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Environmentally friendly alternatives to motorised transport have recently become a topic of discussion among political representatives. To achieve sustainable urban mobility objectives, political discussions have focused on promoting cycling, walking and using public transport in cities. These modes of transportation are attractive ways of commuting to and from work for a portion of the population. In the literature, there is a growing interest in the phenomenon of active commuting. This paper examines the spatio-temporal patterns and practices of active commuting and evaluates their significance in the context of sustainable urban mobility. The empirical research is based on data obtained from a questionnaire survey and semistructured interviews conducted in 2023 in the Brno Metropolitan Area. Based on the data, three categories of active commuting were identified, namely pragmatic active commuting, physical active commuting, and combined active commuting, which differ in the implementation of different spatio-temporal practices. For the development of sustainable transport in the city, it is necessary to promote the construction of pedestrian and bicycle infrastructure, with the aim of making movement for pedestrians and cyclists more straightforward, efficient, and safer, including paying attention to actions leading to the embedding of this mode of transport in the wider societal context.

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone's GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model's ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model's capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin–destination (OD) matrix to better understand how people move within the city. [ABSTRACT FROM AUTHOR]

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A rhyolite boulder collected by R. S. Newall in 1924 from an excavation at Stonehenge has been pivotal to arguments concerning glacial versus human transport of the bluestones to Stonehenge. Initial studies suggested that the boulder came from north Wales, and hence was a probable glacial erratic. New petrographic and geochemical analyses however support it being from Craig Rhos‐y‐Felin in west Wales, the source of much debitage recovered from Stonehenge. Examination of the form and surface features of the boulder provides no evidence for it being erratic. Instead, it is considered to be one more piece of debitage probably derived from a broken‐up monolith. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone’s GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model’s ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model’s capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin–destination (OD) matrix to better understand how people move within the city.

Individuals choose transport modes to travel based on their preferences and journey characteristics. The availability of autonomous vehicles (AVs) on the market will affect the traditional mode choice models. In this regard, this research studies three transport modes: conventional car, privately-owned autonomous vehicle (PAV), and shared autonomous vehicle (SAV). The potential changes in transport mode choice are evaluated, where the impact of changes of some sociodemographic and travel variables on choices are estimated. A discrete choice modeling approach is applied in this study to develop a transport choice model. A stated preference (SP) approach is used, where 306 answers are collected in Hungary. A discrete choice experiment (DCE) is designed to collect the choices of people. Multinomial Logit (MNL) model is applied to the data to develop a transport mode choice model, where differentiations between some parameters across transport modes are presented. People are varied in their willingness to use a transport mode across groups, such as the income, the family size, and the current transport mode. The results demonstrate that people are more likely to use a conventional car than PAV, while they are more likely to use PAV than SAV. The output of this study can be used to support policy implications in the AV era.

This research was designed to investigate the factors affecting the frequency of use of ride-hailing in a fast-growing metropolitan region in Southeast Asia, Kuala Lumpur. An intercept survey was used to conduct this study in three potential locations that were acknowledged by one of the most famous ride-hailing companies in Kuala Lumpur. This study used non-parametric and machine learning techniques to analyze the data, including the Pearson chi-square test and Bayesian Network. From 38 statements (input variables), the Pearson chi-square test identified 14 variables as the most important. These variables were used as predictors in developing a BN model that predicts the probability of weekly usage frequency of ride-hailing. According to the final model, the attitude of the commuters towards the speed of ride-hailing over hailing regular taxis was the most important and presented in all probability conditions. Several related studies also identified ride-hailing speed as one of the top reasons for using this travel option. The findings of this study imply that commuters still compare the ride-hailing services with the traditional taxis in Kuala Lumpur, especially in terms of complementarity to other modes, ease of payment, ease of access, and speed. It is critical to have a sustainable strategy for keeping commuters’ satisfaction at the highest level because if the ride-hailing services cannot meet the commuters’ expectations, they may switch back to conventional transport options.

