The impact of long-term travel demand changes on mixed decision problems of mass transit lines construction and vehicles’ depots location

Piotr Sawicki,

Szymon Fierek

The paper is concentrated on solving a mixed decision problem of mass transit line construction and vehicles’ depots location (MTLC&VDL). The authors have iteratively solved this problem as a function of scenariobased, long-term travel demand changes, and finally have analysed the generated results. As a product of the computations, it has been proved that the solution of the mixed MTLC&VDL decision problem depends on changes in travel demand, both in terms of the line construction and the location of the depot. 5 and 10% increase of travel demand leads to changes in the optimal mass transit line’s configuration, while the change of the optimal depot location takes place with 10% of the travel demand change. The results have implied a conclusion that to make strategic decisions on transport systems and solving mixed decision problems, forecasting travel demand changes (its volume and structure) over the long-term horizon has to be performed.
Słowa kluczowe: mass transit lines construction, vehicles’ depots location, travel demand, exact optimisation, traffic modelling

[1]        Abdallah T., Sustainable mass transit. Challenges and opportunities in urban public transportation,Elsevier, Amsterdam, Oxford,Cambridge MA,2017.

[2]        Baaj M.H., Mahmassani H.S., An AI-based approach for transit route system planning and design,Journal of Advanced Transportation, 25/1992, 187-210.

[3]        Canca D., Barrena E., The integrated rolling stock circulation and depot location problem in railway rapid transit systems,Transportation Research Part E: Logistics and Transportation Review, 109/2018, 115-138.

[4]        Cancela H., Mauttone A., Urquhart M.E., Mathematical programming formulations for transit network design,Transportation Research Part B, 77/2015, 17-37.

[5]        Ceder A., Public transit planning and operation: Theory, modeling and practice,Butterworth-Heinmann, Elsevier Ltd, Oxford2007.

[6]        Dial R.B., Bunyan R.E., Public transit planning system,Socio-Economic Planning Sciences, 1, 1968, 345-362.

[7]        Dubois D., Bel G., Libre M., A set of methods in transportation network seethes and analysis,Operations Research, 30/1979,797-808.

[8]        FHWA, Mitigating Traffic Congestion-The Role of Demand-Side Strategies, prepared by ACT, Report No. FHWA-HOP-05-001, October 2004.

[9]        Hamdouni M., Desaulniers G., Soumis F., Parking buses in a depot using block patterns: A Benders decomposition approach for minimizing type mismatches, Computers and Operations Research, 34 (11)/2007, 3362- 3379.

[10]    Hamdouni M., Soumis F., Desaulniers G., Parking buses in a depot with stochastic arrival times, European Journal of Operational Research, 183 (2)/2007, 502-515.

[11]    Kupka P., Sawicki P., Optymalizacja lokalizacji zajezdni tramwajowej w systemie komunikacji miejskiej, Logistyka, 2/2015, 462-472.

[12]    Lownes N., Machemehl R.B., Exact and heuristic methods forpublic transit circulator design,Transportation Research, Part B, 44 (2)/2010, 309-318.

[13]    Sawicki P., Fierek S., Problem jednoczesnego wyznaczania przebiegu linii i lokalizacji zajezdni w systemie transportu zbiorowego, Prace Naukowe Politechniki Warszawskiej –Transport, 119/2017, 429-444.

[14]    Sawicki P., Fierek S., Mixed public transport lines construction and vehicle’s depots location problems, [in:]Macioszek E., Sierpiński G., (eds.), Recent Advances in Traffic Engineering and Transport Networks and Systems,TSTP 2017, Lecture Notes in Networks and Systems, Springer, 21/2018, 213-224.

[15]    Schöbel A., Line planning in public transportation: models and methods,OR Spectrum, 34/2012, 491-510.

[16]    Szeto W.Y., Wu Y., A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong,European Journal of Operational Research, 209 (2)/2011, 141-155.

[17]    Teodorović D., Janić M., Transportation engineering. Theory, practice and modeling. Elsevier Inc., Oxford, Cambridge, MA, 2017.