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A novel approach for transit transfer stations design optimization in densely populated cities
Available online at www.sciencedirect.com
ScienceDirect
Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science 130 (2018) 1013–1018
The 9th International Symposium on Frontiers in Ambient and Mobile Systems (FAMS 2018)
A novel approach for transit transfer stations design optimization in densely populated cities Azucena Román-de la Sanchaa, Juan M. Mayoralb*, and Luis I. Romána a aGraduate
Graduate student, Institute of Engineering, National Autonomous University of Mexico, Building No. 4, P.O. Box 04510, Mexico City, Mexico b bResearcher, Institute of Engineering, National Autonomous University of Mexico, Building No. 4, P.O. Box 04510, Mexico City, Mexico
Abstract Efficiency assessment of transit transfer stations (TTS) located in densely populated urban areas is one of the most challenging tasks for metropolitan mobility entities. Currently the lack of practice oriented tools and the complexity of operation patterns make this kind of evaluations a fuzzy process. This paper presents a novel, integral and sustainable approach to evaluate relative TTS efficiency using technical, social and environmental variables. The methodology proposed is based on an optimization model using Data Envelopment Analysis (DEA). A set of operation variables are defined and efficiency frontier curves are developed in order to stablish optimal operation values. 36 TTS located in Mexico City Metropolitan Area (MCMA) are presented as case study. Findings point that TTS medium size stations are more suitable to reach a better balance among technical, social and environmental objectives. This methodology represents a useful tool to assess the different operational elements of TTS to improve the whole station efficiency. © 2018 The Authors. Published by Elsevier B.V. © 2018 The under Authors. Published by B.V. Program Chairs. Peer-review responsibility of Elsevier the Conference Peer-review under responsibility of the Conference Program Chairs. Keywords: Data Envelopment Analysis; Transit transfer stations; Urban transport interchange; Transit efficiency
1. Introduction Transit transfer stations (TTS) are essential elements to guarantee a suitable travel connection among urban transit networks. Moreover, they may have positive social impacts as they contribute to urban integration and social equity.
* Corresponding author. Tel.: +52 55 56233600 ext 8469. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs.
1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. 10.1016/j.procs.2018.04.141
Azucena Román-de la Sancha et al. / Procedia Computer Science 130 (2018) 1013–1018 Román-de la Sancha et al., / Procedia Computer Science 00 (2015) 000–000
1014 2
Intermodal passenger transport plays a main role in the mobility of cities where transport offer includes a great variety of modes going from particular modes to a wide range of public modes such as bus, trolley, BRT systems, metro, light rail, heavy rail, etc. In highly populated urban areas the operational efficiency of stations oriented to modal interchange is often compromised due to a lack of technical resources designed to periodically evaluate and monitor their operational elements. The proper performance of these weak but essential links of transit networks brings mobility closer to the expectations transportation companies, users and environmental and social entities. On the other hand, an inefficient performance is translated to important economic, social and environmental negative impacts such as high maintenance costs, a reduction in the quality of service and increases in energy consumption and emissions of toxic gases levels. According to Schakenbos et al.1, several studies indicate that passengers dislike interchanges, nonetheless almost no trip is completed with only one mode of transport. Hernández et al. 3 mentioned that currently few opportunities for direct journeys exist when choosing to use public transport; most trips require a transfer at a stop or interchange at one point or another. The assessment of operational elements to improve efficiency of this kind of infrastructure is still a complex task in the design phase, due to the lack of practice-oriented procedures and the limited research focused in this transportation field. Some studies had been conducted in the intermodal passenger transport, (Schakenbos1, Román-de la Sancha et al.2, Hernández et al.3, Allard and Moura4, Geurs et al.5, Guo et al.6, Chen and Lin7, Cheng et al.8, Kang et al.9, Martins de Sá et al.10). Nonetheless, no previous studies have considered a sustainable and integrated framework including economic, social and environmental objectives. Data Envelopment Analysis (DEA) is a widely used optimization model to evaluate efficiency and productivity. In the transportation field studies such as Lai et al.11, Mallikarjun12 and Caulfield et al.13 have applied different DEA submodels to evaluate operation performance of airlines, airports and ports. More specifically, Jordá14, Kang et al. Zhang et al.15, Lao and Liu16 and Karlaftis17 have developed methodologies based on DEA to determine bus companies, bus lines and other urban transit systems efficiency . The purpose of this paper is to present a novel approach to evaluate TTS efficiency and a tool to determine the opportunity areas and actions to be carried out in order to improve the stations performance. The method explicitly incorporates the technical (economical), passenger service (social) and environmental quality as efficiency parameters of the global TTS performance. The approach is introduced studying 36 of the most important TTS located in the Mexico City Metropolitan Area (MCMA). Two of them, Chapultepec and Zapata, were selected to further study in order to analyze the improvement strategy based on the results. Nomenclature Cnn BPL TA Tt Ti At EC BC CO2 S