People spend approximately 90% of their day in confined spaces (at home, work, school or in transit). During these periods, exposure to high concentrations of atmospheric pollutants can pose serious health risks, particularly to the respiratory system. The objective of this paper is to define a framework of the existing literature on the assessment of air quality in various transport microenvironments. A total of 297 papers, published from 2002 to 2021, were analyzed with respect to the type of transport microenvironments, the pollutants monitored, the concentrations measured and the sampling methods adopted. The analysis emphasizes the increasing interest in this topic, particularly regarding the evaluation of exposure in moving cars and buses. It specifically focuses on the exposure of occupants to atmospheric particulate matter (PM) and total volatile organic compounds (TVOCs). Concentrations of these pollutants can reach several hundreds of µg/m3 in some cases, significantly exceeding the recommended levels. The findings presented in this paper serve as a valuable resource for urban planners and decision-makers in formulating effective urban policies. [ABSTRACT FROM AUTHOR]

The research shows a comparative analysis of land transportation infrastructure where there is an intermodal competition in Peru, specifically between the Road and the Rail in the Central Region. Based on this analysis, it is established which means of freight transportation are used to carry goods such as solid bulks, by? incorporating producers and consumers who compete for mining logistics to the Callao Port. The results show that transferring the mining freight from the road to the rail reduces traffic congestion from heavy truck use.

Abstract Background There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing. Methods The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode. Results The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization. Conclusion Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions.

Urban parks benefit the mental health of older adults by providing outdoor leisure space. However, the potential impact of spatial accessibility to urban parks on older adults' mental health was under-researched, particularly in regard to various spatial scales and transport modes. Using linear mixed models, this study assessed the association between spatial accessibility to urban parks and two typical mental health issues of older adults (i.e. depression and anxiety) based on a mental health survey among 1,231 older adults in Beijing, China. Spatial accessibility of each community to parks via walking and public transport modes at local, regional, and global scales was calculated based on travel time by application programming interface of online map. Results show that the spatial accessibility of urban parks at all three scales and with different transport modes had a significant impact on older adults' anxiety; accessibility at the regional scale had the greatest impact. Older adults' depression was affected primarily by regional park accessibility in walking mode. Findings have important practical implications for spatial-accessibility improvements to urban parks and for constructing age-friendly urban spaces. [ABSTRACT FROM AUTHOR]

The technology of autonomous vehicles is gaining more and more emphasis these days. In the near future the technological developments will make it possible for vehicles to travel on the roads without human intervention. However, downstream users have differing views on this new mode of transport. The aim of our research was to explore the opinions of different social generation groups and traffic groups about fully autonomous self-driving (SAE Level 5). In our research, we conducted an online self-report questionnaire survey. The questionnaire was completed by 223 people. The results were analyzed from several perspectives. The results showed that opinions and expectations in the field of autonomous vehicles differed by generation group, gender and primary mode of transport. [ABSTRACT FROM AUTHOR]

Relief supply warehouses are material warehouses built to cope with natural disasters and guarantee the supply of life necessities. Great earthquakes (with Ms ≥ 7) are overwhelming natural disasters beyond human control. The efficiency in rescuing earthquake victims depends on if the construction preparations of large relief supply warehouses have fully assessed the influence weights of the factors of large earthquake disasters. We innovatively proposes a method for selecting ideal site of relief supply warehouses in terms of seismic evaluation and the quantification of site selection criteria. We assess disaster-affected areas and screen the transport mode of relief supplies based on seismic prediction and remote sensing recognition, quantify and classify the site selection criteria of relief supply warehouses in China, and finally determine the ideal site. Taking a seismically-active region as a study case, we identify seven major disaster-affected areas, estimate the largest safe flight range (255 km) of a helicopter in post-earthquake transport of relief supplies, and pre-selecte 37 candidate sites. We suggest that the spatial distances between candidate sites and essential transport hubs (such as airports, railway stations, and highway entrances/exits) and the construction planning of the city are crucial indicators for the ideal site selection. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Ninety-east Ridge (NER) is located in the southern Bay of Bengal in the northeast Indian Ocean and is composed of pelagic and hemipelagic sediments. In addition to contributions from marine biomass, the ridge also contains terrestrially sourced sedimentary material. However, considerable disagreement remains regarding the origin of these terrestrial materials and transport pathways. This paper discusses the collection of seafloor surface sediments and three sediment cores recovered from the northern region of the NER, as well as the analysis of clay minerals, Sr-Nd isotopes, and sediment grain size. The ages of the three core sediments are constrained by AMS 14C dating to better establish the source and transport pathways of the terrestrial materials within NER sediments over the past 35000 years. The research results show that the Qinghai-Tibet Plateau is the predominate source of terrigenous sedimentary material in the NER. In the plateau, the crustal materials were weathered and stripped and then transported to the Andaman Sea via the Irrawaddy River. From there, the material was transported westward by monsoon-driven circulation to the northernmost part of the NER before being transported to the south for final deposition. This transport mode has changed little over the past 35000 years. However, during the rapidly changing climate of the Younger Dryas (12.9~11.5 ka BP), there were some variations in the input amount, grain size, and Sr-Nd isotope value of the source material. The above conclusions are significant for re-evaluating the source of terrigenous sediments, the temporal and spatial changes in transport modes, and the sensitivity of the NER to climatic shifts.

