https://orcid.org/0009-0004-9327-789X
Figures
Abstract
In order to further study the expansion characteristics of left-turning non-motorized vehicles at intersections and the relationship between expansion characteristics and vehicle-bicycle conflicts, the trajectory point data of left-turning non-motorized vehicles are extracted using video trajectory tracking technology, and construct the cubic curve expansion envelope equation with the highest fitting degree. For the purpose of quantifying the expansion degree of non-motor vehicles after starting, two intersections in Guangxi Zhuang Autonomous Region were selected for case analysis, and the numerical range of expansion degree of the intersection with a left-turn waiting area and the intersection without a left-turn waiting area was obtained. Study the mathematical relationship between the expansion degree and its influencing factors, and establish the multivariate nonlinear regression equation between the expansion degree and the left-turn non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the left-turn green light time. Analyze the vehicle-bicycle conflicts caused by the expansion of left-turning non-motorized vehicles, determine the essential factors affecting the number of non-motorized vehicles, and establish the multiple linear regression equation between the number of non-motorized vehicles and the number of left-turning non-motorized vehicles, the expansion degree, and the number of parallel non-motorized vehicles, the results show that the model has high accuracy. By analyzing the expansion characteristics of left-turning non-motorized vehicles at intersections, the relationship between different influencing factors and the expansion degree is obtained. Then the vehicle-bicycle conflicts under the influence of expansion characteristics is analyzed, providing theoretical ideas for improving traffic efficiency and optimizing traffic organization at intersections.
Citation: Feng T, Liu J, Liang C, Tian X, Chen C, Liu K (2023) Analysis of left-turn behaviors of non-motorized vehicles and vehicle-bicycle conflicts. PLoS ONE 18(9): e0291504. https://doi.org/10.1371/journal.pone.0291504
Editor: Jing Zhao, University of Shanghai for Science and Technology, CHINA
Received: July 26, 2023; Accepted: August 30, 2023; Published: September 14, 2023
Copyright: © 2023 Feng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
1.Introduction
With the rapid development of global urbanization, the number of cars continues to increase, and the riding space of non-motor vehicles is squeezed and compressed. The mixed traffic composed of non-motor vehicles, motor vehicles, and pedestrians interferes with each other on the road, which has become an urban traffic problem. One of the roots of [1, 2]. As a green transportation mode, bicycle transportation has the characteristics of punctuality, travel flexibility, environmental protection, and health. It has become an excellent supplement to developing urban public transport systems and relieves road traffic pressure [3–5].
The intersection is the node of the urban road network, and the vehicle-bicycle conflicts at the intersection has become a hidden danger point for road traffic safety. In order to solve the problem of vehicle-bicycle conflictsat intersections and further improve traffic efficiency, scholars have researched the problem of vehicle-bicycle conflicts within intersections. Scholars mainly conduct research on non-motor vehicle traffic characteristics, mechanism research, vehicle-bicycle conflicts discrimination and prediction, safety impact assessment, andvehicle-bicycle conflicts technology application [6–14]. Shladover [15] et al. used video detection technology to capture the trajectory of bicycles to obtain the time distribution characteristics of bicycles crossing intersections. Jing Chunguang [16] conducted an in-depth study on the vehicle-bicycle conflictsof intersections and established a calculation model for the critical value of left-turn bicycle flow in traffic conflicts for two-phase intersections. In order to assess the safety conflict between motor vehicles and bicycles, Puscar [17] et al. used a computer vision system for safety diagnosis. In the case of different intersection layouts and right-of-way, the yield rate of bicycles may change significantly. Guo Y [18] et al. used the automatic traffic conflict technology to evaluate the safety of the unconventional left-turn outer lane, and the conflict of the approach road at the intersection with the outer left-turn lane was more serious.
