Methods of solving problems related to the organization of passenger transportation by road transport

Authors

DOI:

https://doi.org/10.46299/j.isjea.20230203.10

Keywords:

organization of passenger transport, road transport, population movement, public service, city transport, management relations, foreign experience, construction of route systems, passenger flow, transport routing

Abstract

One of the areas of public service is the provision of high-quality and timely transportation services to passengers. The organization of passenger transportation by road transport is a set of measures aimed at creating favorable conditions for the movement of the population. The social development of the area depends on the correct organization of the city's public passenger transport. The creation of a universal structure of the management system of carriers of various forms of ownership is important for small cities and towns of the urban type, since the majority of the population in such cities use public transport. The ever-increasing mobility of the population in the world, the reformatting of management relations between various branches of social production place increased demands on the construction of route systems, and on the quality of their planning and management. In these conditions, as foreign experience shows, the most important direction of the efficiency of the functioning of passenger transport systems is the scientific and technical justification of their optimal construction. We will comprehensively consider the list of problems of the rational construction of route systems, namely: methods of collecting and processing information about passenger flows, modeling the passenger transport system of cities, as well as methods, models and software for routing passenger transportation.

References

Xiao, Y., Wang, F., Liu, Y., & Wang, J. (2013). Reconstructing gravitational attractions of major cities in china from air passenger flow data, 2001-2008: A particle swarm optimization approach. Professional Geographer, 65(2), 265-282. doi:10.1080/00330124.2012.679445

Chakamera, C., & Pisa, N. M. (2021). Relationship between air passenger transport, tourism and real gross domestic product in africa: A longitudinal mediation analysis. African Journal of Hospitality, Tourism and Leisure, 10(4), 1200-1214. doi:10.46222/ajhtl.19770720-157

Banks, S. (1951). Relative severity of air line passenger complaints. Journal of Applied Psychology, 35(4), 260-264. doi:10.1037/h0061891

Lukyanov S. A., Ruzhanskaya L. S., Avramenko E. S., Stroev V. V. Restraints on competi-tion in the Russian air passenger market. St Petersburg University Journal of Economic Studies, 2018, vol. 34, issue 1, pp. 134–148. https://doi.org/10.21638/11701/spbu05.2018.107

Liu, X., & Xia, H. (2008). Reverse gravity model based on OD traffic flow of air passengers. Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 43(3), 409-414.

Xie, G., Wang, S., & Lai, K. K. (2014). Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches. Journal of Air Transport Management, 37, 20-26. doi:10.1016/j.jairtraman.2014.01.009

Hofer, C., Kali, R., & Mendez, F. (2018). Socio-economic mobility and air passenger demand in the U.S. Transportation Research Part A: Policy and Practice, 112, 85-94. doi:10.1016/j.tra.2018.01.009

Margaretic, P., Thomas-Agnan, C., & Doucet, R. (2017). Spatial dependence in (origin-destination) air passenger flows. Papers in Regional Science, 96(2), 357-380. doi:10.1111/pirs.12189

Neretin, A. S. (2017). Spatial structure of air passenger transport in european russia. Izvestiya Rossiiskoi Akademii Nauk.Seriya Geograficheskaya, (6), 19-38. doi:10.7868/S0373244417060032

Kim, K., Kim, V., & Kim, H. (2019). Spatiotemporal auto-regressive model for origin–destination air passenger flows. Journal of the Royal Statistical Society.Series A: Statistics in Society, 182(3), 1003-1016. doi:10.1111/rssa.12427

Zhang, X., & Hou, Z. (2010). Study on energy conservation schemes of air-conditioned passenger train based on sustainable development strategy. Paper presented at the 2010 International Conference on Logistics Engineering and Intelligent Transportation Systems, LEITS2010 - Proceedings, 336-339. doi:10.1109/LEITS.2010.5664963

Yan, K. -. (2009). Study on the forecast of air passenger flow based on SVM regression algorithm. Paper presented at the Proceedings - 2009 1st International Workshop on Database Technology and Applications, DBTA 2009, 325-328. doi:10.1109/DBTA.2009.33

