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[1] European Commission. Climate Action: Reducing emissions from transport, http://ec.europa.eu/clima/policies/transport/index_en.htm (accessed March 2016).
[2] Dell'Acqua, G. & Russo, F., Safety performance functions for low-volume roads.The Baltic Journal of Road and Bridge Engineering, 6(4), pp. 225–234, 2011. [Crossref]
[3] Dell'Acqua, G., Russo, F. & Biancardo S.A., Risk-type density diagrams by crash typeon two-lane rural roads. Journal of Risk Research, 16(10), pp. 1297–1314, 2013. [Crossref]
[4] Bifulco, G.N., Pariota, L., Brackstione, M. & McDonald, M., Driving behaviour modelsenabling the simulation of advanced driving assistance systems: Revisiting the actionpoint paradigm. Transportation Research Part C, 36, pp. 352–366, 2013. [Crossref]
[5] Pariota, L., Bifulco, G.N., Galante, F., Montella, A. & Brackstone, M., Longitudinalcontrol behaviour: Analysis and modelling based on experimental surveys in Italyand the UK. Accident Analysis & Prevention, 89, pp. 74–87, 2016. [Crossref]
[6] Cascetta, E., Cartenì, A., Pagliara, F. & Montanino, M., A new look at planning and designingtransportation systems: A decision-making model based on cognitive rationality,stakeholder engagement and quantitative methods. Transport Policy, 38, pp. 27–39,2015. [Crossref]
[7] Cascetta, E., Cartenì, A. & Montanino, M., A behavioral model of accessibility basedon the number of available opportunities. Journal of Transport Geography, 51, pp. 45–58, 2016.
[8] D'Acierno, L., Gallo, M., Montella, B. & Placido, A., The definition of a model frameworkfor managing rail systems in the case of breakdowns. Proceedings of the 16thIEEE Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands,pp. 1059–1064, 2013. [Crossref]
[9] Gallo, M., Simonelli, F., De Luca, G. & De Martinis, V., Estimating the effects of energy-efficient driving profiles on railway consumption. Proceedings of the 15th IEEEInternational Conference on Environment and Electrical Engineering (EEEIC), Rome,Italy, pp. 1059–1064, 2015. [Crossref]
[10] D'Acierno, L., Placido, A., Botte, M. & Montella, B., A methodological approach formanaging rail disruptions with different perspectives. International Journal of MathematicalModels and Methods in Applied Sciences, 10, pp. 80–86, 2016.
[11] Cascetta, E., Transportation Systems Analysis: Models and Applications. Springer:New York, 2009.
[12] Smith M.J., The existence, uniqueness and stability of traffic equilibria. TransportationResearch Part B, 13(4), pp. 295–304, 1979. [Crossref]
[13] Brog, W. & Ampt, E., State of the art in the collection of travel behaviour data. TravelBehaviour for the 1980s, Special Report 201, National Research Council: Washington,DC, 1982.
[14] Ortuzar, J.de D. & Willumsen, L.G., Modelling Transport, 4th ed., John Wiley andSons: Chichester, UK, 2011.
[15] Domencich, T.A. & McFadden, D., Urban Travel Demand: A Behavioural Analysis.Elsevier: New York, 1975.
[16] Horowitz, J., Identification and diagnosis of specification errors in the multinominallogit model. Transportation Research Part B, 15(5), pp. 345–360, 1981. [Crossref]
[17] Manski, C.F. & McFadden, D., Structural Analysis of Discrete Data with EconometricApplications. The MIT Press: Cambridge, MA, 1981.
[18] Ben-Akiva, M. & Lerman, S.R., Discrete Choice Analysis: Theory and Applicationto Travel Demand. The MIT Press: Cambridge, MA, 1985. [Crossref]
[19] Ben-Akiva, M. & Morikawa, T., Estimation of switching models from revealed preferenceand stated intention. Transportation Research Part A, 24(6), pp. 485–495, 1990.
[20] Ortuzar, J. de D., Stated preference in travel demand modelling. Proceedings of the 6thWorld Conference on Transportation Research (WCTR), Lyon, France, 1992.
[21] Lo, H.-P. & Chan, C.-P., Simultaneous estimation of an origin–destination matrix andlink choice proportions using traffic counts. Transportation Research Part A, 37(9), pp.771–788, 2003. [Crossref]
[22] Cascetta, E., Papola, A., Marzano, V., Simonelli, F. and Vitiello, I., Quasi-dynamic estimationof o–d flows from traffic counts: Formulation, statistical validation and performance analysis on real data. Transportation Research Part B, 55, pp. 171–187, 2013. [Crossref]
[23] Lu, C.-C., Zhou, X. & Zhang, K., Dynamic origin–destination demand flow estimationunder congested traffic conditions. Transportation Research Part C, 34, pp. 16–37,2013. [Crossref]
[24] Oi, W.Y. & Shuldiner, P.W., An Analysis of Urban Travel Demands. Northwestern UniversityPress: Evanston, IL, 1962.
[25] Wilson, A.G., Urban and Regional Models in Geography and Planning. John Wiley &Sons: London, 1974.
[26] Botte, M., Di Salvo, C., Placido, A., Montella, B. & D'Acierno, L., A NeighbourhoodSearch Algorithm (NSA) for determining optimal intervention strategies in the case ofmetro system failures. International Journal of Transport Development and Integration,1(1), pp. 63–73, 2016.
[27] D'Acierno, L., Gallo, M., Montella, B. & Placido, A., Analysis of the interaction betweentravel demand and rail capacity constraints. WIT Transactions on the Built Environment,128, pp. 197–207, 2012. [Crossref]
[28] Ercolani, M., Placido, A., D'Acierno, L. & Montella, B., The use of microsimulationmodels for the planning and management of metro systems. WIT Transactions on theBuilt Environment, 135, pp. 509–521, 2014. [Crossref]
[29] Botte, M., Di Salvo, C., Caropreso, C., Montella, B. & D'Acierno, L., Defining economicand environmental feasibility thresholds in the case of rail signalling systemsbased on satellite technology. Proceedings of the 16th IEEE Conference on Environmentand Electrical Engineering (EEEIC), Naples, Italy, 2016. [Crossref]
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Open Access
Research article

