ارائه مدلی جدید برای طراحی شبکه اتوبوسرانی با رویکرد کمینه‌سازی یارانه برونسپاری خطوط

نوع مقاله : علمی - پژوهشی

نویسندگان
شهریار افندی زاده 1
یاسر تقی زاده 2
1 استاد دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران
2 دانشجوی دکتری برنامه‌ریزی حمل و نقل، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران
10.22119/jte.2024.469250.2716
چکیده
برونسپاری خطوط اتوبوس به بخش خصوصی با حداقل یارانه ممکن، از مسائل مهم سازمان‌های اتوبوسرانی در جهان است. تعداد مسافر، تواتر خط، تعداد ناوگان، مسیر خط و هزینه‌های عملیاتی، پارامترهایی هستند که در تعیین یارانه مورد نیاز یک خط اتوبوس نقش دارند. این پارامترها، همان متغیرهای تصمیم‌گیری اصلی در مسئله طراحی شبکه اتوبوسرانی هستند که ارتباط بین این دو مسئله را برجسته می‌سازد. این مقاله مدلی جدید برای طراحی بهینه شبکه اتوبوسرانی ارائه نموده که علاوه بر اهداف مرسوم مانند پوشش تقاضا و کاهش زمان انتظار، کمینه‌سازی یارانه مورد نیاز برای برونسپاری شبکه نیز لحاظ شده است. بدین منظور، ابتدا تابعی برای برآورد یارانه یک شبکه اتوبوسرانی ساخته شده و سپس در تابع هدف مدل پیشنهادی وارد می‌شود. برای حل مدل در شبکه‌های واقعی ترکیبی از روش‌های ابتکاری و فرا ابتکاری مبتنی بر الگوریتم ژنتیک به کار گرفته شده است. روش پیشنهادی در محیط C++  کدنویسی شده و برای شهر تهران به عنوان یک شبکه بزرگ مقیاس پیاده‌سازی گردید. نتایج نشان داد که شبکه پیشنهادی این مطالعه بهبود قابل توجهی نسبت به شبکه وضع موجود دارد و با وجود افزایش پوشش تقاضا به میزان 26/3 درصد، یارانه مورد نیاز به ازای هر نفر به میزان 96/7 درصد کاهش یافته است.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

A New Model for Bus Network Design Considering Minimal Subsidy for Outsourcing Routes

نویسندگان English

Shahriar Afandizadeh 1
Yaser Taghizadeh 2
1 Professor, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
2 Ph.D. candidate, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده English

Outsourcing bus routes to the private sector with minimal subsidies is a significant concern for bus organizations worldwide. Parameters such as the number of passengers, route frequency, fleet size, route path, and operational costs play a crucial role in determining the subsidy for a bus route. These parameters are the main decision variables in the bus network design problem, highlighting the connection between these two issues. This paper presents a new model for the optimal design of a bus network, which considers not only conventional objectives such as demand coverage and reduction of waiting time but also minimizes the subsidy for outsourcing the network. For this purpose, a function for estimating the subsidy of a bus network is constructed and then incorporated into the objective function. To solve the model in real networks, a combination of heuristic and meta-heuristic methods based on the genetic algorithm is employed. The proposed method was coded in a C++ environment and implemented for the city of Tehran as a large-scale network. The results showed that the proposed network in this study has significant improvements compared to the current network, including a 7.96% reduction in required subsidy and a 3.26% increase in demand coverage.

کلیدواژه‌ها English

Bus network design
Subsidy
Outsourcing
Genetic algorithm
-   افندی زاده، ش؛ جوانشیر، ح؛ و الیاسی، ر. (1389). طراحی خطوط شبکه اتوبوسرانی شهری با استفاده از روش جستجوی ممنوع. فصلنامه  مهندسی حمل و نقل، سال اول، شماره چهارم، ص. 13–26.
 
