In this study, we explored whether and how area-wide air pollution affected individuals' activity participation and travel behaviors, and how these effects differed by neighborhood context. Using multi-day travel survey data provided by 403 adults from 230 households in a small urban area in northern Utah, US, we analyzed a series of 20 activity and travel outcomes. We investigated the associations of three different metrics of (measured and perceived) air quality with these outcomes, separately for residents of urban and suburban/rural neighborhoods, and controlled for personal and household characteristics. Our models found some measurable changes in activity and travel patterns on days with poor air quality. In urban areas, people engaged in more mandatory (work/school) activities, whereas there was no discernible change in suburban/rural areas. The total travel time for urban residents increased, driven by increases in trip-making and travel time by public modes (bus) and increases in travel time by private modes (car). On the other hand, suburban/rural residents traveled shorter total distances (mostly through lower vehicle miles traveled), and there was a notable uptick in th
People's attitudes towards personal data sharing have been extensively researched, however, limited research studied their evolving nature in across different stages of a leisure trip. This paper addresses this gap by exploring how leisure travellers' attitudes towards sharing personal data change before, during and after travel. Analysing data from an online survey with 318 participants, we found that participants' privacy attitudes towards sharing different personal data vary based on sharing purposes and travel stages. Interestingly, participants exhibited a more relaxed attitude towards sharing commonly sensitive personal data (e.g., name, gender) compared to other types of personal data. This is likely because sharing such data for travel bookings has become essential and widely accepted among travellers when using booking sites, which is in line with previous work stating that information easily obtainable is typically not seen as highly confidential. Moreover, despite participants' self-reported frequent use of social media platforms, content sharing is minimal on TikTok, YouTube, Snapchat, Pinterest, and Twitter. Conversely, Facebook and Instagram were more common for trave
As global tourism expands and artificial intelligence technology advances, intelligent travel planning services have emerged as a significant research focus. Within dynamic real-world travel scenarios with multi-dimensional constraints, services that support users in automatically creating practical and customized travel itineraries must address three key objectives: Rationality, Comprehensiveness, and Personalization. However, existing systems with rule-based combinations or LLM-based planning methods struggle to fully satisfy these criteria. To overcome the challenges, we introduce TravelAgent, a travel planning system powered by large language models (LLMs) designed to provide reasonable, comprehensive, and personalized travel itineraries grounded in dynamic scenarios. TravelAgent comprises four modules: Tool-usage, Recommendation, Planning, and Memory Module. We evaluate TravelAgent's performance with human and simulated users, demonstrating its overall effectiveness in three criteria and confirming the accuracy of personalized recommendations.
The Traveling Thief Problem (TTP) is a multi-component optimization problem that captures the interplay between routing and packing decisions by combining the classical Traveling Salesperson Problem (TSP) and the Knapsack Problem (KP). The TTP has gained significant attention in the evolutionary computation literature and a wide range of approaches have been developed over the last 10 years. Judging the performance of these algorithms in particular in terms of how close the get to optimal solutions is a very challenging task as effective exact methods are not available due to the highly challenging traveling component. In this paper, we study the tour-optimization component of TTP under a fixed packing plan. We formulate this task as a weighted variant of the TSP, where travel costs depend on the cumulative weight of collected items, and investigate how different distance metrics and cost functions affect computational complexity. We present an $(O(n^2))$-time dynamic programming algorithm for the path metric with general cost functions, prove that the problem is NP-hard even on a star metric, and develop constant-factor approximation algorithms for star metrics. Finally, we also d
The continuous evolution and enhanced reasoning capabilities of large language models (LLMs) have elevated their role in complex tasks, notably in travel planning, where demand for personalized, high-quality itineraries is rising. However, current benchmarks often rely on unrealistic simulated data, failing to reflect the differences between LLM-generated and real-world itineraries. Existing evaluation metrics, which primarily emphasize constraints, fall short of providing a comprehensive assessment of the overall quality of travel plans. To address these limitations, we introduce TripTailor, a benchmark designed specifically for personalized travel planning in real-world scenarios. This dataset features an extensive collection of over 500,000 real-world points of interest (POIs) and nearly 4,000 diverse travel itineraries, complete with detailed information, providing a more authentic evaluation framework. Experiments show that fewer than 10\% of the itineraries generated by the latest state-of-the-art LLMs achieve human-level performance. Moreover, we identify several critical challenges in travel planning, including the feasibility, rationality, and personalized customization of
In this paper, we present formulations and an exact method to solve the Time Dependent Traveling Salesman Problem with Time Window (TD-TSPTW) under a generic travel cost function where waiting is allowed. A particular case in which the travel cost is a non-decreasing function has been addressed recently. With that assumption, because of both the First-In-First-Out property of the travel time function and the non-decreasing property of the travel cost function, we can ignore the possibility of waiting. However, for generic travel cost functions, waiting after visiting some locations can be part of optimal solutions. To handle the general case, we introduce new lower-bound formulations that allow us to ensure the existence of optimal solutions. We adapt the existing algorithm for TD-TSPTW with non-decreasing travel costs to solve the TD-TSPTW with generic travel costs. In the experiment, we evaluate the strength of the proposed lower bound formulations and algorithm by applying them to solve the TD-TSPTW with the total travel time objective. The results indicate that the proposed algorithm is competitive with and even outperforms the state-of-art solver in various benchmark instances
In today's digital era, the use of Social Networks (SNs) and Location-Based SNs (LBSNs) has become integral for travelers seeking Points of Interest (POI) and sharing travel experiences. This trend is supported by the fact that a significant majority of American travelers utilize SNs during their trips. However, the abundance of information available on these platforms presents a challenge in identifying the best options. To address this issue, Recommender Systems (RS) are commonly employed to suggest POIs based on user history, with the integration of contextual information enhancing the quality of recommendations. Notably, incorporating user travel purpose, which is often overlooked but holds potential in characterizing travelers' behavior, can lead to more tailored recommendations. In this study, we propose a model to predict whether a trip is leisure or work-related, utilizing state-of-the-art Automatic Text Classification (ATC) models such as BERT, RoBERTa, and BART to enhance the understanding of user travel purposes and improve recommendation accuracy in specific travel scenarios.
