People Travel Research Articles

Impact of Artificial Intelligence in Urban Tourism

Urban tourism involves travelling to cities that are quite near to the urban precincts. Huge quantum of people travel to nearby cities for entertainment and relaxation. The tourist authorities serve its customers with adequate resources and accessories on an updated mode. Even the advancement of artificial intelligence too reflected in the urban tourist segment in a highly profound manner as the influence of e-technology has highly benefited almost all countries throughout the world. The present study sprouts light into the significance of Artificial Intelligence (AI) in the urban tourist segment and it is analyzed that Artificial Intelligence plays a significant role in the tourism industry and is updated with its latest tools for a highly attractive and profitable tourist climate.

Impact of COVID-19 pandemic on road traffic injuries in Iran: An interrupted time-series analysis.

Globally, the COVID-19 pandemic has affected the number of road accidents and deaths caused by them. The present study aimed to identify the effect of this epidemic on traffic accidents and their casualties in Iran. In this study, Interrupted Time Series Analysis (ITSA) was used in a semi-experimental design to measure the impact of the restrictive policies of COVID-19 on road accidents. Data were collected retrospectively from the Iran Red Crescent Society data set for 31 provinces from March 2017 to February 2022. The information related to the number of road accidents, injuries, deaths, and deaths in the hospital was collected. The Newey-West method is used for estimation. Statistical analyses were carried out using R software version 3.6.1. Since February 2020 in Iran, the reduction in the number of road accidents and the number of injuries and deaths in these accidents was significant at 5% but the reduction of deaths in the scene and hospital was significant at 10%. In general, for all variables, the reduction trend was established only in the first months, and then it had an upward trend. In the early months of the COVID-19 epidemic in Iran, the number of road accidents and their casualties decreased. Policies restricting traffic, quarantine, and fines for violators can be reasons for changing people's behavior and travel patterns and also lead to a reduction in traffic accidents and fatalities. Such studies can explain the importance of the policies in changing behavioural patterns and can be used as a guide in future policies.

RouteVis: Quantitative Visual Analytics of Various Factors to Understand Route Choice Preferences

AbstractAnalyzing the preference of route choice not only facilitates the understanding of individuals' decision‐making behavior, but also provides valuable information for improving traffic management strategies. As the layout of the road network, the variability of individual preferences and the spatial distribution of origins and destinations all play a role in route choice, it is a great challenge to reveal the interplay of such numerous complex factors. In this paper, we propose RouteVis, an interactive visual analytics system that enables traffic analysts to gain insight into what factors drive individuals to choose a specific route. To uncover the relationship between route choice and influencing factors, we design a quantitative analytical framework that supports analysts in conducting closed‐loop analysis of various factors, i.e., data preprocessing, route identification, and the quantification of influence and contribution. Furthermore, given the multidimensional and spatio‐temporal characteristics of the analysis results, we customize a set of coordinated views and visual designs to provide an intuitive presentation of the factors affecting people's travels, thus freeing analysts from tedious repetitive tasks and significantly enhancing work efficiency. Two typical usage scenarios and expert feedback on the system's functionality demonstrate that RouteVis can greatly enhance the capabilities of understanding the travel status.

Non-linear impact of the built environment on metro commuter flows before and after the COVID-19 outbreak: A case study in Guangzhou

The built environment is a crucial factor influencing subway commuting. However, the extent to which the built environment affects metro travel during the COVID-19 pandemic remains under explored. In particular, people's travel patterns and destinations might have changed during the pandemic, influenced by the built environment. The degree to which these influences differ from those in normal circumstances has scarcely been studied. This study investigates the non-linear effects of the built environment on metro commuters within an 800-m radius of subway stations. Analysis of mobile signal data from Guangzhou Metro Line 1 showed that the importance of the built environment was 81.69% and 70.06% before and after the outbreak, respectively. Based on the threshold effects of the built environment on subway passenger flows before and after the pandemic, this study determined the immediate needs of commuters for the built environment after the outbreak. This information can guide planning strategies around subway stations facing public health emergencies. This study highlights the non-linear impact of the built environment on subway passenger flows after an outbreak. Thus, the number of parking lots, the proportion of medical facilities and the distance to the central business district are urgent demands for subway passengers after the outbreak.

