694 research outputs found

    Characterizing the polycentric spatial structure of Beijing Metropolitan Region using carpooling big data

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    Polycentric metropolitan regions are a high-level urbanization form characterized with dynamic layout, fuzzy boundary and various human activity performances. Owing to the complexity of polycentricity, it can be difficult to understand their spatial structure characteristics merely based on conventional survey data and method. This poses a challenge for authorities wishing to make effective urban land use and transport policies. Fortunately, the presence and availability of big data provides an opportunity for scholars to explore the complex metropolitan spatial structures, but there are still some research limitations in terms of data use and processing, unit scale, and method. To address these limitations, we proposed a three-step method to apply the carpooling big data in metropolitan analysis including: first, locating the metropolitan sub-centers; second, delimiting the metropolitan sphere; third, measuring the performance of polycentric structure. The developed method was tested in Beijing Metropolitan Region and the results show that the polycentric metropolitan region represents a hierarchical regional center system: one primary center interacting with seven surrounding secondary centers. These metropolitan centers have a strong attraction, which results in the continuous expansion beyond the administrative boundary to radiate more adjacent jurisdictions. Furthermore, the heterogeneity of human activity performance and role for each regional center is remarkable. It is necessary to consider the specific role of each sub-center when making metropolitan transport and land use policies. Compared with previous studies, the proposed method has the advantages of being more reliable, accurate and comprehensive in characterizing the polycentric spatial structure. The application of carpooling big data and the proposed method would provide a novel perspective for research on the other metropolitan regions

    Chapter 3 - Mobility on demand (MOD) and mobility as a service (MaaS): early understanding of shared mobility impacts and public transit partnerships

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    Technology is changing the way we move and reshaping cities and society. Shared and on-demand mobility represent notable transportation shifts in the 21st century. In recent years, mobility on demand (MOD)—where consumers access mobility, goods, and services on-demand by dispatching shared modes, courier services, public transport, and other innovative strategies—has grown rapidly due to technological advancements; changing consumer preferences; and a range of economic, environmental, and social factors. New attitudes toward sharing, MOD, and mobility as a service (MaaS) are changing traveler behavior and creating new opportunities and challenges for public transportation. This chapter discusses similarities and differences between the evolving concepts of MaaS and MOD. Next, it characterizes the range of existing public transit and MOD service models and enabling partnerships. The chapter also explores emerging trends impacting public transportation. While vehicle automation could result in greater public transit competition in the future, it could also foster new opportunities for transit enhancements (e.g., microtransit services, first- and last-mile connections, reduced operating costs). The chapter concludes with a discussion of how MOD/MaaS partnerships and automation could enable the public transit industry to reinvent itself, making it more attractive and competitive with private vehicle ownership and use

    Analysis of Mexico City transportation systems to address climate change, traffic, social equity, safety, and air pollution health risks

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    This research presents an analysis of Mexico City\u27s transportation systems and how they impact climate change, traffic, social equity, safety, and health risks. The purpose of this research is to propose transportation and energy management strategies to the government of Mexico City to reduce the effects of climate change, traffic, social equity, safety, and health risks. The methodology used in the research includes a traffic analysis, environmental and social impact analysis across Mexico City transportation, an equity analysis, and a SWOT analysis of policies. Through the traffic analysis of the research found that traffic congestion occurs in the northwest region of Mexico City. Traffic is a major problem in Mexico City due to the increase in population since the year 2015. Traffic is considered the central problem of air pollution in Mexico City. The environmental and social impact analysis across Mexico City transportation found that low-socioeconomic status sectors tend to deal with more health, safety, and pollution problems in Mexico City. Through the equity analysis the research recommends that transportation electrification is convenient in the eastern and northeastern areas of Mexico City to reduce air pollution and improve the quality of the transportation modes in the vulnerable zones. Through the SWOT analysis the research found that the policy “Don’t Drive Today” is not bringing down emissions and is increasing the number of vehicles on the road. Recommendations for Mexico City to improve the policy are: 1) Creation of policies or incentives to make citizens invest in electric vehicles (EVs), 2) Carpooling systems, and 3) Intelligent Transportation Systems (ITS)

    A passenger-to-driver matching model for commuter carpooling: Case study and sensitivity analysis

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    For the transport sector, promoting carpooling to private car users could be an effective strategy over reducing vehicle kilometers traveled. Theoretical studies have verified that carpooling is not only beneficial to drivers and passengers but also to the environment. Nevertheless, despite carpooling having a huge potential market in car commuters, it is not widely used in practice worldwide. In this paper, we develop a passenger-to-driver matching model based on the characteristics of a private-car based carpooling service, and propose an estimation method for time-based costs as well as the psychological costs of carpooling trips, taking into account the potential motivations and preferences of potential carpoolers. We test the model using commuting data for the Greater London from the UK Census 2011 and travel-time data from Uber. We investigate the service sensitivity to varying carpooling participant rates and fee-sharing ratios with the aim of improving matching performance at least cost. Finally, to illustrate how our matching model might be used, we test some practical carpooling promotion instruments. We found that higher participant role flexibility in the system can improve matching performance significantly. Encouraging commuters to walk helps form more carpooling trips and further reduces carbon emissions. Different fee-sharing ratios can influence matching performance, hence determination of optimal pricing should be based on the specific matching model and its cost parameters. Disincentives like parking charges and congestion charges seem to have a greater effect on carpooling choice than incentives like preferential parking and subsidies. The proposed model and associated findings provide valuable insights for designing an effective matching system and incentive scheme for carpooling services in practice

    How machine learning informs ride-hailing services: A survey

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    In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

    Congestion Mitigation Measure in Hyderabad

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    The State of Sustainable Transportation at Union College: A Transportation Audit of Union College Students and Faculty

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    Union College has established a Climate Action Plan with the goal of carbon neutrality by 2060 as part of its commitment to sustainability. A significant component of Union’s carbon footprint, however, is student and faculty transportation. The purpose of this research was to analyze the transportation behavior of students and faculty to determine the carbon emissions that result from the use of various transportation methods. Two campus‐wide surveys were conducted; one was distributed to students and the other targeted faculty. For comparison purposes, survey questions were designed to be compatible with, but more focused than, those of a survey conducted in 2007‐08 by the students taking an Introduction to Environmental Science course. The surveys asked students and faculty about the modes of transportation utilized, and parking and travel habits. Using standard formulas, transportation carbon emission analysis determined that a typical faculty member emitted 824 and 1020 kg of carbon in 2011 and 2007, respectively. Similar calculations showed that a typical student emitted 998 and 784 kg of carbon in 2011 and 2007, respectively. By compiling the data related to the type of cars students drive, as well as carpool and trolley participation habits, the study proposes recommendations to improve the transportation culture on campus to make it more sustainable
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