359 research outputs found

    Analysis of the use and perception of shared mobility: A case study in Western Australia

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    The sharing economy has acquired a lot of media attention in recent years, and it has had a significant impact on the transport sector. This paper investigates the existing impact and potential of various forms of shared mobility, concentrating on the case study of Wanneroo, Western Australia. We adopted bibliometric analysis and visualization tools based on nearly 700 papers collected from the Scopus database to identify research clusters on shared mobility. Based on the clusters identified, we undertook a further content analysis to clarify the factors affecting the potential of different shared mobility modes. A specially designed questionnaire was applied for Wanneroo’s residents to explore their use of shared mobility, their future behaviour intentions, and their perspectives on the advantages and challenges of adoption. The empirical findings indicate that the majority of respondents who had used shared mobility options in the last 12 months belong to the low-mean-age group. The younger age group of participants also showed positive views on shared mobility and would consider using it in the future. Household size in terms of number of children did not make any impact on shared mobility options. Preference for shared mobility services is not related to income level. Bike sharing was less commonly used than the other forms of shared mobility

    Exploration of the Current State and Directions of Dynamic Ridesharing

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    Dynamic ridesharing (DRS) is an emerging transportation service based on the traditional concept of shared rides. DRS makes use of web-based real-time technologies to match drivers with riders. Enabling technologies include software platforms that operate on mobile communication devices and contain location-aware capabilities including Global Positioning Systems (Agatz, Erera, Savelsberg, & Wang, 2012). The platforms are designed to provide ride-matching services via smartphone applications differing from early systems that used non-real time services such as internet forums, or telecommunications, where responses were not immediate. The study of DRS is important when considering its role as an emerging transportation demand management strategy. DRS reduces travel demand on singleoccupancy vehicles (SOVs) by filling vehicle seats that are typically left vacant. The most recent statistics of vehicle occupancy rates were measured in 2009 by the National Household Travel Survey (NHTS), conducted by the U.S. Department of Transportation. According to the NHTS, the 2009 occupancy rate for all purposes was a meager 1.67 persons per vehicle (Federal Highway Administration, 2015). Vehicle occupancy rates examined against the total of all registered highway vehicles in the U.S. as of 2012, calculated at 253,639,386 (Bureau of Transportation Statistics, 2015), reveals the magnitude of the impact of SOVs. Left unattended, the ramifications for environmental outcomes is substantial. Among the major energy consuming sectors, transportation\u27s share is largest in terms of total CO2 emissions at 32.9% (Davis, Diegel, & Boundy, 2014, p. 11-15). DRS offers promise to fill empty vehicle seats. Evidence indicates that specific demographic subgroups are inclined to use DRS services. For example, data suggest that the subgroup of 18 to 34-year-olds, the so-called millennials , have negative attitudes towards private car ownership unlike previous age groups (Nelson, 2013). Data collected for this study revealed that the millennial subgroup represents half of all DRS users. Millennials also revealed they tended to use DRS more than other subgroups to replace a private vehicle. Further research is needed to determine if the trend towards DRS by 18 to 34-year-olds represents current economic factors or a fundamental cultural shift away from the SOV transportation model

    Trust Transfer and the Intention to Use App-enabled Carpooling Service

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    In China, with the rapid dissemination of mobile communications technology along with congested traffic and increasingly expensive transportation costs, consumers are turning to smartphone-enabled, ride-sharing services. Sharing economy requires trust in strangers. Based on trust transfer theory and a dyadic conceptualization of trust from cognitive to affective, the purpose of this study is to examine trust building through the use of Didi, a third-party, ride-sharing platform that mediates exchanges among strangers

    Public Transit in the Capital of Enchantment: Improving Rider Information and Services

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    The goal of this project was to increase utilization of the Santa Fe Trails (SFT) public bus system. This was achieved by improving service and quality of information and proposing additional services to better utilize existing resources. To improve quality of information we conducted surveys and personal interactions to assess the quality of information available to riders. To quantify current utilization,we analyzed survey data to see when riders are utilizing the system,how long they are traveling to and from stops,and why riders are utilizing the system. To investigate redistribution of resources and create new services,an express bus was tested and we determined the feasibility of a college shuttle. Finally,research was conducted to assist SFT in future piloting of a feeder system

