Influence of 3D printing on transport: a theory and experts judgment based conceptual model

Consumer 3D printing is on the rise and has the potential to significantly change the transport and logistics sector. Current literature on 3D printing and transport studies does not provide a systematic model of the impact of 3D printing on transport and related (policy relevant) areas, such as traffic safety, location decisions, accessibility and environmental effects. Based on a literature review and two rounds of expert consultation, we propose and refine a conceptual model as a way to approach this gap in the literature. The expert consultation yields that the conceptual model comprises the relevant and important elements for assessing the impact of 3D printing on transport and transport-related challenges. Location, needs and transport resistance are important: (a) city-level hubs are the most likely locations for 3D printers because they can coordinate material flows and gather expertise; (b) mass-individualisation and personification dictates the needs for 3D printers; (c) distribution networks will be organised more efficiently, less empty vehicles, but raw materials still need shipping. However, experts’ opinions diverged on the impact of 3D printing on transport volumes and environmental impacts.     

Exploring the conditions for inclusive port development: the case of Indonesia

Governments around the world are adopting inclusive growth agendas. The ambition to align economic growth ambitions with broader-based social benefits is increasingly embraced by corporations to limit the ‘negative externalities’ and enhance the ‘positive externalities’ of their operations. Therefore, micro-level corporate strategies and macro-level national ambitions meet at the meso-level of networks and clusters. This requires societal spheres to collaborate and search for alternative governance constellations. In this discourse, port development is only recently receiving attention. In March 2018, ports around the world signed the World Ports Sustainability Program declaration, which aims to contribute to the sustainable development goals (SDGs), whilst a number of national port (master) plans have started to include social along with environmental standards. Extant studies on partnering and stakeholder inclusion in port development are proliferating but are primarily aimed at environmental rather than social (inclusion) issues. This paper adopts an exploratory research design to consider conditions for inclusive port development. A novel taxonomy considers port development as a driver for inclusive growth, where partnerships are the missing link between micro-level business strategies and macro-level effects in the port region and economy at large. This paper shows the first findings and delineates areas for further research.     

The university workers’ willingness to pay for commuting

Using a dynamic approach, employing data on job mobility, we demonstrate that university workers?? marginal willingness to pay for reducing commuting distance is about ?0.25 per kilometre travelled. This corresponds to a marginal willingness to pay for reducing commuting time of about 75?% of the net average hourly wage. For females, the willingness to pay is substantially higher than for males. It is also substantially higher for workers that work few hours per day, as predicted by theory.     

Bayesian committee of neural networks to predict travel times with confidence intervals

Short-term prediction of travel time is one of the central topics in current transportation research and practice. Among the more successful travel time prediction approaches are neural networks and combined prediction models (a ‘committee’). However, both approaches have disadvantages. Usually many candidate neural networks are trained and the best performing one is selected. However, it is difficult and arbitrary to select the optimal network. In committee approaches a principled and mathematically sound framework to combine travel time predictions is lacking. This paper overcomes the drawbacks of both approaches by combining neural networks in a committee using Bayesian inference theory. An ‘evidence’ factor can be calculated for each model, which can be used as a stopping criterion during training, and as a tool to select and combine different neural networks. Along with higher prediction accuracy, this approach allows for accurate estimation of confidence intervals for the predictions. When comparing the committee predictions to single neural network predictions on the A12 motorway in the Netherlands it is concluded that the approach indeed leads to improved travel time prediction accuracy.     

Travelers’ compliance with social routing advice: evidence from SP and RP experiments

Transportation - This study examines to what extent travel information can be used to direct travelers to system-optimal routes that may be sub-optimal for them personally, but contribute to...     

Can passenger flow distribution be estimated solely based on network properties in public transport systems?

Transportation - We present a pioneering investigation into the relation between passenger flow distribution and network properties in public transport systems. The methodology is designed in a...     

Carsharing: the impact of system characteristics on its potential to replace private car trips and reduce car ownership

Transportation - This paper aims to explore the potential of carsharing in replacing private car trips and reducing car ownership and how this is affected by its attributes. To that affect, a...     

What are the determinants of the willingness to share rides in pooled on-demand services?

Transportation - Simulation studies suggest that pooled on-demand services (also referred to as Demand Responsive Transport, ridesharing, shared ride-hailing or shared ridesourcing services) have...     

Transportation education in the United States

Abstract

In the last decade the importance of attitude‐related residential self‐selection has frequently been recognized. In addition people can theoretically self‐select them with respect to other location choices, such as job locations, with respect to travel behaviour, or with respect to the exposure to transport externalities such as noise and congestion. In this paper, we argue that insights into self‐selection processes might significantly improve our knowledge on location choices, travel behaviour and transport externalities. We elaborate on options for self‐selection and briefly formulate methodologies for research into self‐selection.     

Multi-period Research Designs for Identifying Causal Effects of Built Environment Characteristics on Travel Behaviour

Abstract

To date, most empirical studies have applied cross-sectional designs to investigate the relationship between the built environment (BE) and travel behaviour (TB). Since these studies cannot identify causal influence, the use of designs that provide data on multiple moments in time seems necessary. This article classifies these designs and describes how they can be applied to identify causality in this relationship. We recommend the use of natural experiments to assess the impact of changes in land use/infrastructure and prospective longitudinal designs to assess the impact of residential or job moves. In addition, the role of the BE can be explored by assessing the impact of (1) deliberate TB change experiments and (2) changes in household circumstances across different spatial contexts over time. The use of randomised experimental designs is recommended for the former and prospective longitudinal designs for the latter. The article concludes with an outlook on future research.