Abstract
Road-rail intermodal travel is one of the important intercity travel modes. However, an intercity travel recommendation method based on single factor ranking cannot satisfy the personalized travel demands of road-rail intermodal passengers. This study improves travel efficiency by using a profile database based on passenger historical ticketing data with the term frequency-inverse document frequency (TP-IDF) and K-means algorithms to explore the road-rail intermodal travel demand differences derived from the passenger heterogeneity. The model uses reward functions based on preference scores and sensitivity characteristics with the Q-learning reinforcement learning algorithm in a road-rail intermodal travel recommendation method based on the passenger heterogeneity profile. The method is applied to the Tianjin-Sihong route as a typical road-rail intermodal travel route from a megacity to small cities with road-rail intermodal travel schemes recommended for three types of passengers with different sensitivities. The results show that the recommended travel schemes shorten travel times by 20% and reduce travel costs by 32% while effectively meeting passenger behavior preferences, sensitivity characteristics and personal demands.
References
[1]
GIVONI M, CHEN X M. Airline and railway disintegration in China: The case of Shanghai Hongqiao integrated transport hub[J]. Transportation Letters, 2017, 9(4): 202-214.
[2]
JIANG Y L, TIMMERMANS H J P, CHEN C, et al. Determinants of air-rail integration service of Shijiazhuang airport, China: Analysis of historical data and stated preferences[J]. Transportmetrica B: Transport Dynamics, 2019, 7(1): 1572-1587.
[3]
LI Z C, SHENG D. Forecasting passenger travel demand for air and high-speed rail integration service: A case study of Beijing-Guangzhou corridor, China[J]. Transportation Research Part A: Policy and Practice, 2016, 94: 397-410.
[4]
RUI H T, WU Q Q. Medium-and long-distance travel mode decision between high-speed rail and civil aviation[J]. China Journal of Highway and Transport, 2016, 29(3): 134-141. (in Chinese)
[5]
YUAN Y L, YANG M, FENG T, et al. Heterogeneity in passenger satisfaction with air-rail integration services: Results of a finite mixture partial least squares model[J]. Transportation Research Part A: Policy and Practice, 2021, 147: 133-158.
[6]
LI X H, LI S Y, CHENG C, et al. Analyzing the air-rail transfer service demand and preference for air-rail integrated hubs[J]. China Transportation Review, 2020, 42(6): 8-12. (in Chinese)
[7]
CHEN L. Research on the intermodal behavior of passengers between the hubs of the Beijing-Tianjin-Hebei urban agglomeration[D]. Beijing: Beijing Jiaotong University, 2020. (in Chinese)
[8]
BOVY P H L. On modelling route choice sets in transportation networks: A synthesis[J]. Transport Reviews, 2009, 29(1): 43-68.
[9]
LI D W, YANG M, JIN C J, et al. Multi-modal combined route choice modeling in the MaaS age considering generalized path overlapping problem[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(4): 2430-2441.
[10]
LIU Z Y, MENG Q. Bus-based park-and ride-system: A stochastic model on multimodal network with congestion pricing schemes[J]. International Journal of Systems Science, 2014, 45(5): 994-1006.
[11]
WONG S C, TONG C O. Estimation of time-dependent origin-destination matrices for transit networks[J]. Transpor- tation Research Part B: Methodological, 1998, 32(1): 35-48.
[12]
HORN M E T. An extended model and procedural framework for planning multi-modal passenger journeys[J]. Transportation Research Part B: Methodological, 2003, 37(7): 641-660.
[13]
BARRETT C, BISSET K, HOLZER M, et al. Engineering label-constrained shortest-path algorithms[C]//Proceedings of the 4th International Conference on Algorithmic Aspects in Information and Management. Shanghai, China: Springer, 2008: 27-37.
[14]
BARRETT C, BISSET K, JACOB R, et al. Classical and contemporary shortest path problems in road networks: Implementation and experimental analysis of the TRANSIMS router[C]//10th Annual European Symposium on Algorithms. Rome, Italy: Springer, 2002: 126-138.
[15]
SONG Y C, LI D W, CAO Q, et al. The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service[J]. Transportation Research Part C: Emerging Technologies, 2021, 132: 103360.
[16]
RENJITH S, SREEKUMAR A, JATHAVEDAN M. An extensive study on the evolution of context-aware personalized travel recommender systems[J]. Information Processing & Management, 2020, 57(1): 102078.
[17]
ABOWD G D, ATKESON C G, HONG J, et al. Cyberguide: A mobile context‐aware tour guide[J]. Wireless Networks, 1997, 3(5): 421-433.
[18]
ZHANG X L. The design and implementation of multimodal travel scheme planning system[D]. Dalian: Dalian University of Technology, 2019. (in Chinese)
[19]
LIU X Y, CHEN Y L, JIA Z P, et al. Recommender system for travel plan based on reinforcement learning[J]. Computer Engineering, 2010, 36(21): 254-256, 259. (in Chinese)
[20]
WEN Y T, YEO J, PENG W C, et al. Efficient keyword-aware representative travel route recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8): 1639-1652.
[21]
GAO Y, CHEN S F, LU X. Research on reinforcement learning technology: A review[J]. Acta Automatica Sinica, 2004, 30(1): 86-100. (in Chinese)
[22]
CHENG Y H, HUANG T Y. High speed rail passenger segmentation and ticketing channel preference[J]. Transportation Research Part A: Policy and Practice, 2014, 66: 127-143.
[23]
BORDAGARAY M, DELL'OLIO L, IBEAS A, et al. Modelling user perception of bus transit quality considering user and service heterogeneity[J]. Transportmetrica A: Transport Science, 2014, 10(8): 705-721.
[24]
RASOULI S, TIMMERMANS H J P. Covariates-dependent random parameters regret-rejoice models of choice behavior: Specification and performance assessment using experimental design data[J]. Transportmetrica A: Transport Science, 2019, 15(2): 485-525.
