The Future of Mobility: Scenarios for China in 2030

Ecola, Liisa; Zmud, Johanna; Gu, Kai; Phleps, Peter; Feige, Irene

doi:10.7249/rr991

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…When a fixed parameter is replaced by a random number that obeys a distribution, the probability of j being chosen without conditions should be the probability of β traversing all possible values, that is, the probability function of the Mixed Logit model can be regarded as the integration of Multinomial Logit probability functions on the β density function, f η q z q , Ω , as shown in Equation (8). For a more reliable estimate of the parameters, the survey data are encoded in Table 1 as a Mixed Logit model.…”

Section: The Basic Principles Of the Mixed Logit Model Based On The Cmentioning

confidence: 99%

“…Its structure has changed significantly; the carbon emission composition of urban transportation has changed accordingly [6,7]. With regards the framework of urban traffic structure, the proportion of rail transit is increasing year by year, contributing to urban traffic energy savings and significantly reducing emissions [8][9][10][11]. Therefore, estimating the energy consumption and carbon emission of rail transit passengers, and constructing a method to forecast the emission reduction of rail transit based on passenger demand, could support the formulation of urban low-carbon transportation policies and the construction of low-carbon infrastructures.…”

Section: Introductionmentioning

confidence: 99%

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Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

et al. 2020

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There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023.

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Section: The Basic Principles Of the Mixed Logit Model Based On The Cmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

et al. 2020

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“…It was important that the students see representative models of scenarios, to learn about their structural and rhetorical form. They read a RAND white paper that had four scenarios on mobility in China in 2030 (Ecola et al 2015), and scenarios from the Institute for the Future on four scenarios of the future of food (Kreit et al 2011). In addition to learning about the compositional mechanics of writing a scenario, I wanted the students to see that scenarios come in fours, typically (although I was having them compose three).…”

Section: Write Representationsmentioning

confidence: 99%

Teaching the Future of Technology in the History Classroom: A Case Study

Staley

2018

World Futures Review

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This article will describe how historians can teach the future of technology. Historians need not alter their traditional methods of historical inquiry to teach the future, and indeed the history classroom is a natural site for foresight education. Historical inquiry begins with questions, and futuring similarly begins with asking the right questions. The historian seeks out evidence, and futurists as well identify drivers and blockers, considering how these drivers and blockers will interact with each other. In contrast to social scientists, historians work with imperfect or incomplete information, an apt description of the state of our evidence about the future. In a manner similar to historians, futurists interpret and draw inferences from evidence. After the research an analysis of the evidence is complete, the historian/futurist writes representations. This article will describe how I employed the historical method to teach the future of technology in a history research seminar, the results produced by the students, and ways that the study of the future can be situated in the history classroom.

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“…Fields such as defense, social policy, and business commonly use horizon scanning to anticipate future technologies and emerging trends (24). In transportation, future scenarios research is becoming an increasingly important means of considering the possible effects of technology such as autonomous vehicles (25)(26)(27). What these methods have in common is that they provide a structured means of soliciting input from a limited number of experts knowledgeable about the topic at hand.…”

Section: Proposed Scenario-based Approachmentioning

confidence: 99%

Conceptual Models of the Effect of Information and Communications Technology on Long-Distance Travel Demand

Erhardt

2017

Transportation Research Record: Journal of the Transportation R

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Rapidly evolving information and communications technology (ICT) poses a risk to the accuracy of long-distance travel (LDT) demand forecasts. The nature of this risk is multifaceted: ICT could reduce demand if people substituted virtual for physical travel, or it could serve as a complement and thereby induce more demand. This paper presents two conceptual models for better understanding the nature of those relationships. Both models start from an existing LDT model and envision how that model can be adapted to capture ICT effects, with one considering the effects explicitly and the other implicitly. When the effects of future technology are being forecast, data collected in the past or in the present, when the future technology does not exist, are inherently limited. In such situations, conceptual models such as these can be applied by using a scenario-based approach to explore a range of reasonable outcomes. This strategy can be an important tool not just for ICT, but also for understanding the risks posed by other future technologies, such as autonomous vehicles.

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The Future of Mobility: Scenarios for China in 2030

Cited by 13 publications

References 6 publications

Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji

Teaching the Future of Technology in the History Classroom: A Case Study

Conceptual Models of the Effect of Information and Communications Technology on Long-Distance Travel Demand

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