People Travel Research Articles

Key Factors Influencing Vehicle Availability Associated with the User Waiting Time of Demand Responsive Transport (DRT) for People with Disabilities

Demand Responsive Transport (DRT) services for the safe and comfortable travel of people with disabilities have long waiting times and significant variations in time and space. Due to these characteristics, user dissatisfaction with DRT services continues to increase. This study aimed to identify factors that affect vehicle availability, which is closely related to waiting time, and to prepare measures to solve problems in waiting time. The data used for the analysis was composed of spatial units, so Moran's I factor was used to test for spatial dependence. After confirming the presence of spatial dependence, spatial regression models optimized for spatial data analysis were estimated. As a result of predicting the model using five independent variables, four variables, Medical Infrastructure Concentration Index (MICI), Disabled Population Concentration Index (DPCI), Depot Size, and Demand Variability Index (DVI), were found to be statistically significant at a 95% confidence level, except for congestion level. The analytical results indicate that the frequency of vehicle operation is low in areas with a low population of the disabled, little medical infrastructure related to the disabled, and irregular DRT demand, which may affect increasing user waiting time. Therefore, supply-oriented policies to increase the number of available vehicles, such as adding more depots or deploying more vehicles, will contribute significantly to reducing user waiting time in these areas.

Will people travel because of envy? The influence of travel experience sharing on the post-90s WeChat users

ABSTRACT Many people share travel experience on WeChat Moments (WM). At present, most scholars have analysed the phenomenon from the perspective of destination image construction, but few of them investigate the uniqueness of the WM platform and the social factors of WM are rarely discussed. Based on the social comparison theory, this paper regards the sharing of travel experience on WM as an inducing factor of upward social comparison in WM, and explores its influence on travel intention. The role of benign envy and potential tourists’ self-presentation in this mechanism are also explored. Through experimental design and survey instruments, it is found that high-quality travel experience sharing on WM may increase potential tourists’ travel intention. In addition, high-quality travel experience sharing can induce benign envy, which will increase potential tourists’ travel intention as well. When the level of potential tourists’ self-presentation is high, the relationship between WM travel experience sharing and benign envy is stronger. The results provide theoretical insights into travel experience sharing. Practically, the potential tourists’ benign envy and self-presentation intention can be used to benefit tourism destination marketing.

Travel during Pregnancy: A Web-Based Survey of People Who Have Been Pregnant within the Past 10 Years

Travel is frequent among many populations, including pregnant people. The focus of this online survey was to better understand the travel practices of people who have been pregnant within the last ten years. An online survey was conducted for three months through social media posts on Facebook and Twitter. Previously pregnant people were asked questions about where they traveled, if they cancelled any travel plans, and travel-related discussions with their obstetric provider. During the three months the survey was open, 469 participants completed the survey. A total of 390 (83.2%) participants traveled domestically, while 114 (24.3%) traveled internationally or between non-contiguous states within the United States of America (USA). Of these respondents, 170 (44.2%) of the domestic travelers and 69 (61.1%) of the international travelers reported discussing travel plans with their OB provider. Additionally, 49 (10.5%) participants cancelled at least one domestic trip and 30 (6.41%) cancelled at least one international trip. Regarding travel discussions, 6 (3.6%) participants who traveled domestically and 2 (2.9%) who traveled internationally reported that their OB provider initiated the conversation. Many pregnant people choose to travel domestically and internationally. However, it is also clear that not all travelers discuss plans with their OB provider, and in few cases does the provider initiate the conversation. Given the frequency with which people travel, pregnant people and their OB providers should have conversations regarding travel to minimize potential risks.

Research on Vietnamese Attention in Aviation Insurance

The article has clarified the theoretical basis of interest and aviation insurance products. The aviation insurance business is still underdeveloped while the aviation industry is an important factor in Vietnam's economic development, which makes aviation development unsustainable. To solve the research purpose set out, the research team conducted a survey on the understanding and perception of the benefits of aviation insurance products with 196 Vietnamese customers traveling by plane. The results of the data analysis show that the majority of people traveling by plane do not know about aviation insurance products. Many interviewees indicated that they knew that the cost of purchasing aviation insurance products was very high and that they knew through an aviation insurance product consultant. Respondents also said that aviation accidents rarely happen, so they see the benefits of this product are not high. In addition, based on descriptive statistical data of the survey results, the gender factor did not affect the understanding and perception of the benefits of aviation insurance products.