The expansion characteristic of non-motorized vehicles is a particular attribute, especially when turning left and crossing the street. Research has shown that this characteristic is critical to keeping the vehicle running smoothly and operating efficiently. Therefore, in-depth research on the expansion characteristics of non-motor vehicles is of great significance for improving the release efficiency and safety of intersections. At present, many scholars have carried out research on the expansion characteristics of non-motorized vehicles. Among them, the expansion phenomenon of bicycle traffic flow is described by introducing the concept of expansion degree [19], which provides a theoretical basis for the traffic design of mixed-vehicle intersections. At the same time, the researchers also explored the interference impact of non-motor vehicle expansion on motor vehicles [20–22]. They quantitatively analyzed the impact of non-motor vehicles on the traffic capacity and operation delay of motor vehicles. In addition, the dynamic and static factors affecting the expansion characteristics of electric bicycles in the intersection vehicle-road environment are also analyzed, and the mathematical relationship between the influencing factors and the expansion degree is determined [23].
Scholars have done much research to extract vehicle trajectory data from traffic observation videos. By automatically tracking the trajectories of motor vehicles, non-motor vehicles, and pedestrians in the video and obtaining their real motion coordinates, this technology is useful for identifying traffic conflicts, evaluating the severity of conflicts, predicting traffic conflicts, and providing a theoretical basis for improving intersection management. It is of great significance to the analysis and evaluation of traffic safety [24–30].
At present, the research on the expansion characteristics of non-motor vehicles mainly focuses on the motion model and traffic flow model, the causes and influencing factors of expansion characteristics, and the relationship between traffic characteristics and expansion characteristics. However, the current academic research rarely discusses how to use the research results of non-motor vehicle expansion characteristics to carry out traffic safety assessments and establish the relationship between non-motor vehicle inflation characteristics and traffic conflicts. Therefore, this paper deeply studies the expansion characteristics of left-turning non-motor vehicles at intersections and their relationship with vehicle-bicycle conflicts. By analyzing the expansion characteristics of left-turning non-motor vehicles, the conflict risk between non-motor vehicles and motor vehicles is discussed. Based on these research results, we can better carry out traffic safety assessments and establish the relationship between the expansion characteristics of non-motor vehicles and traffic conflicts, which can provide suggestions for future traffic planning and management. In addition, this paper also innovatively puts forward the relationship model between left-turning non-motor vehicle flow, the number of parallel non-motor vehicles, the left-turning green time, and the expansion degree. It provides a scientific basis for intersection signal control and traffic planning.
This study takes the signalized intersection as the research object. Through field investigation and video analysis, the expansion characteristics of left-turning non-motor vehicles at the intersection are deeply studied considering whether there is a left-turn waiting area at the intersection, which provides theoretical ideas for improving the traffic efficiency at the intersection and optimizing the traffic organization. In addition, based on the changes in vehicle space-time trajectory data, the concept of expansion degree is introduced to quantify the expansion degree of non-motor vehicles after starting, which provides a theoretical basis for the traffic design of plane intersections. The mathematical relationship model of left-turning non-motor vehicle expansion degree and its influencing factors is established. Then, the important influencing factors of the number of vehicle-bicycle conflicts caused by the expansion characteristics of the left-turning non-motor vehicle at the intersection are determined, and the multiple linear regression model of the number of vehicle-bicycle conflicts at the intersection is established.
Future research can further explore the behavioral characteristics of non-motor vehicles, the behavioral characteristics of non-motor vehicle drivers, and the impact of the traffic environment on expansion characteristics. In urban traffic planning, it is necessary to consider the expansion characteristics of non-motor vehicles and determine the width and number of non-motor lanes, as well as the length and width of the left turn area at the intersection, to improve the traffic capacity of the road. In terms of traffic signal control, through traffic signal control and optimization, the left green time and non-motor vehicle flow can be reasonably allocated at intersections to reduce the impact of non-motor vehicle expansion characteristics on intersection traffic. In addition, the intelligent signal control system can automatically adjust the signal timing scheme according to the real-time traffic flow and the expansion characteristics of non-motor vehicles, to improve the traffic efficiency of the intersection. In terms of traffic management, differentiated management strategies can be proposed according to the differences of expansion characteristics of non-motor vehicles of different types and speeds, to improve the safety and efficiency of non-motor traffic. These research results can provide scientific basis and technical support for urban road design and traffic planning. Therefore, in future research, it is necessary to further explore the mechanism and law of the expansion characteristics of non-motor vehicles and their impact on traffic flow, to improve the scientificity and practicability of urban road design and traffic planning.