Baikgaki, O. A., & Daw, O. D. (2013). The determinants of domestic air passenger demand in the republic of south africa. Mediterranean Journal of Social Sciences, 4(13), 389-396. doi:10.5901/mjss.2013.v4n13p389

Muweis, J., & Łamasz, B. (2019). The development of the aviation fuel market in poland and changes in civil passenger traffic. Polityka Energetyczna, 22(1), 153-168. doi:10.33223/epj/105527

Li, Y., Yang, B., & Cui, Q. (2019). The effects of high-speed rail on air passenger transport in china. Applied Economics Letters, 26(9), 745-749. doi:10.1080/13504851.2018.1494798

Zhang, F., Ning, Y., & Lou, X. (2021). The evolutionary mechanism of china's urban network from 1997 to 2015: An analysis of air passenger flows. Cities, 109 doi:10.1016/j.cities.2020.103005

Korkmaz, E., & Akgüngör, A. P. (2021). The forecasting of air transport passenger demands in turkey by using novel meta-heuristic algorithms. Concurrency and Computation: Practice and Experience, 33(16) doi:10.1002/cpe.6263

Danchev, S., Paratsiokas, N., & Vettas, N. (2022). The impact of the concession of 14 regional greek airports on passenger traffic. Journal of Industry, Competition and Trade, 22(1), 51-67. doi:10.1007/s10842-021-00378-0

Al-Saad, S., Ababneh, A., & Alazaizeh, M. M. (2019). The influence of airport security procedures on the intention to re-travel. European Journal of Tourism Research, 23, 127-141.

Lee, H. -. (2009). The networkability of cities in the international air passenger flows 1992-2004. Journal of Transport Geography, 17(3), 166-175. doi:10.1016/j.jtrangeo.2008.07.011

Ida, Y. (1993). The pattern of air passenger flows in japan. Geographical Review of Japan, Series B, 66(1), 18-34. doi:10.4157/grj1984b.66.18

Correnti, V., Caprì, S., Ignaccolo, M., & Inturri, G. (2007). The potential of rotorcraft for intercity passenger transport. Journal of Air Transport Management, 13(2), 53-60. doi:10.1016/j.jairtraman.2006.11.009

Burns, M. C., Roca Cladera, J., & Moix Bergadà, M. (2008). The spatial implications of the functional proximity deriving from air passenger flows between european metropolitan urban regions. GeoJournal, 71(1), 37-52. doi:10.1007/s10708-008-9144-x

Li, H., Wang, H., Bai, M., & Duan, B. (2019). The structure and periodicity of the chinese air passenger network. Sustainability (Switzerland), 11(1) doi:10.3390/su11010054

Elwakil, O. S., Windle, R. J., & Dresner, M. E. (2013). Transborder demand leakage and the US-canadian air passenger market. Transportation Research Part E: Logistics and Transportation Review, 57, 45-57. doi:10.1016/j.tre.2013.01.005

Xu, J., Qiu, R., Tao, Z., & Xie, H. (2018). Tripartite equilibrium strategy for a carbon tax setting problem in air passenger transport. Environmental Science and Pollution Research, 25(9), 8512-8531. doi:10.1007/s11356-017-1163-z

Valutytė, R. (2020). Striking a healthier balance between air passenger rights and air carriers’ vital interests in the light of COVID-19. Entrepreneurship and Sustainability Issues, 8(2), 546-558. doi:10.9770/jesi.2020.8.2(33)

Kuo, S. -., & Chen, S. -. (2013). What drives business and leisure air passenger transport demand. Transactions of Nanjing University of Aeronautics and Astronautics, 30(1), 88-95.

