1
|
Wu R, Li L, Shi H, Rui Y, Ngoduy D, Ran B. Integrated driving risk surrogate model and car-following behavior for freeway risk assessment. Accid Anal Prev 2024; 201:107571. [PMID: 38608507 DOI: 10.1016/j.aap.2024.107571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 03/25/2024] [Accepted: 04/06/2024] [Indexed: 04/14/2024]
Abstract
Drivers' risk perception plays a crucial role in understanding vehicle interactions and car-following behavior under complex conditions and physical appearances. Therefore, it is imperative to evaluate the variability of risks involved. With advancements in communication technology and computing power, real-time risk assessment has become feasible for enhancing traffic safety. In this study, a novel approach for evaluating driving interaction risk on freeways is presented. The approach involves the integration of an interaction risk perception model with car-following behavior. The proposed model, named the driving risk surrogate (DRS), is based on the potential field theory and incorporates a virtual energy attribute that considers vehicle size and velocity. Risk factors are quantified through sub-models, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. The DRS model is applied to assess driving risk in a typical scenario on freeways, and car-following behavior. A sensitivity analysis is conducted on the effect of different parameters in the DRS on the stability of traffic dynamics in car-following behavior. This behavior is then calibrated using a naturalistic driving dataset, and then car-following predictions are made. It was found that the DRS-simulated car-following behavior has a more accurate trajectory prediction and velocity estimation than other car-following methods. The accuracy of the DRS risk assessments was verified by comparing its performance to that of traditional risk models, including TTC, DRAC, MTTC, and DRPFM, and the results show that the DRS model can more accurately estimate risk levels in free-flow and congested traffic states. Thus the proposed risk assessment model provides a better approach for describing vehicle interactions and behavior in the digital world for both researchers and practitioners.
Collapse
Affiliation(s)
- Renfei Wu
- School of Transportation, Southeast University, Nanjing, China; Institute of Transport Studies, Monash University, Australia; Joint Research Institute on Internet of Mobility between Southeast University and University of Wisconsin-Madison, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, Nanjing, China
| | - Linheng Li
- School of Transportation, Southeast University, Nanjing, China; Joint Research Institute on Internet of Mobility between Southeast University and University of Wisconsin-Madison, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, Nanjing, China
| | - Haotian Shi
- Department of Civil and Environmental Engineering, University of Wisconsin-Madison, United States
| | - Yikang Rui
- School of Transportation, Southeast University, Nanjing, China; Joint Research Institute on Internet of Mobility between Southeast University and University of Wisconsin-Madison, Southeast University, China; Jiangsu Key Laboratory of Urban ITS, Nanjing, China.
| | - Dong Ngoduy
- Institute of Transport Studies, Monash University, Australia.
| | - Bin Ran
- School of Transportation, Southeast University, Nanjing, China; Joint Research Institute on Internet of Mobility between Southeast University and University of Wisconsin-Madison, Southeast University, China; Department of Civil and Environmental Engineering, University of Wisconsin-Madison, United States
| |
Collapse
|
2
|
Hyun KK, Jeong K, Tok A, Ritchie SG. Assessing crash risk considering vehicle interactions with trucks using point detector data. Accid Anal Prev 2019; 130:75-83. [PMID: 29544655 DOI: 10.1016/j.aap.2018.03.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 02/10/2018] [Accepted: 03/01/2018] [Indexed: 06/08/2023]
Abstract
Trucks have distinct driving characteristics in general traffic streams such as lower speeds and limitations in acceleration and deceleration. As a consequence, vehicles keep longer headways or frequently change lane when they follow a truck, which is expected to increase crash risk. This study introduces several traffic measures at the individual vehicle level to capture vehicle interactions between trucks and non-trucks and analyzed how the measures affect crash risk under different traffic conditions. The traffic measures were developed using headways obtained from Inductive Loop Detectors (ILDs). In addition, a truck detection algorithm using a Gaussian Mixture (GM) model was developed to identify trucks and to estimate truck exposure from ILD data. Using the identified vehicle types from the GM model, vehicle interaction metrics were categorized into three groups based on the combination of leading and following vehicle types. The effects of the proposed traffic measures on crash risk were modeled in two different cases of prior- and non-crash using a case-control approach utilizing a conditional logistic regression. Results showed that the vehicle interactions between the leading and following vehicle types were highly associated with crash risk, and further showed different impacts on crash risk by traffic conditions. Specifically, crashes were more likely to occur when a truck following a non-truck had shorter average headway but greater headway variance in heavy traffic while a non-truck following a truck had greater headway variance in light traffic. This study obtained meaningful conclusions that vehicle interactions involved with trucks were significantly related to the crash likelihood rather than the measures that estimate average traffic condition such as total volume or average headway of the traffic stream.
Collapse
Affiliation(s)
- Kyung Kate Hyun
- Department of Civil Engineering, University of Texas at Arlington, 416 Yates St., 425 Nedderman Hall, Arlington, TX, 76019, United States.
| | - Kyungsoo Jeong
- Department of Civil and Environmental Engineering, Intelligent Transportation Systems Lab., 77 Massachusetts Avenue, Building 1-180, Massachusetts Institute of Technology, United States.
| | - Andre Tok
- Institute of Transportation Studies, 4000 Anteater Instruction and Research Building (AIRB), University of California, Irvine, Irvine, CA, 92697, United States.
| | - Stephen G Ritchie
- Department of Civil and Environmental Engineering, 4014 Anteater Instruction and Research Building (AIRB), Institute of Transportation Studies, University of California, Irvine, Irvine, CA, 92697, United States.
| |
Collapse
|