This article investigates the conceptual and empirical aspects of hinterland connectivity, focusing on container ports in India as a case study. It aims to clarify the complex and multidimensional nature of hinterland connectivity within the context of container ports by addressing a key conceptual issue. The study proposes that port-hinterland connectivity goes beyond direct links and emphasises the strategic relationships with key logistics centres in the hinterland. By employing centrality measures from social network analysis, the study evaluates the overall connectivity of Indian container ports, considering both direct and indirect connections from 2009 to 2021, to identify which ports have robust connections to key logistics centres. The results obtain by using eigenvector centrality reveal that an extensive hinterland network does not necessarily guarantee better hinterland connectivity for container ports. Additionally, the quality of connections, particularly to hubs with high betweenness centrality, is crucial for achieving a high degree of hinterland connectivity. These findings provide valuable policy insights, highlighting the importance of ports fostering closer relationships with key logistics hubs to effectively enhance their connectivity. Based on these findings, the paper offers policy recommendations for India aimed at improving connectivity in alignment with corresponding market shares. This research will serve as a foundation for further exploration of the interplay between port-hinterland connectivity and port market shares from a hinterland perspective, especially in a country like India, where hinterlands significantly overlap. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has forced many conferences and educational events to shift from in-person to online, significantly reducing the carbon footprint associated with these activities. Workshops are a common pattern of thematic learning at the university level, usually involving a series of activities, such as gathering, learning, and dining, for participants from different regions. However, unlike a three-day conference, workshops usually last for seven days or more, resulting in a non-negligible carbon footprint. To resolve this challenge, we have developed a model that provides recommendations for minimizing the carbon footprint of workshops. Using data from the DigitalFUTURES International Workshop on architecture education at Tongji University in China, we calculated the carbon footprint of scenarios with varying workshop durations, participation modes, and transportation methods. Our results show that online workshops can reduce the carbon footprint by up to 88% compared to in-person workshops. Hybrid workshops, which combine online and in-person participation, can also lead to significant carbon reductions, with a 46% online participation rate resulting in an 82% reduction in carbon footprint. However, we recommend that in-person participation be maintained to ensure efficient learning and effective communication. Our work provides a sustainable solution for organizing future workshops with a reduced carbon footprint.

Background: There has been an increased focus on active transport, but the measurement of active transport is still difficult and error-prone. Sensor data have been used to predict active transport. While heart rate data have very rarely been considered before, this study used random forests (RF) to predict transport modes using Global Positioning System (GPS), accelerometer, and heart rate data and paid attention to methodological issues related to the prediction strategy and post-processing. Methods: The RECORD MultiSensor study collected GPS, accelerometer, and heart rate data over seven days from 126 participants living in the Ile-de-France region. RF models were built to predict transport modes for every minute (ground truth information on modes is from a GPS-based mobility survey), splitting observations between a Training dataset and a Test dataset at the participant level instead at the minute level. Moreover, several window sizes were tested for the post-processing moving average of the predicted transport mode. Results: The minute-level prediction rate of being on trips vs. at a visited location was 90%. Final prediction rates of transport modes ranged from 65% for public transport to 95% for biking. Using minute-level observations from the same participants in the Training and Test sets (as RF spontaneously does) upwardly biases prediction rates. The inclusion of heart rate data improved prediction rates only for biking. A 3 to 5-min bandwidth moving average was optimum for a posteriori homogenization. Conclusion: Heart rate only very slightly contributed to better predictions for specific transport modes. Moreover, our study shows that Training and Test sets must be carefully defined in RF models and that post-processing with carefully chosen moving average windows can improve predictions. [ABSTRACT FROM AUTHOR]

