DOI : https://doi.org/10.18517/ijaseit.14.2.19324

Exploring Trip Chain Pattern Choices at Urban Relaxation Facilities: A Case Study of Lego-Lego in Central Point of Indonesia

Hajriyanti Yatmar (1), Muhammad Isran Ramli (2), Sakti Adji Adisasamita (3), Andi Sitti Chaerunnisa (4)
(1) Department of Civil Engineering, Universitas Hasanuddin, Jl. Poros Malino, Km. 6, Gowa, 92171, Indonesia
(2) Department of Civil Engineering, Universitas Hasanuddin, Jl. Poros Malino, Km. 6, Gowa, 92171, Indonesia
(3) Department of Civil Engineering, Universitas Hasanuddin, Jl. Poros Malino, Km. 6, Gowa, 92171, Indonesia
(4) Department of Naval Architecture, Universitas Hasanuddin, Jl. Poros Malino, Km. 6, Gowa, 92171, Indonesia
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How to cite (IJASEIT) :
Yatmar, Hajriyanti, et al. “Exploring Trip Chain Pattern Choices at Urban Relaxation Facilities: A Case Study of Lego-Lego in Central Point of Indonesia”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 2, Apr. 2024, pp. 582-91, doi:10.18517/ijaseit.14.2.19324.
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Lego-Lego at Central Point of Indonesia (CPI) is one of the famous relaxation facility areas currently hosting numerous visitors, specifically on weekends. People tend to spend their time at the first multi-purpose facilities in the morning and afternoon, causing traffic congestion and long queues at the entrance and exit. Therefore, this study explored the trip chain activity pattern and behavior of visitors using the facilities provided in Lego-Lego. This study was considered necessary because the government and stakeholders need to understand the trip chain pattern choices preferred by the people to give infrastructure, manage traffic problems, and formulate relevant regulations. The data were collected through interview-based questionnaires randomly distributed to visitors and analyzed using a multinomial logistic regression model to determine individual and trip characteristics. The results showed the variables with significant impacts on the trip chain and the trip chain models for the morning and afternoon were similar. It was also discovered that the probability of selecting trip chain pattern 1 reduced as the time activity increased while the probability of selecting patterns 2, 3, and 4 increased. This indicated that time activity influenced the trip chain activity pattern, but the cost was not. Another important observation was that the greater the diversity of facilities influencing the activity-travel patterns, the more time was required to engage in the activities.

L. Wan, J. Tang, L. Wang, and J. Schooling, “Understanding non-commuting travel demand of car commuters – Insights from ANPR trip chain data in Cambridge,” Transp Policy (Oxf), vol. 106, pp. 76–87, Jun. 2021, doi: 10.1016/j.tranpol.2021.03.021.

K. G. Logan, J. D. Nelson, C. Osbeck, J. D. Chapman, and A. Hastings, “The application of travel demand management initiatives within a university setting,” Case Stud Transp Policy, vol. 8, no. 4, pp. 1426–1439, Dec. 2020, doi: 10.1016/j.cstp.2020.10.007.

D. Esztergár-Kiss, “Trip Chaining Model with Classification and Optimization Parameters,” Sustainability 2020, Vol. 12, Page 6422, vol. 12, no. 16, p. 6422, Aug. 2020, doi: 10.3390/SU12166422.

L. Basri Said and I. Syafey, “The scenario of reducing congestion and resolving parking issues in Makassar City, Indonesia,” Case Stud Transp Policy, vol. 9, no. 4, pp. 1849–1859, Dec. 2021, doi:10.1016/j.cstp.2021.10.004.

D. Bautista-Hernández, “Urban structure and its influence on trip chaining complexity in the Mexico City Metropolitan Area,” http://www.tandfonline.com/action/authorSubmission?journalCode=rupt20&page=instructions, vol. 8, no. 1, pp. 71–97, Jan. 2019, doi: 10.1080/21650020.2019.1708784.

M. Mwale, R. Luke, and N. Pisa, “Factors that affect travel behaviour in developing cities: A methodological review,” Transp Res Interdiscip Perspect, vol. 16, p. 100683, Dec. 2022, doi:10.1016/j.trip.2022.100683.

M. Lin, Z. Huang, T. Zhao, Y. Zhang, and H. Wei, “Spatiotemporal Evolution of Travel Pattern Using Smart Card Data,” Sustainability 2022, Vol. 14, Page 9564, vol. 14, no. 15, p. 9564, Aug. 2022, doi:10.3390/SU14159564.