Transportation modes available (Connectivity) Total bus platform length Transfer area Average transfer time Transfer index Automatization Energy consumption Black carbon emissions CO2 emissions Users satisfaction
2. Methodology DEA is a multivariable optimization model originally designed by Charnes et al.18 as a method to measure the relative efficiency of a set of decision-making units (DMU) characterized by multiple variables acting as inputs and outputs where efficiency is defined as a productivity measure of a given amount of resources (inputs) used to obtain a certain production (outputs). Some of the model advantages are that each data set can be introduced using different units, also that the model can be adjusted to consider scale variation of data and that the solution may be proposed from two approaches: input oriented, meaning that inputs (resources) are minimized in order to obtain a fixed value of outputs (production); or output oriented where output (production) is maximized using a given amount of inputs
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(resources). From the observations made in a set of DMU it is possible to identify those with maximum efficiency values and build an envelope or efficiency frontier that allows to establish the best operation value of the set. This value indicates the maximum quantity of outputs that can be obtained from a given combination of inputs. By graphically representing these values, the inefficient units are "enveloped" by the frontier. Several DEA sub-models have been developed in the past years offering different analysis approaches. Following the methodology proposed by Román-de la Sancha2 a Slack Based Model, SBM- DEA, considering variable return to scale is here applied, (1) and (2). According to Jordá14, SBM- DEA considers a DMU efficient only if there is no more possibilities to reduce the resources or to increase the production. Then, the slack is the excess or deficit of an inefficient DMU. Once slacks are computed, variations of original values can be carried out in order to obtain an improvement in the TTS performance. 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 Τ = 𝑡𝑡 −
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝛵𝛵 = 𝑡𝑡 −
1
𝑚𝑚
∑𝑚𝑚 𝑖𝑖=1
𝑆𝑆𝑖𝑖−
𝑥𝑥𝑖𝑖𝑖𝑖0
𝑆𝑆 + ∑𝑛𝑛𝑗𝑗=1 𝑗𝑗 𝑦𝑦 𝑛𝑛 1
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑡𝑡𝑡𝑡: 1 = 𝑡𝑡 +
𝑗𝑗𝑗𝑗0
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑡𝑡𝑡𝑡: 1 = 𝑡𝑡 +
1
𝑛𝑛
∑𝑛𝑛𝑗𝑗=1
𝑦𝑦𝑗𝑗𝑗𝑗0
,
∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝑆𝑆𝑖𝑖− = 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖0 ∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑦𝑦𝑗𝑗𝑗𝑗 − 𝑆𝑆𝑗𝑗+ = 𝑡𝑡𝑡𝑡𝑗𝑗𝑗𝑗0 , Δ𝑘𝑘 , 𝑆𝑆𝑖𝑖− , 𝑆𝑆𝑗𝑗+ ≥ 0, 𝑡𝑡 > 0, 𝑖𝑖 = 1, … , 𝑚𝑚; 𝑗𝑗 = 1, … , 𝑛𝑛; 𝑘𝑘 = 1, … , 𝐾𝐾.
𝑆𝑆 − ∑𝑚𝑚 𝑖𝑖 𝑚𝑚 𝑖𝑖=1 𝑥𝑥 1
𝑆𝑆𝑗𝑗+
𝑖𝑖𝑖𝑖0
,
∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝑆𝑆𝑖𝑖− = 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖0 , ∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑦𝑦𝑗𝑗𝑗𝑗 − 𝑆𝑆𝑗𝑗+ = 𝑡𝑡𝑡𝑡𝑗𝑗𝑗𝑗0 , Δ𝑘𝑘 , 𝑆𝑆𝑖𝑖− , 𝑆𝑆𝑗𝑗+ ≥ 0, 𝑡𝑡 > 0,
𝑖𝑖 = 1, … , 𝑚𝑚; 𝑗𝑗 = 1, … , 𝑛𝑛; 𝑘𝑘 = 1, … , 𝐾𝐾.
3. Case Study
(1)
(2)
Mexico City and its metropolitan area are one the most populated urban regions in the world. With more than the 22 million trips made daily, currently mobility is an increasingly important element in the daily life of people. Moreover, intermodal trips play a main role since 50% from these trips includes at least one modal change and over 70% are made using the public transport network19,20. In order to attend the high mobility demand, public transport offer includes bus, BRT system, trolleys, metro, light rail and suburban heavy rail, however low capacity vehicles such as vans and small buses dominate the offer. There is also an increasingly use of bicycle and bike sharing systems. Furthermore, 48 Transit transfer stations are strategically located throughout the city connecting in most cases to a metro or rail line. Geometry and operational characteristics may vary importantly from one station to another as well as daily affluences. 20 TTS were chosen to be analyzed as case study. Table 1 shows the variables used as inputs and outputs for each dimension of analysis as well as the model orientation. Table 1. Efficiency dimensions and selected input and output variable Dimension
Inputs
Outputs
Technical (Input oriented)
Transfer area (m2) Bus platform length (m) Automatization (%) Connectivity (number of available modes) Transfer area (m2) Capacity (pax/day) Transfer index (related to transfer time) Transfer area (m2) CO2 emissions (ton/day) BC emissions (ton/day) Energy consumption (MWH)
Demand (pax/day)
Passenger service (Output oriented) Environmental (Input oriented)
User’s satisfaction (%)
Demand (pax/day)
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3.1. Data base Data regarding transit offer and demand, stations geometry and operation characteristics are presented in Table 2. As noticed all TTS chosen herein attend at least 80,000 pax/day. Table 2. TTS used in the analysis operational parameters No.
