Multimodal Transportation最新文献

Pricing optimization is key to the airlines’ profitability. Oftentimes, the premium cabin demand falls short of the seat capacity, making economy cabin seat upgrading a common practice. The common procedure in the airline industry solves the revenue management optimization for premium cabin first and then to determine the number of seats left for lower cabin upgrading, which together with the lower cabin seat capacity, allows to solve the lower (economy) cabin optimization. In comparison, this paper develops models to combine all the cabin capacities, including economy and premium (business/first), and solve the optimization simultaneously. The models consider cases with deterministic and random demand, respectively. Numerical tests use actual production data and indicate that the proposed models outperform the current ones in practice.

Frankfurt Airport is a role model for intermodal infrastructure, given its excellent degree of air, rail, and road connectivity. The connection Cologne-Frankfurt, for instance, is frequently used as the prime example for air-rail mode substitution, where a high-frequent rail service from Cologne Main Station to the integrated air-rail terminal at Frankfurt Airport completely replaced air service between the two cities in the year 2007. In recent years, Lufthansa and Deutsche Bahn have gradually expanded this role model into a whole network product covering 24 cities connected for Frankfurt Airport, advertised as Lufthansa Express Rail. In this study, we analyze the potential benefits of Lufthansa Express Rail for the 24 cities in terms of journey time and journey fare, comparing it to flights departing from the 24 origin cities directly. Our results indicate that the network and schedule together do not lead to obvious advantages for either time-sensitive or price-sensitive passengers, compared to the prime example Cologne-Frankfurt. Our study contributes to the analysis of air-rail integration, highlighting the difficulties which are inherent when extending a prime example corridor into a working network product.

Over the past two decades, transportation engineers and urban planners have become increasingly interested in using performance measures to capture roadway infrastructure effects on bicyclists' comfort levels. In this study, a tool is developed for evaluating Ohio's local roads and identifying opportunities for improvement. This begins by assessing the available data sources required to develop a customized model for Ohio, United States. Stakeholder interviews and data assessment outcomes indicate that most local jurisdictions do not have access to all relevant data for a complete bicycle level of traffic stress (LTS) analysis. Therefore, we extend the original bicycle LTS framework by formulating effective methods to deal with missing data. The designed methods are based on Ohio's functional classification system, and existing data on speed limits, traffic volumes, and bicycle facility widths. Our approach enables agencies to conduct bicycle LTS analysis when critical data elements are missing (such as posted speed limits, traffic volumes, and bicycle facility widths). For example, compared to the assignments with completed Mid-Ohio region data, the proposed approach reaches a 90% match in bicycle LTS score assignments in urban areas when road traffic volumes are missing. Local communities in Ohio and beyond can adopt the proposed approach to achieve interim and temporary results when collecting accurate data is not feasible due to time and cost considerations.

The current revolutions of automation, electrification, and sharing are reshaping the way we travel, with broad implications for future mobility management. While much uncertainty remains about how these disruptive technologies would exactly impact demand for future mobility and enhancement of transportation supply, it is clear that innovative demand management is equally important as smart supply technology development in solving worsening traffic problems in big cities. In this work, we will discuss the significances, opportunities, and challenges of demand management in the era of smart transportation. Innovative ways of travel demand management for road transportation, public transit, and smart mobility are described, including tradable travel credit schemes for road congestion mitigation, revenue-preserving and Pareto-improving strategies for peak-hour transit demand management, and a novel reward scheme integrated with surge pricing in a ride-sourcing market.