Research in Transportation Economics最新文献

This paper discusses an overarching framework that integrates latent variable models and prediction success with receiver operator characteristics (ROC) curves. In three illustrative examples that focus on trip purpose, health market indicators, and resources, the paper employs binary logit models and ROC methodology to identify factors that best discriminate individuals' trips to out-patient health care facilities. Data for the examples are a three-year longitudinal survey of persons 45 years and older in China. The study contributes to the sparse empirical economics literature using ROC methodologies and more broadly to the transportation and health literatures. The ROC applications provide new insights on health care trips, finding that out-patient trips for treatment, paying out-of-pocket costs, and lack of monetary resources are salient discriminators in one's trip choice decisions.

Given the dynamic nature of Urban Freight Transport (UFT) processes, the involved transport and logistics operators face with internal and external issues that should tackle to improve last-mile levels of service and decrease total costs while performing delivery operations. Customers (i.e., freight receivers) perceive the level of service through the acceptance of their requests, while total operational costs are mainly determined by the total travel costs (i.e., distance and/or time) required to accomplish the customers' request. In addition, the vehicle-kilometres travelled are related to the externalities produced. Given that the actors involved in the process operate in a stochastic environment (with changes that can occur both in terms of demand – receivers' requests, and in supply – travel times), collaboration and coordination among the operators could play a key role in meeting the customers' requests as well as in reducing both internal and external delivery costs. Therefore, the paper proposes an UFT modelling framework that integrates collaboration and coordination processes among the different involved actors, and allows the benefits to be assessed. The model has a multi-agent architecture based on microsimulation. In particular, the multi-agent architecture allows us to point out the different actors’ responses to various internal (e.g., delivery requests) and external (e.g., delivery times) changes occurring in the daily delivery operations. It consists of three layers. The first one simulates the interactions among actors operating collaboratively. The second layer microsimulates the collaborative processes of information management. Finally, a third layer integrates the two previous layers, facilitating a decision-making process in such a dynamic context. The whole modelling framework is tested in a real case study in which it is possible to validate pros and cons of working in a collaborative and coordinative environment. The results show significant benefits from actors/operators involved in the process and subsequently can address the policy/measure implementation towards a more sustainable and liveable city.

Given the severity and effects of global warming, countries are undertaking attempts to restrict emissions through research, carbon taxes, and other means and policies. Over the last decade, one promising emerging concept in the sharing economy has been crowdshipping. It is currently in its early phases, with little research on its sustainability, particularly while operating in inter-city or inter-urban areas. This study focuses on the sustainability assessment of inter-urban crowdshipping through a case study approach, where the existent literature is scarce. The research employs a multi method quantitative approach with a case study, where descriptive analysis is used to explore the case company's operations. It addresses the other research questions by performing environmental and financial impact assessment considering two relevant scenarios with postal services and transporter operations. Data from the carrier's (Nimber AS, Norway) platform were extracted over 6 months and complemented with a survey for analysis. When compared to alternative scenarios in which 3 PL service providers are utilized, crowdshipping appeared to contribute to lower emissions. It also seemed to be more cost-effective for the users. Unpublished evidence from the ongoing EU LEAD project suggests that access to micro-hubs could be one important urban logistics policy instrument to enhance efficiency for crowdshipping platforms.

We estimated the effect of surface metering technology deployed at Charlotte Douglas International Airport (CLT) by the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) on fuel and CO₂ savings. We employed a difference-in-differences strategy, covariate-balancing methods, and a fixed-effects model. Furthermore, we used a doubly robust strategy that combines matching with parametric regression using datasets constructed by data-driven approaches, which minimizes discretion in selecting comparison units. Our estimates from the daily panel of US carriers from November 2015 to November 2019 suggested that about 39.156 kg (95% confidence interval (CI) 64.017–13.436) of fuel and 120.599 kg (CI 197.174 kg–41.382 kg) of CO₂ could be saved per flight even when the single-engine taxiing rate was as high as 75% during the entire deployment phase. At the same time, our results suggested a concerning pattern: taxi-out time savings generated by the technology diminished over time.

Drones are expected to allow faster and more efficient last mile parcel delivery while at the same time reducing relevant costs. This studies’ purpose is to understand what factors influence consumer acceptance in Germany. The Unified Theory of Acceptance and Use of Technology model was expand using an incremental approach. The construct performance expectancy was replaced with the three context-specific constructs, environmental performance expectancy, speed expectancy, and relative advantage of contact-free delivery. Four perceived risk constructs (i.e., privacy risk, safety risk, noise risk, financial risk) were added. Eleven hypotheses were developed and tested via an online survey. The data were analyzed using structural equation modeling in Statistical Package for the Social Sciences. Relative advantage of contact-free delivery, hedonic motivation, and social influence are the highlights among the factors investigated for their influence on consumer acceptance. The influence on the perceived risk constructs privacy risk and safety risk varies depending on drone usage experience and gender. The value of the study stems from the proposed extended Unified Theory of Acceptance and Use of Technology model, tailored to a new technology context (drones for last mile parcel delivery) and a different cultural setting (Germany). This research contributes to the multidisciplinary approach of existing drone research by adding a consumer perspective to the mainly logistics provider-oriented engineering, informatics, and operations research.

