A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles
Using a cellular automaton model, this paper studied the evolution mechanism of traffic incidents affecting the capacity of urban expressway under the mixed traffic environment of manual driving and automatic driving. It showed that the length of the automated-driving early-warning zone could affect...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/9523819 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566058715709440 |
|---|---|
| author | Jiawen Wang Shaobo Li Yining Lu Lubang Wang |
| author_facet | Jiawen Wang Shaobo Li Yining Lu Lubang Wang |
| author_sort | Jiawen Wang |
| collection | DOAJ |
| description | Using a cellular automaton model, this paper studied the evolution mechanism of traffic incidents affecting the capacity of urban expressway under the mixed traffic environment of manual driving and automatic driving. It showed that the length of the automated-driving early-warning zone could affect the capacity of expressway. Specifically, the early-warning zone is divided into an accelerate lane-changing area, a decelerate lane-changing area, and a forced lane-changing area. The areas vary according to the distance between the vehicle and the location of incident. Based on the study, this paper establishes a codirectional two-lane cellular automaton model. The analysis showed that the capacity of the urban expressway varies under different combinations of early-warning area length and division ratio of early-warning zone. In the case of two-lane reduction caused by traffic incidents, the capacity of the expressway is optimized when the length of early-warning zone is between 450 and 600 m, and the ratio of accelerate zone, decelerate zone, and forced zone to the length of early-warning zone is, respectively, 75%, 10%, and 15%. In addition, this study showed that the capacity will rise with the increase in automated vehicles. |
| format | Article |
| id | doaj-art-4717b2c5cf714bb4b2050be0e6c94bd0 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-4717b2c5cf714bb4b2050be0e6c94bd02025-02-03T01:05:17ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/95238199523819A Division Method of Determining the Early-Warning Zone on an Expressway for Automated VehiclesJiawen Wang0Shaobo Li1Yining Lu2Lubang Wang3Business School, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSH MH. Urban Planning & Design Research Institute, Shanghai 200093, ChinaBusiness School, University of Shanghai for Science and Technology, Shanghai 200093, ChinaLogistics and E-Commerce College, Zhejiang Wanli University, Ningbo, 315100, ChinaUsing a cellular automaton model, this paper studied the evolution mechanism of traffic incidents affecting the capacity of urban expressway under the mixed traffic environment of manual driving and automatic driving. It showed that the length of the automated-driving early-warning zone could affect the capacity of expressway. Specifically, the early-warning zone is divided into an accelerate lane-changing area, a decelerate lane-changing area, and a forced lane-changing area. The areas vary according to the distance between the vehicle and the location of incident. Based on the study, this paper establishes a codirectional two-lane cellular automaton model. The analysis showed that the capacity of the urban expressway varies under different combinations of early-warning area length and division ratio of early-warning zone. In the case of two-lane reduction caused by traffic incidents, the capacity of the expressway is optimized when the length of early-warning zone is between 450 and 600 m, and the ratio of accelerate zone, decelerate zone, and forced zone to the length of early-warning zone is, respectively, 75%, 10%, and 15%. In addition, this study showed that the capacity will rise with the increase in automated vehicles.http://dx.doi.org/10.1155/2020/9523819 |
| spellingShingle | Jiawen Wang Shaobo Li Yining Lu Lubang Wang A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles Discrete Dynamics in Nature and Society |
| title | A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles |
| title_full | A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles |
| title_fullStr | A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles |
| title_full_unstemmed | A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles |
| title_short | A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles |
| title_sort | division method of determining the early warning zone on an expressway for automated vehicles |
| url | http://dx.doi.org/10.1155/2020/9523819 |
| work_keys_str_mv | AT jiawenwang adivisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles AT shaoboli adivisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles AT yininglu adivisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles AT lubangwang adivisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles AT jiawenwang divisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles AT shaoboli divisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles AT yininglu divisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles AT lubangwang divisionmethodofdeterminingtheearlywarningzoneonanexpresswayforautomatedvehicles |