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Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user's data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user's age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest.

Mobility problems in large cities of Ukraine and Eastern Europe are complicated by the fact that the increase of private transport volume significantly exceeds street and road network`s capacity. This is most noticeable during peak periods in terms of daylight hours and throughout the year. From the point of sustainable mobility view, this negative phenomenon significantly affects urban public transport, which does not have separate dedicated traffic lines. This article analyzes the issue regarding the deterioration of the transport situation in large cities. The reason for this is the increase in traffic on main streets during the day peaks, as well as the presence of seasonal traffic factors. If the issue of the occurrence and traffic jams duration and the increase in the correspondence time of private transport is sufficiently studied, then the problems of changing the schedules of public transport and taking into account the increase in the trip duration depending on the time of year need to be clarified. The routes of public transport, which do not have a separate infrastructure and move in the general flow together with private cars, were chosen for the study. According to the results of remote monitoring of public transport, a change in the trip duration and time lost due to the boarding and disembarking of passengers on similar trolleybus routes in different seasons was established. Based on the obtained data, a matrix of trip duration unevenness coefficients for public transport routes was formed, and a measure of the seasonality effect on these indicators was established. The obtained results make it possible to quantitatively determine the influence of the season and time of the day on the change in the trip duration, which can be applied in further studies using simulation tools and for practical use in drawing up seasonal traffic schedules. The results of the research complement the currently relevant scientific works, which concern the problems of seasonal mobility, as well as the influence of the social infrastructure objects functioning (schools, kindergartens, and other educational institutions) of cities on the peak load of the street and road network, which extends the duration of traffic not only for private but also public transport.

BackgroundQuantification of movement intensity and energy utilization, together with frequency of trips, duration, distance, step counts and cadence, is essential for interpreting the character of habitual walking for transport, and its potential support of health. The purpose of the study is to illuminate this with valid methods and novel perspectives, and to thereby provide a new basis for characterizing and interpreting walking in relation to health outcomes.MethodsHabitual middle-aged commuting pedestrians (males = 10, females = 10) were investigated in the laboratory at rest and with maximal treadmill and cycle ergometer tests. Thereafter, levels of oxygen uptake, energy expenditure, ventilation, heart rate, blood lactate, rated perceived exertion, cadence, number of steps, duration, distance, and speed were recorded during the normal walking commute of each participant in Greater Stockholm, Sweden. The number of commutes per week over the year was self-reported.ResultsWalking in the field demanded about 30% more energy per km compared to level treadmill walking. For both sexes, the walking intensity in field was about 46% of maximal oxygen uptake, and energy expenditure amounted to 0.96 kcal · kg−1 · km−1. The MET values (males: 6.2; females: 6.5) mirrored similar levels of walking speed (males: 5.7; females: 5.9 km · h−1) and levels of oxygen uptake (males: 18.6; females: 19.5 mL · kg−1 · min−1). The average number of MET-hours per week in a typical month was 22 for males and 20 for females. This resulted in a total weekly energy expenditure of ~1,570 and 1,040 kcal for males and females, respectively. Over the year, the number of walking commutes and their accumulated distance was ~385 trips and 800 km for both sexes.ConclusionWalking in naturalistic field settings demands its own studies. When males and females walk to work, their relative aerobic intensities and absolute energy demands for a given distance are similar. It is equivalent to the lower part of the moderate relative intensity domain. The combination of oxygen uptake, trip duration and frequency leads to high and sustained levels of MET-hours as well as energy expenditure per week over the year, with a clear health enhancing potential. Based on this study we recommend 6000 transport steps per day, or equivalent, during five weekdays, over the year, in order to reach optimal health gains.

