Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data
Abstract
:1. Introduction
2. Computational Framework
2.1. Data Collection and Filtering of Trip Ddata
2.2. Computing Alternative Trips
2.3. Quantifying Trip Attributes
2.4. Changes in Travel Time and Its Relation to Emission Reduction and Physically Active Distance
2.5. Weather Context and Its Influence on Physically Active Travel
3. Case Study Data
4. Case Study Results
4.1. Summative Potential Changes with Lower-Carbon Alternatives
4.2. Potential Travel Time Changes, Emission Reduction, and Physically Active Travel Distance
4.3. Influence of Travel Time Threshold Variance
4.4. Alternatives in Relation to the Weather Context
5. Discussion and Conclusions
5.1. Highlights of Case Study Findings on the Potential for Modal Shift
5.2. Usefulness of Understanding Modal Shift Potential
5.3. Challenges and Potentials for Data Collection and Public Engagement using Smartphone Apps
5.4. Accuracy and Noise in Sampling and Computation
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Data Availability
References
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Component | Parameters |
---|---|
Summative potential changes with lower-carbon alternatives |
|
Potential travel time changes, emission reduction, and physically active travel distance |
|
Influence of travel time threshold variance |
|
Alternatives in relation to the weather context |
|
Observed Trip | Total Duration | Total Distance (km) | Duration Traveled by Each Mode | Distance Traveled by Each Mode (km) |
Walk→Car→Walk | 00:43:43 | 19.9 | Car: 00:41:03 Walk: 00:02:40 | Car: 19.78 Walk: 0.12 |
Computed Alternative Trip | Total Duration | Total Distance (km) | Duration Traveled by Each Mode | Distance Traveled by Each Mode (km) |
Walk→Bus→Walk→Train→Walk | 00:45:28 | 18.5 | Bus: 00:11:00 Train: 00:19:00 Walk: 00:15:28 | Bus: 2.9 Train: 14.9 Walk: 0.7 |
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Bagheri, M.; Mladenović, M.N.; Kosonen, I.; Nurminen, J.K. Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data. Sustainability 2020, 12, 5901. https://doi.org/10.3390/su12155901
Bagheri M, Mladenović MN, Kosonen I, Nurminen JK. Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data. Sustainability. 2020; 12(15):5901. https://doi.org/10.3390/su12155901
Chicago/Turabian StyleBagheri, Mehrdad, Miloš N. Mladenović, Iisakki Kosonen, and Jukka K. Nurminen. 2020. "Analysis of Potential Shift to Low-Carbon Urban Travel Modes: A Computational Framework Based on High-Resolution Smartphone Data" Sustainability 12, no. 15: 5901. https://doi.org/10.3390/su12155901