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on Transport Economics |
| By: | Wiefel, Jennifer |
| Abstract: | Recent advancements in intelligent technologies and sensor-based data collections pave the way for autonomous driving and facilitate a radical transformation of today’s mobility. Based on auspicious market projections, traditional automotive manufacturers and technology companies invest heavily in the development of autonomous vehicles (AVs). In addition to the profits that the industry expects from self-driving vehicles, this new type of mobility should also solve societal issues like reducing traffic accidents and fatalities by eliminating human driving errors. More efficient autonomous driving is expected to bring improvements in terms of fewer congestions and less fuel consumption, thereby reducing greenhouse emissions. Besides, AVs pledge to entail advantages for their users. Specifically, they increase mobility for the disabled and the older generation. In contrast, younger passengers associate autonomous driving with improved productivity and an enhanced hedonic experience as non-driving activities, such as working or watching a movie, are made possible. Contrary to the above expectations, people also raise concerns regarding self-driving vehicles. They are worried about whether the sensors and systems can correctly interpret complex environmental conditions. Above all, there are doubts whether the technology, even being intelligent, can react appropriately in critical traffic situations made up of humans who sometimes behave unpredictably. In case of unavoidable traffic accidents, ethical questions come into play regarding how the vehicle makes decisions that could result in a person being injured or killed. Finally, the new and sophisticated technology could have vulnerabilities that can be exploited by cybercriminals or allow unauthorized third parties to obtain passenger data. Motivated by the anticipated improvements that AVs entail and the breadth of factors that might influence their adoption, a large body of research investigating relevant adoption factors has accumulated. In order to collect, organize, and combine extant findings, research paper A conducts a structured literature review on the acceptance of autonomous vehicles. Based on 58 articles, it develops an AV acceptance framework consisting of individual user characteristics, vehicle characteristics, and political/societal elements. The framework indicates for each factor whether available research results identify the effect as either positively or negatively significant. Thereby, the paper also sheds light on diverging construct operationalizations, aiming to support researchers in comparing available findings. Eventually, paper A proposes future research avenues across various themes and methods, which build a foundation for further research pursued in this dissertation’s subsequent papers. However, solely balancing significant against non-significant results can come to wrong conclusions since the sample size alone can lead to varying significance levels. Because of this, paper B builds on the literature review and conducts a meta-analysis to include further quantitative analyses. It calculates the mean effect sizes for each AV acceptance factor based on published research results. By doing so, the paper identifies attitude, perceived usefulness, efficiency, trust in AVs, safety, and subjective norms to correlate most strongly with the behavioral intention to use an automated car. A subsequent moderator-analysis shows that almost all acceptance factors are influenced by the study’s methodology and location, the AV’s level of automation, and the examined ownership model, i.e., private cars, car sharing, or public transport. In doing so, paper B observes that most of the available research is on privately owned AVs and hence lacks to assess public as well as shared automated mobility. To fill this gap, paper C investigates characteristics relevant for automated mobility as a service (AMaaS). Based on 23 exploratory interviews with the general public, the paper derives a set of AMaaS requirements. Mobility experts sort these requirements based on commonalities so that a cluster analysis can conceptualize the expected AMaaS characteristics from a practitioner’s view. The paper identifies traffic safety, information privacy, cybersecurity, regulations, flexibility, accessibility, efficiency, and convenience to be relevant service characteristics. It discusses each required characteristic and thereby delineates the constructs’ scopes so that subsequent research can build appropriate measurement instruments. Besides, paper C discovers strongly diverging priorities regarding the respective service characteristics when comparing the potential users’ conversation shares with the experts’ relevance ratings. Paper D builds on the qualitative results of paper C as it develops and validates a hierarchical quality scale for AMaaS. The paper proposes a theoretical model and operationalizes the previously identified service characteristics. Throughout multiple empirical studies with 1,431 participants, the proposed quality scale is refined iteratively until satisfactory psychometric properties are achieved. Nomological validity ensures the scale’s predictability. Paper D progresses research from focussing on the mere acceptance of autonomous driving to the user’s quality perception, which significantly influences user satisfaction and the success of AMaaS. This, in turn, is necessary to realize the promised benefits of autonomous driving in a sustainable manner. |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:dar:wpaper:130864&r= |
| By: | Morteza Taiebat; Elham Amini; Ming Xu |