• Trips less than one hour are the most common length for transporting horses by road. • Trail and leisure riding were the most common reasons for equine transportation. • The reasons for transporting horses by road vary by discipline and length of trip. • Vital signs are not usually recorded before and/or after trips of 3 h or less. • One-third of horses transported on short journeys were over the age of 15 years. Horses are regularly transported in the United States (U.S.); however, how, and why horses travel by road has not been explored. Consequently, an online nationwide survey was conducted to understand 1) the most common reasons for travel; 2) the types of journeys undertaken when being transported by road in the U.S. and 3) the general management practices when transporting for 3 h or less. Responses were collected from 1294 participants with at least one response from every state in the continental U.S. The most common survey taker was a female (93.9 %), adult amateur (81.2 %), horse owner (64.6 %) who rode recreationally (33.1 %) and transported their own horse (79.4 %). The most common reasons for travel were for trail or leisure riding (34.2 %) followed by showing and competition (25.3 %); however, this varied by discipline. The most common trip duration was less than one hour (46.8 %), with only 12.4 % of the most common trip durations being 4 h or more. The most common specific horse transported by road for 3 h or less was an adult (age 5-15; 59.0 %), Quarter Horse (21.2 %), used for pleasure or trail riding (44.3 %). The biggest concern when transporting was injury to the horse (26.7 %), whilst the biggest factor when planning to travel was the weather (24.1 %). These results provide insight into why horses are being transported by road in the U.S. and that it is more common to transport horses for shorter durations. [ABSTRACT FROM AUTHOR]

The demand for public transport on the example of Lviv city is analyzed in the paper. The aim of the trip, the duration of the trip, vehicle occupancy rate, walking approach to the stop, and the choice of public transport mode are determined as the main impact factors. The questioning of 1100 users of public transport is carried out. During the formation of transport strategy of public transport system functioning in large cities, it is necessary to consider such factors of functioning as optimal, from the view of transport provision, location of places of work and study, trips are up to 45 min; vehicle occupation rate is up to 80% (on the most routes); the walking distance is up to 600 m; vehicles of large capacity. The provision of these indicators in transport systems will provide an opportunity to increase the effectiveness and enlarge the demand for public transport services as a result of qualitative functioning.

Animals constantly test the borders of their own ecological niche and tend to expand their range, which is now additionally challenged by global climate change. Following human exploitation throughout the Southern Ocean in the 19th and the beginning of the 20th century, numbers of King Penguin breeding pairs have increased and former breeding sites have been re-colonized. Since 2010 a breeding colony became (re-)established at Bahía Inútil, Strait of Magellan, Tierra del Fuego, Chile. The aims of this study were to study the foraging ecology of King Penguins at this new breeding site, which is characterized by a set of different environmental variables as it is located within the confined environment of the Magellan Strait, more than 300 km from the open ocean. During the course of this study, thirty-two birds were successfully equipped with external devices that recorded 206 foraging trips by breeding and non-breeding birds. With one exception, all birds foraged throughout the year exclusively in the Magellan Strait with the main foraging areas located within 100 km from the colony. The diving activities of 15 King Penguins were recorded during 59 foraging trips, the deepest dive was 160 m and the longest dive lasted 6.75 mins. Based on a representative subsample of 3000 dives, mean dive depth was 32 ± 34 m and mean dive duration 117 ± 84 s. Accordingly, foraging trip durations throughout the year were significantly shorter than those recorded for conspecifics elsewhere. In accordance with these changes in foraging behavior, stomach contents from seven birds showed a mix of fish and squid, with Falkland sprats Sprattus fuegensis as the main prey item present in all samples. The implications of these behavioral adaptations are discussed with regard to this unusual confined foraging environment and predicted changes in the performance of King Penguins breeding elsewhere following global change.

Studies scrutinizing the tangible and intangible factors regarding the length of stay (LOS) in a destination are rare. The emotional factors have not always been integrated into this analysis. We have contributed to fill this gap in the literature considering the degree of happiness with the tourist destination. We used a sample of 1253 tourists and three regression models were estimated (OLS regression model, a Weibull survival model and a zero-truncated negative binomial) to study the LOS. We verified that the emotional factor related with happiness affects the LOS. Furthermore, regarding the managerial/practical implications it is important to highlight that the tourists who intend to visit the city have gastronomic and wine experiences, and, through their contact with the cultural heritage of the city, they will stay for a longer period of time. In addition to economic factors, as expenditures, there are also emotional and experiential aspects that influence LOS and these have to be included in the communication of a tourist destination. The attributes of a destination are not enough to influence the LOS. The destination must also be a set of experiences that will increase the happiness of the tourist with the destination. [ABSTRACT FROM AUTHOR]

