An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation

Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children’s travel and activity participation during the post-school period have direct implication for adults’ activity-travel patterns. A better understanding of children’s after school activity-travel patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. In this paper, data from the 2002 Child Development Supplement of the Panel Study of Income Dynamics is used to undertake a comprehensive assessment of the post-school out-of-home activity-location engagement patterns of children aged 5–17 years. Specifically, this research effort utilizes a multinomial logit model to analyze children’s post-school location patterns, and employs a multiple discrete–continuous extreme value model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others’ activity-travel pattern characteristics impact children’s after school activity engagement patterns.     

Does working from home reduce CO2 emissions? An analysis of travel patterns as dictated by workplaces

This research provides new evidence about the relationship between travel behavior, workplace diversification, and environmental impact in the United Kingdom using data from the National Travel Survey for the period between 2002 and 2017. The path analysis approach based on SEM handles both direct and indirect effects and allows for a comprehensive study of travel behavior, trade-off effects, and work and non-work trips. The results suggest that workplace diversification is often reflected by longer average distances for work trips, which are often associated with more remote residential locations. Findings also show that for some categories, such as teleworkers and home-based workers, trade-off effects are observed between work and non-work trips, which increase CO2 emission levels.     

Does driving behavior matter? An analysis of fuel consumption data from heavy-duty trucks

In a case study of a Norwegian heavy-duty truck transport company, we analyzed data generated by the online fleet management system Dynafleet. The objective was to find out what influenced fuel consumption. We used a set of driving indicators as explanatory variables: load weight, trailer type, route, brake horsepower, average speed, automatic gearshift use, cruise-control use, use of more than 90% of maximum torque, a dummy variable for seasonal variation, use of running idle, use of driving in highest gear, brake applications, number of stops and rolling without engine load. We found, via multivariate regression analysis and corresponding mean elasticity analysis, that with driving on narrow mountainous roads, variables associated with infrastructure and vehicle properties have a larger influence than driver-influenced variables do. However, we found that even under these challenging infrastructure conditions, driving behavior matters. Our findings and analysis could help transport companies decide how to use fleet management data to reduce fuel consumption by choosing the right vehicle for each transportation task and identifying environmentally and economically benign ways of driving.     

Working on the train: from ‘dead time’ to productive and vital time

The conventional approach to the study of travel time is to see it as ??dead time??, i.e. time that should be minimized. In this paper, we study time-use on trains, especially in relation to the use of information and communication technology for work purposes. The empirical results are based on a survey of rail travellers in Norway in 2008. It was found that a high proportion of ordinary commuters and business people work on board while travelling by train, i.e. 35?% of commuters and 43?% of business people. Nearly every fourth commuter gets their travel time approved as working hours. Most respondents had some sort of electronic device with them on board, and 25?% travelling for work-related purposes use a laptop computer. Only 10?% report that their travel time is of no use. Knowledge of how travel time is utilized is indispensable in the discussion about the evaluation of travel time in cost-benefit analysis. The use of travel time can also be important for choice of transport mode in the assessment of travel time versus work options.     

How can the maritime industry meet Sustainable Development Goals? An analysis of sustainability reports from the social entrepreneurship perspective

Sustainability Development Goals (SDGs) are a comprehensive agenda agreed upon globally that aims to stimulate actions towards economic, environmental and social sustainability. Being one of the key stakeholders, the international maritime industry plays an important role in contributing to global sustainability. By applying the concept of social entrepreneurship (SE), this study aims to examine (1) the basic and extended responsibilities (SDG 1–SDG 16) and (2) the potential collaborations within the value chain (SDG 17) concerning SDG implementation in maritime industry. To achieve these, we conduct a content analysis of sustainability reports published by container shipping liners and terminal operators from 2016 to 2019. More specifically, manual text classification is adopted to categorise the text content of sustainability reports based on 17 SDGs, and automatic text mining is employed to further identify the key roles of maritime industry related to each SDG. A unified framework is proposed, which points to varied motives and levels of comprehensiveness of the sustainability efforts by the maritime industry. This framework reveals the theoretic process of maritime industry's transitional involvement in sustainability from the SE perspective. It also creates managerial implications regarding the resource allocation strategies by maritime industry in meeting SDGs.     

