Research on Intelligent Tourism Application Based on Big Data

Dai, ZhiQiang; Xiang, Chang-guo

doi:10.2991/icemc-17.2017.98

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The imbalance between parking supply and demand is particularly acute in the central areas of first and second-tier cities [4] , causing significant inconvenience for citizens' travel, negatively impacting urban environments, and hindering socio-economic development. The issue of parking difficulties has become a pressing challenge in current urban development [5] . China, yet overseas developments in the parking sector are not lagging behind.…”

Section: Introductionmentioning

confidence: 99%

Research on Strategies to Address Urban Parking Issues in China

Sun

2024

FHSS

View full text Add to dashboard Cite

In the process of urbanization in China, the scarcity of land resources has led to an increasingly prominent contradiction between the growth of automobile stocks and parking space availability. The escalating number of private vehicles has become a significant contributor to traffic congestion. This paper aims to delve into the issue of urban parking difficulties and explore associated management strategies. Firstly, through a literature analysis approach, a comprehensive examination of domestic and international parking problems is conducted, revealing a severe situation in China that necessitates the formulation of pertinent strategies. Secondly, case studies on different regions are undertaken from the perspectives of current parking situations and management countermeasures, providing valuable insights and references for addressing China's parking challenges. In response to urban parking difficulties, this paper proposes two strategies: first, "source expansion" by increasing the supply of parking spaces, and second, "streamlining" by restricting private vehicle access. To address parking challenges, strategies such as constructing landscape eco-parking lots, intelligent multi-level garages, developing smart parking systems, and implementing tidal parking are suggested. Additionally, shared parking is considered an effective method to reduce the number of privately owned vehicles in urban areas. Furthermore, resolving urban parking issues should prioritize social benefits, advocate environmentally friendly transportation, and encourage more residents to enjoy higher-quality urban travel experiences.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on Strategies to Address Urban Parking Issues in China

Sun

2024

FHSS

View full text Add to dashboard Cite

show abstract

“…In this regard, the promotion of intelligent tourism is highly valued; it can help in developing the tourism industry and improving its services. There are other technologies used, such as big data (Miah et al, 2017), cloud (Zhiqiang and Changguo, 2016) and Apache Spark (Ntaliakouras et al, 2019). Alomari et al (2021) defined big data term as a large growing dataset that include heterogeneous formats: Structured, unstructured and semi-structured data.…”

Section: Introductionmentioning

confidence: 99%

Twitter Sentiment Analysis for Reviewing Tourist Destinations in Saudi Arabia using Apache Spark and Machine Learning Algorithms

Alasmari¹,

Abdelhafez²

2022

Journal of Computer Science

View full text Add to dashboard Cite

The appearance of big data has created new challenges for data analysis teams especially dealing with unstructured data in text form. Many applications increasingly include a large amount of this type of data. Example of such data is data collected from Twitter. Adequate use of Machine Learning (ML), big data tools and social media platforms can solve several problems. The aim of this research is to apply sentiment analysis using Arabic tweets of tourism in Saudi Arabia and determine the most visited places. Ara Senti corpus was used as the labelled data to perform machine learning for sentiment analysis to deal with the Arabic morphology. The three-classes classification (Positive, Negative, or Neutral) was performed using Decision Tree, Random Forest, Logistic Regression and Naïve Bayes. The results showed that the highest performance achieved was 86% using Logistic Regression with Term Frequency-Inverse Document Frequency (TF-IDF) representation and Naïve Bayes with Bag-of-Words model compared with both random forest and decision tree. The trainable classifier was applied to predict classes on collected data from Twitter for reviewing Kingdom of Saudi Arabia (KSA) destinations to finally present a rating of the most visited places on KSA. There are five most visited places in Saudi Arabia (Riyadh, Alula, Hail, Taif and Tabuk).

show abstract

Research on Intelligent Tourism Application Based on Big Data

Cited by 2 publications

References 5 publications

Research on Strategies to Address Urban Parking Issues in China

Research on Strategies to Address Urban Parking Issues in China

Twitter Sentiment Analysis for Reviewing Tourist Destinations in Saudi Arabia using Apache Spark and Machine Learning Algorithms

Contact Info

Product

Resources

About