2.Materials and methods
2.1 Intersection coordinate system
For a typical cross-level intersection, the x-axis is the rear end line of the pedestrian crossing at the south entrance, and the y-axis is the rear end line of the pedestrian crossing at the west entrance. In this way, the two-dimensional coordinate system of the intersection is established, as shown in Fig 1. For irregular intersections, the corresponding coordinate system can be established by transforming the coordinates. The rear end line of one side of the crosswalk is selected as the x-axis, the apex of the rear end line is the origin, and the y-axis is established perpendicular to the x-axis direction through the origin, and the coordinates of the intersection are constructed.
2.2 Expansion envelope of left-turning non-motor vehicles
Data collection and processing.
This paper selects two intersections of Luban Road-Daxuedong Road in Nanning City and Guizhong Avenue-Yingbin Road in Laibin City for data collection. Both intersections are typical four-phase intersections, and diversion islands are set up. There is a left-turn waiting area at the intersection of Luban Road-Daxuedong Road, but there is no left-turn waiting area at the intersection of Guizhong Avenue-Yingbin Road. The characteristic parameters of the intersection are shown in Table 1. UAV aerial photography obtains the traffic data during weekday evening peak hours of 17:30–19:30. The intersections are photographed for 3 hours, respectively, and a total of 102 cycles of samples are obtained. Using Tracker software to extract data from the collected video, The frame rate is 30.0 fps, and the measurement range in the video is set to the area surrounded by four crosswalks. Extract non-motor vehicle trajectory, flow, density, speed, and other data through video. Non-motor vehicle track point data is a collection of coordinate points of non-motor vehicles in the intersection coordinate system with the cycle as the time interval.
The Tracker software is used to open the collected traffic video with a resolution of 1920 × 1080. First, the origin of the intersection coordinate system is determined, the direction of the x and y axes is determined, the two ends of the correction rod are pulled to both ends of a certain standard length in the video with the mouse, and the corresponding real length is input, to determine the spatial coordinate system of the video. Then set the start and end time bar of the video to complete the setting of the time coordinate system of the video. Finally, the tracking place is set according to the object to be tracked, the mouse selects the object to be tracked, and the automatic tracking mode is selected to track the tracked object. The automatic tracking mode will mark the location of each tracked object,as shown in Fig 2, and the corresponding position and value will also be synchronously marked in the graph and table generated by the software. Due to the small area and fast-moving speed of non-motor vehicles in the aerial video footage taken by drones, sometimes the automatic tracking software can not accurately track the situation, then the mouse is used to manually mark the position of each frame of the tracking object. After the final tracking is completed, the software can calculate the output chart and table, including the time of tracking the object, the horizontal coordinate point, and the vertical coordinate.
Data analysis of vehicle routing.
According to the research of Alhajyaseen (2013), the path of a left-turning vehicle at a signalized intersection can be represented by a spline curve composed of 5 segments. This polygonal line comprises a straight line segment, an Euler spiral curve segment, and a circular curve segment. The path starts from the straight segment, then transitions to a linear Euler curve segment with a curvature of ., followed by a circular curve segment with a constant curvature of
, then an Euler curve segment with a linear curvature gradient of
, and finally ends in a straight line segment. In order to uniquely determine this polygonal line, five parameters need to be defined: A1,A2, the starting point (BP) entering the Euler spiral curve and the end point (EP) exiting the Euler spiral curve, as shown in Fig 3.
Through actual observation, the actual left-turn motor vehicle flow trajectory is found to conform to the assumed curve equation, as shown in Fig 4.
When the green light is released at the intersection of Luban Road-Daxuedong Road, the flow of non-motor vehicles turning left expands and spreads to both sides, presenting an hourglass-shaped distribution, as shown in Fig 5. However, the two-way left-turning non-motor vehicle trajectories at the intersection of Guizhong Avenue-Yingbin Road respectively showed a tightening trend on both sides of the widest middle, as shown in Fig 6. Construct the expansion envelopes D1 and D2 of left-turn non-motorized vehicles, as shown in Fig 7.