To remain competitive and respond to rapidly changing markets, we need to increase flexibility in today's global marketplace. In this respect, the selection of the appropriate mode of transport is one of the most important functions to be performed by logistics. The selection of the appropriate mode of transport is a multi-criteria problem involving both quantitative and qualitative criteria. This paper deals with the selection of the mode of transport using the Analytic Hierarchy Process method (AHP). AHP is a method of decomposing a complex unstructured situation into simpler components to create a hierarchical system problem. This paper describes a general model of selection of transport mode using AHP including its application to a manufacturing company that selects the appropriate mode of transport from three potential transport modes. The aim of this paper is to create a useful decision support tool for selection of the transport mode using the AHP method within distribution logistics of motor fuels. This tool helps companies to make the right decision on the choice of transport mode by taking into account different importance of the different criteria that influence the decision-making process.

Daily commuting significantly contributes to the overall exposure to particulate matter (PM) in urban areas, underscoring the need to understand the factors influencing PM exposure. This study assessed personal exposure levels and inhalation doses of PM 2.5 , and PM 10 across various transportation modes in Tehran, Iran. The study included buses, open-window taxis, subways, walking, and cycling for over 125 trips. Data were collected during the morning and evening peak hours using low-cost sensors, with an average trip distance of 7.50 km. On average, buses exhibited the highest mean PM 2.5 concentration (25.0 μ g m 3 ), followed by subways (17.0 μ g m 3 ), taxis (14.4 μ g m 3 ), cycling (13.5 μ g m 3 ), and walking (8.80 μ g m 3 ). Pearson correlation analysis showed a strong relationship between traffic volume and PM concentrations in taxi and bus modes (r = 0.78 and 0.56, respectively). Subway commuters experienced the lowest PM 2.5 inhalation dose (2.50 μg), whereas bus commuters experienced the highest (6.20 μg). Pedestrians had the highest average inhalation dose of PM 2.5 with 20.7 μg due to longer trip durations. The daily average PM 2.5 levels in all transportation modes exceeded the WHO thresholds by 1.60 times. These findings offer crucial insights into personal exposure concentrations among commuters and serve as a basis for effective air quality management plans and broader global investigations of air pollution. [Display omitted] • Assesses personal exposure levels to PM 2.5 , and PM 10 across various transportation modes. • Buses consistently exhibited the highest mean PM 2.5 concentrations, followed by subways, taxis, cycling, and walking. • Pedestrians face the highest average inhalation doses due to the longer trip times. [ABSTRACT FROM AUTHOR]

• Explores how perceived accessibility is shaped across travel modes and trip purposes. • Sociodemographic, accessibility indicator and transport-related correlates are tested. • Transport resources, habits, and attitudes exhibit the strongest correlations. • Significant disparities in perceived accessibility between car and other modes. Accessibility frequently serves as a central objective in policy and planning due to its significance for travel behavior, health, and economic development. However, until recently, there has been limited attention given to how individuals perceive their accessibility, and even less focus on how these perceptions differ across various transportation modes and trip purposes. This study explores how sociodemographic traits, accessibility indicators, as well as transport resources, attitudes, and habits correlate with perceived accessibility. We assess whether these correlations differ based on transport mode (bicycle, car, public transport, walking) and trip purpose (commuting, grocery shopping, dining out, reaching the city center). Based in the Gothenburg Region, Sweden, this study uses web-panel survey data to capture perceived accessibility. Sixteen ordinal regression models were applied, each tailored to a specific transport mode-activity combination. The results highlight the importance of all categories of correlates in shaping perceived accessibility. Specifically, transport resources, habits, and attitudes exhibit the strongest correlations. Notably, car access, positive car-related attitudes, and frequent car usage are linked to lower perceived accessibility for walking, cycling, and public transport but higher perceived car accessibility. Future studies should consider disaggregating their analyses based on travel mode, as significant disparities exist, particularly between perceived accessibility by car and alternative modes. [ABSTRACT FROM AUTHOR]

