| Title | Methodology to estimate the need for direct-current fast-charging stations along highways in Canada |
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| Author | Wu, Y ; Ribberink,
H |
| Source | Journal of Transportation Engineering, Part A: Systems vol. 146, issue 10, 04020108, 2020 p. 1-11, https://doi.org/10.1061/JTEPBS.0000437 |
| Image |  |
| Year | 2020 |
| Alt Series | Natural Resources Canada, Contribution Series 20200307 |
| Publisher | American Society of Civil Engineers (ASCE) |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf |
| Province | Ontario |
| NTS | 31B; 31C; 31G; 31M |
| Area | Ottawa; Toronto; Hawkesbury |
| Lat/Long WENS | -80.0000 -74.0000 46.0000 43.2500 |
| Subjects | Science and Technology; Transport; energy; electric power; Electric vehicles; Infrastructures; Road networks; Methodology |
| Illustrations | tables; plots; bar graphs |
| Released | 2020 07 16 |
| Abstract | Many research works have focused on solving facility location problems to optimize the distribution of direct current fast charging (DCFC) stations along highways. However, before such optimization
studies can be done, a reasonable estimate is needed of the required number of DCFCs for these highways. Unfortunately, many highways lack the detailed traffic count data required to make these estimates. This study developed a methodology for
forecasting the need for DCFC stations along highways using only classic traffic count information such as annual average daily traffic (AADT), which is one of the most readily available types of data in many countries, including Canada. This method
was developed using data from highway sections with more detailed traffic count information. Detailed historical traffic data of different highway sections first are analyzed thoroughly and categorized into groups of traffic flow patterns that then
can be employed to predict traffic flow for other locations where only less-detailed data, such as the AADT, are available. The methodology describes a way to estimate the peak traffic flow and the long-distance traffic fraction on the highway, so
that the equation developed to predict the number of long distance-traveling electric vehicles (EVs) is complete and solvable. The methodology was applied in two case studies for different highway sections in Ontario, and the need for DCFCs under
various scenarios of the EV adoption rate was presented. The case studies showed that the methodology developed in this study can be used successfully to guide the planning of EV fast charging infrastructure along highways using only conventional
traffic data. |
| GEOSCAN ID | 326953 |
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