At an intersection with a left turn waiting area, a motor vehicle may enter the intersection ahead of time and make a left turn in the area marked with a “Left turn waiting area” sign. When the flow of motor vehicles is large, the left turn waiting area is completely occupied by motor vehicles. When the green light starts, the left turn non-motor vehicles need to wait for the gap to pass through the left turn waiting area. Therefore, the street crossing track of the left turn non-motor vehicles is relatively more curved, and more attention needs to be paid to the driving situation of motor vehicles, so the speed and acceleration are relatively low. When there is no left turning zone for motor vehicles, the track of non-motor vehicles is relatively straight, and non-motor vehicles can complete the left turning operation more smoothly. So the velocity and acceleration are relatively high.
2.3 Curve fitting
According to the actual observation of the trajectory point data of left-turning non-motorized vehicles, statistics and drawing analysis are carried out to determine the left-turning non-motorized vehicle expansion envelope D1, D2 at the intersection, where each circle represents a trajectory point. Use SPSS software to fit curves on D1 and D2, as shown in Figs 8–11. In order to represent the curves more accurately, comparing ten mathematical models, and the results are shown in Table 2.
According to the data in Table 2, it can be seen that the fitting degree of D1, D2 and the cubic curve model is closest to 1, and the fitting effect is the best. To sum up, the cubic curve model of the expansion envelope D1 and D2 of the left-turning non-motorized vehicle is established as follows:
(1)
In the formula, y is the ordinate of the track point of the non-motor vehicle; x is the abscissa of the track point of the non-motor vehicle; β1, β2, β3 are respectively coefficient; β0 is constant.
The cubic curve model fitting analysis is carried out on the expansion envelopes D1 and D2, and the parameter values in the model are obtained as shown in Tables 3 and 4:
The curve equation of the expansion envelope D1 can be obtained from Table 3 (2)
The curve equation of the expansion envelope D2 can be obtained from Table 4 (3)
3.Expansion degree model of left-turning non-motor vehicles
In order to reflect the expansion effect of non-motor vehicle flow, the concept of expansion degree is introduced to describe the expansion phenomenon of non-motor vehicle flow. The width of the most apparent expansion in the passage space is defined as the maximum expansion width W, and the relationship between the maximum expansion width and the average width Db of the road occupied by bicycles after non-motorized vehicles start to expand is shown in Eq 4.
(4)
In the formula, Db is the average lateral width of the road occupied by a single vehicle after non-motor vehicles start to expand, m; W is the maximum expansion width, m; Nb is the horizontal parallel number of left-turning non-motor vehicles when the expansion width is maximum, vehicles.
The expansion degree is the ratio of the lateral traffic flow density after non-motor vehicles start to expand and the lateral density of non-motor vehicles when they stand still in line.
(5)
In the formula, pk is the expansion degree; D0 is the average lateral width of the road occupied by a single vehicle when the non-motorized vehicles stand in line, generally 0.8m.
The factors that may affect the expansion of left-turning non-motorized vehicles include: left-turning non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the time of green light for left-turning. Figs 12–14 visually shows the relationship between the expansion degree of left-turning non-motorized vehicles and variables.
It can be found that the expansion degree presents a quadratic function curve trend with the increase of the variable value. Fig 12 shows that under the same non-motor vehicle flow conditions, the expansion degree of the intersection with a left-turn waiting area is smaller than that of an intersection without a left-turn waiting area. This is because of the setting of the left-turn waiting area, Left-turning motor vehicles occupy the original passage space of non-motor vehicles, and a large number of left-turning non-motor vehicles can only pass in a small space, resulting in a slight expansion, which affects the passage efficiency of non-motor vehicles. In the case of a small intersection space rather than a large motor vehicle flow, the orderly flow of non-motor vehicles will be guaranteed, and the traffic rate of motor vehicles will also increase accordingly.
For the intersection of Luban Road-University East Road, when the left-turn non-motor vehicle flow is less than 115, it increases with the increase of the left-turn non-motor vehicle flow, from 2.0 to 2.42, and when its flow is more significant than 115, the expansion degree starts With the increase of left-turning non-motor vehicle traffic, it gradually decreased to 1.85; the expansion degree of Guizhong Avenue-Yingbin Road intersection increased from 2.22 to 2.7, and when the non-motor vehicle traffic was less than 85, it decreased to 2.15.
