Autonomous-Shared Vehicle Management System

Yıl 2023, Cilt: 26 Sayı: 1, 81 - 92, 27.03.2023

Erdi Şener , Aslı Sebatlı Sağlam , Fatih Çavdur

https://doi.org/10.2339/politeknik.931490

Cited By: 4

Öz

In this study, a decision support system prototype is developed to solve the demand-based vehicle assignment problem within the scope of autonomous-shared vehicle management systems. First, a database suitable for the demand structure is designed for the developed decision support system prototype. Then, a user-friendly, web-based interface is designed so that customer requests can be stored and viewed by the system administrator in an integrated system design. An example case study is used to illustrate the system implementation where the city center of Bursa with its high urban traffic density is considered. In the case study, three parking stations are assumed to be located in three different central districts (namely Yıldırım, Osmangazi and Nilüfer) through the east-west direction of the city in order to meet customer demands. A multi-commodity network flow problem-based model is used to solve the vehicle assignment problem. It is thought that it will be beneficial in terms of the efficiency of the rapidly growing enterprises of shared-vehicle services to optimize their vehicle relocations and other operations using decision support systems similar to the one developed in this study.

Anahtar Kelimeler

Shared vehicle system, Vehicle allocation problem, Vehicle fleet management, Network flow model, Decision support systems

Otonom-Paylaşımlı Araç Yönetim Sistemi

Öz

Bu çalışmada, otonom-paylaşımlı araç yönetim sistemleri kapsamında, talep tabanlı araç atama problemi çözümü için bir karar destek sistemi prototipi geliştirilmiştir. Öncelikle, geliştirilen karar destek sistemi prototipi için talep yapısına uygun bir veritabanı tasarlanmıştır. Ardından, bütünleşik bir sistem yapısı içerisinde, müşteri taleplerinin kaydedilebilmesi ve sistem yöneticisi tarafından görüntülenebilmesi için kullanıcı dostu web tabanlı bir arayüz tasarlanmıştır. Geliştirilen sistemin çalışması bir örnek uygulama üzerinde gösterilmesi amacıyla kent trafik yoğunluğu yüksek olan şehirlerden Bursa şehir merkezi ele alınmıştır. Oluşturulan uygulama kapsamında, müşteri taleplerini karşılamak üzere şehrin doğu-batı ekseni doğrultusundaki üç merkez ilçesinde (Yıldırım, Osmangazi ve Nilüfer) olmak üzere üç farklı konumda park istasyonu belirlenmiştir. Araç atama probleminin çözümü için çok-ürünlü ağ akış problemi bazlı bir model kullanılmıştır. Günümüzde paylaşımlı araç hizmetleri için hızla artmakta olan girişimlerin, araç konumlandırmalarını ve diğer operasyonlarını bu çalışmada kurgulanana benzer karar destek sistemleri kullanarak optimize etmelerinin verimlilik açısından faydalı olacağı düşünülmektedir.

Anahtar Kelimeler

Paylaşımlı araç sistemi, Araç atama problemi, araç filo yönetimi, ağ akış modeli, karar destek sistemleri

