| dc.creator |
Loulizi, Amara |
|
| dc.creator |
Bichiou, Youssef |
|
| dc.creator |
Rakha, Hesham A. |
|
| dc.date |
2019-05-20T11:47:54Z |
|
| dc.date |
2019-05-20T11:47:54Z |
|
| dc.date |
2019-05-13 |
|
| dc.date |
2019-05-19T07:04:16Z |
|
| dc.date.accessioned |
2023-03-01T18:53:34Z |
|
| dc.date.available |
2023-03-01T18:53:34Z |
|
| dc.identifier |
Amara Loulizi, Youssef Bichiou, and Hesham Rakha, “Steady-State Car-Following Time Gaps: An Empirical Study Using Naturalistic Driving Data,” Journal of Advanced Transportation, vol. 2019, Article ID 7659496, 9 pages, 2019. doi:10.1155/2019/7659496 |
|
| dc.identifier |
http://hdl.handle.net/10919/89570 |
|
| dc.identifier |
https://doi.org/10.1155/2019/7659496 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/281738 |
|
| dc.description |
The time gap is defined as the time difference between the rear of a vehicle and the front of its follower, which affects both safety and the saturation flow rate of a roadway segment. In this study, naturalistic driving data were examined to measure time gaps from seven different drivers in a car-following scenario within steady-state conditions. The measurements were taken from a 13-km section of a Dulles Airport access road in Washington, DC. In total, 168,053 time gap samples were obtained covering seven speed intervals. Analysis of the data revealed a large variation in time gaps within individual drivers’ driving data, with coefficients of variation as high as 63.8% observed for some drivers. Results also showed that the variability within drivers was more significant at speeds higher than 54 km/h. In addition, there was a large variability between drivers. At speeds above 108 km/h, minimum time gaps left by some drivers could be 1.6 times longer than those left by others. Several statistical distributions were used to fit the data of the seven drivers as well as the data for all drivers combined for each speed interval. The selected distributions passed the goodness-of-fit (Kolmogorov-Smirnov, Chi-square, and Anderson-Darling) criteria only when the number of samples was reduced. Data reduction was not performed randomly, but rather in a manner intended to maintain the same observed distribution when all the samples were used. It is therefore recommended that empirical measures of distributions be used in traffic microsimulation software rather than theoretically fit distributions obtained based on statistical tests. This will lead to better naturalistic traffic behavior simulations, resulting in more precise predicted measures of performance (travel time, fuel consumption, and gas emissions). |
|
| dc.description |
Published version |
|
| dc.format |
application/pdf |
|
| dc.format |
application/pdf |
|
| dc.format |
text/xml |
|
| dc.language |
en |
|
| dc.publisher |
Hindawi Publishing Corp |
|
| dc.rights |
Creative Commons Attribution 4.0 International |
|
| dc.rights |
http://creativecommons.org/licenses/by/4.0/ |
|
| dc.rights |
Copyright © 2019 Amara Loulizi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
|
| dc.title |
Steady-State Car-Following Time Gaps: An Empirical Study Using Naturalistic Driving Data |
|
| dc.title |
Journal of Advanced Transportation |
|
| dc.type |
Article - Refereed |
|
| dc.type |
Text |
|