| Abstract: | Ride-hailing is rapidly changing urban and personal transportation. Ride sharing or pooling is important to mitigate negative externalities of ride-hailing such as increased congestion and environmental impacts. However, there lacks empirical evidence on what affect trip-level sharing behavior in ride-hailing. Using a novel dataset from all ride-hailing trips in Chicago in 2019, we show that the willingness of riders to request a shared ride has monotonically decreased from 27.0% to 12.8% throughout the year, while the trip volume and mileage have remained statistically unchanged. We find that the decline in sharing preference is due to an increased per-mile costs of shared trips and shifting shorter trips to solo. Using ensemble machine learning models, we find that the travel impedance variables (trip cost, distance, and duration) collectively contribute to 95% and 91% of the predictive power in determining whether a trip is requested to share and whether it is successfully shared, respectively. Spatial and temporal attributes, sociodemographic, built environment, and transit supply variables do not entail predictive power at the trip level in presence of these travel impedance variables. This implies that pricing signals are most effective to encourage riders to share their rides. Our findings shed light on sharing behavior in ride-hailing trips and can help devise strategies that increase shared ride-hailing, especially as the demand recovers from pandemic. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.12696&r= |
| By: | Fournier, Nicholas; Huang, Amy; Skabardonis, Alexander |
| Abstract: | Complete streets movement is a national effort to return to traditional streets in our cities to enhance livability, safely, accommodate all modes of travel, provide travel choices, ease traffic congestion, and promote healthier communities. The California Department of Transportation (Caltrans) and several local agencies in the State have developed implementation plans for complete streets. In this project, we developed and tested improved strategies and analysis methodologies for complete streets, taking into consideration the emerging advances in technology on control devices and data availability from multiple sources. The proposed improvements to the Highway Capacity Manual (HCM) methodology for bicycle LOS, accounts for protected bicycle lanes, traffic exposure, bicycle delay and pavement quality index. A survey was also used to calibrate the proposed bikeway evaluation models. Signal control strategies for complete streets were developed and tested, including signal optimization for pedestrians, bicycles and Transit Signal Priority (TSP) along major travel corridors in San Francisco. |
| Keywords: | Engineering |
| Date: | 2021–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:cdl:itsrrp:qt2gd0t4cd&r= |
| By: | Florent Laroche (LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | In this paper we explore the effect of COVID 19 on the long-distance transport services in France. These services have been strongly impacted by the different lockdowns imposed. The research question has for objective to characterize the market before COVID 19 to understand its adaptation and the strategies of the stakeholders faced with sanitary regulations. The empirical research is based on a large panel of data collected on four routes in France from 2019 to 2021 for four modes. The first finding is the severe crisis in terms of supply during the first lockdown in March 2020. During the rest of the year 2020 and 2021, services increased slowly until recovering a similar level to that of 2019 for rail and carpooling. This is not yet the case for coach and air services. The second finding is the market concentration to the advantage of the dominant mode, train, especially in comparison to air, still subject to difficulty because of the reduction of the business market. The last finding highlights the persistence of conventional services during the different lockdowns and the high variability of low cost services. Finally, low-cost services were recovering services faster in autumn 2021 than conventional services, thereby increasing their market share. |
| Keywords: | COVID-19,Public Transport,Interurban Mobility,Market organization,Working Paper du LAET |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-03522931&r= |
| By: | Prateek Bansal; Rubal Dua |
| Abstract: | China and India, the world's two most populous developing economies, are also among the world's largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of new car buyers in China and India to such policy levers and drivers including income. Furthermore, we estimate the potential for rebound effect and the effectiveness of a feebate policy. To accomplish this, we developed a joint discrete-continuous model of car choice and usage based on revealed preference survey data from approximately 8000 new car buyers from India and China who purchased cars in 2016-17. Conditional on buying a new car, the fuel consumption in both markets is found to be relatively unresponsive to fuel price and income, with magnitudes of elasticity estimates ranging from 0.12 to 0.15. For both markets, the mean segment-level direct elasticities of fuel consumption relative to car price and fuel economy range from 0.57 to 0.65. The rebound effect on fuel savings due to cost-free fuel economy improvement is found to be 17.1% for India and 18.8% for China. A revenue-neutral feebate policy, with average rebates and fees of up to around 15% of the retail price, resulted in fuel savings of around 0.7% for both markets. While the feebate policy's rebound effect is low - 7.3% for India and 1.6% for China - it does not appear to be an effective fuel conservation policy. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.08995&r= |
| By: | Angela Acocella; Chris Caplice; Yossi Sheffi |
| Abstract: | In the for-hire truckload market, firms often experience unexpected transportation cost increases due to contracted transportation service provider (carrier) load rejections. The dominant procurement strategy results in long-term, fixed-price contracts that become obsolete as transportation providers' networks change and freight markets fluctuate between times of over and under supply. We build behavioral models of the contracted carrier's load acceptance decision under two distinct freight market conditions based on empirical load transaction data. With the results, we quantify carriers' likelihood of sticking to the contract as their best known alternative priced load options increase and become more attractive; in other words, carriers' contract price stickiness. Finally, we explore carriers' contract price stickiness for different lane, freight, and carrier segments and offer insights for shippers to identify where they can expect to see substantial improvement in contracted carrier load acceptance as they consider alternative, market-based pricing strategies. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.02367&r= |