Vertical is Beneficial but volume is Irrelevant: Optimization of urban guide signs based on spatial representation of road networks

Urban guide signs, a fundamental component of traffic sign systems, convey both directional and locational information. Previous studies mainly focused on the font or volume of information, while little attention was paid to the layout of text-based Chinese guide signs, which is an unregulated area but crucial in practical applications and related to people's travel safety. This study investigates the impact of text layout and information volume on the spatial representation of road networks through two experimental studies, examining the effects of different designs on path determination and global road network knowledge. The results indicate that the text layout of urban road guide signs significantly influences the formation of spatial representation of the road network. Specifically, vertical guide signs displaying road names on both sides proved more effective than horizontal ones. While the volume of road name information does not markedly affect the formation of spatial representation, the arrangement of road names does influence the determination of information volume, with vertical layouts facilitating the presentation of more information. It is anticipated that these design recommendations for road signs can effectively mitigate the incidence of road traffic accidents.

Examining ride sourcing services as an emerging mode in Metro Vancouver: Insights into trip characteristics and impacts on multimodal competitions

The availability and utilization of ride-sourcing services have the potential to transform how people travel. While these services could improve mobility and accessibility, they could also attract users away from active modes and public transit and increase congestion and emissions. Understanding the impacts of transportation network companies (TNCs) on the transportation system is critical to ensure that the benefits of ride-sourcing are captured, and its negative externalities are minimized. This study uses web-based survey data administered to Metro Vancouver residents to explore the characteristics of ride-sourcing trips and the early impacts of ride-sourcing use on mode choice, given that TNCs are new to the study area. Additionally, this study utilizes stated preference experiments and error-components mixed logit models to examine the influence of sociodemographic characteristics and attitudinal factors on mode choice decisions for commuting and non-commuting trips. The results offer insights into the relationship between ride-sourcing and private vehicles, local and regional transit, taxi, and active modes (such as walking and cycling). Furthermore, model results highlight the heterogeneity in mode substitution behavior across population segments. This study can help planners and agencies capitalize on the advantages of TNCs and better integrate ride-sourcing into the transportation system.

Optimal joint strategies of introducing live streaming and online travel agency channels in the tourism supply chain

ABSTRACT With the advent of e-commerce, online travel platforms such as live streaming and online travel agency (OTA) are increasingly developing, which has changed the way people travel and has exerted a significant impact on people’s daily life. In reality, tourist attractions face the challenge of whether introducing live streaming channel, and tour operators face the challenge of whether entering OTAs. To address these challenges, our paper develops theoretical models and presents some insights. The results show that the optimal joint strategy depends on primarily on the fixed fee and annual service fee, but is also affected by other factors such as the size of user traffic, the interactive and review experience, and the consumer's preference. Interestingly, tour operators entering OTAs is always beneficial to tourist attractions, but tourist attractions introducing live streaming is always detrimental to tour operators. Besides, when offline channel and live streaming channel coexist, the total travel package demands will not increase after adding an OTA channel. At last, when considering consumer returns, tourist attractions are more willing to introduce live streaming, but the willingness of tour operators to join OTAs is related to tourists’ acceptance of the OTA channels.

Application of inertial navigation high precision positioning system based on SVM optimization

With the advancement of semiconductor technology, pedestrian navigation and positioning technology based on smartphones is becoming increasingly important in people's travel. However, precise positioning is challenging due to the use of inertial measurement units in low-cost smartphones and the complex motion states of pedestrians. To navigate and locate pedestrians in complex motion states, a method for converting between smartphone coordinate systems and navigation coordinate systems was studied and designed, and the errors of the built-in sensors of smartphones were analyzed and calibrated. In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. To solve the classification problem of multiple human motion states, a multi classification model was constructed and adjacent gait correlation constraints were introduced to correct the classification results. Experiments indicated that the sum of squared errors for traditional algorithms estimating pedestrian trajectories was 0.92, whereas the optimized algorithms produced an improved sum of squared errors of 0.26. Consequently, the average sum of squared errors was reduced by 71.74 %, and the convergence speed increased by 55.56 %. The pedestrian trajectory prediction algorithm optimized by support vector machine could significantly lift the positioning and navigation efficiency, with a correct recognition rate of over 93 % and a position recognition accuracy of 78.8 % - 88.4 %. By optimizing recognition of the motion state of pedestrians, more accurate determination of their position and motion state can be achieved.

