dynamic bus holding strategies for schedule reliability: optimal linear control and performance analysis. Transp Res Part B Methodol 45:1831–1845. doi:10.1016/j.trb.2011.07.009

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful and useful comments that helped to improve this paper.

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Correspondence to Jesper Bláfoss Ingvardson.

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Ingvardson, J.B., Jensen, J.K. & Nielsen, O.A. Analysing improvements to on-street public transport systems: a mesoscopic model approach. Public Transp 9, 385–409 (2017). https://doi.org/10.1007/s12469-016-0151-x

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