Revisiting port pricing; a proposal for seven port pricing principles

A review of seven large landlord port authorities around the world reveals a notable diversity of pricing structures. While port authorities increasingly act as commercial undertakings, port pricing often seems to be not driven by commercial considerations. In this paper, we argue that ports can be regarded as “business ecosystems” with multiple users. This provides a valuable perspective on pricing and raises the question of whether ports can be regarded as two-sided markets. We argue this is not the case. The business ecosystem perspective provides a basis for deducing seven pricing principles for port authorities that are detailed in the paper and illustrated with cases these principles. These pricing principles are broadly follow a direct user pays approach; capture value from “non-core” tenants; price aggressively for activities with a high strategic value; differentiate pricing based on price elasticity and connectivity improvements; maximize revenue from long-term lease agreements, price port dues competitively; critically consider differentiation of charges based on environmental performance; and finally use incentives to align interests of terminal operators and shipping lines. We conclude that the ecosystem perspective is central to the understanding of pricing decisions of port authorities and that various pricing issues deserve more attention.     

Impacts of built environment and emerging green technologies on daily transportation greenhouse gas emissions in Quebec cities: a disaggregate modeling approach

This paper aims to investigate the impact of the built environment (BE) and emerging transit and car technologies on household transport-related greenhouse gas emissions (GHGs) across three urban regions. Trip-level GHG emissions are first estimated by combining different data sources such as origin–destination (OD) surveys, vehicle fleet fuel consumption rates, and transit ridership data. BE indicators for the different urban regions are generated for each household and the impact of neighborhood typologies is derived based on these indicators. A traditional ordinary least square (OLS) regression approach is then used to investigate the direct association between the BE indicators, socio-demographics, and household GHGs. The effect of neighborhood typologies on GHGs is explored using both OLS and a simultaneous equation modeling approach. Once the best models are determined for each urban region, the potential impact of BE is determined through elasticities and compared with the impact of technological improvements. For this, various fuel efficiency scenarios are formulated and the reductions on household GHGs are determined. Once the potential impact of green transit and car technologies is determined, the results are compared to those related to BE initiatives. Among other results, it is found that BE attributes have a statistically significant effect on GHGs. However, the elasticities are very small, as reported in several previous studies. For instance, a 10 % increase in population density will result in 3.5, 1.5 and 1.4 % reduction in Montreal, Quebec and Sherbrooke, respectively. It is also important to highlight the significant variation of household GHGs among neighborhoods in the same city, variation which is much greater than among cities. In the short term, improvements on the private passenger vehicle fleet are expected to be much more significant than BE and green transit technologies. However, the combined effect of BE strategies and private-motor vehicle technological improvement would result in more significant GHGs reductions in the long term.     

Immigrant status and commute distance: an exploratory study based on the greater Golden Horseshoe

With 8.76 million residents in 2011, the population of Southern Ontario’s Greater Golden Horseshoe (GGH) has grown dramatically over the past decades, driven by net domestic in-migration and immigration. Corresponding to its growth in population, commuting distances and times within the region have grown as well. Yet, despite the number of immigrants that the region attracts on a yearly basis, there is comparatively little information on commute distances. Consequently, this paper examines commuting distance amongst immigrants in the GGH. Specifically, it evaluates commute distance by immigrant status (immigrants and native born), along with how commute distance differs by arrival cohort and ethnic and racial population groups. Results indicate that commute distance increases with increasing duration of residence, with differences by race and ethnicity.     

I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice

The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.     

The San Francisco Travel Quality Study: tracking trials and tribulations of a transit taker

In helping understand the dynamics of travel choice behavior and traveler satisfaction over time, multi-day panel data is invaluable (McFadden in Am Econ Rev 91(3): 351–378, 2001). The collection of such data has become increasingly feasible thanks to smartphones, which researchers can use to present surveys to travelers and to collect additional information through the phones’ location services and other sensors. This paper describes the design and implementation of the San Francisco Travel Quality Study, a multi-day research study conducted in autumn 2013 with 838 participants. The objective of the study was to investigate the link between transit service quality, the satisfaction and subjective well-being of transit riders, and travel choice behavior, with a particular interest in the influence of travelers’ choice history and personal experiences on future transit use. For that purpose, a rich panel data set was collected from multiple sources, including a number of mobile travel experience surveys capturing traveler satisfaction and emotions, two online surveys capturing demographics, attitudes and mode choice intentions, as well as high-resolution phone location data and transit vehicle location data. By fusing the phone location data with transit vehicle location data, individual-level transit travel diaries could be automatically created, and by fusing the location data with the survey responses, additional information about the context of the responses could be derived. While the behavioral and satisfaction-related findings of the study are detailed in other publications, this paper is intended to serve two purposes. First, it describes the study design, data collection effort and challenges faced in order to provide a learning opportunity for other researchers considering similar studies. Second, it discusses the key sociodemographic data and characteristics of the study population in order to provide a foundation and reference for further publications that make use of the data set described here. The authors would like to invite other researchers to collaborate with them on the evaluation of the data.     

Literature review on surveys investigating the acceptance of automated vehicles

Due to the potential of automated vehicles to offer a multitude of advantages to the travelers and therefore influence their daily routines, it is essential to monitor the public’s opinion on this particular technological development. The goal of a number of surveys in recent years was therefore not only to elicit the general acceptance of the technology but to additionally explore when, how and why respondents were inclined to make use of it. This is the first literature review on surveys regarding automated vehicles with the intention to investigate the various methods currently being applied and the conclusions they lead to. In addition to comparing the general results in terms of the distributions of the response variables, the surveyed explanatory variables are categorized and analyzed according to their influence in different experiments. Based on these investigations, this review identifies research gaps that can be addressed in future experiments.     

The use of a hazard-based duration model for imputation of missing personal income data

This paper suggests using a proportional hazard model to predict personal income, for the purpose of imputing missing income data in household travel surveys. The model has a hazard function that comprises two multiplicative components: (1) a non-parametric baseline hazard function that is dependent only on the income level and (2) a function that is dependent only on the other personal attributes of the survey respondents (excluding income). To estimate and validate the model, data is drawn from a travel characteristics survey conducted in Hong Kong in year 2001. The model is found to have a much higher accuracy when compared with a conventional ordered probit model based on the assumption that the logarithm of income is normally distributed.

Use of an ANP to prioritize managerial responsibilities of maritime stakeholders in environmental incidents: An oil spill case

While maritime pollution is often caused at the operational level, the causes can generally be traced back to shortcomings in the ship–shore interface and to poor communication among stakeholders. Here, the environmental responsibilities of maritime stakeholders are systematically analyzed and quantified using an analytic network process. This approach ensures that the most important dependencies and feedbacks among the responsibilities which is applied to a real marine situation (an oil spill) to validate the theoretical basis. A number of latent links between stakeholders’ responsibilities and the catastrophic impacts of the spill are identified.