A Survey on Demand-Responsive Transportation for Rural and Interurban Mobility

  1. Pasqual Martí 1
  2. Jaume Jordán 1
  3. María Angélica González Arrieta 2
  4. Vicente Julian 1
  1. 1 Universidad Politécnica de Valencia
    info

    Universidad Politécnica de Valencia

    Valencia, España

    ROR https://ror.org/01460j859

  2. 2 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
IJIMAI

ISSN: 1989-1660

Año de publicación: 2023

Volumen: 8

Número: 3

Páginas: 43-54

Tipo: Artículo

DOI: 10.9781/IJIMAI.2023.07.010 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: IJIMAI

Resumen

Rural areas have been marginalized when it comes to flexible, quality transportation research. This review article brings together papers that discuss, analyze, model, or experiment with demand-responsive transportation systems applied to rural settlements and interurban transportation, discussing their general feasibility as well as the most successful configurations. For that, demand-responsive transportation is characterized and the techniques used for modeling and optimization are described. Then, a classification of the relevant publications is presented, splitting the contributions into analytical and experimental works. The results of the classification lead to a discussion that states open issues within the topic: replacement of public transportation with demandresponsive solutions, disconnection between theoretical and experimental works, user-centered design and its impact on adoption rate, and a lack of innovation regarding artificial intelligence implementation on the proposed systems.