This paper examines the importance of commercial revenue on optimal airport charges in a Hotelling-type duopoly airports competition. Each airport offers multi-products to heterogeneous consumers (airlines and passengers) and sets commercial and landing charges and serves. The airport-airline bundle competes for leisure and business passengers. The setting of landing charges under different regulatory regimes is investigated. We demonstrate that in the leisure travel market, which ignores schedule delay cost, the optimal landing fee is invariant to the regulatory scheme, and concession revenue is determined by an airport’s home market size. In the business travel market, the optimal landing charge is smaller if concession revenue is included in setting the landing fee than if it is not included. In the former case, increasing passenger volume does not guarantee increases in airports’ aeronautical revenue, and a negative impact may exist if the weight of concession profit out of total profit is small.

Some recent studies have highlighted the importance of considering the temporal context as a potential factor influencing time valuation estimates. However, in studies that compare shorter and longer-run choice settings, various elements tend to differ between the two settings, rendering it difficult to infer what drives short- and long-run estimates apart. This paper focuses on the comparison between time valuations associated with one-off vs. repeated events. We present the results of a lab experiment on the valuation of waiting time, which has been carefully designed such that the only element varying between the two treatments is whether the choice has a repetitive character or concerns a one-off event. We find no significant differences between the two treatments, and hence can conclude that the repetitive character of a choice situation is unlikely to drive differences between short- and long-run estimates, at least if the concerned travel or waiting times are relatively short (¡10 min).

In recent years, car-sharing has emerged and developed in different cities in China, supplementing other urban transportation modes, but also triggering fierce competition with some of them, such as taxis. In this study, we use the car-sharing orders and GPS trajectory data of a company in Beijing, China, to discuss the competitive advantages, market, and development trends of car-sharing from the perspective of travel costs. By building a travel costs comparison model, we calculate the travel costs of car-sharing and taxis under the same travel scenario, comparing their cost differences in the dimensions of travel mileage, duration, utilization efficiency, etc., and locating the cost advantage space of car-sharing. Further, we discuss the impact of different elements of travel (such as time cost, waiting time, etc.) on the cost advantage of travel by car-sharing. The results reveal that, under the current pricing conditions, the cost advantage of car-sharing is particularly obvious when the rental duration is not too long (less than 14 h), the utilization efficiency is relatively high, and the stop time is shorter than 8 h. We also find that it makes great cost advantage of using car-sharing in periods or areas of urban taxi shortages. In the cases of short travel, car-sharing have bigger cost advantage for the middle and low-income groups. In addition, the popularity of bike-sharing can also increase the cost advantage of car-sharing, which indicates that the synergy of different travel models could promote the development of car-sharing. However, when people travel long, the cost advantage of car-sharing would be minimally affected by the elements, such as people's income and the development of bike-sharing. In other words, car-sharing would keep its cost advantage in the cases of long travel, especially when its utilization efficiency is high and the travel stop time is short.

Although cable car services have become an integral part of the transit system in many cities in the world, their specific technology has never been studied from the viewpoint of the links between optimal design and marginal cost pricing. Here we formulate, solve and apply a first model to represent the specific features of a cable car system considering operators' and users' costs. The links among cabins’ density, their speed and capacity, frequency, slope, and others, are formulated to obtain the optimal design variables as a function of demand volume, a system cost function, marginal and average costs, and first best money prices and possible subsidies. An application with sensitivity analysis illustrates the model.

Omnichannel distribution is a retail innovation that provides a seamless purchasing experience to customers through cohesive experience across channels, cross-channel integration, and integrated assistance. Blurring the lines between offline and online shopping, concepts like “Buy-Online-Pickup-In-Store” (BOPIS) and “Buy-In-Store-Ship-Direct” (BSSD) are increasingly becoming accepted in retail operations. While many retailers are still in a nascent phase of integrating online channels and physical stores, consumer-centric studies are called for to investigate the diffusion of these new strategies in the evolving marketplace. Our study explores the key adoption determinants of these new omnichannel strategies, focusing on the case of India. A detailed online survey was used to collect data for a sample of 311 Indian consumers. Econometric analysis reveals the main purchase influencing factors. We find that a quick purchase process, elimination of product delivery delays, delivery and shipping costs, ease of receiving product, retail system reliability and, trust in retailer are key adoption determinants. Purchase returnability is only weakly associated with BOPIS purchase choices while payment security has no significant effect. Among six demographic variables, only gender and age are found to differ significantly between the two concepts. These insights from this study should be useful for retailers to design omnichannel strategies and for transport policy makers to predict the future growth of e-commerce related transport movements.