scholarly journals Spatial-Temporal Response Patterns of Tourist Flow under Real-Time Tourist Flow Diversion Scheme

2020 ◽  
Vol 12 (8) ◽  
pp. 3478 ◽  
Author(s):  
Guang Yang ◽  
Yan Han ◽  
Hao Gong ◽  
Tiantian Zhang
Keyword(s):  
Real Time ◽  
Residence Time ◽  
Stated Preference ◽  
Flow Diversion ◽  
Flow Density ◽  
Response Patterns ◽  
Tourist Behavior ◽  
Theoretical Support ◽  
Behavior Adjustment ◽  
Tourist Flow

This paper excavates tourist decision-making mechanism under the real-time tourist flow diversion scheme (RTFDS) and evaluates the tourist flow diversion effect of RTFDS. To meet the objectives, the stated preference survey and tourist flow survey of the Summer Palace were implemented. The tourist behavior adjustment model and tourist flow diversion simulation model were established. The results show that: (a) For core tourist spots, 66.5% and 16.5% of tourists will choose “behavior adjustment” and “no longer adjustment” under RTFDS, these behavior adjustments all shorten tourists’ residence time in tourist spots; (b) When the tourist congestion perception degree equals 4 or 5, tourists tend to adopt behavior adjustment or the individuals adopt no longer adjustment instead of cognitive adjustment when they face low tourist congestion perception degree, which equals 1 or 2; (c) When core tourist spots’ residence time is reduced by 10% and 20%, there are 60% and 73% time nodes where core tourist spots’ tourist flow density is less than or equal to the condition of null information, there are 73% and 60% time nodes where periphery tourist spots’ density is more than the condition of null information. The simulation results showed that some tourists could be guided from core tourist spots to periphery tourist spots through releasing RTFDS information. The research can provide theoretical support for the implementation of RTFDS, and alleviate the congestion inside the tourist attraction.

Health Monitoring Research Based on Fiber Bragg Grating Sensing Technology in Concrete Pouring

2015 ◽  
Vol 734 ◽  
pp. 64-70
Author(s):  
Kai Zhu ◽  
Si Guo Zheng ◽  
Gang Liu ◽  
Qing Chao Zhang
Keyword(s):  
Fiber Bragg Grating ◽  
Real Time ◽  
3D Models ◽  
Bragg Grating ◽  
Theoretical Support ◽  
Sensing Technology ◽  
Safety And Reliability ◽  
Time Observation ◽  
Temperature Strain ◽  
Technical Guidance

Aiming at the problem of hard to monitor features such as temperature, strain in time in large concrete pouring construction process during the construction of urban underground substation, this paper presents applying fiber Bragg grating sensors to collect temperature and strain data in the process of concrete pouring, then demodulating them, and transmitting them to the computer terminal, finally proceeding the real-time online monitoring. Moreover, this paper establishes the 3D models of temperature field and strain field on the basis of one pouring pile, then proceeds dynamic simulation and real-time observation, which can provide certain theoretical support and technical guidance for the site construction, further improve work efficiency, safety and reliability.

A Theory-Driven Algorithm for Real-Time Crime Hot Spot Forecasting

2019 ◽  
Vol 23 (2) ◽  
pp. 174-201 ◽  
Author(s):  
YongJei Lee ◽  
O SooHyun ◽  
John E. Eck
Keyword(s):  
Real Time ◽  
Hot Spots ◽  
High Efficiency ◽  
Hot Spot ◽  
State Dependence ◽  
High Volume ◽  
Population Heterogeneity ◽  
Forecasting Models ◽  
Theoretical Support ◽  
Underlying Mechanisms

Real-time crime hot spot forecasting presents challenges to policing. There is a high volume of hot spot misclassifications and a lack of theoretical support for forecasting algorithms, especially in disciplines outside the fields of criminology and criminal justice. Transparency is particularly important as most hot spot forecasting models do not provide their underlying mechanisms. To address these challenges, we operationalize two different theories in our algorithm to forecast crime hot spots over Portland and Cincinnati. First, we use a population heterogeneity framework to find places that are consistent hot spots. Second, we use a state dependence model of the number of crimes in the time periods prior to the predicted month. This algorithm is implemented in Excel, making it extremely simple to apply and completely transparent. Our forecasting models show high accuracy and high efficiency in hot spot forecasting in both Portland and Cincinnati context. We suggest previously developed hot spot forecasting models need to be reconsidered.