Many tourism travel decisions are interrelated. From modelling viewpoint incorporating these interrelations is important for understanding the consequences of exogenous shocks, as well as the intended and unintended consequences of policy. This paper addresses two tourist choices in the temporal dimension that were previously only handled as if they were mutually exclusive. The paper adopts a discrete-continuous model framework to simulate tourists' behaviours in selecting travel seasons (choice model) and associated trip duration (accelerated failure time model) and estimates the correlation between these two tourism decisions conditional on the covariates. The empirical analysis is based on Australian domestic tourism data and reveal the factors influencing both decisions as well as those that influence only one or the other. The model results, which is tested with data from select years between 1999-2018, including years of significant exogenous shocks, show mixed evidence of stability and changes in the parameters. Building on this evidence, the paper concludes with underlying temporal choice behaviours of tourists that may be of relevance during- and post-COVID19 environment. [ABSTRACT FROM AUTHOR]

The fixed public charging stations (FCS) network is challenged by widespread of electric vehicle (EV) uses. Therefore, there is exploitation of the many parks spread over the territory of a smart city by means of mobile charging stations (MCS). That can be set up or moved anywhere as needed. This allows for the rapid expansion of the charging infrastructure. In this work, we propose an architecture system consisting of a set of algorithms to manage electric vehicle charging plans in terms of minimizing journey time, including waiting and charging time at charging stations (CS). Thus, During the CS selection decision, the system takes into consideration the amount of sufficient energy for the EV to reach the specified CS, the remaining amount of energy in stock if the selected CS is the MCS type, the CS Real-time status, and the first-come-first-served policy based on providing charge seats in CS. Moreover, the dynamically system regulates each FCS at its peak time of its MCS operation, ensuring a semi-permanent equilibrium in electrical grid usage and reducing congestion by changing the flow of vehicles that are directed towards FCSs for charging. The evaluation results demonstrate, in the context of the Helsinki City scenario, the effectiveness of the proposed system and algorithms, in terms of achieving the above-mentioned objectives. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the travel behavior changes in Thessaloniki, Greece aiming to understand them and explore the factors that affect them under the COVID-19 mobility restriction measures. Socioeconomic and mobility data from two questionnaire surveys, one year before and during the COVID-19 lockdown of April 2020 (with 1462 and 196 responses respectively), were compared by utilizing a wide variety of inductive statistical tests. Ordinary Least-Squares regression models and Cox proportional hazards duration models were employed to explore any concurrent socioeconomic effect on travel behavior patterns. Results showed that the number of daily trips per person was on average decreased by 50% during the lockdown. This decrease was much greater for the non-commuting trips. Trips on foot were increased, private car was mainly used for commuting and public transport modal shares were heavily reduced. Trip durations were generally increased, as travelling was considered a recreational activity per se. The starting times of the first trips of the day were more evenly distributed throughout the day and many travelers only started their first trips late in the afternoon. Older travelers generally maintained their mobility behavior patterns despite their higher vulnerability to COVID-19 disease. Lower-income travelers were likely to make more daily trips. Male travelers tended to make higher-duration trips compared to their female counterparts. Since pandemics may become recurring events in the future, our findings provide for a better understanding of their influence on mobility and support the design of customized policies to fulfill sustainable mobility objectives during lockdown circumstances.

Continuous developments in automotive technology brought about the reality of having an increasing number of autonomous vehicles (AV) on roadways soon. However, this advancement comes with the uncertainty of how the interaction between the autonomous and non-autonomous (non-AV) vehicles will play out. Using traffic simulators to model both AVs and non-AVs, and studying the effect of AVs on roadway mobility can help researchers in learning what to expect in real life. In this paper, an open-source simulator called Simulation of Urban Mobility (SUMO) was used to develop AVs by modifying Intelligent Driver Model (IDM) car following and LC2013 lane changing models. Then, three actual roadways in Columbus, Ohio were modeled, and 40 simulations were run for each traffic network. The first set of simulations focused on the impacts that AVs have on vehicle roadway mobility with non-AVs when all the AVs have the same autonomy level. The second set of simulations had mixed AV autonomy levels (SAE Level 2, 3 and 4) with mixed AV and non-AV traffic. The analysis revealed that mobility of AVs and non-AVs increased generally as autonomy level increased. When the AV autonomy levels were mixed, the non-AVs experienced lower average speeds and decreased roadway mobility. [ABSTRACT FROM AUTHOR]

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user's data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user's age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train , a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest.

Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on humanmobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogengeneration time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions. [ABSTRACT FROM AUTHOR]

Today, smart cities are turning to electric transport, carpooling and zero emission zones. The growing number of electric vehicles on the roads makes it increasingly necessary to have a public charging infrastructure. On the other hand, the main limitations of electric vehicles are the limited range of their batteries and their relatively long charging times. To avoid having problems to recharge, electric vehicle drivers must plan their journeys more thoroughly than others. At the goal of optimizing trip time, drivers need to automate their travel plans based on a smart charging solution, which will require the development of new Vehicle-to-Grid applications that will allow at the charging stations to dynamically interact with the vehicles. In this paper, we propose an architecture based on an algorithm allowing the management of charging plans for electric vehicles traveling on the road to their destination, in order to minimize the duration of the drivers’ journey including waiting and charging times. The decision taken by the algorithm based on the exploration of the data of each public supply station according to its location, number of vehicles in the queue, number of charging sockets, and rates of service.

Public transit provides an affordable and reliable transport option especially to the vulnerable groups. However, the relevance of transit accessibility to the daily mobility of different social strata has not been fully understood. It remains unclear to what extent the low-income may benefit from enhanced transit accessibility compared to others. Focusing on an Asian metropolis—Hong Kong, this study investigates the interplay between transit accessibility and daily trip duration with a particular focus on the low-income population via a machine-learning approach (Gradient Boosting Decision Tree). Our findings indicate that network accessibility by Mass Transit Rail (MTR) exerts a weaker effect on the duration of mandatory and discretionary trips of the low-income than for the non-low-income for these trips. This implies the presence of possible barriers of using MTR among the low-income. Moreover, marked threshold effects are identified for both MTR and bus accessibility especially in relation to the mandatory and maintenance trips of the low-income. Based on these findings, policy recommendations are proposed to help strengthen the linkage between improvement of transit accessibility and equitable mobility conditions across society. [ABSTRACT FROM AUTHOR]

Studies scrutinizing the economic aspects of student tourism at the micro-level are rare in the literature. Set against this background, the present study examines the variation in a sample of Norwegian students' length of stay at summer vacation destinations in 2014. Three estimation methods – an OLS regression model, a Weibull survival model and a zero-truncated negative binomial regression model – provide qualitatively similar results regarding how a number of determinants affect length of stay. In particular, the results show that daily trip costs, booking time, tourism and trip motives, trip month and gender explain much of the variation in length of stay. Of special interest is the comparison of two segments differing on when trip duration is determined: the “pre-fixed” returners (75% of the sample) and the “open” returners (25%). In this regard, the results suggest that the “open” returners stay longer on their trips than the “pre-fixed” ones. Finally, the study provides some implications for future length of stay research. [ABSTRACT FROM AUTHOR]

This study analyses the determinants of cycling expenditure by means of a Tobit regression analysis, based on a dataset of 5,157 cyclists. Using a heterodox economic approach, 23 different variables are combined into two commonly used variable groups in the field of sports expenditure (socio-demographics, sports intensity variables) and two variable groups [socio-economic cycling capital, and attitudes, interests, opinions (AIOs)] that are less frequently incorporated. With all variables included in the Tobit regression, sex, trip duration, frequency, number of cycling variants practised, visiting cycling websites, and practicing road bicycle racing or mountain bike influence cycling expenditure positively. A negative association is found with competitive riding and cycling drop out. It is suggested that marketers of cycling services and cycling apparel should meet the cyclist's need for identification instead of focusing solely on socio-demographic factors. [ABSTRACT FROM AUTHOR]

Are longer trips more strenuous or unpleasant than shorter ones? This paper examines this question using data from the American Time Use Survey’s well-being module, which queried individuals about the extent to which they felt happiness, pain, sadness, stress, and fatigue during three randomly selected daily activities. Over 22,000 instances of individuals traveling are observed, including their trip duration, mode, purpose, and demographic and geographic information. Each emotion, plus a constructed, composite mood variable, is regressed on trip duration. Overall, the relationship between trip duration and traveler mood is not strong, which is unsurprising given prior findings on the limited impact of activities on mood. However, there is a statistically significant and negative association between trip duration and mood, primarily because of rising stress, fatigue and sadness on long trips. This is particularly true for drivers, while negative emotions do not rise with increasing trip duration for auto passengers. This suggests strain rises as the result of operating the vehicle for long periods, not traveling in an auto per se. Long bicycle trips are more painful than shorter ones, probably due to the physical demands of the mode, and long train trips are associated with less sadness. For commutes, long trips significantly degrade the mood of both drivers and bus riders, in the latter case probably due in part to vehicle crowding and standing. The findings imply that reducing the duration of trips, for example through land use policies that reduce trip distances, or congestion reduction, would have emotional benefits. Policies to promote ridesharing instead of solo driving for long trips may increase traveler mood in the aggregate. Improving bus service or substituting rail for bus for long commute trips may also improve traveler mood. [ABSTRACT FROM AUTHOR]