Kaynakça

• [1] Chapman L., “Transport and climate change: a review”, Journal of transport geography, 15(5): 354-367, (2007).
• [2] Rigole P. J., “Study of a shared autonomous vehicles based mobility solution in Stockholm”, Master of Science Thesis, Industrial Ecology, Royal Institute of Technology, (2014).
• [3] Shaheen S. A., Cohen A. P. and Roberts J. D., “Carsharing in North America: Market growth, current developments, and future potential”, Transportation Research Record, 1986(1): 116-124, (2006).
• [4] Shaheen S. and Cohen A., “Worldwide carsharing growth: An international comparison”, Transportation Research Record, 1992(1): 81-89, (2007).
• [5] Fan W. D., “Optimizing strategic allocation of vehicles for one-way car-sharing systems under demand uncertainty”, Journal of the Transportation Research Forum, 53(3): 7-20, (2014).
• [6] Nourinejad M. and Roorda M. J., “Carsharing operations policies: a comparison between one-way and two-way systems”, Transportation, 42(3): 497-518, (2015).
• [7] Klingman D., Napier A. and Stutz, J., ”NETGEN: A program for generating large scale capacitated assignment, transportation, and minimum cost flow network problems”, Management Science, 20(5): 814-821, (1974).
• [8] Dantzig G. B. and Fulkerson D. R., “Computation of maximal flows in networks”, Naval Research Logistics Quarterly, 2(4): 277-283, (1955).
• [9] Ford L. R. and Fulkerson D. R., “Maximal flow through a network”, Canadian journal of Mathematics, 8(1): 399-404, (1956).
• [10] Ford Jr L. R. and Fulkerson D. R., “A suggested computation for maximal multi-commodity network flows”, Management Science, 50(12): 1778-1780, (2004).
• [11] Jorge D. and Correia G., “Carsharing systems demand estimation and defined operations: a literature review”, European Journal of Transport and Infrastructure Research, 13(3): 201-220, (2013).
• [12] Fan W. D., “Management of dynamic vehicle allocation for carsharing systems: Stochastic programming approach”, Transportation research record, 2359(1): 51-58, (2013).
• [13] Narayanan S., Chaniotakis E. and Antoniou C. “Shared autonomous vehicle services: A comprehensive review”, Transportation Research Part C: Emerging Technologies, 111: 255-293, (2020).
• [14] Wang F., Yang M. and Yang R., “Dynamic fleet management for cybercars”, 2006 IEEE Intelligent Transportation Systems Conference, Toronto, ON, Canada, 1246-1250, (2006).
• [15] Fagnant D. J. and Kockelman, K. M., “The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios”, Transportation Research Part C: Emerging Technologies, 40: 1-13, (2014).
• [16] Martinez L. and Crist P., “Urban Mobility System Upgrade–How shared self-driving cars could change city traffic”, International Transport Forum, Paris, (2015).
• [17] Chen T. D., Kockelman K. M. and Hanna J. P., “Operations of a shared, autonomous, electric vehicle fleet: Implications of vehicle & charging infrastructure decisions”, Transportation Research Part A: Policy and Practice, 94: 243-254, (2016).
• [18] Sayarshad H., Tavassoli S. and Zhao F., “A multi-periodic optimization formulation for bike planning and bike utilization”, Applied Mathematical Modelling, 36(10): 4944-4951, (2012).
• [19] Nair R., Miller-Hooks E., Hampshire R. C. and Bušić, A., “Large-scale vehicle sharing systems: analysis of Vélib'”, International Journal of Sustainable Transportation, 7(1): 85-106, (2013).
• [20] Alonso-Mora J., Samaranayake S., Wallar A., Frazzoli E. and Rus D., “On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment”, Proceedings of the National Academy of Sciences, 114(3): 462-467, (2017).
• [21] Bai W., Quan J., Fu L., Gan X. and Wang X., “Online fair allocation in autonomous vehicle sharing”, GLOBECOM 2017-2017 IEEE Global Communications Conference, Singapore, 1-6, (2017).
• [22] Spieser K., Treleaven K., Zhang R., Frazzoli E., Morton D. and Pavone M., “Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in Singapore”, Road Vehicle Automation, Springer, Cham, (2014).
• [23] Wen J., Zhao J. and Jaillet P., “Rebalancing shared mobility-on-demand systems: A reinforcement learning approach”, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan, 220-225, (2017).
• [24] Kek A. G., Cheu R. L. and Chor M. L., “Relocation simulation model for multiple-station shared-use vehicle systems”, Transportation research record, 1986(1): 81-88, (2006).
• [25] Kek, A. G., Cheu R. L., Meng Q. and Fung C. H., “A decision support system for vehicle relocation operations in carsharing systems”, Transportation Research Part E: Logistics and Transportation Review, 45(1): 149-158, (2009).
• [26] de Souza F., Gurumurthy K. M., Auld J. and Kockelman K. M., “A repositioning method for shared autonomous vehicles operation”, Procedia Computer Science, 170: 791-798, (2020).
• [27] Jorge D., Correia G. H. and Barnhart C., “Comparing optimal relocation operations with simulated relocation policies in one-way carsharing systems”, IEEE Transactions on Intelligent Transportation Systems, 15(4): 1667-1675, (2014).
• [28] Winter M. K. E., Cats O., Martens K. and van Arem B., “Relocating strategies under parking constraints for a fleet of shared automated vehicles”, hEART 2018: 7th Symposium of the European Association for Research in Transportation, Athens, Greece, 1-7, (2018).
• [29] Tang Q., Zhang Y. and Zhou M., “Vehicle repositioning under uncertainty”, SSRN, (2020).
• [30] Powell W. B., “A stochastic model of the dynamic vehicle allocation problem”, Transportation science, 20(2): 117-129, (1986).
• [31] Frantzeskakis L. F. and Powell W. B., “A successive linear approximation procedure for stochastic, dynamic vehicle allocation problems”, Transportation Science, 24(1): 40-57, (1990).
• [32] Cheung R. K. and Chen C.Y., “A two-stage stochastic network model and solution methods for the dynamic empty container allocation problem”, Transportation science, 32(2): 142-162, (1998).
• [33] Fan W., Machemehl R. B. and Lownes, N. E., “Carsharing: Dynamic decision-making problem for vehicle allocation”, Transportation Research Record, 2063(1): 97-104, (2008).
• [34] Cokyasar T. and Larson J., “Optimal assignment for the single-household shared autonomous vehicle problem”, Transportation Research Part B: Methodological, 141: 98-115, (2020).
• [35] Liu J., Mirchandani P. and Zhou X., “Integrated vehicle assignment and routing for system-optimal shared mobility planning with endogenous road congestion”, Transportation Research Part C: Emerging Technologies, 117: 102675, (2020).
• [36] Zhang D., Liu Y. and He S., “Vehicle assignment and relays for one-way electric car-sharing systems”, Transportation Research Part B: Methodological, 120: 125-146, (2019).
• [37] Bazaraa M. S., Jarvis J. J. and Sherali H.D., “Linear Programming and Network Flows”, 4, John Wiley and Sons, ISBN 978-0-470-46272-0, New Jersey, (2010).