An Analytical Methodology for Extending Passenger Counts in a Metro System

r. di mauro,
m. botte,
l. d'acierno
Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Italy
International Journal of Transport Development and Integration
|
Volume 1, Issue 3, 2017
|
Pages 589-600
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
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Abstract:

The planning of a rail system requires the definition of travel demand in terms of passenger (or freight) flows for sizing physical and technological elements (such as number of trains, signalling system type, length and width of platforms). Moreover, once a system has been set up and functional elements have been acquired, system management in terms of services and related timetables requires knowledge of travel demand flows. Much has been written about the methods and techniques for estimating travel demand by means of analytical models (calibrated by surveys), statistical processing of survey data and/ or correcting model results by using properly collected traffic counts. However, whatever the adopted approach, it is necessary to proceed with survey campaigns to acquire experimental data. Obviously, the greater the number of detected data (and related acquisition costs and times), the greater the accuracy of travel demand estimations. Hence, in real cases, a fair compromise between survey costs and estimation accuracy has to be struck.

In this context, we propose an analytical methodology for identifying space–time relations between passenger counts to reduce the amount of data to be surveyed without affecting estimation accuracy. In particular, our proposal is based on defining analytical functions to provide boarding and alighting flows depending on the station (space component) and the time period (time component) in question. Finally, in order to show the feasibility of the proposed methodology and related improvements with respect to traditional approaches, we applied our proposal to the case of a real metro line in Naples (Italy) by comparing different levels of detail in passenger surveys.