scholarly journals Investigate Tourist Behavior through Mobile Signal: Tourist Flow Pattern Exploration in Tibet

2020 ◽  
Vol 12 (21) ◽  
pp. 9125
Author(s):  
Lina Zhong ◽  
Sunny Sun ◽  
Rob Law ◽  
Liyu Yang
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Flow Pattern ◽  
Limited Attention ◽  
Destination Marketing ◽  
Tourist Behavior ◽  
And Diffusion ◽  
Main Center ◽  
Tourist Flow

Identifying the tourist flow of a destination can promote the development of travel-related products and effective destination marketing. Nevertheless, tourist inflows and outflows have only received limited attention from previous studies. Hence, this study visualizes the tourist flow of Tibet through social network analysis to bridge the aforementioned gap. Findings show that the Lhasa prefecture is the transportation hub of Tibet. Tourist flow in the eastern part of Tibet is generally stronger than that in the western part. Moreover, the tourist flow pattern identified mainly includes “(diverse or balanced) diffusion from the main center”, “clustering to the main center”, and “diffusion from a clustered circle”.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Yang Liu ◽  
Jing Shi ◽  
Meiying Jian
Keyword(s):  
Real Time ◽  
Logit Model ◽  
Intelligent Transportation System ◽  
Stated Preference ◽  
Wait Time ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Random Coefficients ◽  
Information Uncertainty ◽  
Real Time Information

One important function of Intelligent Transportation System (ITS) applied in tourist cities is to improve visitors’ mobility by releasing real-time transportation information and then shifting tourists from individual vehicles to intelligent public transit. The objective of this research is to quantify visitors’ psychological and behavioral responses to tourism-related ITS. Designed with a Mixed Ranked Logit Model (MRLM) with random coefficients that was capable of evaluating potential effects from information uncertainty and other relevant factors on tourists’ transport choices, an on-site and a subsequent web-based stated preference survey were conducted in a representative tourist city (Chengde, China). Simulated maximum-likelihood procedure was used to estimate random coefficients. Results indicate that tourists generally perceive longer travel time and longer wait time if real-time information is not available. ITS information is able to reduce tourists’ perceived uncertainty and stimulating transport modal shifts. This novel MRLM contributes a new derivation model to logit model family and for the first time proposes an applicable methodology to assess useful features of ITS for tourists.

scholarly journals Impacts of Real-Time Transit Information on Bus Passengers’ Travel Choices Based on Travel Behaviour Survey

2021 ◽  
Vol 33 (4) ◽  
pp. 539-550
Author(s):  
Yajuan Deng ◽  
Mingli Chen
Keyword(s):  
Real Time ◽  
Stated Preference ◽  
Travel Behaviour ◽  
Route Selection ◽  
Travel Mode ◽  
Travel Choice ◽  
Departure Time ◽  
Calculation Results ◽  
Research Findings ◽  
The Relationship

Real-time transit information (RTI) service can provide travellers with information on public transport and guide them to arrange departure time and travel mode accordingly. This paper aims to analyse travellers’ choices under RTI by exploring the relationship between the related variables of RTI and passengers’ travel choice. Based on the stated preference (SP) survey data, the ordinal logistic regression model is established to analyse the changing probability of passengers’ travel behaviour under RTI. The model calculation results show that travellers getting off work are more likely to change their travel choice under RTI. When data from the control and experimental groups are compared, the differences in route selection are significant. Specifically, passengers with RTI have a more complex route selection than those without, including their changes of travel mode, departure time, vehicles, and stop choices. The research findings can provide insights into the optimisation of intelligent transit information systems and the strategy of RTI. Also, the analysis of passengers’ travel choice under RTI in the transit network can help to improve network planning.

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