<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The sports characteristics of E-bikes increase the conflict between traffic participants. In this paper, the criteria of E-bike traffic conflict are given according to time distance. Based on the rough set theory, the redundant rule reduction method is developed, and the following conclusions are drawn: 1. Signal lights and video monitoring points have little effect on alleviating the traffic conflict between e-vehicles and motor vehicles; 2. Setting isolation facilities, non-motor vehicle lanes and traffic police can effectively reduce the traffic conflict. The results show that this method can effectively analyse the main influencing factors of traffic conflict.</p>
</abstract>

The sports characteristics of E-bikes increase the conflict between traffic participants. In this paper, the criteria of E-bike traffic conflict are given according to time distance. Based on the rough set theory, the redundant rule reduction method is developed, and the following conclusions are drawn: 1. Signal lights and video monitoring points have little effect on alleviating the traffic conflict between e-vehicles and motor vehicles; 2. Setting isolation facilities, non-motor vehicle lanes and traffic police can effectively reduce the traffic conflict. The results show that this method can effectively analyse the main influencing factors of traffic conflict.

Research on management and control strategy of E-bikes based on attribute reduction method

Xi’an University of Architecture and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The sports characteristics of E-bikes increase the conflict between traffic participants. In this paper, the criteria of E-bike traffic conflict are given...

The kernel matrix
C1	C2	C3	C4	C5	C6	C7
Empty					0	0
0	0	1	0	0	0	1
0	0	1	0	0	1	0
0	0	0	1	0	1	0
0	0	1	0	0	1	1
0	0	0	1	0	1	1
0	0	1	0	0	2	0
1	0	1	0	0	2	1
1	0	0	1	0	2	1

	Observing through conflict Average time (seconds)		Observing through conflict Average time (seconds)
Left turn E-bike through straight large car	4.6	Straight E-bike through the right turn large car	1.75
Left turn E-bike through straight small car	3.7	Straight E-bike through right turn small car	0.94
Left-turning motor vehicle crossing straight E-bike	3.08

U	C							D	U	C							D
U	C1	C2	C3	C4	C5	C6	C7	D	U	C1	C2	C3	C4	C5	C6	C7	D
1	0	1	0	0	0	0	0	0	12	0	0	1	0	1	0	1	0
2	0	0	0	1	0	1	1	2	13	0	1	1	1	0	2	1	1
3	0	0	0	0	0	2	1	2	14	1	1	1	1	0	2	0	1
4	0	1	0	0	1	2	1	2	15	0	1	1	1	0	2	0	1
5	0	0	0	1	1	1	1	2	16	0	0	0	0	0	1	0	2
6	0	1	0	0	1	2	1	2	17	1	0	1	0	1	0	0	1
7	0	0	1	1	0	1	0	0	18	0	0	1	1	0	1	0	1
8	0	1	0	1	0	2	0	0	19	0	0	1	0	1	1	1	0
9	1	1	1	1	0	0	1	0	20	1	1	1	1	0	2	1	0
10	0	0	0	0	1	0	1	1	21	0	1	0	0	0	2	1	2
11	0	0	1	0	1	1	1	0	22	1	1	1	1	1	0	0	0

Research on management and control strategy of E-bikes based on attribute reduction method

Published Online: May 26, 2021

Page range: 161 - 170

Received: Nov 29, 2020

Accepted: Feb 26, 2021

DOI: https://doi.org/10.2478/amns.2021.2.00020

Keywords
mixed traffic, E-bike, Traffic conflict safety, management and control strategy, attribute reduction

© 2021 Li Yingfeng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Analysis of factors affecting traffic conflicts.

Critical gap observation statistics of conflict between motor vehicles and E-bikes.

An information system instance.

Traffic conflict and influencing factors data.