TTS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Pantitlán Indios Verdes Taxqueña Chapultepec Rosario Universidad Const. 1917 Zaragoza Ciudad Azteca B. P. Aéreo Martín Carrera Tacuba Politécnico Zapata La Raza Tacubaya C. Caminos Observatorio M.A. Quevedo Santa Martha
Demand (pax/day)
Capacity (pax/day)
1,100,000 1,736,240 950,000 948,460 750,000 721,704 500,000 631,285 230,000 634,032 200,000 599,030 200,000 452,380 185,000 576,937 180,000 423,050 135,000 450,255 135,000 627,215 130,000 790,100 120,000 573,095 115,000 936,856 115,000 848,144 115,000 1,450,480 93,000 389,587 85,000 542,408 80,000 483,354 80,000 662,044
TA (m2)
BPL (m)
88,949 64,714 38,006 30,233 41,699 22,587 38,973 19,443 19,842 10,538 19,312 11,400 14,930 8,734 20,296 4,702 93,470 15,928 976 23,769
3,110 2,900 1,130 970 2,701 1,164 1,970 1,325 2,110 832 1,236 823 956 324 991 560 2,404 717 165 1,143
Tt (min) 8.75 12.47 6.00 18.55 10.00 8.50 7.00 6.50 8.00 6.00 10.00 3.50 9.00 10.00 6.83 11.00 8.00 21.00 6.33 7.50
Ti
Cnn
At (%)
4 5 6 2 5 3 3 2 4 3 5 2 4 4 6 3 3 4 3 3
60 40 40 40 60 20 40 40 70 40 40 20 40 40 20 40 40 40 20 40
0.69 0.48 0.84 0.14 0.62 0.70 0.79 0.81 0.73 0.84 0.62 0.98 0.67 0.62 0.79 0.56 0.73 0.00 0.82 0.76
EC (MWh) 55,217 12,938 62,148 37,988 34,944 31,547 12,833 18,132 13,367 24,551 29,970 25,902 37,386 37,017 59,713 56,770 12,593 17,067 28,115 16,335
CO2 (ton) 150.8 186.63 62.51 79.73 68.87 77.59 63.22 88.69 31.37 47.17 47.23 23.59 28.18 21.28 32.9 55.18 22 21.33 8.21 29.51
BC (ton) 19.28 22.48 8.02 11.39 9.93 9.91 8.04 11.33 4.02 6.03 9.64 2.46 2.39 2.48 1.55 5.76 2.63 4.48 1.69 6.16
S (%) 60 40 60 60 80 60 40 20 60 40 40 20 60 80 40 40 60 40 40 20
3.2. Results An initial analysis of current conditions of TTS was carried out to obtain efficiency values for each TTS, scores are presented in Figure 1. 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00
Technical Efficiency
Passenger Service
Environmental Efficiency
Figure 1. Efficiency scores obtained
Then, Chapultepec and Zapata, were analyzed in more detail to define the variation required in order to improve their technical efficiency. Slacks and projection values were computed for the variables used as inputs in technical efficiency dimension (transfer area, bus platform length and automatization), results are shown in Table 3 highlighting
Azucena Román-de la Sancha et al. / Procedia Computer Science 130 (2018) 1013–1018 Author name / Procedia Computer Science 00 (2015) 000–000
1017 5
the two TTS selected. As depicted in Table 3, slacks analysis results indicate that a reduction in the transfer area of Chapultepec and Zapata stations will produce an increase in technical efficiency. Similarly, bus platform length and automatization facilities are also unexploited or exceeded in these stations therefore a reduction is also suggested for this variables. Figure 2 depicts the technical frontier curve build form the best values of the efficiency scores originally obtained. In the same graphics, Chapultepec and Zapata were added using actual values of transfer area as well as variation values suggested by the slack analysis. In the case of Chapultepec, transfer area was modified from 32,233 to 24,189 m2 which meant a slight improvement in the efficiency score, from 0.80 to 0.87. Regarding Zapata station, a variation from 8,734 to 2,910 m2 implied going from 0.50 to 0.73 of technical efficiency score. Furthermore, it was confirmed that the simultaneous variation of all inputs associated to technical efficiency delivers a final score of 1.00 for Chapultepec and Zapata stations. Table 3. Slacks and projections of technical dimension DMU 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20
TTS Pantitlán Indios Verdes Taxqueña Chapultepec Rosario Universidad Const. 1917 Zaragoza Ciudad Azteca B. P. Aéreo Martín Carrera Tacuba Politécnico Zapata La Raza Tacubaya C. Caminos Observatorio M.A. Quevedo Santa Martha
Slack TA 0.00 0.00 0.00 -6044.16 -32432.70 0.00 -31364.76 -12663.79 -13339.13 -6522.22 -15296.22 -1419.42 -11743.25 -5823.60 -13016.79 -1791.60 -91775.51 -14675.66 0.00 -22793.00
Slack BPL Projection BPL 0.00 3110.00 0.00 2900.00 0.00 1130.00 -200.07 769.93 -2319.96 381.04 0.00 1164.00 -1632.16 337.84 -1008.77 316.23 -1800.97 309.03 -587.78 244.22 -991.78 244.22 -241.75 581.25 -733.39 222.61 -108.59 215.41 -534.63 456.38 -344.59 215.41 -2220.28 183.72 -544.80 172.20 0.00 165.00 -978.00 165.00
Slack At 0.00 0.00 0.00 -7.46 -35.52 0.00 -16.42 -16.87 -47.01 -18.36 -18.36 0.00 -18.81 -18.96 0.00 -18.96 -19.61 -19.85 0.00 -20.00
Projection At 60.00 40.00 40.00 32.54 24.48 20.00 23.58 23.13 22.99 21.64 21.64 20.00 21.19 21.04 20.00 21.04 20.39 20.15 20.00 20.00
Technical Efficiency Frontier and Slacks (Transfer Area)
1,200 Demand (thousands of pax/day)
Projection TA 88949.00 64714.00 38006.00 24188.84 9266.30 22587.00 7608.24 6779.21 6502.87 4015.78 4015.78 9980.58 3186.75 2910.40 7279.21 2910.40 1694.49 1252.34 976.00 976.00
E Frontier
Pantitlán
1,000
Indios Verdes
800
Taxqueña
600
slack Chapultepec
400 200 0
Universidad
slack Zapata M.A. Quevedo 0
10
20
30
40 50 60 Transfer Area (thousands of m2)
70
80
