Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

     

Crowdsourced data for bicycling research and practice

ABSTRACT

Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.     

Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus,OH

Transportation - Autonomous vehicles (AVs) could change travel patterns of the population significantly and with the rapid improvements in AV technology, transportation planners should address AV...     

New appraisal values of travel time saving and reliability in Great Britain

Transportation - This paper provides an overview of the study ‘Provision of market research for value of time savings and reliability’ undertaken by the Arup/ITS Leeds/Accent consortium...     

Trip generation modeling using data collected in single and repeated cross‐sectional surveys

The majority of US metropolitan regions still use the four‐step urban transportation modeling system to develop their travel forecasts. Trip generation, the first step of this system, has as objective of predicting the expected total travel demand in a region. The commonly used methods in planning practice for predicting this expected total travel demand typically use only the most recent cross‐sectional data available from a study region for model development, which ties the resulting travel‐forecast model to the economic environment prevailing at the time of data collection. Applying such models to generate forecasts of travel in economic environments significantly different from those embodied in the estimated model parameters could result in greater errors than would otherwise be the case. To address the aforementioned problem, this paper proposes the development of trip generation models estimated on multiple independent cross‐sectional datasets collected in the same urban region but at different times representing different economic environments. Data used in the research were collected in cross‐sectional household travel behavior surveys undertaken in the Greater Toronto Area, Canada in 1986, 1996, 2001, and 2006. The results lead to the conclusion that well‐specified models, estimated on pooled multiple cross‐sectional datasets, yield travel predictions in the base and horizon years, respectively, that have smaller error compared with corresponding travel predictions generated with single cross‐sectional models. Copyright © 2012 John Wiley & Sons, Ltd.     

Is accessibility relevant in trip generation? Modelling the interaction between trip generation and accessibility taking into account spatial effects

The influence of accessibility to opportunities in trip generation continues to be debated in the specialised literature given its relevance to simulate phenomena such as induced demand. This article estimates multiple linear regression models (MLR), spatial autoregressive models (SAR), spatial autoregressive models in the error term (SEM) and spatially filtered Poisson regression models (SPO) to discover whether or not accessibility is a significant factor in trip generation using data from the urban area of Santander (Spain). The results obtained provide evidence which shows that, on an intraurban scale, more accessibility to opportunities decreases trip production in private vehicle for work purpose, whereas it increases trip production in other transport modes for non—mandatory purposes. For the correct interpretation of the estimated parameters it was important to consider the direct and indirect effects of the independent variables in the SAR production models. Finally, the validation of the models showed that the SAR and SEM models had a mean squared error slightly lower than the MLR models in predicting overall trip production. This was because the spatial models reduced the correlation of the residuals present in the MLR models. Furthermore, the SPO models performed better in validation mode than all the continuous models.     

Re-evaluation of the widely applied force-frequency relation for frequency-modulation AFM under solution

Frequency-modulation atomic force microscopy （FM-AFM） is a highly versatile tool for surface science. Besides imaging surfaces, FM-AFM is capable of measuring interactions between the AFM probe and the surface with high sensitivity, which can provide chemical information at sub-nanometer resolution. This is achieved by deconvoluting the frequency shift, which is directly measured in experiments, into the force between the probe and sample. At present, the widely used method to perform this deconvolution has been shown to be accurate under high quality （high-Q） factor vacuum conditions. However, under low quality （low-Q） factor conditions, such as in solution, it is not clear if this method is valid. A previous study apparently verified this relation for experiments in solution by comparing the force calculated by this equation with that obtained in separate experiments using the surface force apparatus （SFA）. Here we show that, in solution, a more direct comparison of the force calculated by this relation with that directly measured by the cantilever deflection in AFM reveals significant differences, both qualitative and quantitative. However, we also find that there are complications that hinder this comparison. Namely, while contact with the surface is clear in the direct measurements （including the SFA data）, it is less certain in the FM-AFM case. Hence, it is not clear if the two methods are measuring the same tip-sample distance regimes. Thus, our results suggest that a more thorough verification of this relation is required, as application of this formulation for experiments in solution may not be valid.     

The economic impact of commercial fisheries and marine‐related activities: A critical review of northeastern input‐output studies

Abstract

The application of the input‐output method to regional analysis of coastal zone production and consumption activities is examined. A review of previous Northeastern input‐output studies provides a framework for analyzing selected methodological procedures utilized in this type of analysis. While commercial fishing and other marine‐related activities have been shown to have important economic impact on coastal communities, the review indicates that in certain cases the methodological procedures adopted tended to yield an upward bias to the estimation of economic impacts. Standardization of procedures is recommended for future studies.     

Port competitiveness in container traffic from an internal point of view: the experience of the Port of Algeciras Bay

The objective of this article is to analyse the factors that affect port competitiveness from the perspective of the agents and companies that operate in one of the Mediterranean ports most active in container traffic: the Port of Algeciras Bay (PAB). It is essential to understand the perspective of the suppliers of port services, in order to apply the necessary judgment when formulating policies to strengthen the competitive position of a port. The model selected to meet the objective outlined is based on the ‘extended diamond’ of Porter. The universe for the study comprises all the institutions and companies involved in the containerization process in the Port of Algeciras Bay. Within this methodology, two procedures are utilised. First, from the quantitative perspective, a survey has been conducted to obtain responses to two groups of questions, one dealing with situation and the other with perceptions. Second, and from a qualitative perspective, several open interviews have been conducted to corroborate the responses obtained to the questionnaire. From the data obtained in the survey, a model has been estimated that minimises the sum of the residuals in absolute value, utilizing the L1 regression; this model enables the competitive advantages and disadvantages of the PAB, from the point of view of the port operators, to be determined by an analysis of residuals. This analysis demonstrates that the variables representing greater competitive advantages are associated with trans-shipment, and with the maritime accessibility of the Port to vessels, whereas those generating greater disadvantages are associated with rail transport (which, according to those surveyed, has resulted in the diversion of significant volumes of traffic to other Spanish or European ports with better communications, particularly Valencia, Barcelona and Gioia-Tauro, and with the state of competition in container trans-shipment activities.     

Understanding the Relationship Between Physical and Virtual Representations of Transit Agencies

Abstract

This study examines whether physical attributes of transit agencies, such as agency size, make a difference in how transit websites are designed, and how transit information is distributed. The objective of this study is to see if there is a relationship between physical and virtual representations of transit agencies. A rating instrument is developed for evaluating the quality of transit websites. Our findings suggest that transit agency size plays a key role in determining website quality: When the size of transit system is large, the information about the agency is too complex to be effectively presented on web pages. Thus, the quality of the large agencies’ websites is lower than medium-sized agencies. Instead, we find that large agencies attempt to design more user-friendly sites, and provide advanced information searching tools to compensate for low information quality. Policy implications for transit agencies are discussed.