Relationship between urban tourism traffic and tourism land use: A case study of Xiamen Island

Yueer Gao

Yanqing Liao

Donggen Wang

Yongguang Zou

DOI: https://doi.org/10.5198/jtlu.2021.1799


Abstract

The development of tourism leads to changes in land-use demands and patterns, which are complex and dynamic, in tourist cities. Functional differences in land use produce different travel needs and have different impacts on traffic, especially on tourism. This paper explores the relationship between tourism land use and tourism traffic. Taking Xiamen Island as an example, using multivariable linear regression models, tourism land use is divided into accommodation land use, shopping land use and restaurant land use as the independent variables of the model; and the origin-destination (OD) density of traffic analysis zones (TAZs) during National Day in 2018 (October 1 to 5) is chosen as the dependent variable. To compare the different impacts between tourism land use and tourism traffic during the tourism and non-tourism periods, the non-tourism period (March 11 to 15) is further studied. The results show the following: (1) Xiamen, as a tourism city, has not only regular traffic but also tourism traffic, and traffic during the tourism period is totally different than that in the non-tourism period. (2) Tourism land use has a considerable impact on both tourism traffic and non-tourism traffic, but the impact is greater during the tourism period than the non-tourism period. (3) In the morning peak hour of both the tourism period and the non-tourism period, accommodation land use shows prominent effects on traffic. In the evening peak hour, shopping land use significantly impacts traffic. The study provides a basis for urban tourism land use adjustment to achieve the sustainable development of tourism traffic.


References

Alaigba, D., Soumah, M., & Banjo, M. (2017). Heterogeneity index for the assessment of relationship between land-use pattern and road traffic congestion in Apapa-Oworoshoki Express Way, Lagos Metropolis. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-1/W1, 189–196. https://doi.10.5194/isprs-archives-XLII-1-W1-189-2017

Bi, L. (2015). Evaluation of ecological tourism land-use benefit based on the coupling relationship. Paper presented at the International Conference on Economics, Social Science, Arts, Education and Management Engineering, Xi’an, Shaanxi, China.

Boavida-Portugal, I., Rocha, J., & Ferreira, C. C. (2016). Exploring the impacts of future tourism development on land use/cover changes. Applied Geography, 77, 82–91. https://doi.org/10.1016/j.apgeog.2016.10.009

Bordoloi, R., Mote, A., Sarkar, P. P., & Mallikarjuna, C. (2013). Quantification of land-use diversity in the context of mixed land use. Procedia - Social and Behavioral Sciences, 104, 563–572. https://doi.org/10.1016/j.sbspro.2013.11.150

Gan, Z., Feng, T., Wu, Y., Yang, M., & Timmermans, H. (2019). Station-based average travel distance and its relationship with urban form and land use: An analysis of smart card data in Nanjing City, China. Transport Policy, 79, 137–154. https://doi.org/10.1016/j.tranpol.2019.05.003

Gao, Y., Chen, S., Zheng, C., & Bian, J. (2016). Road network capacity of tourist site’s periphery based on FCD: Taking Xiamen Island as an example. Progress in Geography, 35, 1529–1539. (Chinese).

Gao, Y., Cui, G., Xu, C., & Bian, J. (2019). Road traffic status in the surrounding of urban tourist attractions based on FCD. Economic Geography, 39(3), 225–231. (Chinese).

Han, P. (2012). A study of roundabout traffic flow distribution based on the attraction of land use. Paper presented at the World Automation Congress (WAC), Puerto Vallarta, Mexico.