Figure 2. Efficiency frontier of technical dimension and Slacks projection
90
100
1018 6
Azucena Román-de la Sancha et al. / Procedia Computer Science 130 (2018) 1013–1018 Román-de la Sancha et al., / Procedia Computer Science 00 (2015) 000–000
4. Conclusions A methodology to evaluate and improve TTS efficiency has been presented considering three important dimensions in modern urban areas: technical, social and environmental. Input and output variables showing the best fit for the objectives pursued in each dimension were determined. Then, the model was applied to evaluate the efficiency of 20 TTS located in Mexico City, the corresponding frontier curves were generated while a slack analysis enable to identify particular variations needed in order to improve efficiency scores. Transfer area, used as input in the technical efficiency dimension, was modified for Chapultepec and Zapata stations using the projection resulting from the slack analysis. Scores form the analysis using the new values of the transfer area show a clear increase in the technical efficiency of the two stations studied. The variation of the three inputs consider for technical efficiency: transfer area, bus length platform and automatization; led to obtain the best efficiency score for the selected stations. The research opens the door for the development of technical material to define proper steps and features of well operated and designed TTS. The methodology represents a practical tool to evaluate and improve this kind of transportation infrastructure while the variables considered could be easily adapted to any conditions of TTS. Nevertheless, finding the best balance among variables in order to achieve the greatest sustainable efficiency values remains as the main challenge. References 1. Román-de la Sancha A., Mayoral J., Román L. 2016. Modeling urban transfer stations efficiency. Procedia Computer Science, Vol. 83, pp 1825. 2. Schakenbos R., La Paix L.., Nijenstein S., Geurs K.T., 2015. Valuation of a transfer in a multimodal public transport. Transport Policy, Vol. 46 pp, 72-81. 3. Hernández S., Monzon A., de Oña R., 2015. Urban transport interchanges: A methodology for evaluating perceived quality. Transportation Research Part A, Vol. 84 pp, 31-43. 4. Allard R.F., Moura F., 2015. The Incorporation of Passenger Connectivity and Intermodal Considerations in Intercity Transport Planning. Transport Reviews, Vol. 36 pp, 251-277. 5. Geurs K.T., La Paix L., Van Weperen S., 2016. A multi-modal network approach to model public transport accessibility impacts of bicycle-train integration policies. European Transportation Research, Vol. 25 pp, 1-15. 6. Guo X., Sun H., J Wu., Jin J., J Zhou., Gao Z., 2016. Multiperiod-based timetable optimization for metro transit networks. Transportation Research Part B, Vol. 96 pp, 46-67. 7. Chen X., Lin L., 2016. The Integration of Air and Rail Technologies: Shanghai’s Hongqiao Integrated Transport Hub. Journal of Urban Technology, Vol. 23 pp, 23-46. 8. Cheng Y.H.., Chen S.Y., 2015. Perceived accessibility, mobility, and connectivity of public transportation systems. Transportation Research Part A, Vol. 77 pp, 386-403. 9. Kang L., Zhu X., 2015. A simulated annealing algorithm for first train transfer problem in urban railway networks. Applied Mathematical Modelling, Vol. 40 pp, 419-435. 10. Martins de Sá E., Contreras I., Cordeau JF., 2015. Exact and heuristic algorithms for the design of hub networks with multiple lines. European Journal of Operational Research, Vol. 246 pp, 186-198. 11. Lai P, Potter A, Beynon M, Beresford A. 2015. Evaluating the efficiency performance of airports using an integrated AHP/DEA-AR technique. Transport Policy Vol. 42; pp, 75-85. 12. Mallikarjun S. 2014. Efficiency of US airlines: A strategic operating model. Journal of Air Transport Management; Vol. 43: pp, 46-56. 13. Caulfield B, Bailey D, Mullarkey S. 2013 Using data envelopment analysis as a public transport project appraisal tool. Transport Policy; pp, 29–85. 14. Jordá P., 2012. Metodología de evaluación de la eficiencia de los servicios de autobús urbano. Aplicación a las grandes ciudades españolas en el periodo 2004–2009. Ph.D. Thesis, Universidad Politécnica de Madrid, pp 67-80. 15. Zhang J., Wang S., Zhang Z., Zou K., Shu Z., 2015. Characteristics on hub networks of urban rail transit networks. Physica A, Vol. 447 pp, 502-507. 16. Lao Y, Liu L. 2009. Performance evaluation of bus lines with data envelopment analysis and geographic information systems. Computers, environment and urban systems; Vol. 33: pp, 247-255. 17. Karlaftis MG. 2004. A DEA approach for evaluating the efficiency and effectiveness of urban transit systems. European Journal of Operational Research; Vol. 152: pp, 354-364. 18 Charnes A., Cooper W., Rhodes E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, Vol. 2, pp 429-444. 19. Survey: Origin-Destination (2007), National Institute of Statistics and Geography, Mexico 20. www.procdmx.cdmx.gob.mx [official page of the government of Mexico City 21. MaxDEA Basic [computer program]. Version 5.0. Beijing Realworld Research & Consultation Company Ltd; 2014.