| By: | Elena Perra |
| Abstract: | Ethnic favouritism has long been considered by scholars as intrinsic in explaining sub-optimal economic growth in African countries. Our case study, Ethiopia, represents an unicum in the African political context, as ethnicity has been institutionalised as the key element of the post-authoritarian state order, yielding a system that has been labelled “ethnic federalism†. This paper aims to analyse whether this particular institutional setting has proven to be a deterrent to logics of ethnic favouritism in the allocation of public goods. In order to do so, the study exploits a national scale road investment project spanning almost twenty years, the Ethiopian Road Sector Development Programme. We seek to assess whether the politically dominant ethnicity, Tigrays, have benefitted disproportionately from the project with respect to other ethnically identified Ethiopian regions. By exploiting a novel dataset containing spatially explicit information on the location of new road constructions and road surface im- provements, we leverage quasi-experimental econometric methods in order to identify a causal effect of coethnicity with the Tigray People’s Liberation Front, the dominant component of the Ethiopian People’s Revolutionary Democratic Front, in the reception of new road construction and road improvements. The main contribution of this paper resides in the quantification of the disproportional allocation of road investments. We find that ethnic Tigrays obtain on average 5-7% more roads with respect to other ethnic groups, once pre-treatment characteristics are balanced across treatment and control units. Moreover, the result is consistent when ex- pressed in terms of road improvements, with road speed on Tigray territories increasing by an additional 10 km/h with respect to non-Tigray observations. These results may be considered as evidence of ethnically unbalanced economic growth inside the Ethiopian territory. |
| Keywords: | Infrastructure, Roads, Ethnic Favouritism, Ethiopia, GIS |
| JEL: | H54 H77 J15 R42 O15 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:frz:wpaper:wp2022_01.rdf&r= |
| By: | Cochran, Abigail L. (University of North Carolina at Chapel Hill); McDonald, Noreen; Prunkl, Lauren; Vinella-Brusher, Emma; Wang, Jueyu; Oluyede, Lindsay; Wolfe, Mary |
| Abstract: | Objective: To investigate transportation barriers to accessing health care services during the COVID-19 pandemic among high-frequency health care users. Data Sources: Between June 21 and July 23, 2021, primary survey data were collected for a sample of patients in North Carolina. Study Design: The study analyzed the prevalence of arriving late to, delaying, or missing medical care and examined how transportation barriers contributed to negative health care outcomes. Data Collection Methods: A web-based survey was administered to North Carolina residents aged 18 and older in the UNC Health system who were enrolled in Medicaid or Medicare and had at least six outpatient medical appointments in the past year. 323 complete responses were analyzed to investigate the prevalence of reporting transportation barriers that resulted in having arrived late to, delayed, or missed care, as well as relationships between demographic and other independent variables and transportation barriers. Qualitative analyses were performed on text response data to explain transportation barriers. Principal Findings: Approximately 1 in 3 respondents experienced transportation barriers to health care between June 2020 and June 2021. Multivariate logistic regressions indicate individuals aged 18–64 were significantly more likely to encounter transportation barriers. Costs of traveling for medical appointments and a lack of driver or car availability emerged as major transportation barriers; however, respondents explained that barriers were often complex, involving circumstantial problems related to one’s ability to access and pay for transportation as well as to personal health. Conclusions: To address transportation barriers, we recommend more coordination between transportation and health professionals and the implementation of programs that expand access to and improve patient awareness of health care mobility services. We also recommend transportation and health entities direct resources to address transportation barriers equitably, as barriers disproportionately burden younger adults under age 65 enrolled in public insurance programs. |
| Date: | 2021–12–22 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:qf7kt&r= |
| By: | Beck, Ben; Winters, Meghan; Nelson, Trisalyn; Pettit, Christopher; Saberi, Meead; Thompson, Jason; Seneviratne, Sachith; Nice, Kerry A; Zarpelon-Leao, Simone; Stevenson, Mark |
| Abstract: | Background: Extensive research has been conducted exploring associations of built environment characteristics and biking. However, these approaches have often lacked the ability to understanding the interactions of built environment, population and bicycle ridership. To overcome these limitations, this study aimed to develop novel urban biking typologies using unsupervised machine learning methods. Methods: We conducted a retrospective analysis of travel surveys, bicycle infrastructure and population and land use characteristics in the Greater Melbourne region, Australia. To develop the urban biking typology, we used a k-medoids clustering method. Results: Analyses revealed 5 clusters. We highlight areas with high bicycle network density and a high proportion of trips made by bike (Cluster 1; reflecting 12% of the population of Greater Melbourne, but 57% of all bike trips) and areas with high off-road and on-road bicycle network length, but a low proportion of trips made by bike (Cluster 4, reflecting 23% of the population of Greater Melbourne and 13% of all bike trips). Conclusion: Our novel approach to developing an urban biking typology enabled the exploration of the interaction of bicycle ridership, bicycle network, population and land use characteristics. Such approaches are important in advancing our understanding of bicycling behaviour, but further research is required to understand the generalisability of these findings to other settings. |
| Date: | 2021–11–25 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:8w7bg&r= |