Travel Characteristics Research Articles

Coupling travel characteristics identifying and deep learning for demand forecasting on car‐hailing tourists: A case study of Beijing, China

AbstractOnline car‐hailing, with its advantages of convenience and efficiency, has quickly become popular among tourists, playing a crucial role in the accessibility of scenic spots. Due to the particularities of tourist travel behaviour and the complexity of travel supply and demand around scenic spots, research on car‐hailing tourists is relatively lacking at this stage. Based on multi‐source data, this study couples the identifying of travel characteristics, by introducing the concept of service dependency degree, with a Bayesian optimization–long short‐term memory–convolutional neural network (BO‐LSTM‐CNN) method to conduct multi‐task online car‐hailing demand forecasting. The evaluation of the dependency degree primarily encompasses the establishment of evaluation indices and the application of the entropy weight method and natural breakpoint method. The BO‐LSTM‐CNN model utilizes Bayesian optimization for hyperparameter tuning, LSTM for temporal variable processing, and CNN for the fusion of multi‐source information related to weather, space, and online car‐hailing attributes. Extracting online car‐hailing tourist travel orders based on spatial–temporal constraints, the proposed methods are applied to 72 scenic spots in Beijing, China. According to their dependency degree, Beijing's scenic spots are categorized into three levels of dependency on online car‐hailing services, from high to low. The outstanding forecasting efficacy of the proposed model for various scenic spots is verified through comparison tests with several benchmark models. Consequent to these findings, mobility service improvement strategies are specifically proposed for each class of scenic spots, which can provide valuable insights for the relevant tourism traffic management personnel.

Demographic and travel characteristics and self-reported predeparture SARS-CoV-2 testing behavior in air passengers entering the United States from foreign destinations from July to September 2021.

From January 2021 to June 2022, the United States Centers for Disease Control and Prevention required predeparture SARS-CoV-2 testing for all air passengers arriving into the United States from a foreign country. Using data collected during a surveillance project, we described predeparture testing behavior among a convenience sample of international air passengers entering the United States from July to September 2021 at six US ports of entry. We analyzed pairwise relationships between self-reported test type, test timing, demographic and travel characteristics, and COVID-19 vaccination status using chi-square and Fisher's exact tests. Participants were more likely to get a NAAT versus antigen test if they identified as non-Hispanic Asian or Pacific Islander (68.2%, n = 173), non-Hispanic Black (62.6%, n = 147), or if they preferred not to report race and ethnicity (60.8%, n = 209) when compared to those who identified as non-Hispanic White (47.1%, n = 1086, all p < 0.05). Those who identified as Hispanic or Latino (n = 671) were less likely to get a NAAT than the non-Hispanic White group (39.5% vs. 47.1%, p < 0.05). Participants arriving in the US from the Americas were less likely to get a NAAT (38.5%, n = 871) compared to those arriving from Europe (45.5%, n = 1165, p < 0.05). Participants who reported receiving their predeparture test 2 days or 3 or more days before departure were more likely to report receiving a NAAT (52.2%, n = 879, and 60.2%, n = 410, respectively) than those who reported testing within 1 day (41.4%, n = 1040, all p < 0.001) of departure. Test type was significantly associated with race and ethnicity, departure region, and test timing. Differences likely reflected regional disparities in the availability of tests at the time of the activity. Discrepancies in predeparture test timing and type worldwide may have consequences for the effectiveness and equity of travel requirements in future pandemics.

