Exploring temporal heterogeneity in an intercity travel network: A comparative study between weekdays and holidays in China

doi:10.1007/s11442-020-1821-9

Abstract

Abstract:

A largely unexplored application of “Big Data” in urban contexts is using human mobility data to study temporal heterogeneity in intercity travel networks. Hence, this paper explores China’s intercity travel patterns and their dynamics, with a comparison between weekdays and holidays, to contribute to our understanding of these phenomena. Using passenger travel data inferred from Tencent Location Big Data during weekdays (April 11-15, 2016) and National Golden Week (October 1-7, 2016), we compare the spatial patterns of Chinese intercity travel on weekdays and during Golden Week. The results show that the average daily intercity travel during Golden Week is significantly higher than that during weekdays, but the travel distance and degree of network clustering are significantly lower. This indicates temporal heterogeneity in mapping the intercity travel network. On weekdays, the three major cities of Beijing, Shanghai, and Guangzhou take prominent core positions, while cities that are tourism destinations or transportation hubs are more attractive during Golden Week. The reasons behind these findings can be explained by geographical proximity, administrative division (proximity of cultural and policy systems), travel distance, and travel purposes.

Key words: big data, weekdays, National Golden Week, human mobility, travel time heterogeneity, China

LI Tao, WANG Jiaoe, HUANG Jie, GAO Xingchuan. Exploring temporal heterogeneity in an intercity travel network: A comparative study between weekdays and holidays in China[J].Journal of Geographical Sciences, 2020, 30(12): 1943-1962.

Figures/Tables 13

Table 1

Figure 1

Figure 2

Figure 3

Table 2

Figure 4

Table 3

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

References 37

[1]	Anne A, Laurent P, 2015. Socio-occupational and geographical determinants of the frequency of long-distance business travel in France. Journal of Transport Geography, 43:28-35. doi: 10.1016/j.jtrangeo.2015.01.004
[2]	Arbués P, Baños J F, Mayor M et al., 2016. Determinants of ground transport modal choice in long-distance trips in Spain. Transportation Research Part A: Policy and Practice, 84:131-143.
[3]	Aultman-Hall L, Ullman H, 2020. Long-distance and intercity travel: Who participates in global mobility? In: Konstadinos G, Goulias, Adam W Davis (ed.). Mapping the Travel Behavior Genome. Elsevier:187-207.
[4]	Buehler R, 2011. Determinants of transport mode choice: A comparison of Germany and the USA. Journal of Transport Geography, 19(4):644-657.
[5]	Chen W, Xiu C, Ke W et al., 2015. Hierarchical structures of China's city network from the perspective of multiple traffic flows. Geographical Research, 34(11):2073-2083. (in Chinese)
[6]	Dargay J M, Clark S, 2012. The determinants of long distance travel in Great Britain. Transportation Research Part A: Policy and Practice, 46(3):576-587.
[7]	De Montis A, Chessa A, Campagna M et al., 2010. Modeling commuting systems through a complex network analysis: A study of the Italian islands of Sardinia and Sicily. Journal of Transport and Land Use, 2(3):39-55.
[8]	De Witte A, Hollevoet J, Dobruszkes F et al., 2013. Linking modal choice to motility: A comprehensive review. Transportation Research Part A: Policy and Practice, 49:329-341.
[9]	Derudder B, Liu X, Kunaka C et al., 2014. The connectivity of South Asian cities in infrastructure networks. Journal of Maps, 10(1):47-52.
[10]	Feng C, Xie D, Ma X et al., 2014. Functional polycentricity of the urban region in the Zhujiang River Delta based on intercity rail traffic flow. Scientia Geographica Sinica, 34(6):648-655. (in Chinese)
[11]	Garmendia M, Ureña J. M, Coronado J M, 2011. Long-distance trips in a sparsely populated region: The impact of high-speed infrastructures. Journal of Transport Geography, 19(4):537-551.
[12]	Guimerà R, Amaral L A N, 2005. Cartography of complex networks: Modules and universal roles. Journal of Statistical Mechanics: Theory and Experiment, (2):P02001.
[13]	Jia T, Feng Z, Xiao Q, 2018. Activity space of regional high speed rail corridor in information era: Conceptual model and research framework. Geographical Research, 37(9):1789-1801. (in Chinese)
[14]	Jiao J, Wang J E, Jin F et al., 2016. Impact of high-speed rail on inter-city based on the passenger train network in China, 2003-2013. Acta Geographica Sinica, 71(2):265-280. (in Chinese)
[15]	Jin C, Cheng J, Xu J, 2018. Using user-generated content to explore the temporal heterogeneity in tourist mobility. Journal of Travel Research, 57(6):779-791.
[16]	Jin F, 2001. A study on network of domestic air passenger flow in China. Geographical Research, 20(1):31-39. (in Chinese)
[17]	Kuhnimhof T, Buehler R, Wirtz M et al., 2012. Travel trends among young adults in Germany: Increasing multimodality and declining car use for men. Journal of Transport Geography, 24:443-450.
[18]	Kuhnimhof T, Collet R, Armoogum J et al., 2009. Generating internationally comparable figures on long-distance travel for Europe. Transportation Research Record, 2105(1):18-27.
[19]	Li J, Ye Q, Deng X et al., 2016. Spatial-temporal analysis on spring festival travel rush in China based on multisource big data. Sustainability, 8(11):1184.
[20]	Limtanakool N, Dijst M, Schwanen T, 2005. On the participation in medium- and long-distance travel: A decomposition analysis for the UK and the Netherlands. Tijdschrift voor Economische en Sociale Geografie, 97(4):389-404.
[21]	Limtanakool N, Dijst M, Schwanen T, 2006. The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium- and longer-distance trips. Journal of Transport Geography, 14:327-341.
[22]	Liu W, Shi E, 2016. Spatial pattern of population daily flow among cities based on ICT: A case study of "baidu migration". Acta Geographica Sinica, 71(10):1667-1679. (in Chinese)
[23]	Liu Y, Sui Z, Kang C et al., 2014. Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLoS One, 9(1):e86026. doi: 10.1371/journal.pone.0086026 pmid: 24465849
[24]	Luo Z, 2010. Study on the functional polycentricity of Yangtze River Delta. International Urban Planning, 25(1):60-65. (in Chinese)
[25]	Moeckel R, Fussell R, Donnelly R, 2015. Mode choice modeling for long-distance travel. Transportation Letters, 7(1):35-46.
[26]	Neal Z, 2014. The devil is in the details: Differences in air traffic networks by scale, species, and season. Social Networks, 38:63-73.
[27]	Pan J, Lai J, 2019. Spatial pattern of population mobility among cities in China: Case study of the national day plus Mid-Autumn Festival based on Tencent migration data. Cities, 94:55-69. doi: 10.1016/j.cities.2019.05.022
[28]	Rosvall M, Bergstrom C T, 2008. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences of the United States of America, 105(4):1118-1123. doi: 10.1073/pnas.0706851105 pmid: 18216267
[29]	Wang J, Du D, Huang J, 2020. Inter-city connections in China: High-speed train vs. inter-city coach. Journal of Transport Geography, 82:102619.
[30]	Wang J, Jin F, 2007. China’s air passenger transport: An analysis of recent trends. Eurasian Geography and Economics, 48(4):469-480.
[31]	Wang J E, Jing Y, 2017. Comparison of spatial structure and organization mode of inter-city networks from the perspective of railway and air passenger flow. Acta Geographica Sinica, 72(8):1508-1519. (in Chinese)
[32]	Wei Y, Xiu C, Liu Z et al., 2016. Spatial pattern of city network in transitional China based on the population flows in "chunyun" period. Scientia Geographica Sinica, 36(11):1654-1660. (in Chinese)
[33]	Xu J, Li A, Li D et al., 2017. Difference of urban development in China from the perspective of passenger transport around spring festival. Applied Geography, 87:85-96.
[34]	Yang H, Dobruszkes F, Wang J et al., 2018. Comparing China’s urban systems in high-speed railway and airline networks. Journal of Transport Geography, 68:233-244.
[35]	Ye Y, Han M, Chen J, 2018. Intercity passenger travel mode choice behavior based on trip chain. Journal of Tongji University (Natural Science), 46(9):1234-1240. (in Chinese)
[36]	Yuan Y, Lu Y, Chow T E et al., 2020. The missing parts from social media-enabled smart cities: Who, where, when, and what? Annals of the American Association of Geographers, 110(2):462-475.
[37]	Zhang W, Derudder B, Wang J et al., 2020. An analysis of the determinants of the multiplex urban networks in the Yangtze River Delta. Tijdschrift voor Economische en Sociale Geografie, 111(2):117-133.

