Understanding Visitors’ Responses to Intelligent Transportation System in a Tourist City with a Mixed Ranked Logit Model

Liu, Yang; Shi, Jing; Jian, Meiying

doi:10.1155/2017/8652053

Cited by 14 publications

(11 citation statements)

References 29 publications

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“…Researchers do this using various data types. First, most studies use traditional tourism data, including questionnaire surveys [2,10,11], quantitative statistics from the statistics department [12,13], etc. However, these data are expensive and time-consuming, and there is a gap between the data collection time and the analysis time [14], and they cannot be used to refine the travel behavior of tourists within the region [15].…”

Section: Related Work 21 Tourism Datamentioning

confidence: 99%

Influential Factor Detection for Tourism on the Qinghai-Tibet Plateau Based on Social Media Data

Bao

et al. 2021

IJGI

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Tourism is playing an important role in the economic development of the Qinghai-Tibet Plateau (QTP). To better develop tourism in this region, the spatial heterogeneity of influencing factors on tourism needs to be studied. Using the spatial distribution of tourism potential from social media data, this paper analyzes the influencing factors of tourism on the QTP from the perspective of spatial heterogeneity. We extract microblogs related to travel topics connected to the QTP in 2017 from Sina Weibo to capture tourism potential. Then, factors considered from six aspects (tourism resources, amenities, transportation, geography, population, and the economy) are selected, and a geographic detector (Geodetector) is employed to detect the explanatory power of these factors for tourism potential. The results indicate different influential tourism factors in Qinghai and Tibet. In Qinghai, the main factors are hotels, tourist attractions, and road network density, and the explanatory power of the factors mainly comes from eastern and western Qinghai. In Tibet, the main factors are road network density, regional GDP (Gross Domestic Product), and urban land. It is suggested that tourism in the central region of Qinghai can be improved by enhancing the publicity and utilization of tourism resources, and Tibet should enhance tourism resource utilization and improve tourism amenities and infrastructure.

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Section: Related Work 21 Tourism Datamentioning

confidence: 99%

Influential Factor Detection for Tourism on the Qinghai-Tibet Plateau Based on Social Media Data

Bao

et al. 2021

IJGI

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“…To handle and integrate the complex data from different sources efficiently and to satisfy various user groups, different models are used for intelligent transportation systems, such as agent-based traffic management models (Sciences et al 2011), cognitive rationality-based decision-making models (Cascetta et al 2015) and mixedranked logit models (Liu et al 2017).…”

Section: Transport and Traffic Managementmentioning

confidence: 99%

Cloud, Edge, and Mobile Computing for Smart Cities

Liu

Yang

et al. 2021

The Urban Book Series

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Smart cities evolve rapidly along with the technical advances in wireless and sensor networks, information science, and human–computer interactions. Urban computing provides the processing power to enable the integration of such technologies to improve the living quality of urban citizens, including health care, urban planning, energy, and other aspects. This chapter uses different computing capabilities, such as cloud computing, mobile computing, and edge computing, to support smart cities using the urban heat island of the greater Washington DC area as an example. We discuss the benefits of leveraging cloud, mobile, and edge computing to address the challenges brought by the spatiotemporal dynamics of the urban heat island, including elevated emissions of air pollutants and greenhouse gases, compromised human health and comfort, and impaired water quality. Cloud computing brings scalability and on-demand computing capacity to urban system simulations for timely prediction. Mobile computing brings portability and social interactivity for citizens to report instantaneous information for better knowledge integration. Edge computing allows data produced by in-situ devices to be processed and analyzed at the edge of the network, reducing the data traffic to the central repository and processing engine (data center or cloud). Challenges and future directions are discussed for integrating the three computing technologies to achieve an overall better computing infrastructure supporting smart cities. The integration is discussed in aspects of bandwidth issue, network access optimization, service quality and convergence, and data integrity and security.

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“…The lack of proper tourism activity data has always been a significant constraint in tourism research [3,4]. However, as the Internet continues to develop, tourists' "digital footprint" has generated a large amount of data, making it possible to acquire customer-centered personal spatial-temporal behavior and contextual information.This information has long-time sequences, large quantities, high precision, and many other advantages, which can effectively reduce the cost and inconvenience caused by offline questionnaires and interviews [5]. This helps researchers understand tourists' spatio-temporal behavior patterns in destinations and their interactions with the tourism environment.…”

Section: Introductionmentioning

confidence: 99%

Chinese Tourists in Malaysia: An Analysis of Spatio–Temporal Behavior Based on Tourism Digital Footprints

Cao

Teo

2022

IEEE Access

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Travel digital footprints can be used to analyze tourists' spatio-temporal behavior to customize travel plans and recommendations. This study focuses on Chinese tourists in Malaysia as the research subject and uses the tourism digital footprint of Chinese tourists' travel texts on Qunar.com as the primary data source. It connects traditional quantitative analysis with complex network analysis to study tourists' behavior, time pattern, and complex network effects. The research results were as follows: (1) there was an uneven distribution of core tourism nodes with structural holes in Malaysia, which form a network pattern of imbalanced power and fierce internal competition; (2) the digital footprints of Chinese tourists were mainly concentrated in traditional tourist hot spots in Malaysia; (3) besides the competition between domestic islands, Thailand and Singapore are Malaysia's main competitors for Chinese tourists. Based on the above research results, this paper proposes the following suggestions: (1) support the emerging or potential tourist destinations vigorously based on consolidating the existing traditional tourist cities; (2) allow core cities to play the full role of actively attracting tourists to transfer to next-level destinations; (3) to conduct digital precision marketing by relying on the tourists' consumption behavior big data; (4) integrate regional resources, refine the tourism market, and develop characteristic tourism products differently.

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Understanding Visitors’ Responses to Intelligent Transportation System in a Tourist City with a Mixed Ranked Logit Model

Cited by 14 publications

References 29 publications

Influential Factor Detection for Tourism on the Qinghai-Tibet Plateau Based on Social Media Data

Influential Factor Detection for Tourism on the Qinghai-Tibet Plateau Based on Social Media Data

Cloud, Edge, and Mobile Computing for Smart Cities

Chinese Tourists in Malaysia: An Analysis of Spatio–Temporal Behavior Based on Tourism Digital Footprints

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