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Analysis of Carbon Footprint and Comfort for Bus System Regarding Optimum Daily Trips


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1 Pamukkale University, Faculty of Engineering, Dept. of Civil Engineering, 20070, Denizli, Turkey
 

Sustainable and smart management of public transportation systems depends on continuous monitoring and analysis of the data collected at regular intervals. One of the key research topics in this area is determining bus service frequency and developing appropriate schedules. The number of buses and their frequencies have a significant impact on the entire transportation system, affecting all users of the network. To address this issue, bus service frequencies during peak hours should be determined based on passenger demand. In this study, the daily frequencies of the bus system of a city (Denizli, Turkey) were investigated, the carbon footprints of the system were analyzed, and suggestions were provided. This is determined by the model developed regarding the linear optimization method. The goal programming approach is used in the analysis. The existing frequencies are compared with that provided by the goal programming approach. Additionally, the results obtained are investigated by a cost analysis regarding different benefit rates (carbon footprint, total kilometer, and passenger comfort) for the public. By implementing the recommended bus service frequencies, significant financial and environmental improvements can be achieved.

Keywords

Bus system, Carbon footprint, Linear goal programming, Passenger comfort, Public transportation.
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  • Vuchic R V, Urban Transit: Operations, Planning and Economics (John Wiley and Sons Inc., New Jersey) 2005, 466– 612, https://doi.org/10.1111/j.1467-9787.2006.00453_2.x.
  • Chakroborty P, Optimal Routing and Scheduling in Transportation: using Genetic Algorithm to Solve Difficult Optimization Problems (Indian Institute of Technology Transportation Engineering, Kanpur, India) 2017, 33, https://doi.org/10.1080/03081060108717668.
  • Ceder A, Urban transit scheduling: framework, review, and examples, ASCE J Urban Planning Dev, 128(4) (2002) 225–244, https://doi.org/10.1061/(ASCE)0733-9488(2002) 128:4(225).
  • Ceder A, Public Transit Planning and Operation. Theory, Modelling, and Practice (Butterworth-Heinemann, UK), (2007) 640, https://doi.org/10.1201/b12853.
  • Alp S, Use of linear goal programming approach in bus lines, Istanbul Ticaret Univ J Appl Sci, 1(13) (2008) 73–91.
  • Uludag N, Modelling Bus Lines by Fuzzy Optimization and Linear Goal Programming Approaches, Ph.D. Thesis, Pamukkale University Institute of Applied Sciences, Denizli, Turkey 2010.
  • Murat Y S, Kutluhan S & Uludag N, Use of fuzzy optimization and linear goal programming approaches in urban bus lines organization, Soft Computing Ind Appl, 223 (2014) 277–287, https://doi.org/10.1007/978-3-319-00930- 8_33.
  • Deri A, Determination of Public Transportation Demand Values and Optimization of Schedules by Smart Card Data, MSc. Thesis, Dokuz Eylul University Institute of Applied Sciences, Civil Engineering Department, Izmir, Turkey, 2012.
  • Lin N, Weimin M & Xiaoxuan C, Bus frequency optimization considering user behavior based on mobile bus applications, IET Intell Transp Syst, 13 (2019) 596–604, https://doi.org/10.1049/iet-its.2018.5012.
  • Shyue K C & Trun-Shaw C, Optimal headway and route length for a public transit system under the consideration of externality, J Eastern Asia Soc Transp Stud, 6 (2005) 4001–4016, https://doi.org/10.11175/EASTS.6.4001.
  • Jing-Quan L & Head K L, Sustainability provisions in the bus-scheduling problem, Transp Res Part D: Transp Environ, 14 (2009) 50–60, https://doi.org/10.1016/j.trd. 2008.11.001.
  • Nesheli M M, Ceder A, Ghavamirad F & Thacker S, Environmental impacts of public transport systems using real-time control method, Transp Res Part D: Transp Environ, 51 (2017) 216–226, https://doi.org/10.1016/j.trd. 2016.12.006.
  • Liuhui Z, Optimization of Headway, Stops, and Time Points Considering Stochastic Bus Arrivals, Ph.D. Thesis, New Jersey Institute of Technology, New Jersey, USA, 2016.
  • Demirkollu M, Analysis of Bus System Schedules by Goal Programming Approach, MSc. Thesis, Pamukkale University Institute of Applied Sciences, Denizli, Turkey, 2017.
  • Yih-Long C & Kiran D, Win QSB, Version 2.0. (Wiley) 2002.
  • Transit Cooperative Highway Research Program (TCRP), Transit Capacity and Quality of Service Manual (Transportation Research Board, Washington) 2013, 3–28, https://onlinepubs.trb.org/onlinepubs/tcrp/tcrp_rpt_165fm.pdf.
  • UK Department for Transport journey planner, Carbon Emission Assumptions, 2007, http://www.transportdirect.info/ Web/Downloads/TransportDirectCO2Data.pdf.
  • Asian Development Bank, Reducing Carbon Emissions from Transport Projects', Reference Number: EKB: REG 2010-16, Evaluation Knowledge Brief (2010).
  • https://www.carbonindependent.org/20.html, (access 27 Sep 2023).
  • DEFRA, Passenger transport emissions factors: Methodology paper, 2007 http://www.defra.gov.uk/environment/business/ envrp/pdf/passenger-transport.pdf

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  • Analysis of Carbon Footprint and Comfort for Bus System Regarding Optimum Daily Trips

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Authors

Yetis Sazi Murat
Pamukkale University, Faculty of Engineering, Dept. of Civil Engineering, 20070, Denizli, Turkey
Muhammed Demirkollu
Pamukkale University, Faculty of Engineering, Dept. of Civil Engineering, 20070, Denizli, Turkey
Selim Saldiroglu
Pamukkale University, Faculty of Engineering, Dept. of Civil Engineering, 20070, Denizli, Turkey

Abstract


Sustainable and smart management of public transportation systems depends on continuous monitoring and analysis of the data collected at regular intervals. One of the key research topics in this area is determining bus service frequency and developing appropriate schedules. The number of buses and their frequencies have a significant impact on the entire transportation system, affecting all users of the network. To address this issue, bus service frequencies during peak hours should be determined based on passenger demand. In this study, the daily frequencies of the bus system of a city (Denizli, Turkey) were investigated, the carbon footprints of the system were analyzed, and suggestions were provided. This is determined by the model developed regarding the linear optimization method. The goal programming approach is used in the analysis. The existing frequencies are compared with that provided by the goal programming approach. Additionally, the results obtained are investigated by a cost analysis regarding different benefit rates (carbon footprint, total kilometer, and passenger comfort) for the public. By implementing the recommended bus service frequencies, significant financial and environmental improvements can be achieved.

Keywords


Bus system, Carbon footprint, Linear goal programming, Passenger comfort, Public transportation.

References