L. Li, M. Cao, J. Yin, Y. Wang, and S. Mishra, “Observing the Characteristics of Multi-Activity Trip Chain and Its Influencing Mechanism,” KSCE Journal of Civil Engineering, vol. 24, no. 11, pp. 3447–3460, Nov. 2020, doi: 10.1007/S12205-020-1927-8/METRICS.

F. Schneider et al., “Trip chain complexity: a comparison among latent classes of daily mobility patterns,” Transportation (Amst), vol. 48, no. 2, pp. 953–975, Apr. 2021, doi: 10.1007/S11116-020-10084-1/TABLES/3.

W. Q. Al-Salih and D. Esztergár-Kiss, “Linking Mode Choice with Travel Behavior by Using Logit Model Based on Utility Function,” Sustainability 2021, Vol. 13, Page 4332, vol. 13, no. 8, p. 4332, Apr. 2021, doi: 10.3390/SU13084332.

S. S. V. Subbarao, S. N. V. Swaroop, and R. S. Shekhar, “Interrelationships between Mode Choice and Trip-chain Choice decisions: A case of Mumbai Metropolitan Region,” Transportation Research Procedia, vol. 48, pp. 3049–3061, Jan. 2020, doi:10.1016/j.trpro.2020.08.182.

Z. W. Hou, S. Yu, and T. Ji, “Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory,” Applied Sciences 2021, Vol. 11, Page 10058, vol. 11, no. 21, p. 10058, Oct. 2021, doi: 10.3390/APP112110058.

C. Qi, Z. Zhu, X. Guo, R. Lu, and J. Chen, “Examining Interrelationships between Tourist Travel Mode and Trip Chain Choices Using the Nested Logit Model,” Sustainability 2020, Vol. 12, Page 7535, vol. 12, no. 18, p. 7535, Sep. 2020, doi:10.3390/SU12187535.

M. V. Patandianan and H. Shibusawa, “Importance and performance of streetscapes at a tourism destination in Indonesia: The residents’ perspectives,” Frontiers of Architectural Research, vol. 9, no. 3, pp. 641–655, Sep. 2020, doi: 10.1016/j.foar.2020.05.006.

T. Arifin et al., “Forecasting land-use changes due to coastal city development on the peri-urban area in Makassar City, Indonesia,” The Egyptian Journal of Remote Sensing and Space Science, vol. 26, no. 1, pp. 197–206, Feb. 2023, doi: 10.1016/j.ejrs.2023.02.002.

I. Sastrawati and J. Asano, “The Implication of Land Use Change on the Site Shape of Urban Tourism Amenities Case Study: Makassar Old District,” Urban and Regional Planning Review, vol. 8, no. 0, pp. 123–146, 2021, doi: 10.14398/URPR.8.123.

Y. Huang, L. Gao, A. Ni, and X. Liu, “Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China,” J Transp Geogr, vol. 93, p. 103070, May 2021, doi: 10.1016/j.jtrangeo.2021.103070.

F. Schneider et al., “Trip chain complexity: a comparison among latent classes of daily mobility patterns,” Transportation (Amst), vol. 48, no. 2, pp. 953–975, Apr. 2021, doi: 10.1007/S11116-020-10084-1/TABLES/3.

B. E. Bayih and A. Singh, “Modeling domestic tourism: motivations, satisfaction and tourist behavioral intentions,” Heliyon, vol. 6, no. 9, p. e04839, Sep. 2020, doi: 10.1016/j.heliyon.2020.E04839.

P. Cash, O. Isaksson, A. Maier, and J. Summers, “Sampling in design research: Eight key considerations,” Des Stud, vol. 78, p. 101077, Jan. 2022, doi: 10.1016/j.destud.2021.101077.

G. R. Patil, R. Basu, and T. H. Rashidi, “Mode Choice Modeling Using Adaptive Data Collection for Different Trip Purposes in Mumbai Metropolitan Region,” Transportation in Developing Economies 2020 6:1, vol. 6, no. 1, pp. 1–10, Mar. 2020, doi: 10.1007/S40890-020-0099-Z.

B. Bursa, M. Mailer, and K. W. Axhausen, “Intra-destination travel behavior of alpine tourists: a literature review on choice determinants and the survey work,” Transportation 2022 49:5, vol. 49, no. 5, pp. 1465–1516, Feb. 2022, doi: 10.1007/S11116-022-10267-Y.

B. Bursa, M. Mailer, and K. W. Axhausen, “Travel behavior on vacation: transport mode choice of tourists at destinations,” Transp Res Part A Policy Pract, vol. 166, pp. 234–261, Dec. 2022, doi:10.1016/j.tra.2022.09.018.