Outdoor exposure to particulate matter (PM 2.5 and PM 10) in urban areas can vary considerably depending on the mode of transport. This study aims to quantify this difference in exposure during daily travel, by carrying out a micro-sensor measurement campaign. The pollutant exposure was assessed simultaneously over predefined routes in order to allow comparison between different transport modes having the same starting and ending points. During the six-week measurement campaign, the average reference values for PM background concentrations were 13.72 and 17. 92 μ g /m 3 for the PM 2.5 and PM 10 , respectively. The results revealed that the mode with the highest exposure to PM 2.5 adjusted to background concentration (PM 2. 5 N o r m) was the bus (1.65) followed by metro (1.51), walking (1.33), tramway (1.31), car (1.09) and finally the bike (1.06). For PM 10 N o r m , the tramway had the highest exposure (1.86), followed by walking (1.68), metro (1.65), bus (1.61), bike (1.43) and finally the car (1.39). The level of urbanization around the route and the presence of preferential lanes for public transportation influenced the concentration to which commuters were exposed. For the active modes (bike and walking), we observed frequent variations in concentrations during the trip, characterized by punctual peaks in concentration, depending on the local characteristics of road traffic and urban morphology. Fluctuations in particulate matter inside public transport vehicles were partly explained by the opening and closing of doors during stops, as well as the passenger flows, influencing the re-suspension of particles. The car was one of the least exposed modes overall, with the lowest concentration variability, although these concentrations can vary greatly depending on the ventilation parameters used. These results encourage measures to move the most exposed users away from road traffic, by developing a network of lanes entirely dedicated to cycling and walking, particularly in densely populated areas, as well as encouraging the renewal of motorized vehicles to use less polluting fuels with efficient ventilation systems. [Display omitted] • PM 2.5 and PM 10 concentration were compared across six commuting modes. • The three different measurement routes represent different exposure scenarios. • The bus and the tramway are the most exposed modes for PM 2.5 and PM 10 respectively. • Active mode PM exposures are highly dependent on local urban characteristics. • PM concentrations inside cars are strongly influenced by ventilation settings. [ABSTRACT FROM AUTHOR]

Surface sediments such as aeolian sand, loess/sandy loess, and fluvial/alluvial clastics are widespread in the Yarlung Tsangpo (YT) catchment and across the Tibetan Plateau (TP). However, it is debated whether the source of the aeolian sediments is local or remote, involving transport across large regions, and whether the surface sediments, especially aeolian deposits, are a major source of global dust. Here, we use strontium‐neodymium (Sr‐Nd) isotopic analysis and modern observations of dust emissions to investigate the provenance of aeolian sediments from the YT catchment and adjacent areas during different periods (modern, Holocene, Late Pleistocene), and the relationship between surface sediments of the TP and the dust component of sediments in the Japan Sea and the North Pacific Ocean. Strontium‐87/strontium‐86 (87Sr/86Sr) and neodymium isotopic ratio (εNd) show a wide range of variation in different areas (87Sr/86Sr varies from 0.707996 to 0.731078, and εNd from −6.2 to −13.6), influenced by the composition of the clastic material derived from the local parent rock; additionally, there are no significant variations between size fractions. Due to local recycling the aeolian sedimentary systems in the southern TP are supplied with clastic material from proximal sources, and they are unlikely to receive large quantities of long‐range dust transported from regions such as the Taklimakan desert in Central Asia. Dust emission occurs mainly in the dry season (from March to May) and the dust is carried on the upper‐level westerly jet stream and then deposited downwind in the Japan Sea and the North Pacific Ocean, thus contributing to global dust. [ABSTRACT FROM AUTHOR]

