Travel Patterns Research Articles

Travel Demand Estimation for a Special Event using Pervasive Data: A Case Study of G20 Summit

Due to globalization and rapid development, the demand for travel and congestion are increasing, stressing the road networks. Furthermore, road networks are subjected to planned and unplanned disruptions, and these disruptions affect the road network performance by either damaging its components, fluctuating the travel demand, or doing both at the same time. Thus, assessing the change in travel demand and travel patterns during such disruptions is essential. Conventionally, travel demand has been estimated using a household travel survey (HTS). However, the HTS is not feasible for disrupted conditions because of its large sample size requirement and time-consuming methodology. However, with the advent of crowdsourced data, new methodologies have been proposed for estimating the travel demand, but most of these studies have focused on estimating demand for a typical day and not disrupted scenarios. Moreover, data sources utilized till now have low sample sizes or are unavailable for developing countries. To bridge this gap, the current study utilizes an automated planning tool called Rapidex to estimate the travel demand and pattern change for a planned disruption, i.e., the 18th G20 Summit, Delhi, using crowdsourced travel time data from TomTom API. Upon comparing the results of travel demand and travel patterns of typical days with G20 days, it was observed that travel demand was lower during G20. Moreover, it was observed that the zones where railway stations, interstate bus terminals, and G20 activities were focused had higher generation and attraction share than typical days. Other zones within the regulated area had lower generation and attraction rates, which can be because of travel restrictions imposed in these zones.

Assessing the Long-Term Impact of COVID-19 on Travel Behavior: The United Arab Emirates Perspective

The unprecedented COVID-19 pandemic has caused significant short-term travel disruptions and profound changes in daily travel routines. Yet, the long-term impact on travel behavior and mobility remains unclear. The present study examines the enduring effects of COVID-19 on travel patterns in the United Arab Emirates (UAE) through a comprehensive survey assessing long-term impacts and participants' travel behavior changes during different stages of the pandemic. The survey covered travel modes, frequency, duration, and distance, unveiling a pandemic-driven shift towards increased personal vehicle usage due to public transportation concerns. Nevertheless, as the pandemic gradually recedes, individuals are slowly reverting to their pre-pandemic travel habits. Moreover, the results indicate that travel patterns vary depending on the distance traveled. Short-distance travel has largely returned to pre-pandemic levels, while medium-distance travel continues to lag. Intriguingly, post-pandemic levels of long-distance travel have exceeded the pre-pandemic figures, indicating a potential alteration in travel behaviors. These observed changes were substantiated by a paired t-test, demonstrating a significant difference across nearly all periods examined. In summary, this research sheds light on the long-term consequences of COVID-19 on travel behavior and mobility in the UAE. The survey data uncovers a noteworthy transformation in travel patterns during the pandemic, with subsequent gradual reversion to pre-pandemic norms. Notably, the findings suggest potential shifts in travel preferences across different distances, with long-distance travel surpassing pre-pandemic levels. These insights contribute to a deeper understanding of the lasting impacts of COVID-19 on transportation and travel choices.

New Evidence on Nonresponse in Household Travel Surveys

Decline in response rates has increased awareness in the research community for the necessity of more detailed nonresponse analyses. This paper presents results of a follow-up study to the cross-sectional household travel survey, “Mobility in Cities – SrV 2018”. This nonresponse survey covered socio-demographic, travel-related, and trip-related information of 3,108 individuals distributed across the 112 municipalities of the main survey. Response-propensity models were developed which examine drop-out mechanisms and participation probabilities. Education was identified as an explanatory variable; however, there are no indications that this played a factor in weighting for improving the representativeness of reporting-day-specific travel patterns.

Travel pattern analysis and freight trip generation modeling of textile commodities. Case study: Thamrin City Shopping Center, Jakarta

One of the sectors supporting the country’s economic development is the commercial sector. With the increasing population, the demand for consumer goods will increase, impacting trip generation. Thus, it is necessary to conduct a study to predict the trip generation of freight transport in shopping centers, especially in DKI Jakarta, with textile stores as trip generators. This research aims to determine the travel pattern of freight transport and freight trip generation models produced and attracted at the Thamrin City shopping center and to analyze the factors influencing freight trip generation. The research was conducted using a quantitative research method with a cross-sectional. Data collection used a questionnaire given to 105 merchants. Data analysis used linear regression methods with the number of freight trip production and attraction as the dependent variable. The independent variables used are the number of employees and gross floor area. The study results show that the delivery of goods using expedition services is sent to the distribution center before being sent to the destination. While shipping goods using private vehicles, sent directly to the destination location. The gross floor area significantly influences the trips generation. Transportation planners can use this research to estimate the number of trip generation in shopping center areas in the future.

