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Author: Joseph J. Di Gianni Publisher: ISBN: Category : Ridesharing Languages : en Pages : 362
Book Description
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's 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.
Author: Joseph J. Di Gianni Publisher: ISBN: Category : Ridesharing Languages : en Pages : 362
Book Description
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's 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.
Author: Jochen Langheim Publisher: Springer ISBN: 3319198181 Category : Technology & Engineering Languages : en Pages : 238
Book Description
This volume collects selected papers of the 3rd CESA Automotive Electronics Congress, Paris, 2014. CESA is the most important automotive electronics conference in France. The topical focus lies on state-of-the-art automotive electronics with respect to energy consumption and autonomous driving. The target audience primarily comprises industry leaders and research experts in the automotive industry.
Author: Silvia Nittel Publisher: Springer ISBN: 3540799966 Category : Computers Languages : en Pages : 275
Book Description
This volume serves as the post-conference proceedings for the Second GeoSensor Networks Conference that was held in Boston, Massachusetts in October 2006. The conference addressed issues related to the collection, management, processing, ana- sis, and delivery of real-time geospatial data using distributed geosensor networks. This represents an evolution of the traditional static and centralized geocomputational paradigm, to support the collection of both temporally and spatially high-resolution, up-to-date data over a broad geographic area, and to use sensor networks as actuators in geographic space. Sensors in these environments can be static or mobile, and can be used to passively collect information about the environment or, eventually, to actively influence it. The research challenges behind this novel paradigm extend the frontiers of tra- tional GIS research further into computer science, addressing issues like data stream processing, mobile computing, location-based services, temporal-spatial queries over geosensor networks, adaptable middleware, sensor data integration and mining, au- mated updating of geospatial databases, VR modeling, and computer vision. In order to address these topics, the GSN 2006 conference brought together leading experts in these fields, and provided a three-day forum to present papers and exchange ideas.
Author: Yu Zheng Publisher: MIT Press ISBN: 0262039087 Category : Computers Languages : en Pages : 633
Book Description
An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications. Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications. Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field.
Author: Liisa Ecola Publisher: Rand Corporation ISBN: 0833090356 Category : History Languages : en Pages : 119
Book Description
Researchers developed two scenarios to envision the future of mobility in China in 2030. Economic growth, the presence of constraints on vehicle ownership and driving, and environmental conditions differentiate the scenarios. By making potential long-term mobility futures more vivid, the team sought to help decisionmakers at different levels of government and in the private sector better anticipate and prepare for change.
Author: Patrick Vogel Publisher: Springer ISBN: 3319277359 Category : Technology & Engineering Languages : en Pages : 172
Book Description
This monograph presents a tactical planning approach for service network design in metropolitan areas. Designing the service network requires the suitable aggregation of demand data as well as the anticipation of operational relocation decisions. To this end, an integrated approach of data analysis and mathematical optimization is introduced. The book also includes a case study based on real-world data to demonstrate the benefit of the proposed service network design approach. The target audience comprises primarily research experts in the field of traffic engineering, but the book may also be beneficial for graduate students.
Author: Joseph J. Di Gianni
Author: Joseph J. Di Gianni
Author: Zhiwei (Tony) Qin
Author: Jochen Langheim
Author: Nancy McGuckin
Author: Simon Elias Bibri
Author: Silvia Nittel
Author: Yu Zheng
Author: Liisa Ecola
Author: Robert Cervero
Author: Patrick Vogel