Fig 13 shows that when the number of parallel non-motorized vehicles is small, the riding space between the non-motorized vehicles is sufficient, so the degree of expansion increases with the increase of left-turning motor vehicle traffic, while when the number of parallel non-motorized vehicles When it increases to a specific value, the expansion will not increase but decrease because the riding space of non-motor vehicles is limited due to factors such as the left-turn waiting area and motor vehicles turning left in the same direction.
Fig 14 shows that when the green light for non-motorized vehicles turning left is short, non-motorized vehicles tend to bypass the vehicle in front from the outside due to the cyclists’ eagerness to pass through the intersection, resulting in a larger overall traffic expansion width. As the green light time gradually increases, non-motorized vehicles have sufficient passing time so that the rear vehicles in the traffic flow will follow the front vehicles to pass through the intersection in an orderly manner so that the expansion width will show a downward trend. However, when the green light time exceeds a particular value, non-motor vehicles pass in a larger riding space, which increases the lateral diffusion of traffic flow, increasing the expansion width of left-turn non-motor vehicle flow. Nevertheless, when the green light time increases to a specific value, the number of non-motorized vehicles passing in a cycle is limited, so even if the green light time increases, the expansion width will not fluctuate significantly. In order to further verify the relationship between the influencing factors and the expansion degree of left-turning non-motorized vehicles, this paper conducts a Spearman correlation analysis on the left-turning non-motorized vehicle flow, the number of parallel non-motorized vehicles, and the time of green light for left-turning, as shown in Table 5.
Among them, the Spearman correlations of left-turn non-motor vehicle flow, parallel non-motor vehicle quantity, and left-turn green light time are 0.801, 0.863, and 0.602 respectively. The results show that the expansion degree has a high correlation with the left-turn non-motor vehicle flow and the number of parallel non-motor vehicles, while the Spearman correlation coefficient of the left-turn green light time is 0.602, which has a moderate correlation with the expansion degree. The expansion degree pk is affected by factors such as left-turn non-motor vehicle flow, parallel non-motor vehicle quantity, and left-turn green light time, and has a quadratic function relationship with variables, so the following relationship model is established.
(6)
In the formula, b1, b2, b3, b4, b5, b6 are the undetermined parameter, which is obtained according to the actual data fitting analysis; b0 is constant; Q1 is left turn non-motor vehicle flow, (vehicles/cycle); N is the number of parallel non-motor vehicles; T is green turn time (s).
According to the observation data, the nonlinear regression is carried out to obtain the fitted values of each parameter, as shown in Table 6.
The fitting degree of the multiple nonlinear regression equation is R2 = 0.852, indicating that the nonlinear regression equation can better reflect the functional relationship between variables. The multiple nonlinear regression model of the left-turning non-motorized traffic expansion degree is shown in Eq (7).
(7)
4.Research on vehicle-bicycle conflicts caused by expansion characteristics
4.1 Conflict sample collection and processing
This paper collects conflict data between left-turning non-motor vehicles and left-turning motor vehicles. It takes whether motor vehicles and non-motor vehicles have the risk of collision as a qualitative criterion for machine-non-motor conflict discrimination.
Based on the principle of vehicle-bicycle conflicts generation, when the green light is on, non-motorized vehicles turning left start to accelerate quickly. Due to the characteristics of lateral expansion, when motor vehicles in the same direction pass through the intersection, some non-motorized vehicles encroach on motor vehicles in the same direction. Motor vehicles and some non-motor vehicles have horizontal conflicts.
Time to Collision (TTC) is a commonly used method for conflict discrimination. Internationally, 2.0s is used as the basis for judging conflicts. Domestic scholars use the 85% cumulative frequency method to obtain a conflict definition standard of TTC less than 1.6s. This paper uses 1.6s TTC as the criterion for judging non-machine conflicts, and the speed, position, acceleration, and other data of conflict samples are extracted, respectively, as shown in Table 7.
According to statistical analysis, the intersection with left turn waiting area can significantly reduce the conflict between non-motor vehicles and motor vehicles. In this type of intersection, the motor vehicle can enter a special area to wait for the left turn, which will result in the motor vehicle running at a lower speed, thus reducing the conflict between the left turn non-motor vehicles and motor vehicles. On the contrary, the intersection without a left turn waiting area often occurs when non-motor vehicles accelerate to make a left turn in front of the motor vehicle after the green light starts. Due to the faster speed of non-motor vehicles, this situation will lead to more frequent conflicts between non-motor vehicles and vehicles.
