Case Studies on Transport Policy最新文献

The expansion of e-scooter sharing system presents a mix of advantages and challenges to the urban transportation system. This research delves into the frequency of shared e-scooter trips on urban road segments in Austin, TX, leveraging a Random Forest model to dissect the influence of built environment and demographic variables on e-scooter trip frequencies. The model was then interpreted using Shapley Additive Explanations and Partial Dependence Plots. Results indicated that presence of bike lanes, distance to city center, violent crime, walkability, and land use are the most important variables. Notably, high shared e-scooter trip frequency often coincides with high incidence of violent crimes. The study further explores the non-linear relationships between e-scooter trip frequency and these key variables, revealing threshold effects and significant shifts in usage patterns. These insights offer valuable guidance for cities in the strategic development and regulation of shared e-scooter services.

This work examines the link between some of the most important transportation companies’ stock performance with the corresponding transportation cryptocurrencies. To do so, airlines, rail, and shipping companies’ stocks are investigated, examining for a probable link with the transportation cryptocurrencies, and also investigating the attributes of this link. The variable-Lag time-series causality is employed to test the channel of causality (if such exists), and the multifractal detrended cross-correlation analysis is utilized to investigate the relationship’s attributes. According to the results, the three transportation industries differ in the way they relate to transportation-related cryptocurrencies. More precisely, the transportation industries differ in the way they relate to transportation cryptocurrencies since airlines, and railway companies affect the price of the cryptocurrencies related to transportation, and cryptocurrencies also affect in a similar magnitude these fields. On the other hand, the shipping companies have a greater effect on the cryptocurrencies, while the cryptocurrencies in very few cases affect these stocks. Finally, a long-run relationship is identified, implying that transportation companies and the corresponding cryptocurrencies are positively and long-term related. The results important because they unveil a link between the transportation companies and the corresponding cryptocurrencies, with this link differing among these industries. This finding should be taken into account in the adoption of relevant technologies from the transportation field.

The COVID-19 pandemic profoundly impacted the tourism and aviation sectors, leading to a shift from international to domestic travel, especially in China. Cultural tourism, including red tourism, was still active and played a pivotal role in China’s domestic tourism during the pandemic. This study used the panel vector autoregression (PVAR) model and the panel Granger causality test to investigate the interplay between red tourism, airline seat capacity, and the COVID-19 cases during the COVID-19 pandemic. This study contributed to the existing literature of the interplay (or the endogenous interaction) among these three endogenous variables. It found that red tourist numbers have significantly affected airline seat capacity in Chinese domestic market in the face of the disruptive effects of COVID-19. Red tourist numbers and airline seat capacity have the positive and negative relationships with the COVID-19 cases of origin cities but not at red tourism destinations. Although it primarily focuses on China’s red tourism, this study highlights avenues for cross-cultural exploration and a deeper understanding of the dynamic interplay among red tourism, aviation, and the impact of the COVID-19 pandemic. This study concludes by offering insights in developing and shaping cultural tourism and industry’s preparedness strategies, helping the tourism and aviation industries recover and thrive in the post-COVID-19 era.

Prior research has found that people’s intentions to use autonomous technologies such as autonomous buses largely depend on factors like trust towards the technology, while little is known about the role played by the shared benefits. Unlike benefits obtained by the individual user of technologies, shared benefits are commonly societal benefits shared by users. We formulated hypotheses to explore the role of shared benefits in driving the use intention of autonomous buses and tested the model with survey data collected from people living in the greater Helsinki metropolitan area. We analyzed data using Covariance-based SEM, and our results show that perceived shared benefits are among the most vital indicators of autonomous bus use intention. To promote citizens’ acceptance of autonomous buses, the public transport authorities and bus operators should ensure that autonomous buses deliver shared benefits through service that is environmentally friendly, comfortable, easy to use, and accessible for all.

Major transportation infrastructure projects are often associated with public disputes and polarised debates. However, by using the example of a controversial infrastructure project in Vienna (the “Lobau highway”), we show that a binary “pro-versus-con” framing does not do justice to the complex realities of such debates. Based on a Q methodological analysis, we reveal four distinct perspectives, which reflect a broad spectrum of “generally pro” and “generally against” positions: (i) More roads, more traffic, (ii) Less politics, more facts, (iii) Better roads, better city, and (iv) The highway must be built. While our analysis points at unexpected overlaps between perspectives and some entry points for consensus building, it also highlights disagreement on fundamental beliefs such as the impact of roads on the environment, the phenomenon of induced traffic, or the right to drive a car. Against this background, we discuss the wider socio-political context of the analysed debate, reflect on the basic premise of consensus building, and derive policy implications.

This paper investigates the Potential for More Walking (PMW) in work tour among car commuters and proposes ways to encourage them to activate this potential. The study utilized a sample of 621 car commuters in the city of Qom, Iran, who expressed a willingness to replace their daily commute pattern with an alternative commute pattern that involves more walking time. The findings of the random effect binary logit model indicated that network patterns with improved connectivity in the residential neighborhood have a significant impact on activating the PMW. Additionally, subjective factors such as positive perceptions towards sidewalk conditions, longer distances between home and workplace, and positive attitudes towards walking were found to be associated with an increased tendency to activate the PMW. Moreover, the study assessed a set of transportation demand management measures that could influence individual behavior towards activating the PMW. The results indicated that pull measures are generally more effective than push measures in promoting walking during work commuting. Furthermore, the measures of turning streets with dense land-uses into pedestrian malls, increasing green spaces on sidewalks, and widening sidewalks were found to have the highest impact on activating the PMW, respectively.

The Covid-19 pandemic has posed significant challenges to the sector, and Türkiye’s strategic location plays a crucial role in crisis management. This study aims to explore the repercussions of the pandemic on the sector and propose appropriate solutions. To identify the most effective solutions for prioritizing the challenges posed by Covid-19, 53 sector experts participated in the study. The Stepwise Weight Assessment Ratio Analysis method was used to weigh the criteria representing these challenges. The solutions were ranked using the Weighted Aggregated Sum Product Assessment and Combined Compromise Solution methods. The results indicate that the primary challenge is the increased shipping cost, followed by the prolongation of the transport time, global-scale impacts, and port congestion. The most appropriate solution identified was alternative vessel loading, followed by mixed-mode transportation usage, reservation guarantees, long-term contracts with shipping operators, integrated logistics services, container purchases, and technological integration.

This study explores the impact of emerging vehicle technologies on direct urban traffic emissions. It investigates emission reduction potential from shifts in fleet compositions and modal choices, especially considering climate change. To achieve this, three key research areas are explored for historical, current, and future scenarios (up to 2050): mode choice, emission factors for different vehicle categories, and diverse vehicle propulsion technologies. The estimation of the modal split is pivotal, developing a methodology utilizing Stated Preference survey data, discrete choice modeling, Monte Carlo simulations, and macroscopic traffic simulations. Future scenarios derive from the reference year’s modal split, and emission factors and fleet compositions are predetermined via an extensive literature review, aiding the assessment of their respective emissions. A subsequent sensitivity analysis identifies the impact of specific parameters on emissions, guiding future research focus. Study results underscore differences in greenhouse gas emissions and primary air pollutants between base and future scenarios.