Wavefield travel time tomography is used for a variety of purposes in acoustics, geophysics and non-destructive testing. Since the problem is non-linear, assessing uncertainty in the results requires many forward evaluations. It is therefore important that the forward evaluation of travel times and ray paths is efficient, which is challenging in anisotropic media. Given a computed travel time field, ray tracing can be performed to obtain the fastest ray path from any point in the medium to the source of the travel time field. These rays can then be used to speed up gradient based inversion methods. We present a forward modeller for calculating travel time fields by localised estimation of wavefronts, and a novel approach to ray tracing through travel time fields. These methods have been tested in a complex anisotropic weld and give travel times comparable to those obtained using finite element modelling while being computationally cheaper.
Extensive empirical studies show that the long distribution tail of travel time and the corresponding unexpected delay can have much more serious consequences than expected or moderate delay. However, the unexpected delay due to the distribution tail of travel time has received limited attention in recent studies of the valuation of travel time variability. As a complement to current valuation research, this paper proposes the concept of the value of travel time distribution tail, which quantifies the value that travelers place on reducing the unexpected delay for hedging against travel time variability. Methodologically, we define the summation of all unexpected delays as the unreliability area to quantify travel time distribution tail and show that it is a key element of two well-defined measures accounting for unreliable aspects of travel time. We then formally derive the value of distribution tail, show that it is distinct from the more established value of reliability (VOR), and combine it and the VOR in an overall value of travel time variability (VOV). We prove theoretically that the VOV exhibits diminishing marginal benefit in terms of the traveler's punctuality requirement
In times of crisis, international travel becomes tenuous and anxiety provoking. The crisis informatics and Human-Computer Interaction (HCI) community has paid increasing attention to the use of Information and Communication Technologies (ICTs) in various crisis settings. However, little is known about the travelers' actual experiences in whole trips in crises. In this paper, we bridge the gap by presenting a study on Chinese travelers' encounters in their international journeys to the US during a multifacet crisis and their use of ICTs to overcome difficulties in the journeys. We interviewed 22 Chinese travelers who had successfully come to the US during the crisis. The findings showed how travelers improvised to reconnect the broken international travel infrastructure. We also discuss the findings with the literature on infrastructure, and crisis informatics, and provide design implications for travel authorities and agencies.
In research on the value of past time, the value of travel time and the value of saving travel time are two different concepts that have been vaguely distinguished over an extended period of time. This paper applies the theory of perspectives to discuss differences in the value of travel time and savings in travel time between different respondent groups and under different models. To this end, this paper designs an RP-SP questionnaire for urban travel behaviour, and collects data on the travel preferences of 409 Guangzhou residents. By introducing potential profiling to capture the effects of heterogeneity between classes, the respondent group is divided into three categories according to its individual socio-economic characteristics such as age, income, educational attainment, etc. After that, the utility function of travelers is established, and the MNL model, MIXL model, S-MNL model, G-MNL model, LCL model and MM-MNL model are selected for the total sample and three types of groups to estimate the time value. This paper emphasizes the definitions of VTT and VTTS, and presents a method for estimating VTT and VTTS using LPA to identify traveler heterogeneity.
Using the 2017 National Household Travel Survey (NHTS), this study analyzes America's urban travel trends compared with earlier nationwide travel surveys, and examines the variations in travel behaviors among a range of socioeconomic groups. The most noticeable trend for the 2017 NHTS is that although private automobiles continue to be the dominant travel mode in American cities, the share of car trips has slightly and steadily decreased since its peak in 2001. In contrast, the share of transit, non-motorized, and taxicab (including ride-hailing) trips has steadily increased. Besides this overall trend, there are important variations in travel behaviors across income, home ownership, ethnicity, gender, age, and life-cycle stages. Although the trends in transit development, shared mobility, e-commerce, and lifestyle changes offer optimism about American cities becoming more multimodal, policymakers should consider these differences in socioeconomic factors and try to provide more equitable access to sustainable mobility across different socioeconomic groups.
While the individual travel implicates the trace of individual mobility decision, group travels signify the possible social relationship behind these traces. Different from online social network, spatial interaction between individuals is a critical yet unknown dimension to understand the collective behaviors in a city. In this paper, based on over 127 million trips in Beijing metro network, we develop a method to distinguish the group travel of friends from the encounter travel of familiar strangers. We find travels of friends are among the most predictable groups. These identified friendships are interwoven and form a friendship network, with structural properties different from encounter network. The topological role of individuals in this network is found strongly correlated with her travel predictability. The overall time savings of about 34190 minutes after redistribution of inefficient group traveler with flexible travel purposes shows the potential of designing specific traffic fares for group travel. Our identification and understanding of group travel may help to develop and organize new traffic mode in the future smart transportation.
Nowadays, artificial neural networks are widely used for users' online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users' intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users' intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users' intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users' actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destinati
Travel behavior prediction is a core problem in transportation demand management and is traditionally addressed using numerical models calibrated on observed data. With recent advances in large language models (LLMs), new opportunities have emerged to model human decision-making through natural language reasoning. This study explores the use of LLMs for travel behavior prediction through two complementary frameworks. The first framework employs a zero-shot prompting strategy, where the prediction task, traveler attributes, and relevant domain knowledge are described in text, enabling the LLM to directly generate predictions without task-specific training data. The second framework uses LLM-generated text embeddings as high-level representations of travel scenarios, which are then combined with conventional supervised learning models to support prediction in small-sample settings. Empirical results show that both approaches achieve performance comparable to, and in some cases competitive with, classical models such as multinomial logit, random forest, and neural networks. These findings suggest that LLMs offer a flexible and data-efficient alternative for travel behavior prediction.
A travel groupoid is an algebraic system satisfying two suitable conditions, which has a relation to graphs. In this article, we characterize travel groupoids on finite complete multipartite graphs, and we give the numbers of travel groupoids on the complete multipartite graphs.
The rapid growth of air and space travel in recent years has resulted in an increased demand for legal regulation in the aviation and aerospace fields. This paper provides an overview of air and space law, including the topics of aircraft accident investigations, air traffic control, international borders and law, and the regulation of space activities. With the increasing complexity of air and space travel, it is important to understand the legal implications of these activities. This paper examines the various legal aspects of air and space law, including the roles of national governments, international organizations, and private entities. It also provides an overview of the legal frameworks that govern these activities and the implications of international law. Finally, it considers the potential for future developments in the field of air and space law. This paper provides a comprehensive overview of the legal aspects of air and space travel and their implications for international and domestic travel, as well as for international business and other activities in the air and space domains.
We present a toy metric of spacetime travel from topological change. A bubble-like baby universe is detached and re-attached from our universe. Depending on where the bubble is re-attached, matter may travel superluminally or backwards-in-time through the bubble. Quasiregular singularities are formed at the detachment and re-attachment spacetime points. The spacetime is traversable and not covered by any horizons. Exotic matter violating energy conditions is required to realize such spacetimes.
We investigate the algorithmic problem of uniformly dispersing a swarm of robots in an unknown, gridlike environment. In this setting, our goal is to study the relationships between performance metrics and robot capabilities. We introduce a formal model comparing dispersion algorithms based on makespan, traveled distance, energy consumption, sensing, communication, and memory. Using this framework, we classify uniform dispersion algorithms according to their capability requirements and performance. We prove that while makespan and travel can be minimized in all environments, energy cannot, if the swarm's sensing range is bounded. In contrast, we show that energy can be minimized by ``ant-like'' robots in synchronous settings and asymptotically minimized in asynchronous settings, provided the environment is topologically simply connected, by using our ``Find-Corner Depth-First Search'' (FCDFS) algorithm. Our theoretical and experimental results show that FCDFS significantly outperforms known algorithms. Our findings reveal key limitations in designing swarm robotics systems for unknown environments, emphasizing the role of topology in energy-efficient dispersion.
Wave travel-time shifts in the vicinity of sunspots are typically interpreted as arising predominantly from magnetic fields, flows, and local changes in sound speed. We show here that the suppression of granulation related wave sources in a sunspot can also contribute significantly to these travel-time shifts, and in some cases, an asymmetry between in and outgoing wave travel times. The tight connection between the physical interpretation of travel times and source-distribution homogeneity is confirmed. Statistically significant travel-time shifts are recovered upon numerically simulating wave propagation in the presence of a localized decrease in source strength. We also demonstrate that these time shifts are relatively sensitive to the modal damping rates; thus we are only able to place bounds on the magnitude of this effect. We see a systematic reduction of 10-15 seconds in $p$-mode mean travel times at short distances ($\sim 6.2$ Mm) that could be misinterpreted as arising from a shallow (thickness of 1.5 Mm) increase ($\sim$ 4%) in the sound speed. At larger travel distances ($\sim 24$ Mm) a 6-13 s difference between the ingoing and outgoing wave travel times is observed; thi