Referencias bibliográficas

  • [1] R. Choudhary, V. Vasudevan, “Study of vehicle ownership for urban and rural households in india,” Journal of Transport Geography, vol. 58, pp. 52–58, 2017, doi: https://doi.org/10.1016/j.jtrangeo.2016.11.006.
  • [2] T. J. Ryley, P. A. Stanley, M. P. Enoch, A. M. Zanni, M. A. Quddus, “Investigating the contribution of demand responsive transport to a sustainable local public transport system,” Research in Transportation Economics, vol. 48, pp. 364–372, 2014.
  • [3] S. C. Ho,W. Szeto, Y.-H. Kuo, J. M. Leung, M. Petering, T.W. Tou, “Asurvey of dial-a-ride problems: Literature review and recent developments,” Transportation Research Part B: Methodological, vol. 111, pp. 395–421, 2018.
  • [4] G. Currie, N. Fournier, “Why most drt/micro-transits fail–what the survivors tell us about progress,” Research in Transportation Economics, vol. 83, p. 100895, 2020.
  • [5] M. Enoch, S. Potter, G. Parkhurst, M. Smith, “Why do demand responsive transport systems fail?,” in Transportation Research Board 85th Annual Meeting, Washington DC, USA, 22-26 Jan 2006.
  • [6] L. Butler, T. Yigitcanlar, A. Paz, “Smart urban mobility innovations: A comprehensive review and evaluation,” IEEE Access, vol. 8, pp. 196034– 196049, 2020, doi: 10.1109/ACCESS.2020.3034596.
  • [7] P. Vansteenwegen, L. Melis, D. Aktaş, B. D. G. Montenegro, F. S. Vieira, K. Sörensen, “A survey on demand-responsive public bus systems,” Transportation Research Part C: Emerging Technologies, vol. 137, p. 103573, 2022.
  • [8] S. Dytckov, J. A. Persson, F. Lorig, P. Davidsson, “Potential benefits of demand responsive transport in rural areas: A simulation study in lolland, denmark,” Sustainability, vol. 14, no. 6, 2022.
  • [9] A. Lakatos, J. Tóth, P. Mándoki, “Demand responsive transport service of ‘dead-end villages’ in interurban traffic,” Sustainability, vol. 12, no. 9, 2020.
  • [10] G. Calabrò, M. Le Pira, N. Giuffrida, G. Inturri, M. Ignaccolo, G. Correia, “Fixed-route vs demand- responsive transport feeder services: An exploratory study using an agent-based model,” J. of Advanced Transportation, vol. 2022, 2022.
  • [11] H. B. Demir, E. Pekel Özmen, S. Esnaf, “Time- windowed vehiclerouting problem: Tabu search algorithm approach,” ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, vol. 11, p. 179–189, Oct. 2022, doi: 10.14201/adcaij.27533.
  • [12] E. Osaba, F. Diaz, “Design and implementation of a combinatorial optimization multi-population meta-heuristic for solving vehicle routing problems,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, pp. 89–90, 12/2016 2016, doi: 10.9781/ijimai.2016.4213.
  • [13] M. Hyland, H. S. Mahmassani, “Operational benefits and challenges of shared-ride automated mobility-on- demand services,” Transportation Research Part A: Policy and Practice, vol. 134, pp. 251–270, 2020.
  • [14] S. Vallée, A. Oulamara, W. R. Cherif-Khettaf, “Maximizing the number of served requests in an online shared transport system by solving a dynamic darp,” in Computational Logistics, Cham, 2017, pp. 64– 78, Springer International Publishing.
  • [15] F. M. Coutinho, N. van Oort, Z. Christoforou, M. J. Alonso-González, O. Cats, S. Hoogendoorn, “Impacts of replacing a fixed public transport line by a demand responsive transport system: Case study of a rural area in amsterdam,” Research in Transportation Economics, vol. 83, p. 100910, 2020.
  • [16] K. Viergutz, C. Schmidt, “Demand responsive - vs. conventional public transportation: A matsim study about the rural town of colditz, germany,” Procedia Computer Science, vol. 151, pp. 69–76, 2019.
  • [17] J. Schlüter, A. Bossert, P. Rössy, M. Kersting, “Impact assessment of autonomous demand responsive transport as a link between urban and rural areas,” Research in Transportation Business Management, vol. 39, p. 100613, 2021.
  • [18] M. van Engelen, O. Cats, H. Post, K. Aardal, “Enhancing flexible transport services with demand- anticipatory insertion heuristics,” Transportation Research Part E: Logistics and Transportation Review, vol. 110, pp. 110–121, 2018.
  • [19] M. Balmer, M. Rieser, K. Meister, D. Charypar, N. Lefebvre, K. Nagel, “Matsim-t: Architecture and simulation times,” in Multi-agent systems for traffic and transportation engineering, IGI Global, 2009, pp. 57–78.
  • [20] J. Bischoff, M. Maciejewski, “Proactive empty vehicle rebalancing for demand responsive transport services,” Procedia Computer Science, vol. 170, pp. 739– 744, 2020.
  • [21] C. Bertelle, M. Nabaa, D. Olivier, P. Tranouez, “A decentralised approach for the transportation on demand problem,” in From System Complexity to Emergent Properties, Springer, 2009, pp. 281–289.
  • [22] S. Tisue, U. Wilensky, “Netlogo: A simple environment for modeling complexity,” in Int. conference on complex systems, vol. 21, 2004, pp. 16–21, Boston, MA.
  • [23] G. Inturri, N. Giuffrida, M. Ignaccolo, M. Le Pira, A. Pluchino, A. Rapisarda, Testing Demand Responsive Shared Transport Services via Agent-Based Simulations, pp. 313–320. Cham: Springer International Publishing, 2018.
  • [24] J. Palanca, A. Terrasa, C. Carrascosa, V. Julián, “Simfleet: a new transport fleet simulator based on mas,” in International Conference on Practical Applications of Agents and Multi-Agent Systems, 2019, pp. 257–264, Springer.
