Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time
An expressway is divided into different road segments according to the entrance-exit toll stations. Based on the driving characteristics of expressways, two methods are proposed to predict the average speed of vehicles on an expressway. One is based on the relationship between the number and the ave...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10117602/ |
| _version_ | 1797829163690754048 |
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| author | Shuoqi Wang Zhanzhong Wang |
| author_facet | Shuoqi Wang Zhanzhong Wang |
| author_sort | Shuoqi Wang |
| collection | DOAJ |
| description | An expressway is divided into different road segments according to the entrance-exit toll stations. Based on the driving characteristics of expressways, two methods are proposed to predict the average speed of vehicles on an expressway. One is based on the relationship between the number and the average speed of vehicles on road segments, and the other is based on traffic situation obtained by Baidu, Gaode, and other online maps API. According to the methods, intelligent driving strategies are adopted to satisfy the desired driving time and achieve the driving task successfully. The main principle of the strategies is to adjust the driving route and speed automatically according to the expressway conditions and desired driving time, and to realize the switch of the driving route between the expressway and provincial highway, or national highway and other non-expressway networks. The speed prediction methods and intelligent driving strategies overcome the shortcomings of the existing expressway traffic volume prediction. It has no complex model but is simple, feasible, fast, and practical, which provides an important theoretical basis for the design of expressway intelligent driving systems. The proposed methods exhibits good innovation and practical applications. |
| first_indexed | 2024-04-09T13:16:02Z |
| format | Article |
| id | doaj.art-cd8cba922e014b12821a64aab1cd63b7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T13:16:02Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cd8cba922e014b12821a64aab1cd63b72023-05-11T23:01:03ZengIEEEIEEE Access2169-35362023-01-0111448544486510.1109/ACCESS.2023.327330410117602Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving TimeShuoqi Wang0https://orcid.org/0000-0002-5345-8053Zhanzhong Wang1https://orcid.org/0000-0001-7838-7610Transportation College, Jilin University, Changchun, ChinaTransportation College, Jilin University, Changchun, ChinaAn expressway is divided into different road segments according to the entrance-exit toll stations. Based on the driving characteristics of expressways, two methods are proposed to predict the average speed of vehicles on an expressway. One is based on the relationship between the number and the average speed of vehicles on road segments, and the other is based on traffic situation obtained by Baidu, Gaode, and other online maps API. According to the methods, intelligent driving strategies are adopted to satisfy the desired driving time and achieve the driving task successfully. The main principle of the strategies is to adjust the driving route and speed automatically according to the expressway conditions and desired driving time, and to realize the switch of the driving route between the expressway and provincial highway, or national highway and other non-expressway networks. The speed prediction methods and intelligent driving strategies overcome the shortcomings of the existing expressway traffic volume prediction. It has no complex model but is simple, feasible, fast, and practical, which provides an important theoretical basis for the design of expressway intelligent driving systems. The proposed methods exhibits good innovation and practical applications.https://ieeexplore.ieee.org/document/10117602/Expresswayintelligent trafficroad network big datatraffic volume forecastroute optimization |
| spellingShingle | Shuoqi Wang Zhanzhong Wang Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time IEEE Access Expressway intelligent traffic road network big data traffic volume forecast route optimization |
| title | Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time |
| title_full | Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time |
| title_fullStr | Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time |
| title_full_unstemmed | Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time |
| title_short | Intelligent Driving Strategy of Expressway Based on Big Data of Road Network and Driving Time |
| title_sort | intelligent driving strategy of expressway based on big data of road network and driving time |
| topic | Expressway intelligent traffic road network big data traffic volume forecast route optimization |
| url | https://ieeexplore.ieee.org/document/10117602/ |
| work_keys_str_mv | AT shuoqiwang intelligentdrivingstrategyofexpresswaybasedonbigdataofroadnetworkanddrivingtime AT zhanzhongwang intelligentdrivingstrategyofexpresswaybasedonbigdataofroadnetworkanddrivingtime |