J. Liu, J. Li, S. C. (Shawn) Jang, and Y. Zhao, “Understanding tourists’ environmentally responsible behavior at coastal tourism destinations,” Mar Policy, vol. 143, p. 105178, Sep. 2022, doi:10.1016/j.marpol.2022.105178.

L. Aultman-Hall, A. Onayev, J. Dowds, and J. J. LaMondia, “Understanding the relationship between mode and destination choices for personal out-of-town travel,” Travel Behav Soc, vol. 31, pp. 399–409, Apr. 2023, doi: 10.1016/j.tbs.2023.02.004.

I. M. A. Wirawan, W. C. W. S. Putri, N. M. D. Kurniasari, K. H. Mulyawan, M. A. Hendrayana, and C. Suharlim, “Geo-mapping of hazards, risks, and travel health services in Bali: Results from the first stage of the integrated travel health surveillance and information system at destination (TravHeSID) project,” Travel Med Infect Dis, vol. 37, p. 101698, Sep. 2020, doi: 10.1016/j.tmaid.2020.101698.

A. Mahdi, J. Hamadneh, and D. Esztergár-Kiss, “Modeling of Travel Behavior in Budapest: Leisure Travelers,” Transportation Research Procedia, vol. 62, pp. 310–317, Jan. 2022, doi:10.1016/J.TRPRO.2022.02.039.

Y. Huang, L. Gao, A. Ni, and X. Liu, “Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China,” J Transp Geogr, vol. 93, p. 103070, May 2021, doi: 10.1016/j.jtrangeo.2021.103070.

Q. Shangguan, J. Wang, T. Fu, S. Fang, and L. Fu, “An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances,” Anal Methods Accid Res, vol. 38, p. 100265, Jun. 2023, doi: 10.1016/j.amar.2022.100265.

X. Zhao, X. Yan, A. Yu, and P. Van Hentenryck, “Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models,” Travel Behav Soc, vol. 20, pp. 22–35, Jul. 2020, doi: 10.1016/J.TBS.2020.02.003.

A. W. Diriye, O. M. Jama, J. W. Diriye, and A. M. Abdi, “Public preference for sustainable land use policies – Empirical results from multinomial logit model analysis,” Land use policy, vol. 114, p. 105975, Mar. 2022, doi: 10.1016/j.landusepol.2022.105975.

H. López-Ospina, Á. Agudelo-Bernal, L. Reyes-Muñoz, G. Zambrano-Rey, and J. Pérez, “Design of a location and transportation optimization model including quality of service using constrained multinomial logit,” Appl Math Model, vol. 89, pp. 428–453, Jan. 2021, doi: 10.1016/j.apm.2020.07.054.

J. W. Feilzer, D. Stroosnier, E. Dugundji, and T. Koch, “Predicting Lessee Switch Behavior using Logit Models,” Procedia Comput Sci, vol. 184, pp. 380–387, Jan. 2021, doi: 10.1016/j.procs.2021.03.048.

J. Fang, X. Yan, I. Bejleri, and C. Chen, “Which trip destination matters? Estimating the influence of the built environment on mode choice for home-based complex tours,” J Transp Geogr, vol. 105, p. 103474, Dec. 2022, doi: 10.1016/j.jtrangeo.2022.103474.

B. Yu et al., “The Effect of Travel-Chain Complexity on Public Transport Travel Intention: A Mixed-Selection Model,” International Journal of Environmental Research and Public Health 2023, Vol. 20, Page 4547, vol. 20, no. 5, p. 4547, Mar. 2023, doi:10.3390/ijerph20054547.

B. Mendes, M. C. Ferreira, and T. G. Dias, “Tourism as a Service: Enhancing the Tourist Experience,” Transportation Research Procedia, vol. 62, pp. 1–8, Jan. 2022, doi: 10.1016/j.trpro.2022.02.001.

S. Acharya, M. Mekker, and J. De Vos, “Linking travel behavior and tourism literature: Investigating the impacts of travel satisfaction on destination satisfaction and revisit intention,” Transp Res Interdiscip Perspect, vol. 17, p. 100745, Jan. 2023, doi:10.1016/j.trip.2022.100745.

S. R. Mary and M. H. Pour, “A model of travel behaviour after COVID-19 pandemic: TripAdvisor reviews,” https://doi.org/10.1080/13683500.2022.2039596, vol. 25, no. 7, pp. 1033–1045, 2022, doi: 10.1080/13683500.2022.2039596.

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