Studying Travel Networks Using Establishment Covisit Networks in Online Review Data

Whenever someone posts an online review of a restaurant, museum, or barbershop, they also leave a trace of where they traveled. The author visualizes travel patterns in 11 North American metropolitan areas using geolocated review data. The data are based on approximately 7 million online Yelp.com reviews posted by 2 million reviewers between 2005 and 2020. First, the author demonstrates how individual travel patterns can be mapped using the review data and discusses the potential applications of such individual-level data. The author then turns to aggregate-level maps, creating establishment covisit networks in which two establishments are linked if multiple reviewers visit both. Maps of establishment covisits reveal various intriguing patterns related to consumption and geography, such as the connections between neighborhoods and the centralization and segregation within a metropolitan area. Establishment covisit maps can also inform researchers about the diffusion of ideas and practices, trends in crime, and gentrification.

What are the impacts of teleworking on activity-travel behaviour? A text mining study

This study aims to understand how teleworking reorganises activity-travel behaviour. While the link between teleworking and mobility was widely studied, most of the research focused on commuting practices by comparing teleworkers to non-teleworkers. However, the coping strategies of teleworkers and their household members (e.g. reorganisation of activities according to the days of the week, and associated travel behaviour) remain insufficiently studied. With a systemic approach to interpersonal relationships, and their activities in the different spheres of their lives (i.e. professional, family, social and personal), the Reinert method we used (top-down hierarchical clustering) confirms and develops our knowledge about the rebound effects of teleworking. Our findings confirm that the work/telework sphere has a structuring role in the daily activity schedule and is associated with other travel motives (e.g. shopping, leisure or other personal activities). Teleworking appears as an adjustment variable, as it offers more freedom, and facilitates the organisation and reconciliation of activities in the different spheres of daily life. Thus, teleworking transforms individuals’ choices, habits and patterns of activities and travel within the household.

Research on System Development of Spatial Clustering in Tourism Recommendation

Abstract This paper investigates the application of exploring spatial clustering techniques in travel recommendation systems to improve recommendation accuracy and user satisfaction. A system that can provide personalized travel information has been designed by analyzing the spatial data and behavioral patterns of travelers. With this approach, the travel experience can be optimized by considering geographic location, user preferences, and tourist attraction characteristics. User data and attraction characteristics are analyzed in this paper to obtain accurate travel recommendations. Combining the traveler’s interest matrix and the attraction area heat algorithm is used to optimize the recommendation process. This recommender system reduces the root mean square error (RMSE) and mean absolute error (MAE) by 0.487% and 21-60%, respectively, compared with the traditional algorithm in the travel recommendation error analysis, and improves the accuracy rate to 96.33%, with a significant increase in the recall rate. The spatial clustering cluster analysis demonstrates that the chosen number of clusters can effectively enhance the clustering quality and maximize the recommendation results. By effectively utilizing spatial data of users and attractions, the system can provide personalized travel recommendations and improve user experience with high accuracy and recall, demonstrating better recommendation performance than traditional methods.

Characterizing mobility patterns and malaria risk factors in semi-nomadic populations of Northern Kenya.

While many studies have characterized mobility patterns and disease dynamics of settled populations, few have focused on more mobile populations. Highly mobile groups are often at higher disease risk due to their regular movement that may increase the variability of their environments, reduce their access to health care, and limit the number of intervention strategies suitable for their lifestyles. Quantifying the movements and their associated disease risks will be key to developing interventions more suitable for mobile populations. Turkana, Kenya is an ideal setting to characterize these relationships. While the vast, semi-arid county has a large mobile population (>60%) and was recently shown to have endemic malaria, the relationship between mobility and malaria risk in this region has not yet been defined. Here, we worked with 250 semi-nomadic households from four communities in Central Turkana to 1) characterize mobility patterns of travelers and 2) test the hypothesis that semi-nomadic individuals are at greater risk of malaria exposure when migrating with their herds than when staying at their semi-permanent settlements. Participants provided medical and travel histories, demographics, and a dried blood spot for malaria testing before and after the travel period. Further, a subset of travelers was given GPS loggers to document their routes. Four travel patterns emerged from the logger data, Long Term, Transient, Day trip, and Static, with only Long Term and Transient trips being associated with malaria cases detected in individuals who carried GPS devices. After completing their trips, travelers had a higher prevalence of malaria than those who remained at the household (9.2% vs 4.4%), regardless of gender and age. These findings highlight the need to develop intervention strategies amenable to mobile lifestyles that can ultimately help prevent the transmission of malaria.