On the Traveling-Creativity Relationship: Effects of Openness to Experience, Cultural Distance, and Creative Self-Efficacy

Abstract It is a common belief that travel allows us to grow, get inspired, and reach fulfillment. Travels offer a highly conducive combination of cognitive resource maximization, positive emotions, and, most importantly, new, out-of-ordinary experiences, enabling creativity to flourish. However, for travel to affect creativity in any way, some conditions must be met. Of key importance are characteristics of travel destinations and travelers’ Openness to experience. This study explores the creativity-traveling relationship by analyzing occurring interactive, mediating, and correlational effects. Also, it compares how traveling and living abroad predict creativity to address the ongoing debate. The analysis comprising 136 participants demonstrated that the traveling-creativity link is stronger among people low on the Openness trait. Further, an indirect effect of creative self-efficacy in the relationship between traveling and creativity was observed. These findings suggest the potential of facilitating creativity through traveling experiences among some groups, but at the same time, they call for more in-depth research on the topic.

INFLUENCE OF TRANSPORT OPERATIONS ON THE CHANGING LOCAL CLIMATE IN NIGERIA’S SOUTHWESTERN CITIES

The activities of transport operations exhibit both positive and negative outcomes. Its negative effects on human health and the sustainability of the environment are of interest in this study. As a result, the study investigated the influence of transport operations on the changing local climate in Nigeria's southwestern cities. It investigated motorists' socioeconomic status (SES) and travel characteristics; the salient components of transport operations contributing to greenhouse gas emissions (GHG emissions); the effect of GHG emissions from transport operations on the local climate; transport externalities contributing to local temperature changes; and the effect of GHG emissions from transport operations on human health in the research location. This study employed a cross-sectional research design, utilizing systematic sampling to collect 580 copies of questionnaires administered to commercial motorists found in queues across two randomly selected parks in each capital city within the region. Both descriptive and inferential statistics (ANOVA and Linear Regression) were used to analyze the data. The study found that 60% of the motorists were middle-aged (aged between 36 and 45). The majority (70%) drive an average of 100 to 150 km, and premium motor spirit (PMS) is the primary source of energy for 95% of them. The nature and condition of the fuel in use are the top-ranked components of transport operations contributing to GHG emissions. The result of the ANOVA revealed a significant variation among the components of transport operations contributing to GHG emissions (F = 28.302, p = 0.000&lt;0.05). Meanwhile, the linear regression results indicate that vehicular GHG emissions have a significant impact on the local climate (F1⁄578=30.091, p=0.000&lt;0.05). Traffic congestion and bad road conditions were the top-ranked transport externalities contributing to the changing local climate. The most common effects of GHG emissions on human health were headaches and difficulty breathing. Considering these findings, this study suggests the best tactics to lessen the effects of GHG emissions from transport operations, which adversely affect the local climate and human health in the study area.

The potential of changing habitual car user travel modes: The case of Czech towns

With the constant and ever-growing pressure from governing bodies for increased sustainability of transportation systems, the need for changes in the travel behaviours of the inhabitants of suburban and urban areas seems inevitable. Thus a shift from cars to vehicles with higher occupancy rates or other more environmental friendly travel modes needs to be a priority. This requires understanding the reasons why people choose cars as their preferred mode of travel for commutes in order to make informed decisions about how to encourage changes in commuter travel behaviors. This paper presents a case study of four medium-sized Czech towns, including a survey concerning pre- and post-COVID-19 travel habits focused on identifying and understanding the willingness of car-dependent commuters to change their mode of travel choice. This three-stage survey revealed a small, COVID pandemia -based effect on the habits of respondents and helped to identify sensitivities survey participants had to particular travel parameters and characteristics that might be used by municipalities to implement informed policies that could lead to changes in travel behaviours.

Monte Carlo-based Charging Demand Forecasting Model and Market Space Study

With the rapid development of China's new energy vehicle industry, charging infrastructure has gradually become a key factor affecting the further development of the new energy vehicle industry. Scientific and reasonable forecasts of the future development scale of the domestic charging infrastructure market have become an important element supporting the business planning of various related parties such as vehicle enterprises, charging pile enterprises, and the energy industry. Based on the distribution data of electric vehicle traveling and charging characteristics obtained from the industry research, this study proposes a set of charging infrastructure prediction model based on the principle of energy supply-demand balance, predicts the development scale of charging infrastructure in the country and key cities, and further analyzes the market competition and future market margin of the key cities, and evaluates the difficulty of future market entry in the key cities, which provides a basis for further development of the charging infrastructure industry. The difficulty of entering the future market in each key city is assessed, providing business decision support for industry participants. In the future, the charging infrastructure development scale prediction results proposed by this study can be used as the input parameters of the charging pile layout planning model to enhance the scientific nature of the charging infrastructure layout and help the industry to develop in a high-quality manner.