An electricity supply system by recovering the waste heat of commercial aeroengine

Rapid development of the aviation industry has brought great convenience to people's travel and goods transportation. The inevitable issues are high level of energy consumption and the huge environment pollution by kerosene combustion. On the other hand, there is a greater requirement for the electricity capacity as well as the power ratings of aircraft generators to meet the continuous intensification of the electrification degree in modern aircraft. The question that is tried to identify in this paper is: can the waste heat in the aircraft be collected to generate power for meeting the electricity demand? In this paper, an electricity supply system driven by the heat collected from the commercial aeroengine wall is first proposed. It is an integrated system by coupling the topping gas turbine with bottomed closed Brayton cycle via the liquid sodium. A comprehensive mathematical model is developed for the system in the aspects of thermodynamics and economics. The analysis results demonstrate that the proposed system is capable of satisfying the megawatt-scale power requirements of commercial aeroengine. The fluid He–Xe (40 g/mol) is a better choice to cycle the given power system than He and air considering its larger net power output, lower compression ratio and smaller component size. The system with He–Xe can supply 1299 kW electricity power when the compression ratio is just about 7.1. In that case, the oil cost savings is about 3.66 percentages lower than that of the original oil consumption.

Research on smart mobility in public transportation and solutions for mobility

As an important part of modern urban transportation system, smart mobility and public transportation are directly related to people's travel convenience, transportation efficiency and sustainable urban development. With the continuous progress of science and technology, smart mobility solutions play an increasingly important role in the field of smart travel and public transportation. As an important support of urban transportation system, public transportation is of great significance in reducing traffic congestion, improving air quality and saving energy. However, traditional public transportation systems often face problems such as irrational route design, improper scheduling, and untimely information transfer. Therefore, the introduction of smart mobility solutions, such as intelligent scheduling systems and passenger information services, will bring new improvement and development opportunities for public transportation. The research significance of this thesis is to conduct an in-depth study on smart mobility solutions in the field of smart mobility and public transportation, so as to explore how to promote the upgrading and improvement of the transportation system at the technical, managerial and policy levels. This paper will focus on the integration of smart mobility and public transportation, exploring the synergies between the two to achieve a more efficient, environmentally friendly and convenient way of traveling.

Cultural intelligence and COVID-induced virtual teams: Towards a conceptual framework for cross-cultural management studies

This article proposes a creative approach to cultural intelligence, as an individual’s capability to function effectively under the condition of cultural diversity. Virtual team collaboration, as stimulated by the COVID-19 pandemic, constitutes a novel, culturally diverse context. We explore how cultural intelligence may shed light onto the requirements of post-COVID virtual and hybrid team collaboration. The contribution of this article to cross-cultural management studies is thus conceptual: by using the concept of cultural intelligence creatively and beyond its classic application, we exemplify a way in which cross-cultural management studies remain relevant, in an increasingly virtual world of work wherein people travel less to other countries, wherein collaboration takes place online and remotely, and wherein national cultural boundaries intersect with other cultural diversity factors.

Urban visual intelligence: Uncovering hidden city profiles with street view images

A longstanding line of research in urban studies explores how cities can be understood through their appearance. However, what remains unclear is to what extent urban dwellers' everyday life can be explained by the visual clues of the urban environment. In this paper, we address this question by applying a computer vision model to 27 million street view images across 80 counties in the United States. Then, we use the spatial distribution of notable urban features identified through the street view images, such as street furniture, sidewalks, building façades, and vegetation, to predict the socioeconomic profiles of their immediate neighborhood. Our results show that these urban features alone can account for up to 83% of the variance in people's travel behavior, 62% in poverty status, 64% in crime, and 68% in health behaviors. The results outperform models based on points of interest (POI), population, and other demographic data alone. Moreover, incorporating urban features captured from street view images can improve the explanatory power of these other methods by 5% to 25%. We propose "urban visual intelligence" as a process to uncover hidden city profiles, infer, and synthesize urban information with computer vision and street view images. This study serves as a foundation for future urban research interested in this process and understanding the role of visual aspects of the city.