4.2 Correlation test
This paper studies the number of conflicts between left-turning motor vehicles and left-turning non-motorized vehicles and finds that the most direct influencing factors are the flow of left-turning non-motorized vehicles, the number of parallel non-motorized vehicles and the expansion degree of left-turning non-motorized vehicles—factors to be examined in detail. Using SPSS software, the Spearman correlation test was carried out on the number of conflicts between left-turning motor vehicles and left-turning non-motorized vehicles and the influencing factors, and the results are shown in Table 8.
Through correlation analysis, it can be obtained that the simple correlation coefficient between the number of vehicle-bicycle conflicts at the intersection and each variable, the linear correlation coefficient between the left-turning non-motorized vehicle flow, the number of parallel non-motorized vehicles, the expansion degree of left-turning non-motorized vehicles and the number of vehicle-bicycle conflicts They are 0.870, 0727, and 0.602, respectively, and the significance is less than 0.01. Therefore, the multivariate linear regression equation of the number of vehicle-bicycle conflicts TC, the number of parallel non-motorized vehicles, and the expansion degree of left-turning non-motorized vehicles are established.
(8)
In the formula, TC is the number of left turn vehicle-bicycle collisions (times/cycle); m0, m1, m2, m3 are regression coefficients.
Perform linear regression on the Model for the Number of vehicle-bicycle Conflicts to obtain the fitting values of each parameter, as shown in Table 9.
Theoretically, when the flow of left-turning non-motorized vehicles, the expansion width of left-turning non-motorized vehicles, and the number of parallel non-motorized vehicles are all 0, the number of vehicle-bicycle Conflicts is 0, so under the premise of TC > 0, The calculation model is shown in Eq (9). The goodness of fit test was performed on the number of vehicle-bicycle conflicts model, as shown in Tables 10 and 11.
(9)
The goodness of fit test and variance analysis was carried out on the the number of vehicle-bicycle conflicts model. The R2 of the regression function was 0.932, indicating that the model could better reflect the relationship between variables. p < 0.001 indicating that the equation has passed the significance test and the linear relationship of the equation is proven to be significant. According to the analysis of variance, F of the model is 225.784.
4.3 Model verification
In order to verify the accuracy of the number of vehicle-bicycle conflicts model, another 36 sets of test sample data were counted, each variable parameter was brought into the model formula, and the left-turn machine non-conflict number was calculated, and the calculated value was compared with the actual measurement Values are compared to judge the degree of relative error between them, as shown in Table 12.
According to the comparison of the calculation results of the number of vehicle-bicycle conflicts model and the actual survey statistics of 36 sets of data, we can draw a conclusion: the relative error value of the model is 6.3077%, which can verify that the model has high accuracy.
5.Conclusion
- Select two intersections in Nanning City and Laibin City, Guangxi, use video trajectory tracking technology to extract the trajectory data of non-motor vehicles turning left to cross the street for analysis, and use SPSS software to perform curve fitting on the expansion envelopes D1 and D2, establish the cubic curve model of the expansion envelope with the highest fitting degree, the fitting coefficient R2 is more significant than 0.990, and the fitting effect is the best.
- According to statistical analysis, the variation range of the expansion degree of the intersection without a left-turn waiting area is 2.10~2.70, and the expansion degree of the intersection with a left-turn waiting area is 2.0~2.42. The left-turning non-motorized vehicle flow, the number of parallel non-motorized vehicles, the time of the left-turning green light, and the expansion degree of left-turning non-motorized vehicles have a strong correlation, and the expansion degree and its influencing factors present an apparent quadratic function trend. The fitting effect is high for the multiple nonlinear regression model of expansion degree and the verification model fitting degree R2 = 0.852.
- According to the determined vehicle-bicycle conflicts threshold, establish a model of left-turning non-motorized vehicle flow, expansion degree, and the number of parallel non-motorized vehicles to the number of left-turning non-conflicted vehicles. After 36 sets of test data verification, the relative error value is 6.3077%, the model has higher accuracy.