An integrated analysis of workers’ physically active activity and active travel choice behavior

Using Texas add-on sample data from the 2009 National Household Travel Survey, this study examines adult workers’ daily active choice decisions in the context of physical activity and attendant health benefits. The study looked at workers’ two choice behaviors: active activity and active travel. The first choice behavior, active activity, is developed as an ordered-response model based on the number of physically active recreational activities pursued during the workday. The second choice behavior, active travel, is developed as a binary-response model that examines workers’ active travel choices—whether or not the worker used any active mode of travel during the same workday. The study improves the understanding and knowledge of observed factors influencing workers’ physically active activity-travel behavior. The study also provides several observations regarding the role (and constraints) of employment in individuals’ active choices. Using a flexible copula modeling methodology, we explore the true correlation (or dependence) between the two behavior choices that could occur due to the presence of unobserved factors, suggesting a simultaneously low or simultaneously high propensity for being physically active across workers. The study findings suggest that transportation and public health policy makers can mutually benefit from encouraging workers to be physically active (from an activity and/or travel perspective). Overall, the study draws attention to the integrated nature of the public health and transportation fields, thereby providing a distinct view of active/inactive choice behavior. To our knowledge, this is the first study exploring a rich variety of components for workers’ active activity-travel behavior through a robust copula approach.     

What encourages people to carpool? An evaluation of factors with meta-analysis

Non-household carpools (where two or more commuters from different residences travel together in the same private vehicle) bring public benefits. To encourage and incentivise it, transport practitioners and researchers must understand its private motivations and deterrents. Existing studies often report conflicting results or non-generalisable findings. Thus, a quantitative systematic review of the literature body is needed. Using meta-analysis, this study synthesised 22 existing empirical studies (representing over 79,000 observations) to produce an integrated review of the carpooling literature. The meta-analysis determined 24 non-household carpooling factors, and their effect sizes. Factors such as number of employees (\(\bar{r} = 0.42\)), partner matching programs (\(\bar{r} = 0.42\)), female (\(\bar{r} = 0.22\)) and fixed work schedule (\(\bar{r} = 0.15\)) were found to have strong effects on carpooling while judgmental factors (such as the motivation to save costs) only exhibited small influence (\(\bar{r} < 0.1\)). Based on the significant effects, the paper discussed prospects for improving carpooling uptake by developing: (i) target demographics, (ii) selling points for marketing, (iii) carpooling partner programs and (iv) multiple employer ‘super-pools’. The results warrant caution due to the small amount of studies synthesised. Transport practitioners might plan carpooling policies based on the findings; and transportation researchers might use the list of factors to model carpooling behaviour.     

Much Ado about Nothing? - An analysis of economic impacts and ecologic effects of the EU-emission trading scheme in the aviation industry

From 2012 on, all CO₂ emissions from flights departing from or arriving at airports within the European Union have to be offset. We analyze the economic and ecological impacts that are caused by an inclusion of the aviation industry into the proposed emissions trading scheme (ETS). Building on the now fixed system design we employ a simulation model to estimate the impacts of the scheme. Our results indicate that financial impacts are highly dependant on external settings, such as allowance prices and demand growth. We show that the financial burden on the aviation industry will be rather modest in the first years after the introduction of the system and therefore induce only low competition distortions. Likewise, emission reductions within air transportation will be comparably low. While aviation will induce a decline of emissions in other sectors, significant absolute reductions within air transportation can only be reached by a more restrictive system design.     

Does European high-speed rail affect the current level of air services? An EU-wide analysis

This paper analyses whether the current provision of air services in Europe is impacted by high-speed rail (HSR). An ex-post analysis is carried out considering 161 routes EU-wide using transnational data. We use censored regressions with special attention paid to the presence of outliers in the sample and to the potential problem of non-normality of error terms. It is found that shorter HSR travel times involve less air services, with similar impact on both airline seats and flights. This impact quickly drops between 2.0- and 2.5-h HSR travel time. The impact of HSR frequencies is much more limited. Hubbing strategies led by the airlines have the opposite effect from HSR, as hubs involve more air services. Airline/HSR integration at the airport and cities being served by both central and peripheral stations have no significant impact. Metropolitan and national spatial patterns may help to better understand intermodal effects.     