Influences of COVID-19 pandemic on travel patterns across past, present, and future

ABSTRACT As societies strive to return to pre-COVID-19 norms, there's growing recognition of the profound changes people have experienced over the past two and a half years. This has prompted discussions on how to shape future transportation strategies to align with evolving lifestyles. Understanding people's travel patterns before, during, and after the pandemic is crucial for shaping post-pandemic actions. This study investigates the relationship between travel patterns across these phases, with a focus on the pandemic's mediating effects. Using structural equation modelling and data from 7,613 U.S. survey responses, we uncover noteworthy shifts in travel behaviour dynamics. During the pandemic, policy measures and sociodemographic factors significantly influenced travel. However, these effects may diminish as measures are lifted and vulnerabilities no longer correlate with sociodemographics. In contrast, past travel behaviour and attitudes, especially favouring alternative modes of transportation, are expected to have a more enduring impact in the post-pandemic era. This shift is likely to emerge ‘strengthened new normal,’ especially among those who reduced travel and embraced alternative modes. Future travel policies should anticipate and accommodate these evolving dynamics. Promoting diverse transportation modes beyond private vehicles will be crucial as travel behaviour undergoes transformation. Thus, policy formulation should consider a range of scenarios to address these changing preferences and behaviours.

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces.

The built environment's impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people's travel experience and travel mode choice. The streets' built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device's power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.

Research and Analysis on Policy Governance Based on Traffic Congestion in Beijing

This paper proposes three-game models to improve Beijing's traffic environment through the analysis method of game theory. First, the author examines the impact of improving drivers' moral character on alleviating traffic congestion. Second, this paper focus on the role of improving the public transportation environment in reducing road stress. Finally, this research examines the possibility of mitigating traffic congestion through a reward-punishment system. Comprehensive analysis of the results of these three game models, this paper conclude that a single policy of license restriction and traffic restriction is not enough to solve the traffic congestion problem in Beijing, and multiple policy measures need to be considered comprehensively. However, these policy measures also face some challenges and limitations, including time and education investment, people's travel needs and transportation network layout, and the design and implementation of the reward and punishment system. Therefore, future research and policy development need to further explore these issues and find more comprehensive and feasible policy solutions. Finally, this article recommends that the Beijing Municipal Government comprehensively consider the results of different game models and formulate comprehensive policy measures based on the actual situation, including improving road transportation facilities, encouraging green travel methods, and promoting intelligent transportation systems. Through these efforts, it is expected to improve Beijing's traffic environment, enhance citizens' travel experience, and achieve the goal of sustainable urban transportation.

Šoštanj v luči modernizacije življenja: higienizacija in elektrifikacija

Šoštanj kept up with other towns in Slovenia when it came to its modernization achievements. The first initiative for the modernization of larger towns was the wish to clean the streets for the sake of hygiene and the prevention of infectious and fatal diseases, especially cholera. Later, street lighting came into play. The construction of the public water supply and sewage system in Šoštanj greatly contributed to the modernization and the hygiene of living conditions as well as the image of the square and the town. The construction of the Celje–Velenje railway line in 1891 had a decisive influence on the development of industry. It also improved transport routes and people's travel habits. Today, Šoštanj is known for the production of electricity (TEŠ), but the first attempts to use electric current date back to the last decades of the 19th century. All the aforementioned influences of modernization had a decisive influence on the development of trade and industry in Šalek Valley.

What makes public transit demand management programmes successful? A systematic review of ex-post evidence

ABSTRACT Transit crowding results in negative experiences and mode change for transit riders and operational challenges for operators. The COVID-19 pandemic initiated an ongoing transformation of how, when, and where people travel, yet the challenge of balancing demand and supply in transportation remained topical. The pandemic has also exposed the traditional approach of infrastructure expansion for being too slow to respond to the challenges of crowding in a timely manner. As such, this paper provides a systematic literature review of the ex-post studies that evaluated the impact of transit demand management strategies. The paper synthesises the findings from 13 different programmes analysed in 20 studies. It is concluded that at least within the scope of the limited number of identified ex-post studies, the practice of alternative work schedules that allow employees greater freedom when to travel is the demand management approach that can bring the most significant crowding reduction. Once that flexibility is expanded, other strategies that appeal to riders’ preferences might have a larger effect as well. The findings of this review aim to encourage transit agencies to develop collaborations with large employers that can introduce alternative work schedules.

Which trust layer better counterbalances the risk impact on travel intentions in a crisis scenario?