-   سیدابریشمی، س؛ خانزاد، ا؛ زرین مهر، ا؛ و ممدوحی، ا. (1396). ارائه یک روش ابتکاری برای طراحی خطوط شبکه حمل ونقل همگانی با استفاده از الگوریتم تولید مسیر. فصلنامه  مهندسی حمل و نقل، سال هشتم، شماره چهارم، ص 643–654.
 
- Amiripour, S. M. M., Ceder, A. (Avi), & Mohaymany, A. S. (2014). Designing large-scale bus network with seasonal variations of demand. Transportation Research Part C: Emerging Technologies, 48, 322–338.
 
- Baaj, M. H., & Mahmassani, H. S. (1990). TRUST: A LISP program for the analysis of transit route configurations. Transportation Research Record, 1283, 125–135.
 
- Baaj, M. H., & Mahmassani, H. S. (1991). AI-based approach for transit route system planning and design. Journal of Advanced Transportation, 25(2), 187–210.
 
- Baaj, M. H., & Mahmassani, H. S. (1995). Hybrid route generation heuristic algorithm for the design of transit networks. Transportation Research Part C, 3(1), 31–50.
 
- Badia, H., Argote-Cabanero, J., & Daganzo, C. F. (2017). How network structure can boost and shape the demand for bus transit. Transportation Research Part A: Policy and Practice, 103, 83–94.
 
- Bayliss, K., Mattioli, G., & Steinberger, J. (2021). Inequality, poverty and the privatization of essential services: A ‘systems of provision’study of water, energy and local buses in the UK. Competition & Change, 25(3–4), 478–500.
 
- Ceder, A. A. (2015). Public Transit Planning and Operation: Modelling, Practice and Behavior, Second Edition. Butterworth-Heinemann.
 
- Ceder, A., & Wilson, N. H. M. (1986). Bus network design. Transportation Research Part B, 20(4), 331–344.
- Chai, S., & Liang, Q. (2020). An improved NSGA-II Algorithm for transit network design and frequency setting problem. Journal of Advanced Transportation, 2020(1), 2895320.
 
- Durán-Micco, J., & Vansteenwegen, P. (2022). A survey on the transit network design and frequency setting problem. Public Transport, 14(1), 155–190.
 
- Duran-Micco, J., Vermeir, E., & Vansteenwegen, P. (2020). Considering emissions in the transit network design and frequency setting problem with a heterogeneous fleet. European Journal of Operational Research, 282(2), 580–592.
 
- Fielbaum, A., Jara-Diaz, S., & Gschwender, A. (2016). Optimal public transport networks for a general urban structure. Transportation Research Part B: Methodological, 94, 298–313.
 
- Filgueiras, M., Gkiotsalitis, K., Yap, M., Cats, O., Lobo, A., & Ferreira, S. (2023). Bus network design and frequency setting in the post-COVID-19 pandemic: The case of London. Journal of Transportation Engineering, Part A: Systems, 149(4), 4023020.
 
- Gemma, A., Cipriani, E., Crisalli, U., Mannini, L., & Petrelli, M. (2024). A Bus Network Design Model under Demand Variation: A Case Study of the Management of Rome’s Bus Network. Sustainability, 16(2), 803.
 
- Gong, M., Hu, Y., Chen, Z., & Li, X. (2021). Transfer-based customized modular bus system design with passenger-route assignment optimization. Transportation Research Part E: Logistics and Transportation Review, 153, 102422.
 
- Hatzenbühler, J., Cats, O., & Jenelius, E. (2022). Network design for line-based autonomous bus services. Transportation, 49(2), 467–502.
 
- Hensher, D. A. (2007). Bus transport: Economics, policy and planning (Vol. 18). Elsevier.
 
- Hu, J., Shi, X., Song, J., & Xu, Y. (2005). Optimal design for urban mass transit network based on evolutionary algorithms. In Advances in Natural Computation (pp. 1089–1100). Springer.
 