Keywords: OD matrix estimation, Public transport management, Traffic count accuracy, Travel demand estimation

1. Introduction

2. Definition of Space–time Relations

3. Application of the Proposed Methodology

4. Conclusions and Research Prospects

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] European Commission. Climate Action: Reducing emissions from transport, http://ec.europa.eu/clima/policies/transport/index_en.htm (accessed March 2016).
[2] Dell'Acqua, G. & Russo, F., Safety performance functions for low-volume roads.The Baltic Journal of Road and Bridge Engineering, 6(4), pp. 225–234, 2011. [Crossref]
[3] Dell'Acqua, G., Russo, F. & Biancardo S.A., Risk-type density diagrams by crash typeon two-lane rural roads. Journal of Risk Research, 16(10), pp. 1297–1314, 2013. [Crossref]
[4] Bifulco, G.N., Pariota, L., Brackstione, M. & McDonald, M., Driving behaviour modelsenabling the simulation of advanced driving assistance systems: Revisiting the actionpoint paradigm. Transportation Research Part C, 36, pp. 352–366, 2013. [Crossref]
[5] Pariota, L., Bifulco, G.N., Galante, F., Montella, A. & Brackstone, M., Longitudinalcontrol behaviour: Analysis and modelling based on experimental surveys in Italyand the UK. Accident Analysis & Prevention, 89, pp. 74–87, 2016. [Crossref]
[6] Cascetta, E., Cartenì, A., Pagliara, F. & Montanino, M., A new look at planning and designingtransportation systems: A decision-making model based on cognitive rationality,stakeholder engagement and quantitative methods. Transport Policy, 38, pp. 27–39,2015. [Crossref]
[7] Cascetta, E., Cartenì, A. & Montanino, M., A behavioral model of accessibility basedon the number of available opportunities. Journal of Transport Geography, 51, pp. 45–58, 2016.
[8] D'Acierno, L., Gallo, M., Montella, B. & Placido, A., The definition of a model frameworkfor managing rail systems in the case of breakdowns. Proceedings of the 16thIEEE Conference on Intelligent Transportation Systems (ITSC), The Hague, The Netherlands,pp. 1059–1064, 2013. [Crossref]
[9] Gallo, M., Simonelli, F., De Luca, G. & De Martinis, V., Estimating the effects of energy-efficient driving profiles on railway consumption. Proceedings of the 15th IEEEInternational Conference on Environment and Electrical Engineering (EEEIC), Rome,Italy, pp. 1059–1064, 2015. [Crossref]
[10] D'Acierno, L., Placido, A., Botte, M. & Montella, B., A methodological approach formanaging rail disruptions with different perspectives. International Journal of MathematicalModels and Methods in Applied Sciences, 10, pp. 80–86, 2016.
[11] Cascetta, E., Transportation Systems Analysis: Models and Applications. Springer:New York, 2009.
[12] Smith M.J., The existence, uniqueness and stability of traffic equilibria. TransportationResearch Part B, 13(4), pp. 295–304, 1979. [Crossref]
[13] Brog, W. & Ampt, E., State of the art in the collection of travel behaviour data. TravelBehaviour for the 1980s, Special Report 201, National Research Council: Washington,DC, 1982.
[14] Ortuzar, J.de D. & Willumsen, L.G., Modelling Transport, 4th ed., John Wiley andSons: Chichester, UK, 2011.
[15] Domencich, T.A. & McFadden, D., Urban Travel Demand: A Behavioural Analysis.Elsevier: New York, 1975.
[16] Horowitz, J., Identification and diagnosis of specification errors in the multinominallogit model. Transportation Research Part B, 15(5), pp. 345–360, 1981. [Crossref]
[17] Manski, C.F. & McFadden, D., Structural Analysis of Discrete Data with EconometricApplications. The MIT Press: Cambridge, MA, 1981.
[18] Ben-Akiva, M. & Lerman, S.R., Discrete Choice Analysis: Theory and Applicationto Travel Demand. The MIT Press: Cambridge, MA, 1985. [Crossref]