When, where, and with whom during crisis: The effect of risk perceptions and psychological distance on travel intentions

We investigate how risk perceptions and psychological distance impacted people's travel intentions during Covid-19. Our findings reveal that traveling to a high-risk destination increased people's risk perceptions of Covid-19, and their risk perceptions at the destination, which, in turn, reduced people's travel intentions. We identify temporal, spatial, and social distance (the “when, where, and with whom” of traveling) as moderators of these effects; while social distance moderates the effect of risk, on risk perceptions, temporal and spatial distance moderate the effect of risk perceptions on travel intentions. We outline theoretical contributions and implications for tourism during crisis.

A novel traffic optimization method using GRU based deep neural network for the IoV system.

At present, China is moving towards the direction of "Industry 4.0". The development of the automobile industry, especially intelligent automobiles, is in full swing, which brings great convenience to people's life and travel. However, at the same time, urban traffic pressure is also increasingly prominent, and the situation of traffic congestion and traffic safety is not optimistic. In this context, the Internet of Vehicles (also known as "IoV") opens up a new way to relieve urban traffic pressure. Therefore, in order to further optimize the road network traffic conditions in the IoV environment, this research focuses on the traffic flow prediction algorithm on the basis of deep learning to enhance traffic efficiency and safety. First, the study investigates the short-time traffic flow prediction by combining the characteristics of the IoV environment. To address the issues that existing algorithms cannot automatically extract data features and the model expression capability is weak, the study chooses to build a deep neural network using GRU model in deep learning for short-time traffic flow prediction, thereby improving the prediction accuracy of algorithm. Secondly, a fine-grained traffic flow statistics approach suitable for the IoV situation is suggested in accordance with the deep learning model that was built. The algorithm sends the vehicle characteristic data obtained through GRU model training into the fine-grained traffic flow statistics algorithm, so as to realize the statistics of traffic information of various types of vehicles. The advantage of this algorithm is that it can well count the traffic flow of multiple lanes, so as to better predict the current traffic status and achieve traffic optimization. Finally, the IoV environment is constructed to confirm the effectiveness of the prediction model. The prediction results prove that the new algorithm has good performance in traffic flow statistics in different scenarios.

Discovering latent topics and trends in autonomous vehicle-related research: A structural topic modelling approach

Autonomous Vehicle (AV) technology is a disruptive transportation technology that promises to revolutionize how people travel. Due to their potential mobility benefits and associated impacts on public health and the environment, they have received massive attention from transportation experts. To date, there have been numerous academic research conducted regarding AVs, and many attempts have been made to summarize these research documents by manually reviewing them. Nevertheless, due to the vast nature of the existing literature, it is challenging to synthesize the themes in AV-related research and to explore their trends to inform future research direction. This study aims to utilize an advanced natural language processing technique to provide a comprehensive overview of the recent developments in AV-related research. The abstracts of 3292 articles published in transportation-based journals from January 2016 to October 2022 were collected and used for this study. A Structural Topic Model (STM) was employed to explore the dominant research themes hidden in the extant literature on AV research, examine their evolution over time, and determine the topics highly associated with developing and developed economies. Overall, the study highlighted that the least common themes in the literature are in the areas of the development of complex vehicle designs, safety, environmental benefits, and the impact of AVs on transportation infrastructure. Nevertheless, themes concerning AV safety and the environment are emerging as hot topics since late 2021. Topics related to investigating user perceptions, policies/approaches to increase user demand, and AV control/stability are highly studied. Besides, they predominantly originated from developed countries. The research themes, their associated trends, and the key policy suggestions drawn from the study are potentially useful for making well-informed research funding decisions, setting research priorities for countries, and guiding the future research endeavours of researchers.

Who are on the road? A study on vehicle usage characteristics based on one-week vehicle trajectory data

ABSTRACT Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management. However, it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use. Thanks to the advances in data collection and processing techniques, multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns. In this paper, based on a week's operation data of 6600 passenger cars in Shanghai, we develop a systematic approach for identifying trips and travel purposes, and classify vehicles into four categories using a Gaussian-Mixed-Model. A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles. We find that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles. The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction, but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.