Using Google mobility data and Apple maps data we track changes in community mobility and transport modes during the COVID-19 Alert levels. Results show that Alert Level 4 – lockdown had a significant impact on the reduction in mobility and variation in transport mode. Mobility and transport mode progressively returned to pre-Alert Level 4 patterns with the exception of public transport. Regional heterogeneity in the variation of public transport use was evident in the data. Containment measures also had a significant negative effect on retail and recreation. Otago had a significantly delayed recovery in retail and recreation relative to other regions. [ABSTRACT FROM AUTHOR]

Active modes of transportation are essential for the promotion of sustainable mobility. Increasing cycling and walking participation has the potential to improve various aspects of traffic congestion and contribute to human health. The goal of this study was to analyse the factors that influence the choice of active modes of transport on a university campus at Joinville in southern Brazil. An online cross sectional survey of students from two nearby universities was also conducted. Based on quota sampling, participants were surveyed regarding their mode of transport to and from campus, as well as their place of residence, socioeconomic data and trip information. A total of 284 responses were received, where only 22.5% of trips were made using active modes of transport. A binomial logistic model was used to identify the factors that influence modal choices. The results of this study indicate that modal choices are influenced by travel time, university, income, and marital status.

This study used sources from selected databases to provide an overview of the role of habit in choosing a mode of travel. The articles were selected from two levels. The first included selected studies that considered the psychological aspects of travel mode choice and travel mode change. These created the context for the issues of mode choice and the change of travel mode. The second level was narrower and included studies that were directly focused on the role of habit in travel mode choice or that took the role of habit into account. These studies were used as the core of the review. Subsequently, the topic of habit and travel mode choice was divided into four fields: the theoretical definition of the concept of habit, both in general and in the context of travel mode choice; the theoretical bases of the selected studies; follow-up strategies for transport-mode change; and the measurements and methods used in the field. The Theory of Planned Behaviour (TPB) is the most often used theory for explaining the travel mode choice. Other approaches include the Norm Activation Model and the Value Belief Norm Theory. The aim for other approaches is usually to trigger conscious choice processes, but most reaearchers believe that travel mode choices are automatic and habitual when made in a stable context. The Comprehensive Action Determination Model is a promising theoretical integration that may be used in future research. It integrates assumptions of the TPB, Norm Activation Model, theoretical habit conception, and ipsative behaviour theory in order to build on habitual processes, intention processes, situation effects, and personal and social norms. As habit seems to have the most important role in the choice of travel modes and the possible changes, it should also be considered for mode-choice models in the automation era.

Abstract The objective of this paper is to study 1) do people living in certain residential environments express more dissatisfaction with general functionality and safety of travel, 2) is dissatisfaction with general functionality and safety of travel related to dissatisfaction with certain transport modes, 3) is the residential environment related to mode-specific travel satisfaction mediated by satisfaction with general functionality and safety of travel, and 4) is dissatisfaction with general functionality and safety of travel related to use of different transport modes. The analyses are based on a Finnish survey (representative sample of 17,996 people, response rate 29.7%) on citizen satisfaction with the transport system and travel chains. Three groups, the satisfied, the neither satisfied nor dissatisfied and the dissatisfied, are analysed according to their residential environment, satisfaction with different transport modes and the use of the modes. The results indicate that the residential environment and the satisfaction with general functionality and safety of travel are related. Results suggest that satisfaction with different modes is partly related to lower options offered by the residential environment and higher expectations towards the residential environment. However, satisfaction with general functionality and safety of travel does not relate to higher expectations in a similar way. Findings from this study indicate that satisfaction with daily travel is a phenomenon related to the travel options offered by the residential environment.