Factors and incentives that affect electric vehicle adoption in Greece

Electric vehicle proliferation is still at exceptionally low level in Greece. The subsidy program “I move electrically” aims to accelerate the transition to EVs providing incentives both for company and private electric vehicles. In order to analyze the factors that affect the consumers’ intention to purchase private electric vehicles in Attica a questionnaire survey was conducted. Discrete choice models were developed to analyze the factors that influence the intention of users to purchase and willingness to pay extra for an EV compared to a conventional car. Travel and demographic characteristics, provision of financial incentives, environmental awareness, charging time and charging infrastructure were found to affect the intention to purchase an EV in Greece. The paper concludes with policy measures and recommendations regarding the charging infrastructure, the provision of financial incentives and marketing policies that could support the adoption of EVs.

TREND KESUKAAN PARK AND RIDE DENGAN METODE STATE PREFERENCE

Palembang city government chose light rail transit (LRT) as public transportation to overcome the problem of traffic congestion. However, the use of public transportation in the city of Palembang is not optimal, so traffic jams occur, It is necessary to develop facilities that can attract people's interest in using public transportation. This research uses the Stated Preference Method to determine socio-economic characteristics, travel characteristics, and people's preferences for the development of Park and Ride facilities in Palembang City. The respondents for this research were 100 LRT transportation passengers. From the research results, it is known that the majority of passengers are college students, aged between 19-25 years, with an income of less than IDR 1,000,000 and the reason for traveling is for recreation or entertainment. Based on descriptive analysis to determine respondents' preferences for the development of the Park and Ride concept offered in the Stated Preference Survey, the results obtained from 6 (six) travel scenarios obtained a total of 2400 choices from 100 respondents. Travel by private vehicle (Option A) was chosen 1512 (63%) times, while travel by the Park and Ride system integrated with public transportation was chosen 888 (37%) times.

Distributional Impact of a Vehicle Miles Traveled Fee

Highway funding is facing a challenge because of difficulties in raising the federal and state fuel taxes. With the increasing use of electric, hybrid, and other forms of fuel-efficient vehicles, it is predicted that gas tax revenue will decrease. One potential alternative, a vehicle miles traveled (VMT) fee, was examined in this study. The tax burden in each household by income group and residential area was analyzed under the current fuel tax system and a VMT-based fee system. This study includes two VMT-based fee scenarios: 1) static—where people do not change their travel behavior and 2) dynamic—where people change their travel behavior because of the change in travel cost. A VMT demand function was derived by examining data from the National Household Travel Survey and, using regression analysis, considering the fuel cost per mile and traveler characteristics. The increase in cost between the current tax system and the VMT fee system was similar across income groups, but increased average urban household payments while decreasing average rural household payments. However, the VMT had a greater impact on the lower-income group in that they decreased their amount of travel.

Study on Induced Passenger Flow Forecast for Intercity High-Speed Rail

To forecast the induced passenger flow of intercity high-speed rail, two related multinomial logit models are applied to describe travelers’ travel choice behavior under changes in the travel environment. The first model describes different choices of travel mode by travelers between origin–destination pairs, while the second model describes the choice of travel frequency by different travelers. By introducing the consumption surplus variable of travelers, the two selected models are correlated and the model is calibrated using data from a behavioral survey and an intention survey. The results show that the polynomial logit model is applicable for forecasting induced passenger flow after changes in the travel environment. The consumption surplus of travelers is the key factor influencing changes in travel frequency, and the social characteristics of travelers and the economic development features of origin and destination cities have significant impacts on travel frequency. In-transit time is the key factor affecting the demand elasticity of travel frequency. The range of demand elasticity for short-distance business and non-business travel is between −0.61 and −0.79. The results of the demand elasticity analysis were applied to predict that the travel frequency of people from Nanjing to Huai’an, Suqian, Lianyungang, and Yancheng will increase by about 45% after the opening of the Nanjing-Huai’an intercity high-speed railway.

Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China

To alleviate travel congestion at peak periods or on congested routes, some measures for urban rail transit (URT) systems including fare incentive schemes and subsidy policies have been widely adopted. Existing measures can alleviate demand during peak periods or on congested routes to a certain extent, but they do not consider the passengers’ satisfaction with their travel experience. Consequently, this study aims to investigate passengers’ acceptance of a fare incentive policy offering a discounted fare during off-peak periods or on uncongested routes before it is implemented in Nanjing, China. To understand passengers’ acceptance of the policy, this study explores the effects of the policy on passengers’ route choices. Furthermore, the differences among passengers with respect to different travel purposes and travel times in route choices have been analyzed. A revealed preference (RP) survey and a stated preference (SP) survey consisting of 463 samples from URT passengers in Nanjing, China, were conducted and analyzed using a random-parameter multinomial logit (RPMNL) model. Results show that socio-economic variables, travel characteristics, travel cost, departure time, and travel distance significantly affect passengers’ route choices. Furthermore, the route choices of passengers with different travelling times and purposes vary. Commuters are most sensitive to travel cost during off-peak periods, followed by non-commuters who travel during peak periods. Departure time and travel distance most significantly affect the route choices of non-commuters during peak periods and off-peak periods, respectively. The findings of this study could assist transit agencies in designing attractive fare incentive policies for public transit passengers and offer valuable insights into effective demand management strategies. Moreover, the lessons derived from this study may guide the implementation of fare incentive policies in Nanjing, China, and elsewhere.

Searching for the Inflection Point of Travel Well-Being from the Views of Travel Characteristics Based on the Ordered Logistic Regression Model

Travel well-being is the subjective feeling of satisfaction that people have while traveling. Previous research focused on its determinants and relationships with subjective well-being ignored. But no quantitative study discusses the effect of characteristics like weekly income and travel time on travel well-being. To demonstrate the quantitative inflection of travel well-being from characteristics, the relevant factors influencing travel well-being as the dependent variable are identified using Pearson correlation analysis and linear regression in this paper. To overcome the limitations of linear regression techniques, ordered logistic regression is applied to establish an analytical model of travel well-being for predicting the response probabilities for different degrees based on combinations of explanatory variables. Both the linear regression and ordered logistic regression models are calibrated by American residents’ travel datasets. The results illustrate that the ordered logistic model fits sample data better than linear regression models. Age, travel time, health status, and resting degree are significantly related to travel well-being. Older people and those who are healthier and better rested are more likely to experience higher levels of travel well-being. Additionally, increased travel time is associated with a significant decrease in travel well-being. Therefore, to enhance people’s travel feelings, policymakers and urban planners can enhance the quality of public transportation services and provide diverse transportation options, while also logically constructing transportation hubs to provide more convenient travel plans.

Predicting modal choice for urban transport using an algebraic equation

Demand estimation and forecasting is an essential step in urban passenger transport planning. Relating the factors that influence the modal choice behavior of individuals facilitates demand estimation. In this study, we develop machine learning models that consider individuals' demographic, socioeconomic, and travel characteristics to justify their mode choice. Two datasets are used to train and validate the models. We use logistic regression and multilayer perceptron models to classify public or private transportation trips. It was observed that a multilayer perceptron model with a low number of parameters could predict modal selection with an accuracy exceeding 90%. We derive an algebraic equation from this result to perform modal selection prediction. Our results show that the models can effectively predict the mode of transportation of individuals based on their demographic and travel characteristics.