Dynamic origin-destination matrix estimation for networks operating under free-flow conditions using macroscopic flow dynamics

The origin-destination (OD) matrix is a crucial requirement for efficient transportation planning, however its estimation still remains one of the most challenging tasks in transportation. This paper proposes a novel methodology for the estimation of dynamic OD matrices. The proposed methodology integrates disaggregated measurements from stationary sensors, e.g. loop detectors, with a macroscopic model that associates OD demands with Traffic counts on specific network links. We investigate networks that operate under free-flow conditions, hence the dynamic OD matrix estimation problem is formulated as a linear mathematical program. Finally, we reformulate the problem to obtain a computationally cheap quadratic programming problem that allows real-time estimation of OD matrices within the time-window under study. We demonstrate the effectiveness of our methodology through a literature network, showcasing its ability to accurately capture travel patterns in a computationally efficient manner, allowing informed decision-making processes in transportation planning and management.

Social aspects of long-distance travel - a study of two survey designs

Major parts of transportation research focus on everyday travel, including qualitative, quantitative and mixed-methods approaches. However, to find strategies to reduce transport-related emissions on a global scale, studies on long-distance travel require greater consideration. This paper presents and compares two independent mixed-methods research approaches exploring social aspects of long-distance travel. While one study is qualitative dominant and focuses on relational aspects of travel decisions, the other is quantitative dominant and originates from the individuals’ needs. Both studies highlight motivational factors as important stimuli for long-distance travel patterns and that part of long-distance travel is due to social commitment.

An Information Fusion Technique for Prognosticating Future Air Passenger Trends

The aviation industry is constantly changing and to keep up with the trends of air passengers we need predictive models. In this paper, we explore the use of Information Fusion methodologies and classical time series techniques to forecast how many passengers will be traveling by air. Predicting passenger demands is a task, due to various factors that influence travel patterns. The existing models often struggle to capture the dynamics in this field so it's crucial to develop accurate forecasting methods. By leveraging information fusion techniques like smoothing and Autoregressive Integrated Moving Average (ARIMA) our research creates models based on historical data of air passenger volumes. These techniques combine machine learning algorithms and time series analysis to identify dependencies and patterns in the dataset. Through evaluations and comparative analyses, our proposed models demonstrate promising capabilities in forecasting future air passenger volumes. Proof-of-concept experiments based on 5-fold cross-validation demonstrate the efficacy of the proposed approach in capturing underlying trends and seasonality within the dataset.

Understanding the Spatial Heterogeneity Impact of Determinants on Ridership of Urban Rail Transit Across Different Passenger Groups

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life‐oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station‐level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station‐level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian‐ or bicycle‐unfriendly areas. Notably, for tourist and life‐oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment.

Can we focus long-distance travel survey sampling? Analyzing travel behavior trends between and within demographic groups

This work determines if there are statistically significant differences long-distance travel behaviors between and within different demographic groups, defined by income, age, and presence of children. This work considers data from three long-distance travel surveys, using ANOVA and Theil Index analyses. Results indicate long-distance sampling frames can be dramatically reduced based on simple combinations of household demographics. In most cases, only one or two demographics were necessary to statistically differentiate between the long-distance travel patterns of different groups. Therefore, practitioners could use these results to create more frequent and focused long-distance travel survey sampling frames reducing survey administrative costs and respondent burden.