Leung, A., Yen, B. T. H., & Lohmann, G. (2017). Why passengers’ geo-demographic characteristics matter to airport marketing. Journal of Travel and Tourism Marketing, 34(6), 833-850. doi:10.1080/10548408.2016.1250698

Liu, H., Xu, Y. “., Stockwell, N., Rodgers, M. O., & Guensler, R. (2016). A comparative life-cycle energy and emissions analysis for intercity passenger transportation in the U.S. by aviation, intercity bus, and automobile. Transportation Research Part D: Transport and Environment, 48, 267-283. doi:10.1016/j.trd.2016.08.027

Zheng, J. -., Lin, J., Allwood, J. M., & Dean, T. (2021). A universal mass-based index defining energy efficiency of different modes of passenger transport. International Journal of Lightweight Materials and Manufacture, 4(4), 423-433. doi:10.1016/j.ijlmm.2021.06.004

Wang, Z. Z., & Liu, X. Y. (2014). Analysis on difference between supply and demand of urban taxi passenger in the case of carpooling doi:10.4028/www.scientific.net/AMM.543-547.4378

Karplus, V. J., Paltsev, S., Babiker, M., & Reilly, J. M. (2013). Applying engineering and fleet detail to represent passenger vehicle transport in a computable general equilibrium model. Economic Modelling, 30(1), 295-305. doi:10.1016/j.econmod.2012.08.019

Zhang, L., & Liu, B. (2010). Highway passenger traffic volume correlation analysis based on gray relational grade. ICETC 2010 - 2010 2nd International Conference on Education Technology and Computer, 4, V4199-V4202. doi:10.1109/ICETC.2010.5529701

Li, Z., & Bo, L. (2010). Highway passenger traffic volume research based on gray - markov prediction model. Paper presented at the 2010 International Conference on Networking and Digital Society, ICNDS 2010, , 2 229-232. doi:10.1109/ICNDS.2010.5479353

Peng, H., & Xu, M. (2012). Study on the model of intercity passengers' trip mode choice based on cluster analysis method doi:10.4028/www.scientific.net/AMM.178-181.1934

Pham, T. Q. M., Lee, G., & Kim, H. (2020). Toward sustainable ferry routes in korea: Analysis of operational efficiency considering passenger mobility burdens. Sustainability (Switzerland), 12(21), 1-22. doi:10.3390/su12218819

Hejin, Y. (2010). A bus passenger flow estimation method based on feature point's trajectory clustering. Paper presented at the Proceedings - 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2010, , 1 426-430. doi:10.1109/ICICISYS.2010.5658589

Guo, J., Xue, Y., Cai, J., Gao, Z., Xu, G., & Zhang, H. (2021). A bus passenger re-identification dataset and a deep learning baseline using triplet embedding. Multimedia Tools and Applications, 80(11), 16425-16440. doi:10.1007/s11042-020-08944-0

Maternini, G., & Cadei, M. (2014). A comfort scale for standing bus passengers in relation to certain road characteristics. Transportation Letters, 6(3), 136-141. doi:10.1179/1942787514Y.0000000020

Fontes, T., Correia, R., Ribeiro, J., & Borges, J. L. (2020). A deep learning approach for predicting bus passenger demand based on weather conditions. Transport and Telecommunication, 21(4), 255-264. doi:10.2478/ttj-2020-0020

Bai, Y., Sun, Z., Zeng, B., Deng, J., & Li, C. (2017). A multi-pattern deep fusion model for short-term bus passenger flow forecasting. Applied Soft Computing Journal, 58, 669-680. doi:10.1016/j.asoc.2017.05.011

Zhao, S. -., Ni, T. -., Wang, Y., & Gao, X. -. (2011). A new approach to the prediction of passenger flow in a transit system. Computers and Mathematics with Applications, 61(8), 1968-1974. doi:10.1016/j.camwa.2010.08.023

Xiao, R., Zhu, J., Zhao, Z., Yu, H., & Du, Y. (2021). A passenger flow prediction method for bus lines based on multiple stepwise regression analysis. Paper presented at the 2021 11th International Conference on Information Science and Technology, ICIST 2021, 452-455. doi:10.1109/ICIST52614.2021.9440559

Wang, X., Chen, S., & Wei, H. (2018). A passenger flow statistic algorithm based on machine learning. Paper presented at the Proceedings - 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2017, , 2018-January 1-5. doi:10.1109/CISP-BMEI.2017.8302042

Xiang, H. -., & Ming, A. -. (2014). A study of the city bus passenger flow intelligent statistical algorithm based on motion vector tracking. Paper presented at the ICIST 2014 - Proceedings of 2014 4th IEEE International Conference on Information Science and Technology, 41-44. doi:10.1109/ICIST.2014.6920327