As a response to the increasing threat to sport events from climate change and expectations around climate change mitigation, sport event managers increasingly engage in environmentally-related initiatives that aim to reduce GHG emissions. One of the major contributors to GHG emissions at large sport events is spectators' travel and, thus, their associated modal choices. Building on the Sport Logistics Framework (SLF) and using the case study of Rapid Vienna, the largest football club in Austria, this study investigates spectators' modal choice to systematically assess the total GHGs emitted by spectators at a professional football home game. Data was obtained from two sources: a) an extensive data set collected on Rapid Vienna season ticket holders, and b) three surveys at home games which, together, constituted 3317 valid responses. The calculation of the GHG emissions resulted in 99,548 kg GHG emissions per home game or 6.0 kg GHG emissions per spectator. It was also found that 42.4 per cent spectators arriving by car emit 71.6 per cent of GHG emissions, while in contrast, 52.8 per cent of spectators using public transport emit 27.1 per cent of GHG emissions. The results also indicate that the possession of an annual public transport ticket seems to determine travel behavior, i.e. the majority of spectators with an annual ticket are using it, while spectators without a ticket are using a car. As such, this paper not only provides an opportunity for academics and managers to benchmark the data in order to identify initiatives to reduce the impact of GHG emissions, but also allows for the systematic measuring of the environmental impact of fan and spectator travel. • Fan travel to professional football games is a major contributor to GHG emissions. • We assess fans' modal choices and their carbon footprint at a football home game. • The home game emits a total 99,548 kg GHG emissions or 6.0 kg GHG emissions per fan. • We found that 42.4% fans arriving by car emit 71.6% of GHG emissions. • The study allows for benchmarking and systematic measuring of fan travel behavior. [ABSTRACT FROM AUTHOR]

The paper includes the research outputs in the area of examining the total costs of delivering products to the customer for a particular transport mode choice. The methodology of choice procedure is presented based on the calculation of threshold distance for different variants of shipments in road and rail transport based on delivery costs. The paper also assesses the possibility of compiling a multi-criteria function for enterprise logistics, that beside the costs takes into account also other aspects as rail transport availability, time for loading and unloading, shipping time or environmental impacts.

High transition costs remain a major barrier to deeply decarbonizing sectors such as transport in many developing countries. Choice of mode and complementary policies are critical to shaping the costs of climate mitigation in the transport sector. This paper investigates the potentials offered by combined technological and behavioural changes stimulated by strategic infrastructure deployment that can facilitate decarbonization of transport in China. Our study is carried out using IMACLIM-R, a state-of-the-art integrated assessment model, which includes a detailed representation of transport dynamics by incorporating the behavioural determinants of mobility in a standard transport modelling framework. More specifically, this behavioural representation considers (i) the spatial organization of residential dwellings and industrial production, (ii) modal shift induced by transport infrastructure deployment and (iii) the intensity of freight transport production and distribution processes. It is found that supplementing carbon pricing with behavioural measures and a decoupling of economic activity from mobility needs can efficiently promote a modal shift towards low-carbon transport modes and reallocate the sectoral distribution of mitigation efforts. This in turn would significantly reduce the macro-economic impacts of deeply decarbonizing Chinese transport activities over the next decades. Complementary policies should focus on infrastructure, fiscal incentives, land-use, building regulations and other policies affecting the ways urban activities are distributed within the city's boundary, along with industrial policies and other regulations that affect where firms choose to locate. Key policy insights To decarbonize China's transport sector an integrated approach is needed due to strong inertia and distributional effects. Complementary policies have to focus on transport infrastructure, fiscal incentives, land-use and building regulations, as well as spatial organization. Transport-related climate policies interconnect with the ways in which urban activities are distributed within the city's boundary, but also with industrial policies and other regulations that affect where firms choose to locate. A suite of policy instruments is required, including larger institutional incentives and greater financial leverage supporting innovations in the transport sector. [ABSTRACT FROM AUTHOR]

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The use of renewable energy and the transformation of transport mode are crucial items for achieving an efficient and clean electrical mobility that allow being competitive on the market. In this context the interface between the power system and the Electric Vehicles (EVs) assumes a strategic role. Specifically, wireless energy transmission, based on Inductive Power Transfer (IPT), is an attractive solution for EVs charging. Moreover, the use of electric bicycles or kick scooters as mode of urban transport is continuously growing because they are lightweight, sustainable, easily parking, flexible and efficient transport devices. Owing to its benefits, the wireless power transfer can be considered suitable for those devices. In fact, IPT can also be exploited for Vehicle‐To‐Grid (V2G), where the wireless power flow can occur from battery to power grid as well. For E‐bike applications, bicycle‐to‐grid or bicycle‐to‐bicycle energy transfer are viable solutions by means of a Bi‐Directional Inductive Power Transfer (BDIPT). In this paper, a 300 W IPT wireless charger prototype for E‐bikes is proposed. Modelling, design, simulation and experimental results of this prototype are provided. Open‐loop and closed‐loop tests have been performed, focusing on system behaviour for different cases of load, distance and misalignment between the coils. [ABSTRACT FROM AUTHOR]

