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Zhang C, Ma Y, Sayed T, Guo Y, Chen S, Fu Y. Exploring the impact of right-turn safety measures on E-bike-heavy vehicle conflicts at signalized intersections. ACCIDENT; ANALYSIS AND PREVENTION 2024; 206:107722. [PMID: 39033583 DOI: 10.1016/j.aap.2024.107722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2024] [Revised: 07/04/2024] [Accepted: 07/15/2024] [Indexed: 07/23/2024]
Abstract
A major safety hazard for e-bike riders crossing an intersection is encountering heavy vehicles turning right in the same direction, which often results in severe casualties. Recently, some cities in China have implemented right-turn safety improvement treatments (i.e., right-turn yielding rules and right-turn warning facilities) at intersections to reduce the occurrence of such accidents. However, the risk perception and behavior of e-bike riders and heavy vehicle drivers dynamically change during the right-turn interaction process, and the safety effects of different right-turn safety measures remain unclear. This study aims to investigate the safety effect of right-turn safety measures on E-Bike-Heavy Vehicle (EB-HV) right-turn conflicts at signalized intersections. The right-turn conflicts and potential influencing factors are extracted from aerial video data, including characteristics of right-turn warning facilities, characteristics and behavior of e-bike riders and heavy vehicle drivers, environmental factors, and traffic-related factors. Moreover, traffic conflict indicators such as the Time to Collision (TTC), Post Encroachment Time (PET), and Jerk are selected and calculated. Multinomial and binary logit models are used to estimate and analyze the EB-HV right-turn conflict severity and drivers yielding behavior. The results reveal that: (a) right-turn warning facilities can decrease the probability of slight and severe EB-HV right-turn conflicts, while the presence of law enforcement cameras could prompt heavy vehicle drivers to comply with the yielding rules and adopt more cautious behavior; (b) increased heavy vehicle speed and acceleration before turning right have strong correlation to illegitimate yielding behavior of the driver and higher EB-HV right-turn conflict severity; and (c) aggressive behavior of e-bike rider increases the severe conflict probability, especially at intersections without right-turn warning facilities. Based on the study findings, several practical implications are suggested to reduce the risk of EB-HV right-turn conflicts, enhance the effectiveness of right-turn safety measures, and improve crossing safety for e-bike riders.
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Affiliation(s)
- Chenxiao Zhang
- School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China; Department of Civil Engineering, The University of British Columbia, Canada
| | - Yongfeng Ma
- School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China.
| | - Tarek Sayed
- Department of Civil Engineering, The University of British Columbia, Canada
| | - Yanyong Guo
- School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China
| | - Shuyan Chen
- School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China
| | - Yuanhang Fu
- School of Transportation, Southeast University, Nanjing 211189, China; Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China
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Sun S, Zhang Z, Zhang Z, Deng P, Tian K, Wei C. How Do Human-Driven Vehicles Avoid Pedestrians in Interactive Environments? A Naturalistic Driving Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:7860. [PMID: 36298210 PMCID: PMC9610887 DOI: 10.3390/s22207860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
One of the major challenges for autonomous vehicles (AVs) is how to drive in shared pedestrian environments. AVs cannot make their decisions and behaviour human-like or natural when they encounter pedestrians with different crossing intentions. The main reasons for this are the lack of natural driving data and the unclear rationale of the human-driven vehicle and pedestrian interaction. This paper aims to understand the underlying behaviour mechanisms using data of pedestrian-vehicle interactions from a naturalistic driving study (NDS). A naturalistic driving test platform was established to collect motion data of human-driven vehicles and pedestrians. A manual pedestrian intention judgment system was first developed to judge the pedestrian crossing intention at every moment in the interaction process. A total of 98 single pedestrian crossing events of interest were screened from 1274 pedestrian-vehicle interaction events under naturalistic driving conditions. Several performance metrics with quantitative data, including TTC, subjective judgment on pedestrian crossing intention (SJPCI), pedestrian position and crossing direction, and vehicle speed and deceleration were analyzed and applied to evaluate human-driven vehicles' yielding behaviour towards pedestrians. The results show how vehicles avoid pedestrians in different interaction scenarios, which are classified based on vehicle deceleration. The behaviour and intention results are needed by future AVs, to enable AVs to avoid pedestrians more naturally, safely, and smoothly.