Observed large-scale changes in climate and oceanography, which are especially pronounced in the Arctic, represent profound challenges for upper trophic predators. Knowledge about the extent to which marine predators are able to adjust to environmental variability is essential in order to assess the impact of changing oceanic conditions on the Arctic ecosystem. The main goal of this study was to investigate the impact of oceanographic conditions that varied on an inter-annual and inter-colony basis on the foraging and breeding strategy of a zooplanktivorous alcid, the little aukAlle alle. The study was conducted in two little auk breeding colonies in Spitsbergen (Hornsund and Magdalenefjord) located in different hydrographical regimes in two contrasting seasons (2009 and 2010). This article provides a comprehensive study linking a number of variables describing foraging ground quality, measured directly at sea, with the birds' foraging strategy, parental efforts, body condition and survival of nestlings. The analyses revealed that under conditions of high water temperature and a low proportion of the preferred food item,Calanus glacialis, in relation to less energetically profitableCalanus finmarchicusin the little auks' foraging grounds, birds increased the overall duration of their foraging trips and decreased the frequency of chick feeding. The resulting lower calorific value of food delivered daily to the chicks entailed reduced chick survival. We might expect that further ocean warming would impair the time and energy budget of parent birds and, in consequence, the breeding success of the little auks. [ABSTRACT FROM PUBLISHER]

Background: Quantification of movement intensity and energy utilization, together with frequency of trips, duration, distance, step counts and cadence, is essential for interpreting the character of habitual walking for transport, and its potential support of health. The purpose of the study is to illuminate this with valid methods and novel perspectives, and to thereby provide a new basis for characterizing and interpreting walking in relation to health outcomes., Methods: Habitual middle-aged commuting pedestrians (males = 10, females = 10) were investigated in the laboratory at rest and with maximal treadmill and cycle ergometer tests. Thereafter, levels of oxygen uptake, energy expenditure, ventilation, heart rate, blood lactate, rated perceived exertion, cadence, number of steps, duration, distance, and speed were recorded during the normal walking commute of each participant in Greater Stockholm, Sweden. The number of commutes per week over the year was self-reported., Results: Walking in the field demanded about 30% more energy per km compared to level treadmill walking. For both sexes, the walking intensity in field was about 46% of maximal oxygen uptake, and energy expenditure amounted to 0.96 kcal · kg - 1 · km - 1 . The MET values (males: 6.2; females: 6.5) mirrored similar levels of walking speed (males: 5.7; females: 5.9 km · h - 1 ) and levels of oxygen uptake (males: 18.6; females: 19.5 mL · kg - 1 · min - 1 ). The average number of MET-hours per week in a typical month was 22 for males and 20 for females. This resulted in a total weekly energy expenditure of ~1,570 and 1,040 kcal for males and females, respectively. Over the year, the number of walking commutes and their accumulated distance was ~385 trips and 800 km for both sexes., Conclusion: Walking in naturalistic field settings demands its own studies. When males and females walk to work, their relative aerobic intensities and absolute energy demands for a given distance are similar. It is equivalent to the lower part of the moderate relative intensity domain. The combination of oxygen uptake, trip duration and frequency leads to high and sustained levels of MET-hours as well as energy expenditure per week over the year, with a clear health enhancing potential. Based on this study we recommend 6000 transport steps per day, or equivalent, during five weekdays, over the year, in order to reach optimal health gains., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Schantz, Olsson, Salier Eriksson and Rosdahl.)