6.Limitations and future work
There are some important limitations in this paper, which can be divided into the following three aspects:
- Insufficient data collection: The research on the expansion characteristics of non-motor vehicles requires a large amount of data support, but the actual data collection is still small, and the data collection under different traffic environments is insufficient, resulting in the reliability and universality of the research results to be improved.
- Single object of study: At present, the research on the expansion characteristics of non-motor vehicles mainly focuses on bicycles, and there are relatively few studies on the expansion characteristics of other non-motor vehicles, so it is necessary to expand the scope of research objects in the future.
- Insufficient research depth: Current research on the expansion characteristics of non-motor vehicles mainly focuses on local traffic environments such as non-motor lanes and intersections, and further research is needed on the impact of the expansion characteristics of non-motor vehicles on urban traffic networks and the overall efficiency of urban traffic.
In the future, the influence of non-motor vehicle driver behavior characteristics and traffic environment on the expansion characteristics of non-motor vehicles can be deeply discussed, and an intersection signal control method based on the expansion characteristics of non-motor vehicles can be proposed. The intersection signal control parameters can be flexibly adjusted according to the differences in the expansion characteristics of non-motor vehicles of different types and speeds, to improve the traffic efficiency and safety of the intersection. In addition, the influence of the expansion characteristics of non-motor vehicles on the width and number of non-motor vehicle lanes and the length and width of the left bend waiting area can also be explored, and reasonable Settings can be made according to the actual situation to meet the demand of non-motor vehicle flow, reduce the traffic safety hazards caused by the expansion characteristics, and formulate a more scientific and reasonable urban traffic planning and design scheme.
Supporting information
S1 File. It contains all the data files for this manuscript.
https://doi.org/10.1371/journal.pone.0291504.s001
(XLSX)
Acknowledgments
We thank those anonymous reviewers and the editor whose comments/suggestions helped improve and clarify this manuscript.
References
- 1. Beura Sambit Kumar and Kumar N Kiran and Bhuyan Prasanta Kumar. Level of service for bicycle through movement at signalized intersections operating under heterogeneous traffic flow conditions. Transportation in developing economies. 2017, 3:1–16.
- 2.
Li, Ting and Hu, Yucong and Chen, Xu and Liang, Yanhao. CICTP 2016. 2016,2067–2075.
- 3. Cherry Christopher and Cervero Robert. Use characteristics and mode choice behavior of electric bike users in China. Transport policy. 2007,14(3):247–257.
- 4.
Wei, LY and Kai, Lü. Bicycle following behavior at signalized intersection. J. Jilin Univ. (Eng. Technol. Ed.) 2008.
- 5. Gao YH and Qu SW and Song XM. Determination of early green time for E-bikes at signalized intersections. J. Jilin Univ. (Eng. Technol. Ed.) 2020, 50(4):1355–1369.
- 6. Meng Jian-ping and Dai Shi-qiang and Dong Li-yun and Zhang Jie-fang. Cellular automaton model for mixed traffic flow with motorcycles. Physica A: Statistical Mechanics and Its Applications. 2007, 380:470–480.
- 7. Ye Xiaofei and Zhu Yi and Wang Tao and Yan Xingchen and Chen Jun and Ran Bin. Level of service model of the non-motorized vehicle crossing the signalized intersection based on riders’ perception data. International journal of environmental research and public health. 2022,19(8):4534. pmid:35457402
- 8. Chen Albert Y and Chiu Yen-Lin and Hsieh Meng-Hsiu and Lin Po-Wei and Angah Ohay. Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences. Transportation research part C: emerging technologies. 2020,119:102744.
- 9. Liu Qiyuan and Sun Jian and Tian Ye and Xiong Lu. Modeling and simulation of overtaking events by heterogeneous non-motorized vehicles on shared roadway segments. Simulation Modelling Practice and Theory. 2020, 103:102072.
- 10.
Chen, Xiaohong and Yue, Lishengsa and Ye, Jianhong and Shi, Xueying. Characteristics of Moped Conflict with Other Vehicles at Intersections. 2015.
- 11. Essa Mohamed and Sayed Tarek. Traffic conflict models to evaluate the safety of signalized intersections at the cycle level. Transportation research part C: emerging technologies. 2018,89:289–302.