Kii, M., Moeckel, R., & Thill, J.-C. (2019). Land use, transport, and environment interactions: WCTR 2016 contributions and future research directions. Computers, Environment and Urban Systems, 77, 101335. https://doi.org/10.1016/j.compenvurbsys.2019.04.002

Krause, C. M., & Zhang, L. (2019). Short-term travel behavior prediction with GPS, land use, and point-of-interest data. Transportation Research Part B: Methodological, 123, 349–361. https://doi.org/10.1016/j.trb.2018.06.012

Łapko, A. (2014). Urban tourism in Szczecin and its impact on the functioning of the urban transport system. Procedia - Social and Behavioral Sciences, 151, 207–214. https://doi.org/10.1016/j.sbspro.2014.10.020

Liu, Y., Wang, F., Xiao, Y., & Gao, S. (2012). Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning, 106(1), 73–87. https://doi.org/10.1016/j.landurbplan.2012.02.012

Manaugh, K., & Kreider, T. (2013). What is mixed use? Presenting an interaction method for measuring land use mix. Journal of Transport and Land Use, 6, 63–72. https://doi.10.5198/jtlu.v6i1.291

Mao, X., Meng, J., & Wang, Q. (2014). Modeling the effects of tourism and land regulation on land-use change in tourist regions: A case study of the Lijiang River Basin in Guilin, China. Land Use Policy, 41, 368–377. https://doi.org/10.1016/j.landusepol.2014.06.018

Marzuki, A., Masron, T., & Ismail, N. (2015). Land-use changes analysis for Pantai Chenang, Langkawi using spatial patch analysis technique in relation to coastal tourism. Tourism Planning & Development, 13, 1–14. https://doi.10.1080/21568316.2015.1076507

Pulugurtha, S. S., Duddu, V. R., & Kotagiri, Y. (2013). Traffic analysis zone level crash estimation models based on land-use characteristics. Accident Analysis & Prevention, 50, 678–687. https://doi.org/10.1016/j.aap.2012.06.016

Song, J., Zhao, C., Zhong, S., Nielsen, T. A. S., & Prishchepov, A. V. (2019). Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques. Computers, Environment and Urban Systems, 77, 101364. https://doi.org/10.1016/j.compenvurbsys.2019.101364

Spyratos, S., & Stathakis, D. (2017). Evaluating the services and facilities of European cities using crowdsourced place data. Environment and Planning B: Urban Analytics and City Science, 45(4), 733–750. https://doi.10.1177/0265813516686070

Wang, X., Zhou, Q., Yang, J., You, S., Song, Y., & Xue, M. (2019). Macro-level traffic safety analysis in Shanghai, China. Accident Analysis & Prevention, 125, 249–256. https://doi.org/10.1016/j.aap.2019.02.014

Williams, A. M., & Shaw, G. (2009). Future play: Tourism, recreation and land use. Land Use Policy, 26, S326-S335. https://doi.org/10.1016/j.landusepol.2009.10.003

Xi, J., Zhao, M., Ge, Q., & Kong, Q. (2014). Changes in land use of a village driven by over 25 years of tourism: The case of Gougezhuang village, China. Land Use Policy, 40, 119–130. https://doi:10.1016/j.landusepol.2013.11.014

Xiamen Municipal Bureau of Culture and Tourism. (2018). A survey of Spring Festival holiday tourism in Xiamen in 2018. http://wlj.xm.gov.cn/zwgk/tjxx/201907/t20190722_2318753.htm

Yu, C., & He, Z.-C. (2017). Analyzing the spatial-temporal characteristics of bus travel demand using the heat map. Journal of Transport Geography, 58, 247–255. https://doi.org/10.1016/j.jtrangeo.2016.11.009

Yu, W., Ai, T., & Shao, S. (2015). The analysis and delimitation of central business district using network kernel density estimation. Journal of Transport Geography, 45, 32–47. https://doi.org/10.1016/j.jtrangeo.2015.04.008

Zhang, T., Sun, L., Yao, L., & Rong, J. (2017). Impact analysis of land use on traffic congestion using real-time traffic and POI. Journal of Advanced Transportation. https://doi.10.1155/2017/7164790

Zhu, Z., Xiong, C., Chen, X., He, X., & Zhang, L. (2018). Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis. Transportation Research Part C: Emerging Technologies, 93, 446–462. https://doi.org/10.1016/j.trc.2018.06.011