  • [25] N. Marković, M. E. Kim, E. Kim, S. Milinković, “A threshold policy for dispatching vehicles in demand-responsive transit systems,” Promet - Trafficamp;Transportation, vol. 31, pp. 387–395, Aug. 2019.
  • [26] S. Liyanage, H. Dia, “An agent-based simulation approach for evaluating the performance of on- demand bus services,” Sustainability, vol. 12, no. 10, 2020.
  • [27] C.-G. Roh, J. Kim, “What are more efficient transportation services in a rural area? a case study in yangsan city, south korea,” International journal of environmental research and public health, vol. 19, no. 18, p. 11263, 2022.
  • [28] C. Wang, M. Quddus, M. Enoch, T. Ryley, L. Davison, “Exploring the propensity to travel by demand responsive transport in the rural area of lincolnshire in england,” Case Studies on Transport Policy, vol. 3, no. 2, pp. 129–136, 2015.
  • [29] A. Anburuvel, W. Perera, R. Randeniya, “A demand responsive public transport for a spatially scattered population in a developing country,” Case Studies on Transport Policy, vol. 10, no. 1, pp. 187–197, 2022.
  • [30] S. E. Schasché, R. G. Sposato, N. Hampl, “The dilemma of demandresponsive transport services in rural areas: Conflicting expectations and weak user acceptance,” Transport Policy, vol. 126, pp. 43–54, 2022.
  • [31] S. A. M. Agriesti, R.-M. Soe, M. A. Saif, “Framework for connecting the mobility challenges in low density areas to smart mobility solutions: the case study of estonian municipalities,” European Transport Research Review, vol. 14, no. 1, p. 32, 2022.
  • [32] H. Poltimäe, M. Rehema, J. Raun, A. Poom, “In search of sustainable and inclusive mobility solutions for rural areas,” European transport research review, vol. 14, no. 1, p. 13, 2022.
  • [33] M. Abdullah, N. Ali, S. A. H. Shah, M. A. Javid, T. Campisi, “Service quality assessment of app-based demand-responsive public transit services in lahore, pakistan,” Applied Sciences, vol. 11, no. 4, p. 1911, 2021.
  • [34] F. Heinitz, “Sustainable development assessment of incentive-driven shared on-demand mobility systems in rural settings,” European Transport Research Review, vol. 14, no. 1, p. 38, 2022.
  • [35] F. Cavallaro, S. Nocera, “Flexible-route integrated passenger–freight transport in rural areas,” Transportation Research Part A: Policy and Practice, vol. 169, p. 103604, 2023.
  • [36] R. Morrison, T. Hanson, “Exploring agent-based modelling for car-based volunteer driver program planning,” Transportation research record, vol. 2676, no. 11, pp. 520–532, 2022.
  • [37] S. Matsuhita, S. Yumita, T. Nagaosa, “A proposal and performance evaluation of utilization methods for tourism of a demand-responsive transport system at a rural town,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022, pp. 2920– 2925, IEEE.
  • [38] F. Bruzzone, M. Scorrano, S. Nocera, “The combination of e-bike-sharing and demand-responsive transport systems in rural areas: A case study of velenje,” Research in Transportation Business & Management, vol. 40, p. 100570, 2021.
  • [39] P. Li, L. Jiang, S. Zhang, X. Jiang, “Demand response transit scheduling research based on urban and rural transportation station optimization,” Sustainability, vol. 14, no. 20, p. 13328, 2022.
  • [40] A. Horni, K. Nagel, K. Axhausen Eds., Multi-Agent Transport Simulation MATSim. London: Ubiquity Press, Aug 2016.
  • [41] T. Rongen, T. Tillema, J. Arts, M. J. Alonso-González, J.-J. Witte, “An analysis of the mobility hub concept in the netherlands: Historical lessons for its implementation,” Journal of Transport Geography, vol. 104, p. 103419, 2022.
  • [42] G. Mariammal, A. Suruliandi, S. P. Raja, E. Poongothai, “An empirical evaluation of machine learning techniques for crop prediction,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. In Press, pp. 1–9, 12/2022 9998, doi: 10.9781/ijimai.2022.12.004.
  • [43] V. K. Solanki, M. Venkaesan, S. Katiyar, “Conceptual model for smart cities: Irrigation and highway lamps using iot,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, pp. 28–33, 03/2017 2017, doi: 10.9781/ijimai.2017.435.
  • [44] M. Qader Kheder, M. Aree Ali, “Iot-based vision techniques in autonomous driving: A review,” ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, vol. 11, p. 367–394, Jan. 2023.
  • [45] P. Martí, J. Jordán, V. Julian, “Best-response planning for urban fleet coordination,” Neural Computing and Applications, pp. 1–20, 2023.
  • [46] P. Martí, J. Jordán, F. De la Prieta, H. Billhardt, V. Julian, “Demandresponsive shared transportation: A self- interested proposal,” Electronics, vol. 11, no. 1, 2022, doi: 10.3390/electronics11010078.
  • [47] A. Ibáñez, J. Jordán, V. Julian, “Improving public transportation efficiency through accurate bus passenger demand,” in Highlights in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection, Cham, 2023, pp. 18–29, Springer Nature Switzerland.
  • [48] P. Martí, J. Llopis, V. Julian, P. Novais, J. Jordán, “Validating state-wide charging station network through agent-based simulation,” in Highlights in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection, Cham, 2023, pp. 158–169, Springer Nature Switzerland.
Fuente de los datos: Dialnet