The logistics environment between China and Korea has been changing rapidly in line with the 'Go West' policy of international trade and logistics area. The policy includes the BRI, Western Development Strategy, and Korea–China FTA. Against this background, the Korea–China train ferry (KCTF) is being newly illuminated as an alternative mode to cope with the logistics environment between Korea and China. One of the key success factors of the KCTF depends on, among others, shippers' perception, which will affect the choice and the successful operation of the KCTF. From this perspective, this paper develops seven hypotheses based on the Theory of Reasoned Action (TRA) and Technology Acceptance Model (TAM). We test these hypotheses using Structural Equation Modeling (SEM) to explore the structural relationship between shippers' behavioral beliefs influencing the perceived usefulness, the attitude toward and intention to choose the KCTF. The test results confirm that among the behavioral beliefs, transport accessibility in association with an efficient network of inland roads and railway networks is an important prerequisite. In addition, we verified a high statistical significance in the structural relationships among perceived usefulness, the attitude toward and the intention to choose the KCTF. [ABSTRACT FROM AUTHOR]

The objective of this paper is to study 1) do people living in certain residential environments express more dissatisfaction with general functionality and safety of travel, 2) is dissatisfaction with general functionality and safety of travel related to dissatisfaction with certain transport modes, 3) is the residential environment related to mode-specific travel satisfaction mediated by satisfaction with general functionality and safety of travel, and 4) is dissatisfaction with general functionality and safety of travel related to use of different transport modes. The analyses are based on a Finnish survey (representative sample of 17,996 people, response rate 29.7%) on citizen satisfaction with the transport system and travel chains. Three groups, the satisfied, the neither satisfied nor dissatisfied and the dissatisfied, are analysed according to their residential environment, satisfaction with different transport modes and the use of the modes. The results indicate that the residential environment and the satisfaction with general functionality and safety of travel are related. Results suggest that satisfaction with different modes is partly related to lower options offered by the residential environment and higher expectations towards the residential environment. However, satisfaction with general functionality and safety of travel does not relate to higher expectations in a similar way. Findings from this study indicate that satisfaction with daily travel is a phenomenon related to the travel options offered by the residential environment. [ABSTRACT FROM AUTHOR]

This study used sources from selected databases to provide an overview of the role of habit in choosing a mode of travel. The articles were selected from two levels. The first included selected studies that considered the psychological aspects of travel mode choice and travel mode change. These created the context for the issues of mode choice and the change of travel mode. The second level was narrower and included studies that were directly focused on the role of habit in travel mode choice or that took the role of habit into account. These studies were used as the core of the review. Subsequently, the topic of habit and travel mode choice was divided into four fields: the theoretical definition of the concept of habit, both in general and in the context of travel mode choice; the theoretical bases of the selected studies; follow-up strategies for transport-mode change; and the measurements and methods used in the field. The Theory of Planned Behaviour (TPB) is the most often used theory for explaining the travel mode choice. Other approaches include the Norm Activation Model and the Value Belief Norm Theory. The aim for other approaches is usually to trigger conscious choice processes, but most reaearchers believe that travel mode choices are automatic and habitual when made in a stable context. The Comprehensive Action Determination Model is a promising theoretical integration that may be used in future research. It integrates assumptions of the TPB, Norm Activation Model, theoretical habit conception, and ipsative behaviour theory in order to build on habitual processes, intention processes, situation effects, and personal and social norms. As habit seems to have the most important role in the choice of travel modes and the possible changes, it should also be considered for mode-choice models in the automation era. [ABSTRACT FROM AUTHOR]