A new car-following model considering acceleration of lead vehicle
- Authors
- Sun, Bingrong; Wu, Na; Ge, Ying-En; Kim, Taewan; Zhang, Hongjun Michael
- Issue Date
- Jan-2016
- Publisher
- VILNIUS GEDIMINAS TECH UNIV
- Keywords
- sensitivity; microscopic traffic flow models; car-following behavior; stability; GM model
- Citation
- TRANSPORT, v.31, no.1, pp 1 - 10
- Pages
- 10
- Journal Title
- TRANSPORT
- Volume
- 31
- Number
- 1
- Start Page
- 1
- End Page
- 10
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/7388
- DOI
- 10.3846/16484142.2014.913534
- ISSN
- 1648-4142
1648-3480
- Abstract
- Abstract. For decades, the General Motors (GM) car following model has received a great deal of attention and provided a basic framework to describe the interactions between vehicles on the road. It is based on the stimulus-response assumption that the following vehicle responds to the relative speed between the lead vehicle and itself. However, some of the empirical findings show that the assumption of GM model is not always true and need some modification. For example, the acceleration of the following vehicle is very sensitive to the sign of the relative speed and because of no term in the model that directly represents the leader's acceleration, the follower's response to the leader's acceleration can be retarded. This paper offers a new car-following model that can be considered as a variant of the GM model that can better capture car following behavior. The new model treats the follower's acceleration as a proportion of a weighted sum of the leader's acceleration and the relative speed between the lead and following vehicles. This paper compares the new model with the original GM model numerically and the characteristics of the new parameters in the model are investigated. It is also shown that the new model overcomes the shortcomings of the original GM model identified in this paper and gives us more instruments to capture the real-world car-following behavior.
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Collections - College of Engineering > ETC > 1. Journal Articles
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