- 12. Zhang GQ. Determination of the severity of traffic conflict at highway intersections. Journal of Hefei University of Technology. 2008,31(5):683–686.
- 13. Guo Yanyong and Sayed Tarek and Essa Mohamed. Real-time conflict-based Bayesian Tobit models for safety evaluation of signalized intersections. Accident Analysis & Prevention. 2020,144:105660. pmid:32623321
- 14. Farah Haneen and Piccinini Giulio Bianchi and Itoh Makoto and Dozza Marco. Modelling overtaking strategy and lateral distance in car-to-cyclist overtaking on rural roads: A driving simulator experiment. Transportation research part F: traffic psychology and behaviour. 2019,63:226–239.
- 15. Shladover Steven E and Kim ZuWhan and Cao Meng and Sharafsaleh Ashkan and Li Jing-Quan. Bicyclist intersection crossing times: Quantitative measurements for selecting signal timing. Transportation research record. 2009,2128(1):86–95.
- 16. Jing CG and Wang DH. Analysis and dealing method to conflict of mixed traffic at typical intersection. China Civil Engineering Journal. 2004,37(6):97–100.
- 17.
Puscar, Federico Martin. Safety diagnosis of vehicle-bicycle interactions using computer vision systems: a case study in Vancouver, BC. 2017.
- 18. Guo Yanyong and Sayed Tarek and Zaki Mohamed H and Liu Pan. Safety evaluation of unconventional outside left-turn lane using automated traffic conflict techniques. Canadian Journal of Civil Engineering. 2016, 43(7):631–642.
- 19. SUN Ming-zheng and YANG Xiao-guang. Research on the theory of traffic design for intersection of mixed traffic. Journal of Highway and Transportation Research and Development. 2004, 8:82–86.
- 20. Chen Jingxu and Wang Wei and Li Zhibin and Jiang Hang and Chen Xuewu and Zhu Senlai. Dispersion effect in left-Turning bicycle traffic and its influence on capacity of left-turning vehicles at signalized intersections. Transportation Research Record. 2014, 2468(1):38–46.
- 21. ZHANG S and He K. Expansion Effect of Left Turning Mixed Bicycle Flow and Vehicle-Bicycle Conflict at Intersections. Journal of Ningbo University of Technology. 2014, 26(02):7–11.
- 22. Ge DaYun. Study on the setting of non-motor vehicle left turn waiting area at typical intersection. IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2021, 831(1):012054.
- 23. Zhao-wei QU and Yu-hong GAO and Xian-min SONG. Effect Analysis of Vehicle-road Environment at Signalized Intersection on the Release and Expansion Characteristics of E-bikes. China Journal of Highway and Transport. 2020, 33(4):126.
- 24. Tageldin Ahmed and Sayed Tarek and Ismail Karim. Evaluating the safety and operational impacts of left-turn bay extension at signalized intersections using automated video analysis. Accident Analysis & Prevention. 2018, 120:13–27. pmid:30077004
- 25. Li Li and Jiang Rui and He Zhengbing and Chen Xiqun Michael and Zhou Xuesong. Trajectory data-based traffic flow studies: A revisit. Transportation Research Part C: Emerging Technologies. 2020, 114:225–240.
- 26.
Chai, Hao and Zhang, Zhipeng and Hu, Hao and Dai, Lei and Bian, Zheyong. Trajectory-Based Conflict Investigations Involving Two-Wheelers and Cars at Non-Signalized Intersections with Computer Vision. Expert Systems with Applications. 2023: 120590.
- 27. Yao Handong and Li Xiaopeng. Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads. Transportation research part C: emerging technologies. 2021,129: 103182.
- 28. Abdeljaber Osama and Younis Adel and Alhajyaseen Wael. Analysis of the trajectories of left-turning vehicles at signalized intersections. Transportation research procedia. 2020, 48: 1288–1295.
- 29. Buch Norbert and Velastin Sergio A and Orwell James. A review of computer vision techniques for the analysis of urban traffic. IEEE Transactions on intelligent transportation systems. 2011, 12(3): 920–939.
- 30. Dias Charitha and Iryo-Asano Miho and Oguchi Takashi. Predicting optimal trajectory of left-turning vehicle at signalized intersection. Transportation Research Procedia. 2017, 21: 240–250.