U	C							D	U	C							D
U	C1	C2	C3	C4	C5	C6	C7	D	U	C1	C2	C3	C4	C5	C6	C7	D
1	0	1	0	0	0	0	0	0	12	0	0	1	0	1	0	1	0
2	0	0	0	1	0	1	1	2	13	0	1	1	1	0	2	1	1
3	0	0	0	0	0	2	1	2	14	1	1	1	1	0	2	0	1
4	0	1	0	0	1	2	1	2	15	0	1	1	1	0	2	0	1
5	0	0	0	1	1	1	1	2	16	0	0	0	0	0	1	0	2
6	0	1	0	0	1	2	1	2	17	1	0	1	0	1	0	0	1
7	0	0	1	1	0	1	0	0	18	0	0	1	1	0	1	0	1
8	0	1	0	1	0	2	0	0	19	0	0	1	0	1	1	1	0
9	1	1	1	1	0	0	1	0	20	1	1	1	1	0	2	1	0
10	0	0	0	0	1	0	1	1	21	0	1	0	0	0	2	1	2
11	0	0	1	0	1	1	1	0	22	1	1	1	1	1	0	0	0

U	C							D	U	C							D
U	C1	C2	C3	C4	C5	C6	C7	D	U	C1	C2	C3	C4	C5	C6	C7	D
1	0	1	0	0	0	0	0	0	12	0	0	1	0	1	0	1	0
2	0	0	0	1	0	1	1	2	13	0	1	1	1	0	2	1	1
3	0	0	0	0	0	2	1	2	14	1	1	1	1	0	2	0	1
4	0	1	0	0	1	2	1	2	15	0	1	1	1	0	2	0	1
5	0	0	0	1	1	1	1	2	16	0	0	0	0	0	1	0	2
6	0	1	0	0	1	2	1	2	17	1	0	1	0	1	0	0	1
7	0	0	1	1	0	1	0	0	18	0	0	1	1	0	1	0	1
8	0	1	0	1	0	2	0	0	19	0	0	1	0	1	1	1	0
9	1	1	1	1	0	0	1	0	20	1	1	1	1	0	2	1	0
10	0	0	0	0	1	0	1	1	21	0	1	0	0	0	2	1	2
11	0	0	1	0	1	1	1	0	22	1	1	1	1	1	0	0	0

Research on management and control strategy of E-bikes based on attribute reduction method

Published Online: May 26, 2021

Page range: 161 - 170

Received: Nov 29, 2020

Accepted: Feb 26, 2021

DOI: https://doi.org/10.2478/amns.2021.2.00020

Keywordsmixed traffic, E-bike, Traffic conflict safety, management and control strategy, attribute reduction

© 2021 Li Yingfeng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Analysis of factors affecting traffic conflicts.

Critical gap observation statistics of conflict between motor vehicles and E-bikes.

An information system instance.

Traffic conflict and influencing factors data.

Keywords
mixed traffic, E-bike, Traffic conflict safety, management and control strategy, attribute reduction

U	C							D	U	C							D
U	C1	C2	C3	C4	C5	C6	C7	D	U	C1	C2	C3	C4	C5	C6	C7	D
1	0	1	0	0	0	0	0	0	12	0	0	1	0	1	0	1	0
2	0	0	0	1	0	1	1	2	13	0	1	1	1	0	2	1	1
3	0	0	0	0	0	2	1	2	14	1	1	1	1	0	2	0	1
4	0	1	0	0	1	2	1	2	15	0	1	1	1	0	2	0	1
5	0	0	0	1	1	1	1	2	16	0	0	0	0	0	1	0	2
6	0	1	0	0	1	2	1	2	17	1	0	1	0	1	0	0	1
7	0	0	1	1	0	1	0	0	18	0	0	1	1	0	1	0	1
8	0	1	0	1	0	2	0	0	19	0	0	1	0	1	1	1	0
9	1	1	1	1	0	0	1	0	20	1	1	1	1	0	2	1	0
10	0	0	0	0	1	0	1	1	21	0	1	0	0	0	2	1	2
11	0	0	1	0	1	1	1	0	22	1	1	1	1	1	0	0	0