This paper conducts a statistical analysis of student travel behavior at Virginia Commonwealth University (VCU). The data source is the ‘University NHTS’ project launched by the Virginia Department of Transportation (VDOT) in 2009. Through this empirical study, it has been found that university student travel behavior is different from that of the general population; urban universities have lower percentages of nonmotorized trips than college-town universities; undergraduate students are likely to make more daily trips than graduate students – similarly, on-campus students make more frequent trips than off-campus students; the most frequent student activities are home and academic activities; and student group categories have virtually no impact on daily activity profiles, though activity types do have a dramatic impact on daily activity profiles. Based on these research findings, the paper makes a series of recommendations regarding trip generation, trip distribution, mode choice, and activity-based modeling. [ABSTRACT FROM PUBLISHER]

Breeding animals face important time and energy constraints when caring for themselves and their offspring. For long-lived species, life-history theory predicts that parents should favor survival over current reproductive attempts, thus investing more into their own maintenance than the provisioning of their young. In seabirds, provisioning strategies may additionally be influenced by the distance between breeding sites and foraging areas, and offshore and inshore species should thus exhibit different strategies. Here, we examine the provisioning strategies of an inshore seabird using a long-term data set on more than 200 Little Penguins, Eudyptula minor. They alternated between two consecutive long and several short foraging trips all along chick rearing, a strategy almost never observed for inshore animals. Short trips allowed for regular provisioning of the chicks (high feeding frequency and larger meals), whereas long trips were performed when parent body mass was low and enabled them to rebuild their reserves, suggesting that adult body condition may be a key factor in initiating long trips. Inshore seabirds do use dual strategies of alternating short and long trips, but from our data, on a simpler and less flexible way than for offshore birds. [ABSTRACT FROM AUTHOR]

The article discusses a study on how the foraging behavior of Adélie penguins affects the amount of food they are able to provide their chicks. Researchers looked into 20 of the paired birds who were raising young in sea filled ice. They found that the amount of time the parents spent foraging away from their chicks had little effect on the amount of food they were able to collect. The amount of time they spent beneath the water, however, did, which researchers attribute to individual skill. They suggest that these differences affect chick growth.

The article focuses on a study which examined the relationship between parental provisioning behaviour and the breeding success of Cape Gannets breeding at Ichaboe Island in Namibia. Nests were checked every hour from sunrise to sunset to record nest attendance of each bird. Feathers were collected from both parents of 10 nests to verify the sexing method. A logistic regression was used in this study to analyse chick survival. Results of the study showed that chick growth experienced an increase from December 19, 2005 to January 9, 2006. It also found that female body condition was greater than those of male. A positive correlation was also found between attendance bouts and trip durations in both sexes.

The article focuses on the foraging and nest-attendance behavior of Little Penguins or Eudyptula minor during incubation. It discusses a study on 10 pairs of Little Penguins at Penguin Parade, Phillip Island, Victoria, Australia, in October-November 2004. The article presents study results such as length of foraging days, as well as sex and body condition as factors on foraging duration. It also states that combining long and short trips contribute to improving survival and reproduction.

A reliable estimation of public bicycle trip characteristics, especially trip distribution and duration, can help decision-makers plan for the relevant transport infrastructures and assist operators in addressing issues related to bicycle imbalance. Past research studies have attempted to understand the relationship between public bicycle trip generation, trip attraction and factors such as built environment, weather, population density, etc. However, these studies typically did not include trip distribution, duration, and detailed information on the built environment. This paper aims to estimate public bicycle daily trip characteristics, i.e., trip generation, trip attraction, trip distribution, and duration using points of interest and smart card data from Nanjing, China. Negative binomial regression models were developed to examine the effect of built environment on public bicycle usage. Totally fifteen types of points of interest (POIs) data are investigated and factors such as residence, employment, entertainment, and metro station are found to be statistically significant. The results showed that 300 m buffer POIs of residence, employment, entertainment, restaurant, bus stop, metro station, amenity, and school have significantly positive effects on public bicycle generation and attraction, while, counterintuitively, 300 m buffer POIs of shopping, parks, attractions, sports, and hospital have significantly negative effects. Specifically, an increase of 1% in the trip distance leads to a 2.36% decrease in the origin-destination (OD) trips or a 0.54% increase of the trip duration. We also found that a 1% increase in the number of other nearby stations can help reduce 0.19% of the OD trips. The results from this paper can offer useful insights to operators in better estimating public bicycle usage and providing reliable services that can improve ridership. [ABSTRACT FROM AUTHOR]

