Can Eco-Driving Evaluation Cross Cities? Data Localization and Behavioral Heterogeneity from Beijing to Toronto Article Swipe
Leqi Zhang
,
Guohua Song
,
Zeyu Zhang
,
Zhiqiang Zhai
,
Junshi Xu
,
Pengfei Fan
,
Yan Ding
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su17093957
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su17093957
The framework of eco-driving evaluation relying on vehicle trajectory data is to quantify the disparities of the fuel consumption for individual driving behavior and to develop a baseline under various traffic conditions. The baseline represents the typical driving behavior in a city, and it is a pivotal parameter for eco-driving evaluation. The applicability of the evaluation method in different cities is overlooked, encompassing the suitability of parameters and the minimum data required. This study aims to investigate whether the evaluation baseline developed with sufficient data can be applied to a new city. The results reveal that the baseline developed in Beijing cannot be directly transferred to the eco-driving evaluation in Toronto due to the significantly more aggressive and competitive driving behavior exhibited by Toronto drivers. This study further examines the minimum data sample size necessary to develop a robust evaluation baseline and proposes a localized method to construct the evaluation system for eco-driving evaluation in different cities.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su17093957
- https://www.mdpi.com/2071-1050/17/9/3957/pdf?version=1745829785
- OA Status
- gold
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409884263
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409884263Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su17093957Digital Object Identifier
- Title
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Can Eco-Driving Evaluation Cross Cities? Data Localization and Behavioral Heterogeneity from Beijing to TorontoWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-04-28Full publication date if available
- Authors
-
Leqi Zhang, Guohua Song, Zeyu Zhang, Zhiqiang Zhai, Junshi Xu, Pengfei Fan, Yan DingList of authors in order
- Landing page
-
https://doi.org/10.3390/su17093957Publisher landing page
- PDF URL
-
https://www.mdpi.com/2071-1050/17/9/3957/pdf?version=1745829785Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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-
https://www.mdpi.com/2071-1050/17/9/3957/pdf?version=1745829785Direct OA link when available
- Concepts
-
Beijing, Transport engineering, Geography, Environmental science, China, Engineering, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 0, 32, 51, 92 |
| abstract_inverted_index.and | 23, 42, 67, 117, 141 |
| abstract_inverted_index.can | 85 |
| abstract_inverted_index.due | 111 |
| abstract_inverted_index.for | 19, 48, 151 |
| abstract_inverted_index.new | 90 |
| abstract_inverted_index.the | 13, 16, 35, 54, 63, 68, 78, 96, 106, 113, 129, 148 |
| abstract_inverted_index.This | 72, 125 |
| abstract_inverted_index.aims | 74 |
| abstract_inverted_index.data | 9, 70, 84, 131 |
| abstract_inverted_index.fuel | 17 |
| abstract_inverted_index.more | 115 |
| abstract_inverted_index.size | 133 |
| abstract_inverted_index.that | 95 |
| abstract_inverted_index.with | 82 |
| abstract_inverted_index.city, | 41 |
| abstract_inverted_index.city. | 91 |
| abstract_inverted_index.study | 73, 126 |
| abstract_inverted_index.under | 28 |
| abstract_inverted_index.cannot | 101 |
| abstract_inverted_index.cities | 59 |
| abstract_inverted_index.method | 56, 145 |
| abstract_inverted_index.reveal | 94 |
| abstract_inverted_index.robust | 138 |
| abstract_inverted_index.sample | 132 |
| abstract_inverted_index.system | 150 |
| abstract_inverted_index.Beijing | 100 |
| abstract_inverted_index.Toronto | 110, 123 |
| abstract_inverted_index.applied | 87 |
| abstract_inverted_index.cities. | 156 |
| abstract_inverted_index.develop | 25, 136 |
| abstract_inverted_index.driving | 21, 37, 119 |
| abstract_inverted_index.further | 127 |
| abstract_inverted_index.minimum | 69, 130 |
| abstract_inverted_index.pivotal | 46 |
| abstract_inverted_index.relying | 5 |
| abstract_inverted_index.results | 93 |
| abstract_inverted_index.traffic | 30 |
| abstract_inverted_index.typical | 36 |
| abstract_inverted_index.various | 29 |
| abstract_inverted_index.vehicle | 7 |
| abstract_inverted_index.whether | 77 |
| abstract_inverted_index.baseline | 27, 33, 80, 97, 140 |
| abstract_inverted_index.behavior | 22, 38, 120 |
| abstract_inverted_index.directly | 103 |
| abstract_inverted_index.drivers. | 124 |
| abstract_inverted_index.examines | 128 |
| abstract_inverted_index.proposes | 142 |
| abstract_inverted_index.quantify | 12 |
| abstract_inverted_index.construct | 147 |
| abstract_inverted_index.developed | 81, 98 |
| abstract_inverted_index.different | 58, 155 |
| abstract_inverted_index.exhibited | 121 |
| abstract_inverted_index.framework | 1 |
| abstract_inverted_index.localized | 144 |
| abstract_inverted_index.necessary | 134 |
| abstract_inverted_index.parameter | 47 |
| abstract_inverted_index.required. | 71 |
| abstract_inverted_index.aggressive | 116 |
| abstract_inverted_index.evaluation | 4, 55, 79, 108, 139, 149, 153 |
| abstract_inverted_index.individual | 20 |
| abstract_inverted_index.parameters | 66 |
| abstract_inverted_index.represents | 34 |
| abstract_inverted_index.sufficient | 83 |
| abstract_inverted_index.trajectory | 8 |
| abstract_inverted_index.competitive | 118 |
| abstract_inverted_index.conditions. | 31 |
| abstract_inverted_index.consumption | 18 |
| abstract_inverted_index.disparities | 14 |
| abstract_inverted_index.eco-driving | 3, 49, 107, 152 |
| abstract_inverted_index.evaluation. | 50 |
| abstract_inverted_index.investigate | 76 |
| abstract_inverted_index.overlooked, | 61 |
| abstract_inverted_index.suitability | 64 |
| abstract_inverted_index.transferred | 104 |
| abstract_inverted_index.encompassing | 62 |
| abstract_inverted_index.applicability | 52 |
| abstract_inverted_index.significantly | 114 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.15999002 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |