Most transport mode choice studies rely on subjective responses to hypothetical questions (stated preference), or on revealed preferences. In stated preference studies, trip characteristics are exact, but there is a range of sources of errors and biases in the responses. Revealed preference surveys suffer the opposite: The choice is exact (i.e. observed) but trip attributes are uncertain - and even more uncertain when it comes to transport modes not chosen. Our dataset goes a long way in solving these problems. The data set combines real travel behaviour and mode choice data from the Norwegian National Transport Survey (NTS) with trip characteristics collected from Google maps travel planner. From the NTS, we have extracted all commute trips conducted by either private car or public transport (PT) into ten major cities in Norway with exact origin and destination coordinates. The NTS data also comprises information about age, gender, household, income and car availability. From Google maps, we have extracted trip characteristics for these trips - for both the mode chosen and the mode not chosen. This data includes total travel time, the number of interchanges, wait time, walk time, and in-vehicle time. This data can be used to study how different trip characteristics influence the probability of choosing PT over private car on commute journeys.
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http://dx.doi.org/10.1016/j.dib.2021.107319 | DOI Listing |
Sci Total Environ
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Professor, Department of Civil Engineering, Toronto Metropolitan University, Toronto M5B 2K3, Ontario, Canada.
In recent years, the impact of transportation activities on carbon (CO) emissions has gained global attention. In China, the severity of CO emissions from transportation is a pressing issue, necessitating the development of effective emission reduction strategies. This study uses taxi GPS data from Xi'an, China, to explore the spatial patterns and influencing factors of CO2 emissions.
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Department of Pediatrics, Hospital Niño Jesús, Universidad Autónoma de Madrid, Madrid, Spain.
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Department of Applied Chemistry, School of Chemistry and Materials Science, Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China, Hefei, Anhui 230026, China.
The global clean energy transition and carbon neutrality call for developing high-performance batteries. Here we report a rechargeable lithium metal - catalytic hydrogen gas (Li-H) battery utilizing two of the lightest elements, Li and H. The Li-H battery operates through redox of H/H on the cathode and Li/Li on the anode.
View Article and Find Full Text PDFSensors (Basel)
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Department of Rehabilitation Sciences, Medical University of South Carolina, Charleston, SC 29425, USA.
Balance deficits are present in a variety of clinical populations and can negatively impact quality of life. The integration of wearable sensors and machine learning technology (ML) provides unique opportunities to quantify biomechanical characteristics related to balance outside of a laboratory setting. This article provides a general overview of recent developments in using wearable sensors and ML to estimate or predict biomechanical characteristics such as center of pressure (CoP) and center of mass (CoM) motion.
View Article and Find Full Text PDFMaterials (Basel)
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State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, China.
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