Psychologists have long since recognized that consumers have limited cognitive ability, and that this prevents them from weighing up all product attributes when making a choice. More recently, a new framework for consumer choice has been developed which takes account of this, by assuming that consumers are drawn to salient features of a product. We apply the framework of salient thinking to a simple transport market in which passengers may attach different weights to the utility of the trip (its comfort and/or duration) and the fare. We find the optimal fare structure in this market, and investigate under which conditions operators use their pricing schedule to focus passengers' attention on fare, and when they direct attention towards trip duration or comfort. Furthermore, we address the quality investments made by operators compared to a rational benchmark. Quality enhancement in this model is increasing in the unit cost of providing the service. We finally discuss the implications of the theory of consumer choice, and the competitive model for transport markets compared to the predictions of a rational model. This leads to several testable situations which could give implications for policy makers and transport firms. • Passengers may have limited cognitive ability and use rules of thumb when choosing between transport services. • Operators place weight on the attribute (price or quality) in which they have an advantage to capture market share. • Consequences are investigated for the optimal fare and quality structures, showing that these can be quite different to predictions from the traditional rational model. • Quality investment by operators depends upon the unit cost of providing the service, in contrast to traditional theory. • Transport regulators take account of behavioural theory, and several testable implications are derived from the model. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the travel behavior changes in Thessaloniki, Greece aiming to understand them and explore the factors that affect them under the COVID-19 mobility restriction measures. Socioeconomic and mobility data from two questionnaire surveys, one year before and during the COVID-19 lockdown of April 2020 (with 1462 and 196 responses respectively), were compared by utilizing a wide variety of inductive statistical tests. Ordinary Least-Squares regression models and Cox proportional hazards duration models were employed to explore any concurrent socioeconomic effect on travel behavior patterns. Results showed that the number of daily trips per person was on average decreased by 50% during the lockdown. This decrease was much greater for the non-commuting trips. Trips on foot were increased, private car was mainly used for commuting and public transport modal shares were heavily reduced. Trip durations were generally increased, as travelling was considered a recreational activity per se. The starting times of the first trips of the day were more evenly distributed throughout the day and many travelers only started their first trips late in the afternoon. Older travelers generally maintained their mobility behavior patterns despite their higher vulnerability to COVID-19 disease. Lower-income travelers were likely to make more daily trips. Male travelers tended to make higher-duration trips compared to their female counterparts. Since pandemics may become recurring events in the future, our findings provide for a better understanding of their influence on mobility and support the design of customized policies to fulfill sustainable mobility objectives during lockdown circumstances., (© 2021 The Authors.)

Background: Knowledge about exercise intensity and energy expenditure combined with trip frequency and duration is necessary for interpreting the character and potential influencing capacity of habitual cycle commuting on e.g., health outcomes. It needs to be investigated with validated methods, which is the purpose of this study. Methods: Ten male and 10 female middle-aged habitual commuter cyclists were studied at rest and with maximal exercise tests on a cycle ergometer and a treadmill in the laboratory. During their normal commute in the Stockholm County, Sweden, their oxygen uptake, heart rate, energy expenditure, ventilation, blood lactate, rated perceived exertion, number of stops, durations, route distances and cycling velocities were monitored with validated methods. The frequency of trips was self-reported. Results: The relative exercise intensity was 65% of maximal oxygen uptake, and the energy expenditure was 0.46 kcal per km and kg body weight for both sexes. Sex differences in MET-values (men, 8.7; women 7.4) mirrored higher levels of cycling speed (20%), body weight (29%), oxygen uptake (54%) and ventilation (51%) in men compared to women. The number of METhours per week during peak cycling season averaged 40 for the men and 28 for the women. It corresponded to a total energy expenditure of about 3,500 and 1,880 kcal for men and women, respectively. The number of trips per year was about 370, and the annual distance cycled was on average 3,500 km for men and 2,300 for women. Conclusion: Cycle commuting is characterized by equal relative aerobic intensity levels and energy requirements for a given distance cycled by men and women. Based on an overall evaluation, it represents a lower range within the vigorous intensity category. The combined levels of oxygen uptake, durations and trip frequencies lead to high levels of METhours and energy expenditure in both men and women during both peak cycling season as well as over the year. Overall, the study presents a novel basis for interpreting cycle commuting in relation to various health outcomes., (Copyright © 2020 Schantz, Salier Eriksson and Rosdahl.)