How do they get by without cars? An analysis of travel characteristics of carless households in California

Transportation - In spite of their substantial number in the U.S., our understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is...     

Bicycling renaissance in North America? An update and re-appraisal of cycling trends and policies

This paper reviews trends in cycling levels, safety, and policies in Canada and the USA over the past two decades. We analyze aggregate data for the two countries as well as city-specific case study data for nine large cities (Chicago, Minneapolis, Montréal, New York, Portland, San Francisco, Toronto, Vancouver, and Washington). Cycling levels have increased in both the USA and Canada, while cyclist fatalities have fallen. There is much spatial variation and socioeconomic inequality in cycling rates. The bike share of work commuters is more than twice as high in Canada as in the USA, and is higher in the western parts of both countries. Cycling is concentrated in central cities, especially near universities and in gentrified neighborhoods near the city center. Almost all the growth in cycling in the USA has been among men between 25-64 years old, while cycling rates have remained steady among women and fallen sharply for children. Cycling rates have risen much faster in the nine case study cities than in their countries as a whole, at least doubling in all the cities since 1990. They have implemented a wide range of infrastructure and programs to promote cycling and increase cycling safety: expanded and improved bike lanes and paths, traffic calming, parking, bike-transit integration, bike sharing, training programs, and promotional events. We describe the specific accomplishments of the nine case study cities, focusing on each city’s innovations and lessons for other cities trying to increase cycling. Portland’s comprehensive package of cycling policies has succeeded in raising cycling levels 6-fold and provides an example that other North American cities can follow.     

How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach

In this paper, we reformulate conventional data envelopment analysis (DEA) models and propose a novel method for evaluating sustainability of suppliers in the presence of interval volume discount offers, fuzzy data, and ordinal data. To this end, we convert all data into interval data. To convert fuzzy data into interval data, we use nearest weighted interval approximation by applying weighting function and we convert each ordinal data into interval one. Then, using enhanced Russell model, interval efficiencies are obtained. After that, using preference degree approach, we rank suppliers. Finally, a case study is presented to illustrate our proposed approach.     

Law and Characteristics Analysis of Domestic Tunnel Construction Accidents from 2006 to 2016北大核心CSCD

With the rapid development of tunnel construction in China, strengthening statistical studies of domestic tunnel construction accidents is of great significance in order to understand the safety status of tunnel construction and the development trends of tunnel construction accidents and for exploring the direction of future scientific re-search. There were 89 tunnel construction accidents in China (not including subway tunnels or municipal tunnels)from 2006 to 2016. The characteristics of these accidents are analyzed in terms of time distribution, spatial distribu-tion, grade and type of accident according to the statistics by means of line charts, bar charts and pie charts. Corre-sponding prevention and control recommendations are put forward considering the factors such as year, month, work-ing day, time period, region, location, grade and type. © 2018, Editorial Office of "Modern Tunnelling Technology". All right reserved.     

An analysis of children’s leisure activity engagement: examining the day of week, location, physical activity level, and fixity dimensions

This paper presents a detailed analysis of discretionary leisure activity engagement by children. Children’s leisure activity engagement is of much interest to transportation professionals from an activity-based travel demand modeling perspective, to child development professionals from a sociological perspective, and to health professionals from an active lifestyle perspective that can help prevent obesity and other medical ailments from an early age. Using data from the 2002 Child Development Supplement of the Panel Study of Income Dynamics, this paper presents a detailed analysis of children’s discretionary activity engagement by day of week (weekend versus weekday), location (in-home versus out-of-home), type of activity (physically active versus passive), and nature of activity (structured versus unstructured). A mixed multiple discrete-continuous extreme value model formulation is adopted to account for the fact that children may participate in multiple activities and allocate positive time duration to each of the activities chosen. It is found that children participate at the highest rate and for the longest duration in passive unstructured leisure activities inside the home. Children in households with parents who are employed, higher income, or higher education were found to participate in structured outdoor activities at higher rates. The child activity modeling framework and methodology presented in this paper lends itself for incorporation into larger activity-based travel model systems where it is imperative that children’s activity-travel patterns be explicitly modeled—both from a child health and well-being policy perspective and from a travel forecasting perspective.