    Changing Car Culture: A Case Study at Binghamton University

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    Binghamton University has a parking problem fostered by the car culture of today. A change in car culture through the shift from single occupancy driving towards higher occupancy transit was identified as a possible solution. An online survey was used to acquire students\u27 opinions and thoughts on the issue. Its 824 responses highlighted variables that were grouped into five overarching themes: Convenience, Quality of Transportation System, Satisfaction with Parking, Comfort with Carpooling, and Perceived Benefits and Drawbacks, which were analyzed under different qualitative and quantitative methods to test for their effect on car culture. Qualitative analysis was conducted using R and SPSS to run Chi-square tests and linear regression models, whilst qualitative analysis was conducted using NVivo to run coding and word frequency queries. These results showed trends in student behavioral intentions, providing the understanding needed to promote initiatives to instigate car culture change and potentially reduce the parking problem

    MOBILITY ANALYSIS AND PROFILING FOR SMART MOBILITY SERVICES: A BIG DATA DRIVEN APPROACH. An Integration of Data Science and Travel Behaviour Analytics

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    Smart mobility proved to be an important but challenging component of the smart cities paradigm. The increased urbanization and the advent of sharing economy require a complete digitalisation of the way travellers interact with the mobility services. New sharing mobility services and smart transportation models are emerging as partial solutions for solving some tra c problems, improve the resource e ciency and reduce the environmental impact. The high connectivity between travellers and the sharing services generates enormous quantity of data which can reveal valuable knowledge and help understanding complex travel behaviour. Advances in data science, embedded computing, sensing systems, and arti cial intelligence technologies make the development of a new generation of intelligent recommendation systems possible. These systems have the potential to act as intelligent transportation advisors that can o er recommendations for an e cient usage of the sharing services and in uence the travel behaviour towards a more sustainable mobility. However, their methodological and technological requirements will far exceed the capabilities of today's smart mobility systems. This dissertation presents a new data-driven approach for mobility analysis and travel behaviour pro ling for smart mobility services. The main objective of this thesis is to investigate how the latest technologies from data science can contribute to the development of the next generation of mobility recommendation systems. Therefore, the main contribution of this thesis is the development of new methodologies and tools for mobility analysis that aim at combining the domain of transportation engineering with the domain of data science. The addressed challenges are derived from speci c open issues and problems in the current state of the art from the smart mobility domain. First, an intelligent recommendation system for sharing services needs a general metric which can assess if a group of users are compatible for speci c sharing solutions. For this problem, this thesis presents a data driven indicator for collaborative mobility that can give an indication whether it is economically bene cial for a group of users to share the ride, a vehicle or a parking space. Secondly, the complex sharing mobility scenarios involve a high number of users and big data that must be handled by capable modelling frameworks and data analytic platforms. To tackle this problem, a suitable meta model for the transportation domain is created, using the state of the art multi-dimensional graph data models, technologies and analytic frameworks. Thirdly, the sharing mobility paradigm needs an user-centric approach for dynamic extraction of travel habits and mobility patterns. To address this challenge, this dissertation proposes a method capable of dynamically pro ling users and the visited locations in order to extract knowledge (mobility patterns and habits) from raw data that can be used for the implementation of shared mobility solutions. Fourthly, the entire process of data collection and extraction of the knowledge should be done with near no interaction from user side. To tackle this issue, this thesis presents practical applications such as classi cation of visited locations and learning of users' travel habits and mobility patterns using historical and external contextual data

    Enhancing Capacity and Managing Demand to Increase Short-Term Throughput on the San Francisco-Oakland Bay Bridge

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    While there are many proposals for fixing congestion between San Francisco and Oakland in California by adding a new bridge or tube, these solutions will take decades to implement even though a solution is needed now. This thesis assesses sixteen different strategies for reducing congestion in the short-term in the four categories of improving transit, promoting carpooling, implementing intelligent transportation systems practices, and incentivizing alternatives to using the Bay Bridge. Top priorities include HOV improvements on the West Grand Avenue and Powell Street onramps, altering WestCAT Lynx and BART transit services, partnering with rideshare apps to increase transit station accessibility (last mile problem), partnering with vanpool/minibus apps, promoting carpooling and implementing a citizen report system for carpool violators, shifting corporate cultures away from requiring employees to drive and drive alone, and lastly, altering land-use planning practices. To reach this conclusion, an inventory of current proposals and relevant research was compiled. Ridership and capacity data for the various modes of transportation across the bay were assessed for shortfalls and opportunities. Through this research and its resultant conclusions, focus can be placed on the best strategies to pursue in the near-term, while the Bay Area waits on a second bridge or tube in the long-term
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