ScienceDirect
Available online at www.sciencedirect.com Procedia Computer Science 00 (2018) 000–000
ScienceDirect
www.elsevier.com/locate/procedia
Procedia Computer Science 130 (2018) 1013–1018
The 9th International Symposium on Frontiers in Ambient and Mobile Systems (FAMS 2018)
A novel approach for transit transfer stations design optimization in densely populated cities Azucena Román-de la Sanchaa, Juan M. Mayoralb*, and Luis I. Romána a aGraduate
Graduate student, Institute of Engineering, National Autonomous University of Mexico, Building No. 4, P.O. Box 04510, Mexico City, Mexico b bResearcher, Institute of Engineering, National Autonomous University of Mexico, Building No. 4, P.O. Box 04510, Mexico City, Mexico
Abstract Efficiency assessment of transit transfer stations (TTS) located in densely populated urban areas is one of the most challenging tasks for metropolitan mobility entities. Currently the lack of practice oriented tools and the complexity of operation patterns make this kind of evaluations a fuzzy process. This paper presents a novel, integral and sustainable approach to evaluate relative TTS efficiency using technical, social and environmental variables. The methodology proposed is based on an optimization model using Data Envelopment Analysis (DEA). A set of operation variables are defined and efficiency frontier curves are developed in order to stablish optimal operation values. 36 TTS located in Mexico City Metropolitan Area (MCMA) are presented as case study. Findings point that TTS medium size stations are more suitable to reach a better balance among technical, social and environmental objectives. This methodology represents a useful tool to assess the different operational elements of TTS to improve the whole station efficiency. © 2018 The Authors. Published by Elsevier B.V. © 2018 The under Authors. Published by B.V. Program Chairs. Peer-review responsibility of Elsevier the Conference Peer-review under responsibility of the Conference Program Chairs. Keywords: Data Envelopment Analysis; Transit transfer stations; Urban transport interchange; Transit efficiency
1. Introduction Transit transfer stations (TTS) are essential elements to guarantee a suitable travel connection among urban transit networks. Moreover, they may have positive social impacts as they contribute to urban integration and social equity.
* Corresponding author. Tel.: +52 55 56233600 ext 8469. E-mail address: [email protected] 1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs.
1877-0509 © 2018 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the Conference Program Chairs. 10.1016/j.procs.2018.04.141
Azucena Román-de la Sancha et al. / Procedia Computer Science 130 (2018) 1013–1018 Román-de la Sancha et al., / Procedia Computer Science 00 (2015) 000–000
1014 2
Intermodal passenger transport plays a main role in the mobility of cities where transport offer includes a great variety of modes going from particular modes to a wide range of public modes such as bus, trolley, BRT systems, metro, light rail, heavy rail, etc. In highly populated urban areas the operational efficiency of stations oriented to modal interchange is often compromised due to a lack of technical resources designed to periodically evaluate and monitor their operational elements. The proper performance of these weak but essential links of transit networks brings mobility closer to the expectations transportation companies, users and environmental and social entities. On the other hand, an inefficient performance is translated to important economic, social and environmental negative impacts such as high maintenance costs, a reduction in the quality of service and increases in energy consumption and emissions of toxic gases levels. According to Schakenbos et al.1, several studies indicate that passengers dislike interchanges, nonetheless almost no trip is completed with only one mode of transport. Hernández et al. 3 mentioned that currently few opportunities for direct journeys exist when choosing to use public transport; most trips require a transfer at a stop or interchange at one point or another. The assessment of operational elements to improve efficiency of this kind of infrastructure is still a complex task in the design phase, due to the lack of practice-oriented procedures and the limited research focused in this transportation field. Some studies had been conducted in the intermodal passenger transport, (Schakenbos1, Román-de la Sancha et al.2, Hernández et al.3, Allard and Moura4, Geurs et al.5, Guo et al.6, Chen and Lin7, Cheng et al.8, Kang et al.9, Martins de Sá et al.10). Nonetheless, no previous studies have considered a sustainable and integrated framework including economic, social and environmental objectives. Data Envelopment Analysis (DEA) is a widely used optimization model to evaluate efficiency and productivity. In the transportation field studies such as Lai et al.11, Mallikarjun12 and Caulfield et al.13 have applied different DEA submodels to evaluate operation performance of airlines, airports and ports. More specifically, Jordá14, Kang et al. Zhang et al.15, Lao and Liu16 and Karlaftis17 have developed methodologies based on DEA to determine bus companies, bus lines and other urban transit systems efficiency . The purpose of this paper is to present a novel approach to evaluate TTS efficiency and a tool to determine the opportunity areas and actions to be carried out in order to improve the stations performance. The method explicitly incorporates the technical (economical), passenger service (social) and environmental quality as efficiency parameters of the global TTS performance. The approach is introduced studying 36 of the most important TTS located in the Mexico City Metropolitan Area (MCMA). Two of them, Chapultepec and Zapata, were selected to further study in order to analyze the improvement strategy based on the results. Nomenclature Cnn BPL TA Tt Ti At EC BC CO2 S
