A Study on a Prediction Model of E-Bike Expansion Degree at Irregular Signalized Intersections
Source abstract
Abstract
The deviations of straight-going traffic at irregular signalized intersections lead to obvious expansion characteristics of e-bikes. This situation increases the possibility of collisions between motor vehicles and e-bikes. In order to study the change of expansion degree of straight-going e-bike at irregular signalized intersections, the video trajectory extraction technology is used to obtain relevant data of e-bikes during green light release periods at irregular signalized intersections. In addition, we combined the flow and spacing characteristics of e-bikes and used a clustering method to analyze the release stage and release groups. Therefore, the Group 1 of e-bikes in the early green light release was determined to be the main research object of expansion degree. According to the static and dynamic factors, a prediction model for the expansion degree of straight-going e-bikes at irregular signalized intersections was established based on the beetle antennae search–back propagation (BAS-BP) neural network model. Finally, the evaluation indexes were compared and analyzed before and after the beetle antennae search (BAS) algorithm optimization. The results showed that the BAS-BP neural network prediction model was better than that of the back propagation (BP) neural network. The results could provide a theoretical reference for improving the efficiency of mixed traffic flow at irregular signalized intersections.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Urban Transport and Accessibility · Vehicle emissions and performance · Traffic Prediction and Management Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China
Funding
Transportation Science and Technology Project of Jiangsu Province
References (26)
Show source-provided references
1. (2021, May 25). Forecast Report on Market Competition Pattern and Future Trend of China’s E-Bike Industry from 2020 to 2026. Available online: https://www.chyxx.com.
2. A Back-of-Queue Model of a Signal-Controlled Intersection Approach Developed Based on Analysis of Vehicle Driver Behavior10.3390/en14041204 ↗
3. The impact of urban characteristics and residents’ income on commuting in China10.1016/j.trd.2017.09.015 ↗
4. Guo, R., Liu, J., and Qi, Y. (2021). Innovative Signal Timing Strategy for Implementing Contraflow Left-Turn Lanes at Signalized Intersections with Split Phasing. Sustainability, 13.10.3390/su13116307 ↗
5. Behavior of Riders of Electric Bicycles at Onset of Green and Yellow at Signalized Intersections in China10.3141/2317-11 ↗
6. Risky riding: Naturalistic methods comparing safety behavior from conventional bicycle riders and electric bike riders10.1016/j.aap.2015.05.016 ↗
7. Analysis of risk-taking behaviors of electric bicycle riders in response to pedestrian countdown signal devices10.1080/15389588.2018.1542138 ↗
8. Study on Traffic Characteristics and Tolerance Design Method of Turn-left Electric Bicycles Space at Typical Signal Control Intersection
9. Traffic conflicts and their contextual factors when riding conventional vs. electric bicycles10.1016/j.trf.2016.06.010 ↗
10. (2021, May 25). JSTV.com. Available online: http://news.jstv.com/a/20160805/123632.shtml.
11. Research on the Improvement Schemes of Irregular Intersection
12. Simulation-based Traffic Organization Optimization for Irregular Intersections
13. Effect Analysis of Vehicle-road Environment at Signalized Intersection on the Release and Expansion Characteristics of E-bikes
14. Dispersion Effect in Left-Turning Bicycle Traffic and Its Influence on Capacity of Left-Turning Vehicles at Signalized Intersections10.3141/2468-05 ↗
15. An algorithm for e-bike equivalents at signalized intersections based on traffic conflict events number10.1016/j.tra.2020.02.007 ↗
16. Mixed Traffic Flow Signal Timing Optimization Method Considering E-Bike Expansion Influence10.1061/JTEPBS.0000478 ↗
17. Evaluation of the dispersion effect in through movement bicycles at signalized intersection via cellular automata simulation10.1016/j.physa.2017.12.130 ↗
18. Modeling and simulation of motor vehicle-electric bicycle traffic flow at signalized intersection
19. Modeling and Simulation of Nonmotorized Vehicles’ Dispersion at Mixed Flow Intersections
20. Jiang, X., and Li, S. (2017). Beetle antennae search without parameter tuning (BAS-WPT) for multi-objective optimization. J. Loss Prev. Process Ind., 34.10.2298/FIL2015113J ↗
21. Joint application of multi-object beetle antennae search algorithm and BAS-BP fuel cost forecast network on optimal active power dispatch problems10.1016/j.knosys.2021.107149 ↗
22. The Assessment of Storm Surge Disaster Loss Based on BAS-BP Model
23. Xu, Z. (2018). Research on Release Characteristics of E-Bikes at Signalized Intersections. [Master’s Thesis, Jilin University].
24. Analysis of Influencing Factors of Urban Intersections Risks Based on Traffic Conflict
25. Zhang, D. (2012). MATLAB Neural Network Application Design, Machine Press.
26. Research on the Early-warning Method of Water Environment Carrying Capacity Based on BP Neural Network: A Case Study of Beiyunhe River Basin