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Affiliation(s)
- Shulei Sun
- Key Laboratory of Automobile Measurement and Control & Safety, Xihua University, Chengdu 610039, China
- Engineering Research Center of Advanced Energy Saving Driving Technology, Ministry of Education, Chengdu 610031, China
| | - Ziqiang Zhang
- Key Laboratory of Automobile Measurement and Control & Safety, Xihua University, Chengdu 610039, China
| | - Zhiqi Zhang
- Key Laboratory of Automobile Measurement and Control & Safety, Xihua University, Chengdu 610039, China
| | - Pengyi Deng
- Key Laboratory of Automobile Measurement and Control & Safety, Xihua University, Chengdu 610039, China
| | - Kai Tian
- Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK
| | - Chongfeng Wei
- School of Mechanical and Aerospace Engineering, Queen’s University Belfast, Belfast BT9 5AG, UK
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Chen W, Wang T, Wang Y, Li Q, Xu Y, Niu Y. Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations. ACCIDENT; ANALYSIS AND PREVENTION 2022; 176:106810. [PMID: 36049285 DOI: 10.1016/j.aap.2022.106810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/16/2022] [Accepted: 08/13/2022] [Indexed: 06/15/2023]
Abstract
Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the "pedestrian priority" policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.
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Affiliation(s)
- Wenqiang Chen
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China
| | - Tao Wang
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China
| | - Yongjie Wang
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China.
| | - Qiong Li
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China
| | - Yueying Xu
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China
| | - Yuchen Niu
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China
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Characterization of Pedestrian Crossing Spatial Violations and Safety Impact Analysis in Advance Right-Turn Lane. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19159134. [PMID: 35897506 PMCID: PMC9331099 DOI: 10.3390/ijerph19159134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/24/2022] [Accepted: 07/25/2022] [Indexed: 12/04/2022]
Abstract
In view of the pedestrian space violation in an advance right-turn lane, the pedestrian crossing paths are divided by collecting the temporal and spatial information of pedestrians and motor vehicles, and the characteristics of different pedestrian crossing behaviors are studied. Combined with the time and speed indicators of conflict severity, the K-means method is used to divide the level of conflict severity. A multivariate ordered logistic regression model of the severity of pedestrian-vehicle conflict was constructed to quantify the effects of different factors on the severity of the pedestrian-vehicle conflict. The study of 1388 pedestrians and the resulting pedestrian-vehicle conflicts found that the type of spatial violation has a significant impact on pedestrian crossing behavior and safety. The average crossing speed and acceleration variation values of spatially violated pedestrians were significantly higher than those of other pedestrians; there is a significant increase in the severity of pedestrian-vehicle conflicts in areas close to the oncoming traffic; the average percentage of pedestrian-vehicle conflicts due to spatial violations increased by 12%, and the percentage of serious conflicts due to each type of spatial violation increased from 18% to 87%, 74%, 30%, and 63%, respectively, compared with those of non-violated pedestrians. In addition, the decrease in the number of lanes and the increase in speed and vehicle reach all lead to an increase in the severity of pedestrian-vehicle conflicts. The results of the study will help traffic authorities to take measures to ensure pedestrian crossing safety.