Transportation modes available (Connectivity) Total bus platform length Transfer area Average transfer time Transfer index Automatization Energy consumption Black carbon emissions CO2 emissions Users satisfaction
2. Methodology DEA is a multivariable optimization model originally designed by Charnes et al.18 as a method to measure the relative efficiency of a set of decision-making units (DMU) characterized by multiple variables acting as inputs and outputs where efficiency is defined as a productivity measure of a given amount of resources (inputs) used to obtain a certain production (outputs). Some of the model advantages are that each data set can be introduced using different units, also that the model can be adjusted to consider scale variation of data and that the solution may be proposed from two approaches: input oriented, meaning that inputs (resources) are minimized in order to obtain a fixed value of outputs (production); or output oriented where output (production) is maximized using a given amount of inputs
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(resources). From the observations made in a set of DMU it is possible to identify those with maximum efficiency values and build an envelope or efficiency frontier that allows to establish the best operation value of the set. This value indicates the maximum quantity of outputs that can be obtained from a given combination of inputs. By graphically representing these values, the inefficient units are "enveloped" by the frontier. Several DEA sub-models have been developed in the past years offering different analysis approaches. Following the methodology proposed by Román-de la Sancha2 a Slack Based Model, SBM- DEA, considering variable return to scale is here applied, (1) and (2). According to Jordá14, SBM- DEA considers a DMU efficient only if there is no more possibilities to reduce the resources or to increase the production. Then, the slack is the excess or deficit of an inefficient DMU. Once slacks are computed, variations of original values can be carried out in order to obtain an improvement in the TTS performance. 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 Τ = 𝑡𝑡 −
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝛵𝛵 = 𝑡𝑡 −
1
𝑚𝑚
∑𝑚𝑚 𝑖𝑖=1
𝑆𝑆𝑖𝑖−
𝑥𝑥𝑖𝑖𝑖𝑖0
𝑆𝑆 + ∑𝑛𝑛𝑗𝑗=1 𝑗𝑗 𝑦𝑦 𝑛𝑛 1
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑡𝑡𝑡𝑡: 1 = 𝑡𝑡 +
𝑗𝑗𝑗𝑗0
𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑡𝑡𝑡𝑡: 1 = 𝑡𝑡 +
1
𝑛𝑛
∑𝑛𝑛𝑗𝑗=1
𝑦𝑦𝑗𝑗𝑗𝑗0
,
∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝑆𝑆𝑖𝑖− = 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖0 ∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑦𝑦𝑗𝑗𝑗𝑗 − 𝑆𝑆𝑗𝑗+ = 𝑡𝑡𝑡𝑡𝑗𝑗𝑗𝑗0 , Δ𝑘𝑘 , 𝑆𝑆𝑖𝑖− , 𝑆𝑆𝑗𝑗+ ≥ 0, 𝑡𝑡 > 0, 𝑖𝑖 = 1, … , 𝑚𝑚; 𝑗𝑗 = 1, … , 𝑛𝑛; 𝑘𝑘 = 1, … , 𝐾𝐾.
𝑆𝑆 − ∑𝑚𝑚 𝑖𝑖 𝑚𝑚 𝑖𝑖=1 𝑥𝑥 1
𝑆𝑆𝑗𝑗+
𝑖𝑖𝑖𝑖0
,
∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝑆𝑆𝑖𝑖− = 𝑡𝑡𝑡𝑡𝑖𝑖𝑖𝑖0 , ∑𝑘𝑘𝑘𝑘=1 Δ𝑘𝑘 𝑦𝑦𝑗𝑗𝑗𝑗 − 𝑆𝑆𝑗𝑗+ = 𝑡𝑡𝑡𝑡𝑗𝑗𝑗𝑗0 , Δ𝑘𝑘 , 𝑆𝑆𝑖𝑖− , 𝑆𝑆𝑗𝑗+ ≥ 0, 𝑡𝑡 > 0,
𝑖𝑖 = 1, … , 𝑚𝑚; 𝑗𝑗 = 1, … , 𝑛𝑛; 𝑘𝑘 = 1, … , 𝐾𝐾.
3. Case Study
(1)
(2)
Mexico City and its metropolitan area are one the most populated urban regions in the world. With more than the 22 million trips made daily, currently mobility is an increasingly important element in the daily life of people. Moreover, intermodal trips play a main role since 50% from these trips includes at least one modal change and over 70% are made using the public transport network19,20. In order to attend the high mobility demand, public transport offer includes bus, BRT system, trolleys, metro, light rail and suburban heavy rail, however low capacity vehicles such as vans and small buses dominate the offer. There is also an increasingly use of bicycle and bike sharing systems. Furthermore, 48 Transit transfer stations are strategically located throughout the city connecting in most cases to a metro or rail line. Geometry and operational characteristics may vary importantly from one station to another as well as daily affluences. 20 TTS were chosen to be analyzed as case study. Table 1 shows the variables used as inputs and outputs for each dimension of analysis as well as the model orientation. Table 1. Efficiency dimensions and selected input and output variable Dimension
Inputs
Outputs
Technical (Input oriented)
Transfer area (m2) Bus platform length (m) Automatization (%) Connectivity (number of available modes) Transfer area (m2) Capacity (pax/day) Transfer index (related to transfer time) Transfer area (m2) CO2 emissions (ton/day) BC emissions (ton/day) Energy consumption (MWH)
Demand (pax/day)
Passenger service (Output oriented) Environmental (Input oriented)
User’s satisfaction (%)
Demand (pax/day)
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3.1. Data base Data regarding transit offer and demand, stations geometry and operation characteristics are presented in Table 2. As noticed all TTS chosen herein attend at least 80,000 pax/day. Table 2. TTS used in the analysis operational parameters No.