- Huang, D., Gu, Y., Wang, S., Liu, Z., & Zhang, W. (2020). A two-phase optimization model for the demand-responsive customized bus network design. Transportation Research Part C: Emerging Technologies, 111, 1–21.
 
- Iliopoulou, C., & Kepaptsoglou, K. (2021). Robust electric transit route network design problem (RE-TRNDP) with delay considerations: Model and application. Transportation Research Part C: Emerging Technologies, 129, 103255.
 
- Laporte, G., Ortega, F. A., Pozo, M. A., & Puerto, J. (2017). Multi-objective integration of timetables, vehicle schedules and user routings in a transit network. Transportation Research Part B: Methodological, 98, 94–112.
 
- Lee, Y.-J., & Vuchic, V. R. (2005). Transit network design with variable demand. Journal of Transportation Engineering, 131(1), 1–10.
 
- Liu, Y., Feng, X., Zhang, L., Hua, W., & Li, K. (2020). A pareto artificial fish swarm algorithm for solving a multi-objective electric transit network design problem. Transportmetrica A: Transport Science, 16(3), 1648–1670.
 
- Mandl, C. E. (1980). Evaluation and optimization of urban public transportation networks. European Journal of Operational Research, 5(6), 396–404.
 
- Mauttone, A., & Urquhart, M. E. (2009). A multi-objective metaheuristic approach for the transit network design problem. Public Transport, 1(4), 253–273.
 
- Momenitabar, M., & Mattson, J. (2021). A multi-objective meta-heuristic approach to improve the bus transit network: a case study of fargo-moorhead area. Sustainability, 13(19), 10885.
 
- Ng, M. T. M., Mahmassani, H. S., Verbas, Ö., Cokyasar, T., & Engelhardt, R. (2024). Redesigning large-scale multimodal transit networks with shared autonomous mobility services. Transportation Research Part C: Emerging Technologies, 104575.
 
- Ngamchai, S., & Lovell, D. J. (2003). Optimal time transfer in bus transit route network design using a genetic algorithm. Journal of Transportation Engineering, 129(5), 510–521.
 
- Pattnaik, S. B., Mohan, S., & Tom, V. M. (1998). Urban bus transit route network design using genetic algorithm. Journal of Transportation Engineering, 124(4), 368–375.
 
- Pinto, J., Santos, M. C., & Matos, P. V. (2021). Contracting out public transit services: an incentive performance-based approach.
 
- Szeto, W. Y., & Wu, Y. (2011). A simultaneous bus route design and frequency setting problem for Tin Shui Wai, Hong Kong. European Journal of Operational Research, 209(2), 141–155.
 
- Tom, V. M., & Mohan, S. (2003). Transit route network design using frequency coded genetic algorithm. Journal of Transportation Engineering, 129(2), 186–195.
 
- Wang, C., Ye, Z., & Wang, W. (2020). A multi-objective optimization and hybrid heuristic approach for urban bus route network design. IEEE Access, 8, 12154–12167.
 
- Wang, J., Miwa, T., Li, D., & Morikawa, T. (2024). Customised bus service design considering flexible vehicle size and transfer incentivization. Transportmetrica A: Transport Science, 2388618.
 
- Xiong, J., Huang, T., Xu, Y., Li, T., & Peng, F. (2022). Optimal network design of battery-powered electric feeder bus with determination of fleet size and number of chargers. Journal of Transportation Engineering, Part A: Systems, 148(1), 4021097.
 
- Yang, Z., Yu, B., & Cheng, C. (2007). A parallel ant colony algorithm for bus network optimization. Computer‐Aided Civil and Infrastructure Engineering, 22(1), 44–55.
 
- Yoo, S., Lee, J. B., & Han, H. (2023). A Reinforcement Learning approach for bus network design and frequency setting optimisation. Public Transport, 15(2), 503–534.
 
- Zhang, T., Ren, G., & Yang, Y. (2020). Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach. Journal of Advanced Transportation, 2020, 7059584.

  • تاریخ دریافت 10 مرداد 1403
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