Research in Transportation Business and Management最新文献

The rapid expansion of the automotive fleet in Latin America, coupled with the excessive utilization of automobiles, has resulted in significant challenges related to traffic congestion and environmental pollution. One of the mechanisms within transportation planning to mitigate excessive car use is utilizing the park and ride (P&R) system. Nevertheless, implementing multimodal hubs as a P&R system in Latin America has been relatively limited despite their prevalence in other global regions as a crucial transportation infrastructure component. Implementing a P&R system requires transportation planners with different types of expertise to study a set of criteria that are ranked in order of importance. To perform these analyses, it is necessary to use multi-criteria methods to understand which criteria are the ultimate important for their location. Our study highlights the gap in the limited implementation of P&R systems, particularly multimodal hubs, in Latin America despite their prevalence in other places such as Europe and the United States that have developed P&R systems in combination with the implementation of public transport systems. The adoption of the Full Consistency Method (FUCOM) with parsimonious preference information fills this gap by providing a flexible and adaptable approach to decision-making in dynamic and unpredictable urban environments like Cuenca in Ecuador. The findings indicate that utilizing this methodology enhances the efficiency and precision in determining the criteria for the placement of the P&R system. In conclusion, consideration of public transport accessibility serves as a foundational aspect for developing P&R system in the Latin American city under investigation. In essence, implementation of the P&R system is closely linked to optimization of public transportation system.

Rural older adults reliant on transportation services represent an historically underserved demographic, yet their specific service needs remain inadequately understood. This study aims to explore important rural transportation service features for senior customers while identifying service gaps and potential misconceptions between senior customers and service providers. Utilizing data from a cross-sectional service survey in rural Wisconsin, 13 important service features were identified by both senior customers and service providers, and grouped into three categories—basic, technological, and special—via exploratory factor analysis. While consensus existed between seniors and providers on the primary importance of basic features, seniors attributed greater significance to technological aspects than service providers anticipated. Differing preferences surfaced between seniors and providers regarding special features. Statistical tests revealed the substantial importance of basic features, particularly among older females, individuals with mobility constraints, and limited leisure activity. Moreover, technological features received higher ratings from older females, younger individuals, and those with internet access. Varied attention towards special features aligned with diverse older adult needs. Service providers, regardless of their characteristics, exhibited consistent prioritization of service features. Our findings emphasize opportunities for rural transport service providers to better serve senior users by enhancing basic service provisions, embracing age-friendly technologies, and tailoring specialized services to meet distinct user requirements within local contexts.

Most European countries have been committed to reducing their carbon footprint, combating climate change, and reducing the air pollution typical in large cities over the past decade. Among current solutions that can be adopted are the replacement of fuel-powered means of transport with electric ones, as well as the introduction of car sharing, bike sharing and electric scooters.

The post-pandemic phase was characterized by a greater propensity to use these means of transport as they were perceived as a healthier choice (for a greater possibility of implementing social distancing) and cheaper (for the diffusion of shared services). The study of modal choice depends on socio-economic structures. The present work analyses data related to socio-economic factors (work, income and other) to examine the tendency to use electric scooters in the metropolis of Palermo, Sicily, through machine learning algorithms.

The comparison of different algorithms allowed us to underline how the multilayer perceptron algorithm obtained the best classification among the minimal sequential optimization algorithms. The findings also highlight middle-income and freelancer people as being more likely to use micro-mobility than others. Contrary to what was thought, these findings revealed that micro-mobility is not just a preferred mode of transport for low-income people or students. These trends will be able to encourage continuous monitoring of the relevant factors and will be able to help political decision-makers to increase and improve the diffusion of micro-mobility and to direct marketing campaigns to the groups identified here.

The global increase in electric vehicle (EV) usage is evident; however, inadequate infrastructure in many regions still creates range anxiety among drivers. To address this concern, this paper presents a two-stage methodology for analyzing green transformation projects at existing gas stations towards determination of the most appropriate location and capacity decisions. The first stage of the methodology involves a state of charge tracking optimization model to enhance the process of making location decisions and the optimization model is run several times by considering different parameter values to evaluate potential locations for electric vehicle charger installation. In the second stage, a simulation model is constructed to explore capacity decisions based on various scenarios related to regional densities. Afterwards, extensively tested experiments are performed through a real case from Trabzon province of the Karadeniz Coastal Road, a geographically significant area in Turkey's Eastern Black Sea region, to obtain remarkable insights and policy recommendations. As a result, policy recommendations derived from this study emphasize the prioritization of charging station installations at high-frequency locations and tailoring the number of charging units to regional density. The significance of this study is to offer adaptable solutions for various locations, enhance practical utility and support informed decision-making in infrastructure planning by providing a proposed two-stage methodology for sustainable transportation planning.

Cruising ride-hailing vehicles exacerbate traffic congestion by generating negative externalities. In contrast, reserved ride-hailing services leverage precise information regarding the departure times and origins-destinations of future trips. Platforms can use this data to dispatch and route drivers more efficiently, thereby reducing the need for cruising. Although previous research has largely concentrated on real-time ride-hailing services, the impact of the built environment on reserved ride-hailing remains unexplored with empirical data. This study integrates multi-source data from Haikou City in China and utilizes the gradient boosting decision tree model, which is an interpretable machine learning approach, to investigate potential relationships between reserved ride-hailing trip demand and the built environment. The rankings of relative importance reveal that factors such as the density of food services, education institutions, accessibility to town centers, and proximity to transportation hubs significantly influence the demand for reserved ride-hailing. Furthermore, the study demonstrates that the aforementioned factors exhibit non-linear effects on the demand for reserved ride-hailing. The findings have policy implications for local governments aiming to promote reserved ride-hailing and enhance urban mobility services.