TTS
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Pantitlán Indios Verdes Taxqueña Chapultepec Rosario Universidad Const. 1917 Zaragoza Ciudad Azteca B. P. Aéreo Martín Carrera Tacuba Politécnico Zapata La Raza Tacubaya C. Caminos Observatorio M.A. Quevedo Santa Martha
Demand (pax/day)
Capacity (pax/day)
1,100,000 1,736,240 950,000 948,460 750,000 721,704 500,000 631,285 230,000 634,032 200,000 599,030 200,000 452,380 185,000 576,937 180,000 423,050 135,000 450,255 135,000 627,215 130,000 790,100 120,000 573,095 115,000 936,856 115,000 848,144 115,000 1,450,480 93,000 389,587 85,000 542,408 80,000 483,354 80,000 662,044
TA (m2)
BPL (m)
88,949 64,714 38,006 30,233 41,699 22,587 38,973 19,443 19,842 10,538 19,312 11,400 14,930 8,734 20,296 4,702 93,470 15,928 976 23,769
3,110 2,900 1,130 970 2,701 1,164 1,970 1,325 2,110 832 1,236 823 956 324 991 560 2,404 717 165 1,143
Tt (min) 8.75 12.47 6.00 18.55 10.00 8.50 7.00 6.50 8.00 6.00 10.00 3.50 9.00 10.00 6.83 11.00 8.00 21.00 6.33 7.50
Ti
Cnn
At (%)
4 5 6 2 5 3 3 2 4 3 5 2 4 4 6 3 3 4 3 3
60 40 40 40 60 20 40 40 70 40 40 20 40 40 20 40 40 40 20 40
0.69 0.48 0.84 0.14 0.62 0.70 0.79 0.81 0.73 0.84 0.62 0.98 0.67 0.62 0.79 0.56 0.73 0.00 0.82 0.76
EC (MWh) 55,217 12,938 62,148 37,988 34,944 31,547 12,833 18,132 13,367 24,551 29,970 25,902 37,386 37,017 59,713 56,770 12,593 17,067 28,115 16,335
CO2 (ton) 150.8 186.63 62.51 79.73 68.87 77.59 63.22 88.69 31.37 47.17 47.23 23.59 28.18 21.28 32.9 55.18 22 21.33 8.21 29.51
BC (ton) 19.28 22.48 8.02 11.39 9.93 9.91 8.04 11.33 4.02 6.03 9.64 2.46 2.39 2.48 1.55 5.76 2.63 4.48 1.69 6.16
S (%) 60 40 60 60 80 60 40 20 60 40 40 20 60 80 40 40 60 40 40 20
3.2. Results An initial analysis of current conditions of TTS was carried out to obtain efficiency values for each TTS, scores are presented in Figure 1. 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00
Technical Efficiency
Passenger Service
Environmental Efficiency
Figure 1. Efficiency scores obtained
Then, Chapultepec and Zapata, were analyzed in more detail to define the variation required in order to improve their technical efficiency. Slacks and projection values were computed for the variables used as inputs in technical efficiency dimension (transfer area, bus platform length and automatization), results are shown in Table 3 highlighting
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the two TTS selected. As depicted in Table 3, slacks analysis results indicate that a reduction in the transfer area of Chapultepec and Zapata stations will produce an increase in technical efficiency. Similarly, bus platform length and automatization facilities are also unexploited or exceeded in these stations therefore a reduction is also suggested for this variables. Figure 2 depicts the technical frontier curve build form the best values of the efficiency scores originally obtained. In the same graphics, Chapultepec and Zapata were added using actual values of transfer area as well as variation values suggested by the slack analysis. In the case of Chapultepec, transfer area was modified from 32,233 to 24,189 m2 which meant a slight improvement in the efficiency score, from 0.80 to 0.87. Regarding Zapata station, a variation from 8,734 to 2,910 m2 implied going from 0.50 to 0.73 of technical efficiency score. Furthermore, it was confirmed that the simultaneous variation of all inputs associated to technical efficiency delivers a final score of 1.00 for Chapultepec and Zapata stations. Table 3. Slacks and projections of technical dimension DMU 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20
TTS Pantitlán Indios Verdes Taxqueña Chapultepec Rosario Universidad Const. 1917 Zaragoza Ciudad Azteca B. P. Aéreo Martín Carrera Tacuba Politécnico Zapata La Raza Tacubaya C. Caminos Observatorio M.A. Quevedo Santa Martha
Slack TA 0.00 0.00 0.00 -6044.16 -32432.70 0.00 -31364.76 -12663.79 -13339.13 -6522.22 -15296.22 -1419.42 -11743.25 -5823.60 -13016.79 -1791.60 -91775.51 -14675.66 0.00 -22793.00
Slack BPL Projection BPL 0.00 3110.00 0.00 2900.00 0.00 1130.00 -200.07 769.93 -2319.96 381.04 0.00 1164.00 -1632.16 337.84 -1008.77 316.23 -1800.97 309.03 -587.78 244.22 -991.78 244.22 -241.75 581.25 -733.39 222.61 -108.59 215.41 -534.63 456.38 -344.59 215.41 -2220.28 183.72 -544.80 172.20 0.00 165.00 -978.00 165.00
Slack At 0.00 0.00 0.00 -7.46 -35.52 0.00 -16.42 -16.87 -47.01 -18.36 -18.36 0.00 -18.81 -18.96 0.00 -18.96 -19.61 -19.85 0.00 -20.00
Projection At 60.00 40.00 40.00 32.54 24.48 20.00 23.58 23.13 22.99 21.64 21.64 20.00 21.19 21.04 20.00 21.04 20.39 20.15 20.00 20.00
Technical Efficiency Frontier and Slacks (Transfer Area)
1,200 Demand (thousands of pax/day)
Projection TA 88949.00 64714.00 38006.00 24188.84 9266.30 22587.00 7608.24 6779.21 6502.87 4015.78 4015.78 9980.58 3186.75 2910.40 7279.21 2910.40 1694.49 1252.34 976.00 976.00
E Frontier
Pantitlán
1,000
Indios Verdes
800
Taxqueña
600
slack Chapultepec
400 200 0
Universidad
slack Zapata M.A. Quevedo 0
10
20
30
40 50 60 Transfer Area (thousands of m2)
70
80