Research on the Pattern of Pet Fostering Based on Internet Technology

In the post pandemic era, people's social and travel needs have gradually increased, and the concept of pet care has gradually become popular. The demand for pet foster care is also increasing day by day. In this environment, we carry out innovative reforms based on customer needs, supported by big data cloud computing technology and artificial intelligence technology. The core innovation of the "Mingchongwu" project lies in utilizing internet technology to achieve precise integration of resources and needs. By serving both supply and demand simultaneously, utilizing the precise positioning capabilities, powerful information processing capabilities, and data analysis capabilities of internet technology, we can find the most suitable foster care point for the foster care provider and seek additional business cooperation for the service provider, achieving a win-win situation for both parties.

ANALISIS UPAYA PEMULIHAN DAN PENINGKATAN KEGIATAN OPERASIONAL BANDARA H. HASAN AROEBOESMAN ENDE PASCA PANDEMI COVID-19

Since early March 2020, the Covid 19 pandemic has hit Indonesia and brought a major disaster to the world of aviation. This is evidenced by the drastic decrease in the number of passengers and aircraft at Ende Airport. Travel requirements and PSBB policies discourage people from traveling by air. Ende Airport's operational activities have decreased drastically due to the Covid 19 pandemic. This study aims to find out what efforts have been made to recover and increase operational activities after the Covid 19 pandemic and what obstacles have become obstacles in efforts to implement recovery and increase operational activities at H. Hasan Aroeboesman Ende after Covid-19.pandemic.
 The research was conducted qualitatively using primary and secondary data. Efforts to restore and increase Ende Airport operational activities after the Covid-19 pandemic are efforts originating from Ende Airport to improve and restore its airport operations after the Covid-19 pandemic. This study uses qualitative methods, obtained from primary and secondary data. Primary data is done by data, observation, interviews, and secondary data is done by documentation and literature study. This research was carried out at Ende Airport in August and September 2022.
 The results of this research are Ende Airport's efforts to improve and restore its post-pandemic airport operations, including: carrying out promotions, trying to create new flight routes, preparing places for rapid tests and coordinating with the local health office to administer vaccines. The obstacles that hinder this effort are the high ticket prices, the views of the public who are still worried about using air transportation services due to the Covid 19 pandemic and a lack of understanding about the requirements for people's travel by air transportation.

Research on Market Share Prediction of Highway Passenger Transport Based on Exponential Smoothing Method

Highway transportation is an important part of our country traffic transportation system, taking on most of the capacity demand in passenger transport markets. But with the rapid development of domestic rail transportation technology, people's travel mode has gradually changed from highway transportation to rail transportation. This makes the highway passenger transport management department must consider the impact of the change of people's travel mode on the highway passenger transport market demand when making production plans. The market share of highway passenger transport is an important index to measure the development level of highway passenger transport, which can directly reflect the supply-demand relationship between highway passenger transport and passenger transport market. Therefore, this paper selects the market share of highway passenger transport as the prediction evaluation index, and predicts and analyzes the market share data of highway passenger transport in Henan Province from 2010 to 2021 based on the exponential smoothing method. It mainly calculates the single exponential smoothing results under several different smoothing coefficients by using the smoothing analysis tool in EXCEL, and determines the optimal smoothing coefficient by taking the minimum root mean square error (RMSE) as the criterion. Then, it solves the lag defect of the single exponential smoothing prediction results by using the quadratic exponential smoothing. Finally, the trend prediction model of highway passenger transport market share in Henan Province is obtained. The prediction results of this model can guide the rational allocation of highway transportation resources and the formulation of passenger transport production plan in Henan Province.

Kentsel Koridorlarda Yürümeyi Teşvik Edici Planlama Ölçütlerinin Belirlenmesi: Trabzon Sakinleriyle Bir Çalışma

Urban corridors, which were organized as pedestrian-oriented in the period before the invention of the automobile have entered the process of being organized as automobile-oriented in the modernization period when cities began to be reshaped. The widespread of automobile usage has been changing people's travel preferences which resulted in the decrease of walkable environments. Scientists from different disciplines conducted studies on walking and walkability. It is necessary to understand the sociocultural structure and environmental features that persuade people to walk. “Walking Preference Survey”, which can be used as a preliminary study in the planning of walkable corridors, was prepared for this research. The survey was conducted with 597 participants in Trabzon/Ortahisar. The survey was subjected to validity and reliability tests for standardization. 8 walkability criterions were reached in the factor analysis applied to the survey within the scope of the constructive validity analysis. In the study, differences in walking preference were observed according to demographic characteristics and travel habits.