[19] Ben-Akiva, M. & Morikawa, T., Estimation of switching models from revealed preferenceand stated intention. Transportation Research Part A, 24(6), pp. 485–495, 1990.
[20] Ortuzar, J. de D., Stated preference in travel demand modelling. Proceedings of the 6thWorld Conference on Transportation Research (WCTR), Lyon, France, 1992.
[21] Lo, H.-P. & Chan, C.-P., Simultaneous estimation of an origin–destination matrix andlink choice proportions using traffic counts. Transportation Research Part A, 37(9), pp.771–788, 2003. [Crossref]
[22] Cascetta, E., Papola, A., Marzano, V., Simonelli, F. and Vitiello, I., Quasi-dynamic estimationof o–d flows from traffic counts: Formulation, statistical validation and performance analysis on real data. Transportation Research Part B, 55, pp. 171–187, 2013. [Crossref]
[23] Lu, C.-C., Zhou, X. & Zhang, K., Dynamic origin–destination demand flow estimationunder congested traffic conditions. Transportation Research Part C, 34, pp. 16–37,2013. [Crossref]
[24] Oi, W.Y. & Shuldiner, P.W., An Analysis of Urban Travel Demands. Northwestern UniversityPress: Evanston, IL, 1962.
[25] Wilson, A.G., Urban and Regional Models in Geography and Planning. John Wiley &Sons: London, 1974.
[26] Botte, M., Di Salvo, C., Placido, A., Montella, B. & D'Acierno, L., A NeighbourhoodSearch Algorithm (NSA) for determining optimal intervention strategies in the case ofmetro system failures. International Journal of Transport Development and Integration,1(1), pp. 63–73, 2016.
[27] D'Acierno, L., Gallo, M., Montella, B. & Placido, A., Analysis of the interaction betweentravel demand and rail capacity constraints. WIT Transactions on the Built Environment,128, pp. 197–207, 2012. [Crossref]
[28] Ercolani, M., Placido, A., D'Acierno, L. & Montella, B., The use of microsimulationmodels for the planning and management of metro systems. WIT Transactions on theBuilt Environment, 135, pp. 509–521, 2014. [Crossref]
[29] Botte, M., Di Salvo, C., Caropreso, C., Montella, B. & D'Acierno, L., Defining economicand environmental feasibility thresholds in the case of rail signalling systemsbased on satellite technology. Proceedings of the 16th IEEE Conference on Environmentand Electrical Engineering (EEEIC), Naples, Italy, 2016. [Crossref]
Cite this:
APA Style
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BibTex Style
MLA Style
Chicago Style
GB-T-7714-2015
Mauro, R. D., Botte, M., & D'acierno, L. (2017). An Analytical Methodology for Extending Passenger Counts in a Metro System. Int. J. Transp. Dev. Integr., 1(3), 589-600. https://doi.org/10.2495/TDI-V1-N3-589-600
R. D. Mauro, M. Botte, and L. D'acierno, "An Analytical Methodology for Extending Passenger Counts in a Metro System," Int. J. Transp. Dev. Integr., vol. 1, no. 3, pp. 589-600, 2017. https://doi.org/10.2495/TDI-V1-N3-589-600
@research-article{Mauro2017AnAM,
title={An Analytical Methodology for Extending Passenger Counts in a Metro System},
author={R. Di Mauro and M. Botte and L. D'Acierno},
journal={International Journal of Transport Development and Integration},
year={2017},
page={589-600},
doi={https://doi.org/10.2495/TDI-V1-N3-589-600}
}
R. Di Mauro, et al. "An Analytical Methodology for Extending Passenger Counts in a Metro System." International Journal of Transport Development and Integration, v 1, pp 589-600. doi: https://doi.org/10.2495/TDI-V1-N3-589-600
R. Di Mauro, M. Botte and L. D'Acierno. "An Analytical Methodology for Extending Passenger Counts in a Metro System." International Journal of Transport Development and Integration, 1, (2017): 589-600. doi: https://doi.org/10.2495/TDI-V1-N3-589-600
Mauro R. D.， Botte M.， D'acierno L.. An Analytical Methodology for Extending Passenger Counts in a Metro System[J]. International Journal of Transport Development and Integration， 2017， 1(3)： 589-600. https://doi.org/10.2495/TDI-V1-N3-589-600