The severe impact of the COVID-19 pandemic on the tourism industry has revived academic interest in evaluating the strategic role of trust in crises. As a force able to mitigate uncertainty and vulnerability, trust can influence people's travel decision-making process. Extant tourism crisis literature concentrates on individual trust levels in isolation, neglecting its multi-faceted nature. Therefore, a research gap emerges in identifying trust layers that most effectively enhance the intention to travel. In order to address this gap, this study adopts a multi-layered trust perspective rooted in the protection motivation theory (PMT). This study aims to analyze the effectiveness of multi-layer trust as a coping mechanism to enhance intention to travel in the cruise industry. This study uses survey data from 661 cruisers and applies structural equation modeling to test hypotheses empirically. Results highlight that trust in the company and interpersonal trust are the most effective antecedents of the intention to travel, effectively mitigating the perceived health risk. Conversely, trust in the vaccine and trust in the certification show no significant influence on the intention to travel. Therefore, in times of crisis, cruise lines should leverage trust in the company and interpersonal trust as strategic tools to counterbalance the perceived health risks and stimulate travel intentions.

Visualising active travel with Pakistani heritage families in Bradford, UK – Photovoice as a tool for change

In this paper we report insights on barriers to active travel among the Pakistani heritage community in Bradford UK, generated using the participatory Photovoice method. This is the largest minoritized ethnic group in Bradford, and though most segments of the UK population need to be more physically active, those of non-white ethnicities, and South Asian heritage in particular, are generally significantly less active than average. Insufficient physical activity (including active travel) contributes to a significant health burden for minoritized communities as well limiting progress with increasing active travel among these communities. Increasing active travel is known to contribute to both positive health outcomes and to key transport decarbonisation aims. To meet public health goals and Net Zero emissions targets by 2050, increasing walking and cycling for short journeys will be essential but it remains difficult to achieve. There is a lack of knowledge relating to how specific cultural groups perceive active travel as an option within their local areas. We sought to explore the use of Photovoice to understand whether this method has potential to enable minoritized communities to actively consider active travel, and to provide connections between these communities and their local authorities. We recruited eight Pakistani heritage families living in Bradford to do this. Their photos were used in semi-structured interviews, and the data collectively analysed. The analysis suggested key barriers to walking and cycling could be grouped into four main categories: personal factors, social factors and those related to the local and wider environment. We also found that people's travel choices in this study are influenced by cognitive biases and social norms that are quite connected to their cultural heritage and societal position as minoritized. There were also both generational and gendered effects that need to be accounted for in intervention design. Based on participants' reflection and feedback, this report concludes participatory techniques like Photovoice offer an opportunity to break the status quo for those who are struggling to make a change by raising their social consciousness and helping them to gather evidence of their concerns.

Exploring unobserved heterogeneity in ICT usage and travel pattern changes as the pandemic subsides: A quasi-longitudinal analysis in Florida

This paper presents a study that explored the behavioral heterogeneity in changes in people's ICT usage and travel patterns at the end of the pandemic. A quasi-longitudinal approach was employed to collect data from Florida residents, capturing their online durations and trip frequencies for various activities before the pandemic and at the end of 2021. Utilizing the latent class analysis (LCA) approach to identify subgroups based on the online activity durations and trip frequencies, four distinct classes were identified. A little more than one third (35%) of the respondents are resilient users who showed minimal changes in both online activity durations and trip frequencies. About 33% of respondents are trip minimizers who maintained similar online activity durations but reduced travel for non-mandatory activities. About 16% of the respondents are substitutive adapters who showed increased online activity durations combined with reduced travel for non-mandatory activities. Another 16% of the respondents are complementary users who demonstrated higher online activity durations as well as trip frequencies for non-mandatory activities. These four latent classes reflect the diverse ways in which people have adjusted their daily routines and activities. The findings offer a starting point for understanding the complexities of behavioral changes in virtual and physical mobility as we transition to the new normal.

Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic Information

Origin-destination (OD) flow contains population mobility information between every two regions in the city, which is of great value in urban planning and transportation management. Nevertheless, the collection of OD flow data is extremely difficult due to the hindrance of privacy issues and collection costs. Significant efforts have been made to generate OD flow based on urban regional features, e.g., demographics, land use, and so on, since spatial heterogeneity of urban function is the primary cause that drives people to move from one place to another. On the other hand, people travel through various routes between OD, which will have effects on urban traffic, e.g., road travel speed and time. These effects of OD flows reveal the fine-grained spatiotemporal patterns of population mobility. Few works have explored the effectiveness of incorporating urban traffic information into OD generation. To bridge this gap, we propose to generate real-world daily temporal OD flows enhanced by urban traffic information in this paper. Our model consists of two modules: Urban2OD and OD2Traffic . In the Urban2OD module, we devise a spatiotemporal graph neural network to model the complex dependencies between daily temporal OD flows and regional features. In the OD2Traffic module, we introduce an attention-based neural network to predict urban traffic based on OD flow from the Urban2OD module. Then, by utilizing gradient backpropagation, these two modules are able to enhance each other to generate high-quality OD flow data. Extensive experiments conducted on real-world datasets demonstrate the superiority of our proposed model over the state of the art.

Still prefer, but not that much: the premium of subway access on house prices during the COVID-19 pandemic period

ABSTRACT Rail access has traditionally been considered a factor that enhances the amenity value of urban areas, resulting in a price premium for properties located near these stations. However, the Covid-19 pandemic has exerted a profound influence on people's mobility patterns and travel behavior within urban areas. One of the most conspicuous changes is a substantial decrease in subway ridership. This study investigates whether the decline in public transit ridership has led to shifts in housing location preferences driven by proximity to rail access during the Covid-19 pandemic. This study focuses on assessing the impact of the pandemic on the premium associated with rail access in relation to house prices. Using approximately 180,000 apartment transaction records from Seoul, South Korea, the empirical findings, based on a difference-in-differences approach, reveal an immediate and significant depreciation effect resulting from this exogenous shock during the 25-month pandemic period. The premium decreases significantly within a 1.2-kilometer radius from a subway station, and this reduction exhibits a distance-decay pattern within the designated area. Highlights Traditional amenity value of rail access challenged by Covid-19. Profound decline in subway ridership during pandemic. Investigation into pandemic's impact on housing location preferences. Assessment of pandemic's effect on rail access premium. Immediate and significant depreciation effect within 1.2 km from subway stations.

Remote Machine Management via Raspberry Pi and the Internet of Things

Today, automation is really important in our lives and in industries. Many big and small companies need to automate their processes. This project is about managing systems using a special type of web server called Embedded Web-server (EWS). With so many electronic devices at home, work, or anywhere else, it's becoming more important to control them remotely. When people travel for work or other reasons, they still want to be able to control their appliances. So, automation is crucial. This project aims to let users control machines using a web server, which is secure and easy to use from anywhere. The control webpage is made using a programming language called PHP and requires a username and password to access. Through this webpage, users can turn switches on and off over the internet. These switches are connected to a tiny computer called a Raspberry Pi, which is connected to various home appliances. The Raspberry Pi uses its pins to communicate with the webpage. It controls the flow of electricity to the appliances through special switches called relay switches. This means you can control your appliances from far away using the Raspberry Pi and the internet. Keywords: Electromagnetic relay switches, Raspberry pi, Sensors, Weaved cloud services.

Exploring the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka

The COVID-19 outbreak created immense disruptions in our daily lives. Travel is one of the major areas severely impacted by the pandemic. Bangladesh experienced two waves of COVID-19 between March 2020 and July 2021. Although several studies focused on significant changes in the travel behavior of urban road users before and during the pandemic, hardly any research examined the differences between the two waves. Hence, it was important to study the differences in travel behavior between the two waves of the COVID-19 pandemic to understand how the effects of travel restrictions, health concerns, and the severity of the pandemic on people's travel decisions change over time. This study intends to investigate the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka and the factors that influence these changes. Data were collected through online questionnaire surveys using the Google form. Voluntary response and convenience sampling techniques were used to collect responses from 447 people in Dhaka. Descriptive statistics and non-parametric tests were conducted to analyze the data. In addition, two multinomial logistic models were developed to identify the factors behind the changes in travel behavior. This study reveals that work and shopping trips increased, whereas work-from-home and online shopping somewhat decreased during the second pandemic wave compared to the first pandemic wave. Most non-car owners who reduced their use of public transportation during the first pandemic increased their use during the second wave. There was no significant increase in the use of active transport modes for work and shopping trips in Dhaka. The perceived risk of COVID-19 infection was relatively lower during the second wave, influencing the respondents to travel more frequently for work and shopping purposes. Ensuring more adaptive public transportation, flexibility to work from home, and creating supportive infrastructures for active transport modes might help to provide a safe, affordable, and efficient transportation system for all during the future waves of the pandemic and other unprecedented events.