Spatiotemporal trip profiles in public transportation reveal city modular structure

Understanding urban mobility patterns within public transportation (PT) systems is key for cities to improve services and promote sustainable mobility. Exploring daily PT riders’ traffic flows using anonymized big data is a first step to analyze when and to where people travel. Nevertheless, assessing the degree to which PT usage patterns correlate with the spatial distribution of points of interest (POI) and subsequently affect the city structure is less attempted. Classic approaches use questionnaires and survey data that are costly and often yielding limited statistical significance. The purpose of this research is to understand associations between travel patterns of urban commuters and the functional organization of a city. To this end, we propose a network-constrained temporal distance measure for modeling PT rider travel patterns from smart card data; and further introduce a fully autonomous approach to describe the span of services available at catchment areas around metro stations. The end result is a detailed analytical prescription of spatiotemporal commuting patterns in the city of Lisbon as well as an analytical contextual information that enables us to understand the functional modular structure of urban facilities. Using the city of Lisbon as the guiding case study, the gathered results confirm the hypothesis that PT rider’s daily flows along the PT network of metro stations reveal a city modular structure.

Research on e‐commerce recommendation system based on matrix factorization algorithm

SummaryThe development of the internet has brought great convenience to people's travel and shopping. More and more people choose to shop online. As e‐commerce continues to grow in scale, the number and variety of products are also growing rapidly, which results in customers taking a lot of time to find the products they want to buy. This problem prevents people from using the Internet quickly and efficiently. In order to solve these problems, personalized recommendation system comes into being. It can directly predict the content that users may be interested in based on their historical behavior, and make personalized recommendations for them in the massive data. Based on the idea of collaborative filtering, this paper adopts matrix factorization method to analyze the sales records of an e‐commerce platform, and analyzes the potential preferences of 686 customers, and gives the top five personalized recommended products StockCode of users.

Prediction of PM2.5 Concentration Based on CNN-BiGRU Model

The issue of air pollution has always been a concern. Bad smog weather not only brings inconvenience to people's travel, but also poses a threat to people's health. PM2.5 concentration is an important indicator of air conditions. Therefore, it is of long-term significance to analyze and predict the concentration of PM2.5. Aiming at the problem that a single machine learning model cannot consider the impact of multiple factors on PM2.5 concentration changes, and the data characteristics are complex, which cannot better capture all the characteristics of the data, and cannot highlight the regularity of PM2.5 changes over time, the construction of a combined model further improves the prediction accuracy. Firstly, based on the PM2.5 concentration values, air quality data, and meteorological data at various stations in New Taipei City, Taiwan Province, through analyzing the spatiotemporal distribution characteristics of the PM2.5 concentration at the target station, as well as the correlation with various pollutant factors and meteorological factors, Spearman correlation analysis is used for feature selection. The combined model CNN-BiGRU constructed in this paper utilizes its unique convolution operation to extract features from one-dimensional data, and combines the circular neural network BiGRU with bidirectional transmission function to model and predict PM2.5 concentration based on the functional advantages of both parties.

Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system

AbstractIn recent decades, the effects of vehicle emissions on urban environments have raised increasing concerns, and it has been recognized that vehicle emissions affect peoples’ choice of housing location. Additionally, housing allocation patterns determine people's travel behavior and thus affect vehicle emissions. This study considers the housing allocation problem by incorporating vehicle emissions in a city with a single central business district (CBD) into a bilevel optimization model. In the lower level subprogram, under a fixed housing allocation, a predictive dynamic continuum user‐optimal (PDUO‐C) model with a combined departure time and route choice is used to study the city's traffic flow. In the upper level subprogram, the health cost is defined and minimized to identify the optimal allocation of additional housing units to update the housing allocation. A simulated annealing algorithm is used to solve the housing allocation problem. The results show that the distribution of additional housing locations is dependent on the distance and direction from the CBD. Sensitivity analyses demonstrate the influences of various factors (e.g., budget and cost of housing supply) on the optimized health cost and travel demand pattern.