Analisis Faktor-faktor Pelayanan Trans Jogja Berdasarkan Persepsi Pengguna Sepeda Motor

In 2021, the population of motorized vehicles in DIY reached 3 million units of which 85% were motorcycles. The growth in the number of motorized vehicles results in increased congestion, pollution, and traffic accidents. To overcome transportation problems in Yogyakarta, in 2008 the government operated BRT (Bus Rapid Transit) based public transportation, Trans Jogja. However, various studies have shown that the load factor of Trans Jogja is very low. This study was conducted to see the characteristics of motorcyclist and latent factors of public transportation services that are prioritized by motorcycle users to switch modes to Trans Jogja.&#x0D; Primary data was obtained online and offline from 455 motorcycle users by answering a research questionnaire consisting of four sections, namely sociodemographic characteristics, travel characteristics and service factors that are prioritized to switch to Trans Jogja. Data analysis was conducted using descriptive methods, crosstab and factor analysis with SPSS.&#x0D; The results of the crosstab analysis show that the sociodemographic characteristics of respondents that correlate with the number of motorcycle ownership are age, income, pocket money, number of family members.Based on the factor analysis of public transport services prioritized by motorcycle users to switch to Trans Jogja, 4 latent factors were obtained, namely Trans Jogja service performance, transit experience, bus facilities and fees and parking.&#x0D; &#x0D; Keywords: Motorcycle, Trans Jogja, mode shift, factor analysis&#x0D;

Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking.

Characteristics of business, pleasure, and VFR segments who travel during risky times

PurposeThis study aims to explore what may have contributed to risk-taking travel behavior during the COVID-19 pandemic and what may be the drivers of satisfaction and intention to travel again. The study compares travelers based on their travel purposes (business, pleasure and visiting friends and relatives [VFR]) and explore the influence of subjective well-being received from their travel activities.Design/methodology/approachData from a structured survey of 323 US residents who traveled during the COVID-19 pandemic were collected on MTurk. Respondents were asked travel behavior questions related to their favorite trips and to rate several multi-item scales measuring the benefits expected and received from their trip, the trip’s contribution to their positive mental and subjective psychological well-being, as well as their satisfaction with the trip and intention to travel in the future. In addition, personality measures focused on risk-taking, thrill-seeking and self-confidence, as well as additional risk-related concepts of optimism bias, probability neglect and proximity to self.FindingsData analysis revealed some differences among respondents who traveled for business, pleasure and VFR purposes. Business travelers were more risk takers and thrill seekers; pleasure travelers achieved more well-being benefits from their favorite trips during the pandemic; however, VFR travelers’ satisfaction and intention to go on similar trips were explained more by the benefits they received from their favorite trips.Originality/valueAlthough several studies addressed consumers’ travel motivation during the COVID-19 pandemic, there is a lack of empirical research comparing the characteristics of travelers based on their travel purposes, as well as their sociodemographics, personality traits and the expected and perceived well-being benefits from traveling.

Exploring the association between socio‐demographic factors and public acceptance towards fully automated vehicles: Insights from a survey in Australia

AbstractFewer studies have investigated the factors affecting fully Automated Vehicles (AVs) acceptance and their association with demographics and travel behaviour in the Australian context. Evidence shows that public opinions and adoption towards AVs may vary by country. Therefore, this study aims to systematically explore the factors affecting public opinion towards fully AVs by conducting a survey in Australia. Specifically, the study examines seven key attributes (benefits/usefulness, ease of use, attitude, data privacy, willingness to pay more, social influence, and trust) towards the opinions of AVs and their association between demographic and travel behaviour. The results from 809 Australian responses show that Australians tend to have a positive attitude toward AVs. Age and years of driving experience are the two most important factors instead of travel characteristics (e.g. frequency of driving cars, travel time by car, and distance of driving cars). Being less exposed to automation technology is likely to be the main reason affecting the adoption of AVs for the two socio‐demographic factors: age and years of driving experience. Males hold a relatively positive attitude towards AVs than females. Likewise, Australians are less likely to pay more for fully AVs than conventional vehicles. Data privacy is also concerning for Australians.