Determination of Urban Traveling Patterns with High Spatial and Temporal Resolution by Using GPS Cell Phone Data

Determination of Urban Traveling Patterns with High Spatial and Temporal Resolution by Using GPS Cell Phone Data

A multi-objective site selection of electric vehicle charging station based on NSGA-II

The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

A Spatio-Temporal Perspective on Commercial Vehicle Travel Patterns in Urban Environments

A Spatio-Temporal Perspective on Commercial Vehicle Travel Patterns in Urban Environments

Mobile data for studying public space, and trips in Mexico City: a study case of six suburban zones

Abstract This study uses anonymized GPS traces to explore travel patterns within six suburban zones and a central area in Mexico City. The descriptive analysis presented in this paper profiles trips by distance and investigates their distribution within each zone. It examines the prevalence of local trips, walkability, and the availability and spread of entertainment sites within 15-min isochrones accessible by foot, bicycle, transit, and private vehicle. Notably, the central zone boasts diverse entertainment offerings, commendable walkability, and a substantial proportion of short and long trips. It is found that GPS traces are within their home. However, the share of long trips for the inhabitants of central zones is considerably more significant than that for the suburbs. The study highlights suburban zones that could benefit from governmental intervention to enhance transportation and pedestrian conditions. Additionally, it identifies other suburban zones that resemble the central areas in terms of walkability, trip distribution by distances, and the accessibility of entertainment places.

The Impact of Online Transportation Services on Indonesian Urban Non-Working Trip Volume and Distribution Pattern: A Case Study in Bandung City

The development of digital technology has made it much easier for most people to access online communication through smartphones. Such a phenomenon has encouraged operators to develop an online transportation service system, which has also been growing in Indonesian urban areas. The urban community accepts the new alternative of the application-based transportation system since it offers a much more efficient way to reach transportation services. Moreover, the online transportation system also offers a much wider range of destination locations. It is believed that the creation of an application-based transportation service system will affect metropolitan areas' social and economic conditions and how urban communities’ travel. The development of this application-based transportation service would change the configuration of urban non-working trip distribution patterns, which should be understood as inputs for the urban transport development plan. This research aims to understand the impact of online transportation services on non-working trip distribution patterns in Indonesian urban areas, with Bandung City as the case study. The method used in this research is statistical descriptive, using respondents' responses to map the influenced trip generation and distribution patterns. The results show that online transportation has enabled the community to travel more easily and reach wider areas. Such changes have formed a new travel pattern that extends the destination locations to a wider peri-urban area. In the future, the government should anticipate urban fringe area land use growth, changing the region into more "urbanized-built-up" areas.

DEPLATFORMING THE SMART CITY: GIVING RESIDENTS CONTROL OVER THEIR PERSONAL DATA

Smart city platforms–encompassing mobile apps, cameras, sensors, algorithms, and predictive analytics—function as surveillance tools. Specifically, these Internet-connected devices and services generate troves of data on residents, including real-time geolocation, energy consumption habits, travel patterns, mobile device identifiers, Internet browsing history, phone contacts, credit card numbers, and much more. The proposed project is focused on the City of Long Beach’s vision to use data in ethical ways that avoid reinforcing existing racial biases and discriminatory decision-making. When fully implemented, this digital rights platform will operationalize both privacy and racial equity as priorities for all deployments of smart city technology. First, the platform will feature text and the open-source iconography that visually conveys how the City of Long Beach uses specific technologies, what data the devices collect and how the City utilizes that data. We plan to strategically deploy these information points across Long Beach, physically adjacent to or digitally embedded within civic technologies, e.g., sensors, cameras, small cells, mobile payment kiosks, and a 311 app. The platform will include a feedback application consisting of access (via QR code or hyperlink) to an online dashboard where users may learn additional details, update data collection preferences, and share comments/concerns with local government officials. The ultimate goal is to develop a backend solution that enables residents to opt-out of data collection. The platform will provide residents with a clear understanding of how local government applies predictive and diagnostic analytics to personal data, and will also empower community members by granting them agency.

Revealing the Impacts of the Pandemic on Travel Behavior by Examining Pre- and Post-COVID-19 Surveys

Recently, the topic of travel behavior and social media usage has been widely discussed. The current study specifically focuses on how specific factors, such as the sociodemographic variables, the number of friends, the social media usage, and the ICT usage, influence their travel patterns based on survey results conducted in pre- and post-COVID-19 times. The effect of the COVID-19 pandemic is taken into consideration to better understand the impact of restrictions on travel attitudes. Statistical analysis is carried out to investigate the survey data. The results show that the pandemic has made a huge impact on general travel behavior, especially in terms of transport mode choice shifting towards individual modes, such as car and walking. The location choice of the participants has a significant connection to the available transport mode and the price range of the place, together with the retrieved information from the ICT devices. Based on the results, it can be seen that the pandemic has deepened the number of close friendships, but younger people do not tend to choose trendy places anymore. In addition, the results show that there is no direct connection between the number of friends and the number of meetings, and the daily online meetings have not replaced all personal meetings.