Periods	Number of nodes	Number of edges	Total linked value	Average	Maximum	Minimum	CV
Weekdays	362	8430	34 817 749	4130	191 590	7	2.22
Golden Week	362	8588	72 681 843	8463	263 724	7	1.94

Region	Period	Zipf	R²	WDC_i	DIT_i	ODI_c
Whole country	Weekdays	1.1378	0.8205	185 200	3.91	0.84
Whole country	Golden Week	1.0988	0.6418	386 605	5.09	0.88
Eastern China	Weekdays	1.2663	0.8494	305 006	6.48	0.80
Eastern China	Golden Week	1.1219	0.7443	581 193	7.67	0.85
Central China	Weekdays	0.8950	0.8172	130 740	2.72	0.89
Central China	Golden Week	0.8503	0.8125	322 530	4.21	0.91
Western China	Weekdays	1.1941	0.7860	126 287	2.65	0.79
Western China	Golden Week	1.2662	0.7458	265 015	3.47	0.83

Rank	DIT_i				Absolute change
Rank	Weekdays	DIT_i	Golden Week	DIT_i	Golden Week-Weekdays
1	Beijing	100.00	Beijing	100.00	Wuhan	10.17
2	Shanghai	78.19	Shanghai	75.13	Chengdu	10.06
3	Shenzhen	66.88	Shenzhen	61.44	Xi’an	8.57
4	Guangzhou	66.65	Guangzhou	61.02	Changsha	5.44
5	Chongqing	51.30	Chengdu	54.94	Qingdao	5.42
6	Chengdu	44.88	Chongqing	52.50	Harbin	5.31
7	Dongguan	27.72	Hangzhou	35.20	Hefei	5.27
8	Hangzhou	25.82	Xi’an	29.75	Nanchang	5.22
9	Zhengzhou	25.10	Zhengzhou	28.85	Suzhou	4.59
10	Wuhan	25.03	Wuhan	26.78	Nanjing	4.35
11	Nanjing	22.43	Dongguan	26.39	Ganzhou	4.11
12	Xi’an	21.18	Suzhou	25.36	Huizhou	4.07
13	Suzhou	20.77	Changsha	23.94	Dalian	3.98
14	Changsha	18.50	Nanjing	23.57	Zhengzhou	3.75
15	Foshan	15.91	Tianjin	18.52	Hengyang	3.68
16	Nanning	15.24	Foshan	17.87	Shenyang	3.64
17	Tianjin	15.20	Kunming	16.60	Yantai	3.53
18	Jinan	14.22	Jinan	16.59	Yancheng	3.44
19	Kunming	13.86	Hefei	16.44	Qingyuan	3.39
20	Guiyang	12.56	Nanning	16.30	Tianjin	3.32