This paper explored the impacts of vehicle-to-infrastructure (V2I) communication on the mixed traffic flow consisting of connected vehicles (CVs) and human-driven vehicles (HVs). We developed a cellular automaton model for mixed flow at the signalized intersection. In addition to considering the motion characteristics of CVs and the influence of HVs on the motion behavior of CVs, the model also considered the influence of signal lights. CVs determine their velocities via V2I communication in order to pass the signal light with less delay and avoid stopping. Through simulations, we found that the presence, frequency and range of V2I communication all make a difference in the mixed flow. Also, 1-Hz communication reduces the number of vehicles within 300 m before the red light from 36 to 26, and the 10-Hz communication reduces one more; 1-Hz communication increases the number of accelerations, but when the frequency increases to 10 Hz, the number of accelerations decreases to the same value as without V2I communication, but the value of number of accelerations increases monotonously with the frequency; traffic delay decreases and capacity increases as the frequency increases. However, as the communication range increases, except that the number of accelerations first decreases and then increases, other traffic characteristics remain unchanged. The number of accelerations reaches a minimum at about 500 m.
机构:
Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Minist Transport, Beijing 100044, Peoples R ChinaBeijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Minist Transport, Beijing 100044, Peoples R China
Ding Shen-zhen
Chen Xu-mei
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机构:
Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Minist Transport, Beijing 100044, Peoples R China
Xuchang Univ, Xuchang 461000, Peoples R ChinaBeijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Minist Transport, Beijing 100044, Peoples R China
Chen Xu-mei
Yu Lei
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机构:
Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Minist Transport, Beijing 100044, Peoples R China
Xuchang Univ, Xuchang 461000, Peoples R ChinaBeijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, Minist Transport, Beijing 100044, Peoples R China
机构:
Boston Univ, Div Syst Engn, 15 St Marys St, Brookline, MA 02446 USA
Delft Univ Technol TU Delft, Delft Ctr Syst & Control, NL-2628 CD Delft, NetherlandsBoston Univ, Div Syst Engn, 15 St Marys St, Brookline, MA 02446 USA
Giammarino, Vittorio
Lv, Maolong
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机构:
Delft Univ Technol TU Delft, Delft Ctr Syst & Control, NL-2628 CD Delft, NetherlandsBoston Univ, Div Syst Engn, 15 St Marys St, Brookline, MA 02446 USA
Lv, Maolong
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Baldi, Simone
Frasca, Paolo
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机构:
Univ Grenoble Alpes, CNRS, INRIA, Grenoble INP,GIPSA Lab, F-38000 Grenoble, FranceBoston Univ, Div Syst Engn, 15 St Marys St, Brookline, MA 02446 USA
Frasca, Paolo
Delle Monache, Maria Laura
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机构:
Univ Grenoble Alpes, CNRS, INRIA, Grenoble INP,GIPSA Lab, F-38000 Grenoble, FranceBoston Univ, Div Syst Engn, 15 St Marys St, Brookline, MA 02446 USA
Delle Monache, Maria Laura
[J].
2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC),
2019,
: 335
-
340
机构:
Hanyang Univ, Grad Sch Urban Studies, Dept Urban & Reg Dev, Seoul 04763, South KoreaHanyang Univ, Grad Sch Urban Studies, Dept Urban & Reg Dev, Seoul 04763, South Korea
Park, Jonghan
Jang, Seunghwa
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机构:
Hanyang Univ, Grad Sch Urban Studies, Dept Urban & Reg Dev, Seoul 04763, South KoreaHanyang Univ, Grad Sch Urban Studies, Dept Urban & Reg Dev, Seoul 04763, South Korea
Jang, Seunghwa
Ko, Joonho
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机构:
Hanyang Univ, Grad Sch Urban Studies, Dept Urban & Reg Dev, Seoul 04763, South KoreaHanyang Univ, Grad Sch Urban Studies, Dept Urban & Reg Dev, Seoul 04763, South Korea
Ko, Joonho
Patella, Sergio Maria
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机构:Hanyang Univ, Grad Sch Urban Studies, Dept Urban & Reg Dev, Seoul 04763, South Korea
机构:
Beijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R ChinaBeijing Jiaotong Univ, State Key Lab Rail Traff Control & Safety, Beijing 100044, Peoples R China