The aim of this paper is to examine the relationships among the dimensions of bus-riding flow experience, relationship quality, and experiential outcome intentions. The data used in this study were acquired from a convenience sample of 535 passengers riding on driverless buses in Hengli Metro Station Bus Terminal of Guangzhou in China. The empirical results display that the dimensions of bus-riding flow experience (goal clarity, curiosity, positive affection, immersion, utility, and sensory challenge) and trust positively influence psychological ownership. Moreover, both trust and psychological ownership positively affect satisfaction. In addition, trust, satisfaction, and psychological ownership positively influence commitment, which in turn leads to the dimensions of experiential outcome intentions (experiential loyalty intention, experiential word-of-mouth intention, experiential riding intention, and willingness to ride on driverless buses). Finally, theoretical and practical implications based on study findings were outlined and recommendations for future research were made.

The determinants of public transport demand are multifaceted, necessitating consideration of various factors that extend beyond the objective aspects of operation. In addition to travel time and costs, it is imperative to comprehend psychosocial factors influencing public transport usage to enhance policies geared toward fostering ridership and sustainable mobility. Empirical evidence suggests that subjective attributes, such as satisfaction and attitudes, exert a discernible influence on travel behavior and the utilization of public transport. Nevertheless, these linkages remain largely unexplored, especially within the context of large public transport networks in cities of the Global South. This paper aims to address this gap by evaluating the impact of demand-side subsidies in the form of travel vouchers on public transport ridership to understand the mediating effect of satisfaction levels according to the users’ socioeconomic characteristics. These satisfaction levels are related to overall satisfaction with transport, employment and educational opportunities, and life. To achieve this, a large-scale randomized controlled experiment was conducted in Bogotá, Colombia, involving a sample of frequent public transport users (N = 1,607). The participants were divided into a treatment group, which received the travel vouchers loaded onto their smart travel cards for four months, and a control group. Hybrid-ordered logit models were employed to estimate the average number of weekly trips undertaken before, during, and after the intervention. The result shows an increase in ridership by the treatment group, which does not solely depend on improved affordability; but is influenced by a complex interplay of socioeconomic factors and shifts in satisfaction levels. This confirmed that travel satisfaction can contribute to increased public transport demand.

The article aims at assessing the efficiency of 46 Baltic maritime container terminals using a Data Envelopment Analysis Slack-Based Model (DEA-SBM) of three inputs (quay length, terminal equipment, depth at the quay) and two outputs (throughput, short-sea shipping connections) and thus verifying two hypotheses regarding obtained efficiency. To facilitate analysis, the Baltic Sea was divided into three subregions: Southern, Central, and Northern. The study addresses a research gap by providing up-to-date information on the Baltic Sea Region (BSR). Industry reports often focus on individual port performance, neglecting the efficiency of their terminals. The findings indicate that Baltic terminals with an annual turnover exceeding 150 thousand TEU are more efficient than their smaller counterparts. However, no significant difference in efficiency was observed among terminals in different subregions of the Baltic Sea. The above statement implies that all maritime container terminals in the Baltic Sea have benefited equally from the recent restrictions imposed on Russia. However, a closer examination of the DEA-SBM efficiency and benchmark results provides a new perspective that may influence the decisions of container terminal operators in the BSR.

Purpose

Railways are crucial as public transport in rural India in connecting the villages with cities. Since COVID-19, most of the trains in rural India have not stopped at small railway stations, which has impacted the mobility of the villagers. This study is conducted in Piprai, a village in North India, to explore the impact of non-halting trains since COVID-19 on young girls in rural India.

Design/methodology/approach

The study adopts a qualitative research design to explore the impact of non-halting trains since COVID-19 on young girls in rural India. Qualitative data was collected from three sources. Two physical participatory workshops were conducted in Piprai in March 2021 and October 2022 with 10 and 12 participants, respectively. A WhatsApp group was formed with six girls for continuous feedback and discussions. A social activist from the same area was the point of contact and was interviewed to understand the overall condition of women in the village.

Findings

The results provide valuable insights into how the lack of public transportation since COVID-19 has affected young girls in rural India. Women's mobility has been limited, which has led to a halt in higher studies, being unable to visit hospitals regularly and meeting relatives, especially parents in different cities. Furthermore, the above reasons have led to anxiety and mental disturbance.

Originality/value

This study is among the first to explore the impact of lack of transportation since COVID-19 on women in rural India. This is the first time any such study has been conducted in the remote area of Bundelkhand, UP, India.

In order to attract the highest possible number of users, municipal authorities and operators of bike sharing systems (BSS) often set very low prices for their services. This frequently results in non-profitability or dependency on public funding. Furthermore, low prices may discourage customers who are concerned regarding the quality of services. In our study, we investigate the impact of socio-demographic characteristics and travel habits on price sensitivity for an e-bike sharing system. We use Van Westendorp's price sensitivity meter (PSM) and the general linear model (GLM) to determine differences in optimal prices for various users and types of BSS. We find that optimal prices may be higher than the ones fixed by authorities and that discounts for female and younger users may result in higher demand for BSS. Moreover, car users are willing to pay less for a BSS than people who never use a car.