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

Pasqual Martí; Jaume Jordán; Angélica González Arrieta; Vicente Julian

doi:10.9781/ijimai.2023.07.010

Authors

Pasqual Martí Universitat Politècnica de València
Jaume Jordán Universitat Politècnica de València
Angélica González Arrieta Universidad de Salamanca
Vicente Julian Universitat Politècnica de València

DOI:

https://doi.org/10.9781/ijimai.2023.07.010

Keywords:

Artificial Intelligence, Demand-Responsive, Intelligent Systems, Optimization, Rural Areas

Supporting Agencies

This work is partially supported by grant PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. Pasqual Martí is supported by grant ACIF/2021/259 funded by the “Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana". Jaume Jordán is supported by grant IJC2020-045683-I funded by MCIN/AEI/ 10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”.

Abstract

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.

Downloads

Download data is not yet available.

References

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.

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.

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.

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.

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.

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.

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.

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.

A. Lakatos, J. Tóth, P. Mándoki, “Demand responsive transport service of ‘dead-end villages’ in interurban traffic,” Sustainability, vol. 12, no. 9, 2020.

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.

H. B. Demir, E. Pekel Özmen, S. Esnaf, “Time- windowed vehicle routing 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.

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.

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.

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.

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.

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.

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.

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.

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.

J. Bischoff, M. Maciejewski, “Proactive empty vehicle rebalancing for demand responsive transport services,” Procedia Computer Science, vol. 170, pp. 739– 744, 2020.

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.

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.

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.

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.

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.

S. Liyanage, H. Dia, “An agent-based simulation approach for evaluating the performance of on- demand bus services,” Sustainability, vol. 12, no. 10, 2020.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A. Horni, K. Nagel, K. Axhausen Eds., Multi-Agent Transport Simulation MATSim. London: Ubiquity Press, Aug 2016.

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.

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.

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.

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.

P. Martí, J. Jordán, V. Julian, “Best-response planning for urban fleet coordination,” Neural Computing and Applications, pp. 1–20, 2023.

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.

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.

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.