Cheng, S., Xu, J., Mu, Q., & Zhang, Y. (2014). A terminal departure passenger traffic prediction method based on the RBF neural network. Paper presented at the CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems - Proceedings of the 14th COTA International Conference of Transportation Professionals, 31-38. doi:10.1061/9780784413623.004

Xie, X. -., Feng, X. -., & Ren, Q. -. (2008). Adjustment plan of passenger traffic special line optimization study. Paper presented at the Proceedings of the 8th International Conference of Chinese Logistics and Transportation Professionals - Logistics: The Emerging Frontiers of Transportation and Development in China, 3791-3796. doi:10.1061/40996(330)556

Pells, S. R. (1989). An approach to the simulation of bus passenger journey times for the journey to work. Transportation Planning and Technology, 14(1), 19-35. doi:10.1080/03081068908717411

Jiao, F., Huang, L., Song, R., & Huang, H. (2021). An improved stl-lstm model for daily bus passenger flow prediction during the covid-19 pandemic. Sensors, 21(17) doi:10.3390/s21175950

Aceves-González, C., May, A., & Cook, S. (2016). An observational comparison of the older and younger bus passenger experience in a developing world city. Ergonomics, 59(6), 840-850. doi:10.1080/00140139.2015.1091513

Tsui, W. H. K., & Fung, M. K. Y. (2016). Analysing passenger network changes: The case of hong kong. Journal of Air Transport Management, 50, 1-11. doi:10.1016/j.jairtraman.2015.09.001

Tu, Y., & Yang, J. (2017). Analysis and forecast of passenger flow based on public transportation IC card and GPS data. Paper presented at the Proceedings of 2016 5th International Conference on Computer Science and Network Technology, ICCSNT 2016, 281-285. doi:10.1109/ICCSNT.2016.8070164

Blinova, T. O. (2007). Analysis of possibility of using neural network to forecast passenger traffic flows in russia. Aviation, 11(1), 28-34. doi:10.1080/16487788.2007.9635952

Antonova, V. M., Grechishkina, N. A., & Kuznetsov, N. A. (2020). Analysis of the modeling results for passenger traffic at an underground station using AnyLogic. Journal of Communications Technology and Electronics, 65(6), 712-715. doi:10.1134/S1064226920060029

Wang, P. -., Hsu, Y. -., & Hsu, C. -. (2021). Analysis of waiting time perception of bus passengers provided with mobile service. Transportation Research Part A: Policy and Practice, 145, 319-336. doi:10.1016/j.tra.2021.01.011

Rahmatulloh, A., Nursuwars, F. M. S., Darmawan, I., & Febrizki, G. (2020). Applied internet of things (IoT): The prototype bus passenger monitoring system using PIR sensor. Paper presented at the 2020 8th International Conference on Information and Communication Technology, ICoICT 2020, doi:10.1109/ICoICT49345.2020.9166420

Chang, H. -., & Wu, S. -. (2010). Applying the rasch measurement to explore elderly passengers' abilities and difficulties when using buses in taipei. Journal of Advanced Transportation, 44(3), 134-149. doi:10.1002/atr.127

Wang, J. (2019). Design of passenger transport modes between cities with environmental sustainability. Paper presented at the Proceedings - 2019 4th International Conference on Electromechanical Control Technology and Transportation, ICECTT 2019, 323-327. doi:10.1109/ICECTT.2019.00080

Fitzová, H., Matulová, M., & Tomeš, Z. (2018). Determinants of urban public transport efficiency: Case study of the czech republic. European Transport Research Review, 10(2) doi:10.1186/s12544-018-0311-y

Published

2023-06-01

How to Cite

Dolia, O., & Dolia, K. (2023). Methods of solving problems related to the organization of passenger transportation by road transport. International Science Journal of Engineering & Agriculture, 2(3), 101–119. https://doi.org/10.46299/j.isjea.20230203.10

Issue

Section

Transport and communications, shipbuilding

Most read articles by the same author(s)