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Wang Y, Su Q, Wang C, Prato CG. Investigating yielding behavior of heterogeneous vehicles at a semi-controlled crosswalk. ACCIDENT; ANALYSIS AND PREVENTION 2021; 161:106381. [PMID: 34479122 DOI: 10.1016/j.aap.2021.106381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 07/31/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
It is well known that pedestrians are vulnerable road users. Their risk of being injured or killed in road traffic crashes is even higher as vehicle drivers often violate traffic rules and do not slow down or yield in front of crosswalks. In order to reduce this risk, many countries have issued strict regulations requiring vehicles to yield to pedestrians in front of crosswalks. While extensive literature exists on the interaction between vehicles and pedestrians, the consideration of heterogeneity in the behavior of vehicles is vastly overlooked. Accordingly, this study analyzes the yielding behavior of three types of vehicles under the "pedestrian priority" policy by processing drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. Moreover, this study proposes four additional indicators to the widely used yielding rate and yielding delay with the aim of evaluating yielding behavior of three types of vehicles. The results show that buses have the best yielding behavior from the perspective of yielding rate, yielding delay, waiting time, yielding angle and waiting site. Buses perform well in observing pedestrian dynamics near crosswalk, and perform exceptionally well in considering the "blind area" of vision. The location of the waiting site in front of the stop line and the length of the waiting time contribute to the safe crossing of pedestrians. In contrast, private cars perform badly in yielding to pedestrians. However, serious polarization can be observed across private cars, as the performance varies across the board. The relaxation of the homogenization assumption of the behavior of vehicles in pedestrian-vehicle interaction, alongside the improvements in the analysis via Machine Vision Intelligent Algorithm of videos acquired via drone, shows the possibility of having a deeper understanding of the yielding behavior of vehicles at crosswalk. The extension of the use of artificial intelligence methods to analyze drone footage has immense potential in understanding road user behavior and hence providing knowledge for crash prevention.
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Affiliation(s)
- Yongjie Wang
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China
| | - Qian Su
- College of Transportation Engineering, Chang'an University, Xi'an 710064, PR China
| | - Chao Wang
- School of Economics and Management, Chang'an University, Xi'an 710064, PR China
| | - Carlo G Prato
- School of Civil Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
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Noh B, Yeo H. SafetyCube: Framework for potential pedestrian risk analysis using multi-dimensional OLAP. ACCIDENT; ANALYSIS AND PREVENTION 2021; 155:106104. [PMID: 33819792 DOI: 10.1016/j.aap.2021.106104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 01/26/2021] [Accepted: 03/23/2021] [Indexed: 06/12/2023]
Abstract
In the past decade, the number of road traffic accidents and fatalities has remained about the same level. One of strategies to protect vulnerable road users (VRUs) is to analyze the factors that cause traffic accident and then to deploy safety facilities. However, most traffic safety systems currently in operation rely on historical data, which is post-facto approach. Thus, it is necessary to prevent accident in advance and to respond in proactive manner before the accident. In this study, we propose a framework for potential pedestrian risk analysis using a multi-dimensional on-line analytical processing (OLAP), called SafetyCube, which enables decision-makers to understand the situations by scrutinizing interactive behaviors between vehicle and pedestrian. First, we collect the behavioral features of traffic-related objects (e.g., vehicles and pedestrians) extracted from closed circuit televisions (CCTVs) deployed on crosswalks throughout the overall urban, and accumulate them in a data warehouse over an extended period in order to construct a data cube model. Then, we conduct comprehensive analyses in multi-dimensional perspective using OLAP operations by varying the abstraction levels. Our analytical experiments are based on three scenarios, and the results show that the vehicle's movement patterns before entering the crosswalk, patterns of changes in speed of vehicles approaching to pedestrians, and so on. Through these results from the proposed analytical system, decision-makers can gain a better understanding of how the vehicles and pedestrians behave near the crosswalk by visualizing their interactions. Further, these insights would be reflected to improve the road environment safer. In order to validate the feasibility and applicability of the proposed system, we apply it to various crosswalks in Osan city, South Korea.
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Affiliation(s)
- Byeongjoon Noh
- Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseung-gu, Daejeon, South Korea.
| | - Hwasoo Yeo
- Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseung-gu, Daejeon, South Korea.
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