Pemodelan Pemilihan Moda Transportasi (Studi Kasus: Fakultas Ekonomi dan Bisnis Universitas Negeri Malang)

A traveler will consider many things in choosing a mode because the availability of transportation modes is variated. The traveler's characteristics, the trip's characteristics, and the facilities of transportation mode characteristics can influence a person's decision in choosing a mode. The study of the selection of modes of transportation to campus by students, lecturers, and other workers at the Faculty of Economics and Business, State University of Malang, aims to describe the characteristics of mode users and determine the mode selection models to go to campus. This study used a descriptive analysis method to determine the characteristics of mode users and the binomial difference logit method for modeling transportation. The results of the study showed that five factors are most influential in choosing a mode between private transportation and public transportation at the Faculty of Economics and Business, the State University of Malang, namely ownership of a driving license, travel distance, the comfort of transportation, the safety of transportation, and availability of transportation which produces a regression model UKP – KU = -11.480 + 4.045 (X7) – 1.263 (X8) + 5.102 (X11) + 2.302 (X12) + 5.693 (X14). This model has an R2 value of 0.743 and a classification plot of 0.927. From the results of the modeling equation, the probability value of mode selection for private transportation is 0.997. In contrast, public transportation gets a probability value of 0.003.

Analisis Pemilihan Moda Transportasi Angkutan Umum Di Kota Kendari

This study aims to determine the characteristics of travelers who use modes of transportation and find out how big the probability by choosing one of transportation mode is in Kendari City. The result show that the characteristics of mode selection based on the reasons that influenced the choice of mode from a total of 73 respondents were as follows, the travelers chose faster time considerations by 48% or as many as 35 people, the travelers who chose security considerations by 11% or as many as 8 people, the travelers who chose convenience considerations by 19% or as many as 14 people, and travelers who chose price considerations by 22% or as many as 22 people. The probability of users of transportation modes in Kendari City shows that people are more likely to use online modes of transportation 51% compared to using public transportation modes of 49%

Pengaruh Kenaikan Harga Bahan Bakar Minyak terhadap Pemilihan Moda Transportasi (Studi Kasus: Perkotaan Yogyakarta)

The increase in world oil prices has led to changes in the price of fuel oil (BBM) in Indonesia. The fuel price change policy impacts various sectors, including the transportation sector. Transportation is one of the basic human needs for moving from one place to another. Various factors are taken into consideration by the community in choosing the mode of transportation to be used. This study aims to analyze the impact of changes in fuel prices on preferences for transportation modes in urban Yogyakarta and to see how people's behaviour changes in mobility. The analytical method used in this research is descriptive and crosstab analysis. Descriptive analysis was used to identify the respondents' characteristics, the journey's characteristics, and the effect of rising fuel prices. Crosstab analysis is used to see the relationship between the characteristics of respondents and the choice of transportation mode before and after the fuel price increase. The data collection method was carried out by distributing questionnaires to the people of Yogyakarta. The choice of transportation mode when fuel oil increases does not change much, whereas in the scenario of changing fuel prices, there is a significant increase. This can be seen from the decrease in car users when there is an increase in the price of fuel oil, while the probability of motorcycle users when the fuel price rises until the fuel price is Rp 12,000 continues to increase, while when the fuel price is Rp 14,000 switch to using public transportation modes, namely the Trans Jogja bus. When the price of fuel oil increases, the frequency of primary trips does not change, while the frequency of secondary trips changes. Transportation costs incurred have increased, and the fuel used has changed; from the first, Pertamax switched to pertalite