Figure 2. Efficiency frontier of technical dimension and Slacks projection
90
100
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4. Conclusions A methodology to evaluate and improve TTS efficiency has been presented considering three important dimensions in modern urban areas: technical, social and environmental. Input and output variables showing the best fit for the objectives pursued in each dimension were determined. Then, the model was applied to evaluate the efficiency of 20 TTS located in Mexico City, the corresponding frontier curves were generated while a slack analysis enable to identify particular variations needed in order to improve efficiency scores. Transfer area, used as input in the technical efficiency dimension, was modified for Chapultepec and Zapata stations using the projection resulting from the slack analysis. Scores form the analysis using the new values of the transfer area show a clear increase in the technical efficiency of the two stations studied. The variation of the three inputs consider for technical efficiency: transfer area, bus length platform and automatization; led to obtain the best efficiency score for the selected stations. The research opens the door for the development of technical material to define proper steps and features of well operated and designed TTS. The methodology represents a practical tool to evaluate and improve this kind of transportation infrastructure while the variables considered could be easily adapted to any conditions of TTS. Nevertheless, finding the best balance among variables in order to achieve the greatest sustainable efficiency values remains as the main challenge. References 1. Román-de la Sancha A., Mayoral J., Román L. 2016. Modeling urban transfer stations efficiency. Procedia Computer Science, Vol. 83, pp 1825. 2. Schakenbos R., La Paix L.., Nijenstein S., Geurs K.T., 2015. Valuation of a transfer in a multimodal public transport. Transport Policy, Vol. 46 pp, 72-81. 3. Hernández S., Monzon A., de Oña R., 2015. Urban transport interchanges: A methodology for evaluating perceived quality. Transportation Research Part A, Vol. 84 pp, 31-43. 4. Allard R.F., Moura F., 2015. The Incorporation of Passenger Connectivity and Intermodal Considerations in Intercity Transport Planning. Transport Reviews, Vol. 36 pp, 251-277. 5. Geurs K.T., La Paix L., Van Weperen S., 2016. A multi-modal network approach to model public transport accessibility impacts of bicycle-train integration policies. European Transportation Research, Vol. 25 pp, 1-15. 6. Guo X., Sun H., J Wu., Jin J., J Zhou., Gao Z., 2016. Multiperiod-based timetable optimization for metro transit networks. Transportation Research Part B, Vol. 96 pp, 46-67. 7. Chen X., Lin L., 2016. The Integration of Air and Rail Technologies: Shanghai’s Hongqiao Integrated Transport Hub. Journal of Urban Technology, Vol. 23 pp, 23-46. 8. Cheng Y.H.., Chen S.Y., 2015. Perceived accessibility, mobility, and connectivity of public transportation systems. Transportation Research Part A, Vol. 77 pp, 386-403. 9. Kang L., Zhu X., 2015. A simulated annealing algorithm for first train transfer problem in urban railway networks. Applied Mathematical Modelling, Vol. 40 pp, 419-435. 10. Martins de Sá E., Contreras I., Cordeau JF., 2015. Exact and heuristic algorithms for the design of hub networks with multiple lines. European Journal of Operational Research, Vol. 246 pp, 186-198. 11. Lai P, Potter A, Beynon M, Beresford A. 2015. Evaluating the efficiency performance of airports using an integrated AHP/DEA-AR technique. Transport Policy Vol. 42; pp, 75-85. 12. Mallikarjun S. 2014. Efficiency of US airlines: A strategic operating model. Journal of Air Transport Management; Vol. 43: pp, 46-56. 13. Caulfield B, Bailey D, Mullarkey S. 2013 Using data envelopment analysis as a public transport project appraisal tool. Transport Policy; pp, 29–85. 14. Jordá P., 2012. Metodología de evaluación de la eficiencia de los servicios de autobús urbano. Aplicación a las grandes ciudades españolas en el periodo 2004–2009. Ph.D. Thesis, Universidad Politécnica de Madrid, pp 67-80. 15. Zhang J., Wang S., Zhang Z., Zou K., Shu Z., 2015. Characteristics on hub networks of urban rail transit networks. Physica A, Vol. 447 pp, 502-507. 16. Lao Y, Liu L. 2009. Performance evaluation of bus lines with data envelopment analysis and geographic information systems. Computers, environment and urban systems; Vol. 33: pp, 247-255. 17. Karlaftis MG. 2004. A DEA approach for evaluating the efficiency and effectiveness of urban transit systems. European Journal of Operational Research; Vol. 152: pp, 354-364. 18 Charnes A., Cooper W., Rhodes E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, Vol. 2, pp 429-444. 19. Survey: Origin-Destination (2007), National Institute of Statistics and Geography, Mexico 20. www.procdmx.cdmx.gob.mx [official page of the government of Mexico City 21. MaxDEA Basic [computer program]. Version 5.0. Beijing Realworld Research & Consultation Company Ltd; 2014.