Zobrazeno 1 - 10
of 580
pro vyhledávání: '"Bin, Ran"'
Publikováno v:
Energy and Built Environment, Vol 5, Iss 4, Pp 529-543 (2024)
The energy consumption of office buildings has increased sharply since the 21st century with the increasing urbanization coverage. At this stage, the energy consumption of office buildings in China mainly comes from air conditioning and lighting. Thi
Externí odkaz:
https://doaj.org/article/7f363e09b82f411d8c7a5b2882bdb829
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 3, Pp 450-466 (2024)
Abstract Vehicular trajectory data collected by connected automated vehicles (CAVs) is minimal due to the low penetration rates (PRs) of CAVs, and fail to capture the characteristics of traffic flow. This study proposes a fully sampled trajectory rec
Externí odkaz:
https://doaj.org/article/657bc31dd6374258bd4f4b81d63f8d72
Publikováno v:
IET Intelligent Transport Systems, Vol 18, Iss 1, Pp 16-28 (2024)
Abstract Real‐time and accurate short‐term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction
Externí odkaz:
https://doaj.org/article/289a031a2b7a409abd4df0a221fe3781
Publikováno v:
IET Intelligent Transport Systems, Vol 17, Iss 11, Pp 2251-2267 (2023)
Abstract This study focuses on the potential of connected and automated vehicles (CAVs) to enhance road traffic safety through the provision of rich physical motion state information. Real‐time risk indicators are crucial for improving driving safe
Externí odkaz:
https://doaj.org/article/6c70571975c64136ae8cc3fe79e3d56a
Publikováno v:
Journal of Intelligent and Connected Vehicles, Vol 6, Iss 2, Pp 79-90 (2023)
Potential field theory, as a theory that can also be applied to vehicle control, is an emerging risk quantification approach to accommodate the connected and self-driving vehicle environment. Vehicles have different risk impact effects on other road
Externí odkaz:
https://doaj.org/article/39f1245088984182b531c6c54de19aae
Publikováno v:
IEEE Access, Vol 11, Pp 28076-28089 (2023)
Data-driven car-following modeling is of great significance to traffic behavior analysis and the development of connected automated vehicle (CAV) technology. The existing researches focus on reproducing the car-following process by capturing the beha
Externí odkaz:
https://doaj.org/article/e96002c554bc44219adae8dc650dfb09
Publikováno v:
Transportation Engineering, Vol 13, Iss , Pp 100153- (2023)
In this paper, we present a new route-based discrete time variational inequality (VI) ideal dynamic user optimal route choice (DUO) model on a transportation network with fixed signal timing at signalized intersections and a relaxation with gradient
Externí odkaz:
https://doaj.org/article/d7199d6f7bc1462e9f35b71b40806f61
Publikováno v:
IET Intelligent Transport Systems, Vol 16, Iss 4, Pp 543-570 (2022)
Abstract This paper investigates the network capacity considering residual queues and connected automated vehicles. First, the weighted network outflow is introduced to measure the network capacity. Then, two bilevel models are built to calculate the
Externí odkaz:
https://doaj.org/article/dc2df68be88246b2ace7ac45704dc156
Publikováno v:
Agronomy, Vol 13, Iss 12, p 3006 (2023)
The aim of the present study is to evaluate the absolute content and accumulation patterns of flavonoid components; to give insight into the accumulation relationships among flavonoid components; to explore the correlation between the content of flav
Externí odkaz:
https://doaj.org/article/f8da7d6469ac4bce8a5cd02096b7ceee
Publikováno v:
IET Intelligent Transport Systems, Vol 16, Iss 3, Pp 363-379 (2022)
Abstract High‐quality lane‐scale traffic data is of great importance to the intelligent transportation system. However, missing values are sometimes inevitable due to the failure of the detectors or the low penetration rates of the connected auto
Externí odkaz:
https://doaj.org/article/0c877f52d06848619c762dee9ff5fd78