Research in Transportation Economics最新文献

This paper presents results from a case study of fare revenue prediction in public transportation in Berlin using machine learning and time series analysis. Our work aims to aid in the implementation of automated revenue controlling and data-driven decision support within existing controlling processes.

We generate forecasts based on fare revenue data for different product segments aggregated on a monthly basis. Additionally, we model exogenous effects using data publicly available.

The results were obtained using a variety of methods including regression methods as well as autoregressive methods and exponential smoothing. Among others, SARIMAX, MLR, LASSO and Ridge were applied.

We evaluate the predictive quality of each method and compare them. Where appropriate, we apply automatic feature selection to improve performance.

Our findings, alongside a discussion of their interpretability, can serve as recommendations for practitioners, supporting them in choosing appropriate methods and suitable exogenous variables to reliably predict the fare revenues of different products.

Recent literature has documented consumers’ imperfect optimization when facing taxes. Despite the rich literature in this area, little research has been conducted in the airline industry context. I exploit the variation of tax changes in the airline industry to analyze how US passengers react to tax and price changes. I show that passengers react more strongly to taxes than to price changes through the use of semi-elasticities. Baseline estimates suggest that the tax elasticity of demand is approximately 1.5 times as strong as the price elasticity of demand. This paper extends the literature by allowing for heterogeneous price and tax responses, and by providing a series of possible rationales that explain how overoptimization can arise in the airline industry. Tax aversion, media coverage, persistent tax increases, psychological factors, and non-standard preferences suggested in the literature could all contribute to explaining passengers’ overoptimization in the airline industry. The findings in this paper have profound contributions both to the economic literature, to the airline industry, and practitioners at both the local and federal levels.

Blind booking consists of selling cheap surprise trips with a set of possible destinations, but without revealing the real destination until the payment is made. In this paper, we develop an economic model to analyse the social and private optimality of this pricing strategy in the airline industry. We perceive opaque products as a pricing strategy managed directly by airlines (without intermediaries) and simultaneously applied with other pricing strategies. Blind booking allows airlines to sell all their seats while maximising revenues and charging different prices in two parallel and independent markets: the transparent and the opaque market. Considering consumers’ risk attitude, airlines must optimally choose the number of seats of each destination to be sold in each market in order to maximise their profits and create an attractive blind product. Our findings suggest that, in general, selling tickets in both markets is optimal for airlines. We show that, even when it is not optimal, it may enhance social welfare. Thus, policymakers, especially those of low-demanded destinations, should encourage airlines to introduce blind tickets. In these destinations, blind tickets imply an additional source of demand, attracting new customers and generating positive economic impacts.

This paper provides a comprehensive analysis of the initial costs and total cost of ownership (TCO) for light-duty battery electric vehicles (BEVs) and fuel cell vehicles (FCVs) from 2020 to 2040, covering cars, SUVs, and light trucks, alongside the infrastructure requirements. Key findings indicate that by 2040, the initial costs for BEVs will align with those of gasoline vehicles if battery costs can reach cell-level $70/kWh (or pack-level $84/kWh). For FCVs, achieving cost parity with gasoline cars before 2040 will be challenging unless the cost of fuel cells decreases to about $40/kW through high-volume production (>500000 units). Regarding 5-year TCOs, both BEVs and FCVs are expected to be close to or slightly lower than those of gasoline vehicles by 2040 across all LDV market segments. Investment analysis for large fleets suggests that by 2040, public fast charging for BEVs could cost $2000/vehicle, and hydrogen refueling for FCVs $1100/vehicle. Additionally, the study assesses the impact of low carbon fuel standard (LCFS) credits on the profitability of refueling stations, concluding that these credits are essential for transforming potentially unprofitable stations into profitable ventures with returns of 5% or higher. This highlights the critical role of LCFS in financing BEVs and FCVs infrastructure.

Numerous studies have attempted to identify how transport investments affect the overall economy. The results of these studies vary considerably. This variation has been explained by factors such as differences in estimation methods as well as differences among countries and sectors. This paper considers another explanation: low statistical power. By revisiting a previous meta-analysis, this paper shows that accounting for statistical power substantially reduces the estimate. Accounting for statistical power also reduces the relevance of estimation methods as well as country and sectoral differences. These results indicate that a larger data set than typically available is required to overcome the issue of low power. The implication of this is that the existing and, perhaps, future studies using macro data are of limited use to policymakers.

Fostering enduring patronage among passengers for public transportation (PT) services is crucial for sustaining ridership. However, a significant concern arises from the inconsistency between passengers' negative attitudes and their robust patronage behaviors toward PT services, particularly evident among the captive riders. This paradox entails the risk of these passengers potentially shifting to alternative transport modes in the future, thereby diminishing ridership. To unravel the intricate dynamics inherent in the relationship between captive riders' attitudes and behaviors, this study introduces a novel integrated model merging Satisfaction-Loyalty Theory (SLT) with Theory of Planned Behavior (TPB). The integrated model seeks to enhance the comprehension of the mechanisms that drive captive riders' ongoing utilization of PT services, specifically within the context of attitude-behavior inconsistency. Evident in a case study involving 637 samples collected in Dalian, China, the integrated model not only shows excellent goodness-of-fit but also outperforms both the conventional SLT and TPB frameworks. It excels in explaining why captive riders may persist in their behaviors towards the service even in the face of dissatisfaction and highlights how the perceived service quality shapes their attitudes and behaviors. This substantially contributes to the development of targeted service improvement strategies for sustaining captive riders’ continued patronage.

In recent years, sustainable eco-friendly vehicles have been demonstrated as an adequate solution for urban deliveries and restricted areas facing traffic congestion and traffic zone limitation. Therefore, in this paper, a novel Decision Support System has been proposed for evaluating the efficiency of e-grocery home delivery through eco-friendly vehicle adoption. A mathematical model, formulated as Electric Vehicle Routing Problem with Time Windows and Partial Recharging (EVRPTW-PR), has been applied for selecting the best zero-emission vehicle for e-grocery home delivery. The comparison of the most emerging electric light-duty vehicles (e-cargo bikes, e-mopeds, and e-vans) has been carried out through key performance indicators related to the drivers’ salary, the total delivery time, the fuel (energy) costs, the vehicle investment costs, and the average payload capacity utilization. The overall evaluation encourages the adoption of zero-emission strategies and helps e-grocery commerce to adopt the best option that fits with the environmental as well as the economic aspects.

The appraisal of different railway technologies for new intercity services is essential in policy-making. While High-Speed Rail (HSR) lines provide time-savings by operating at 300 kph, new Conventional Rail (CR) services at 150 kph can offer cost-effective benefits. Therefore, assessing the value of travel time (VTT) helps determining such benefits, particularly in the Global South where they may be relatively unknown. This paper examines the VTT of hypothetical intercity business and non-business HSR and CR services in Brazil using data from a Stated Preference survey and Multinomial Logit (MNL) and Mixed Multinomial Logit (MMNL) models. Results indicate that VTT for HSR on business trips are higher than for CR services and vary significantly between trip purpose. Moreover, the VTTs for business trips are similar to those of European countries on average, but significantly higher than other Global South countries for either railway technology or trip purpose.