Spatial analysis of the competitiveness of the high-speed train and air transport: The role of access to terminals in the Madrid–Barcelona corridor

This paper analyzes the effect of access and egress time to transport terminals over the spatial competiveness of the high-speed train (HST) in the Madrid–Barcelona (Spain) corridor, one of the densest airline domestic markets in the world. Applying spatial data from 2010 provided by a geographical information system (GIS) to a mode choice model estimated with sample travelers in this corridor, the present study examines whether and how the level-of-service of transport terminals spatially affects the competitiveness or modal distribution of HST and air transport in the provinces of Madrid and Barcelona; and, in particular, the degree of competitiveness that can be accrued by the access time provided by private car and transit in different market segments, especially mandatory and leisure trips. In a number of urban zones near train stations and airports, terminal accessibility clearly favors one transport mode in comparison to the other. Improving terminal accessibility via private car or public transit not only affects the relative access to terminals, but also represents a key strategy for readjusting the market shares of the competing modes in the corridor.     

How much should gasoline be taxed when electric vehicles conquer the market? An analysis of the mismatch between efficient and existing gasoline taxes under emerging electric mobility

The taxation of gasoline is characterized by large variability across countries and recent research has analyzed existing gasoline tax levels from an economic efficiency point of view focusing on conventional internal combustion engine vehicles. Most studies find that existing fuel tax rates do not coincide with economically efficient levels. As long as policymakers do not take action to reduce the resulting efficiency gap, there will be an ongoing welfare loss to the economy. However, the composition of passenger car fleets will probably be subject to fundamental changes in the (near) future due to the emergence of electric mobility. This raises the question of whether the mismatch between current and efficient fuel taxation will persist, shrink, or even exacerbate under emerging electric mobility. This paper aims at answering this question by determining the structure and level of optimal gasoline taxes in the presence of electric vehicles (EVs). First, the optimal (nationwide) gasoline tax is analytically derived employing a general equilibrium approach. It is shown that differences in traffic related marginal external costs among fuel powered cars and EVs affect the corrective Pigouvian component of the optimal gasoline tax while a differential tax treatment influences the fiscal rational of the tax. Second, the model is applied to Germany using differentiated data on e.g. external costs and behavioral responses. Under a wide range of scenarios, the present analyses indicate a strong relationship between optimal gasoline taxes and electric mobility, calling for a downward adjustment of efficient gasoline taxes. The effect is mainly driven by financial incentives for purchasing and using EVs. Since fuel is likely to be undertaxed in many countries, the emergence of electric mobility will therefore close the gap between gasoline taxes in place and economically efficient taxes. On the other side, it will increase the efficiency gap in those countries where gasoline is overtaxed. This also has important implications for policy concerned with environmental objectives. Pushing electric mobility seriously and at the same time taxing gasoline efficiently could actually prevent sufficient CO₂ emission savings. However, at least in the case of Germany, even a downward adjusted optimal gasoline tax under electric mobility is likely to be higher than the current (non-optimal) tax.     

Comparison of road freight transport trends in Europe. Coupling and decoupling factors from an Input–Output structural decomposition analysis

Decoupling road freight transport from economic growth has been acknowledged by the European Union as a key means to improving sustainability. It is therefore important to identify both the coupling and decoupling drivers of road freight transport demand in order to determine possible factors that may contribute to reduce road transport in the future without curbing economic development. This research proposes an Input–Output (IO) structural decomposition analysis (SDA) to explain road freight transport in terms of a set of key factors that have strongly influenced road freight demand in recent decades in European countries—such as economic growth, economic structure and the evolution of road transport intensity (including improvements in both supply and transport systems). This methodological approach allows us to quantify and compare their contribution in different European countries to either increase or decrease road freight transport demand. The empirical basis for this analysis is a dataset of nine European countries which have IO tables and road transport data available from 2000 to 2007, comprising data on domestic production, imports and exports as well as tonne-kms for 11 types of commodity classes. The results show that, as a whole, aggregate road transport demand has grown—driven mainly by economic activity—but this growth has been strongly curbed in some countries by changes in road freight transport intensity and moderately by the dematerialization of the economy. International transport has been also proven to be a key factor driving road freight transport volumes. Moreover, the increased penetration of foreign operators in national haulage markets appears to have reinforced the final decoupling levels observed in some cases.     

Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.