This page has only limited features, please log in for full access.

Dr. Shuaiyang Jiao
College of Transportation Engineering, Chang’an University

Basic Info


Research Keywords & Expertise

0 Numerical Simulation
0 intelligent transportation system
0 Traffic flow modeling
0 Sustainable Transportation
0 car-following

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Research article
Published: 28 January 2021 in Journal of Advanced Transportation
Reads 0
Downloads 0

We investigate the dynamic performance of traffic flow using a modified optimal velocity car-following model. In the car-following scenarios, the following vehicle must continuously adjust the following distance to the preceding vehicle in real time. A new optimal velocity function incorporating the desired safety distance instead of a preset constant is presented first to describe the abovementioned car-following behavior dynamically. The boundary conditions of the new optimal velocity function are theoretically analyzed. Subsequently, we propose an improved car-following model by combining the heterogeneity of driver’s sensitivity based on the new optimal velocity function and previous car-following model. The stability criterion of the improved model is obtained through the linear analysis method. Finally, numerical simulation is performed to explore the effect of the desired safety distance and the heterogeneity of driver’s sensitivity on the traffic flow. Results show that the proposed model has considerable effects on improving traffic stability and suppressing traffic congestion. Furthermore, the proposed model is compatible with the heterogeneity of driver’s sensitivity and can enhance the average velocity of traffic flow compared with the conventional model. In conclusion, the dynamic performance of traffic flow can be improved by considering the desired safety distance and the heterogeneity of driver’s sensitivity in the car-following model.

ACS Style

Lei Zhang; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Yan Huang. An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity. Journal of Advanced Transportation 2021, 2021, 1 -12.

AMA Style

Lei Zhang, Shengrui Zhang, Bei Zhou, Shuaiyang Jiao, Yan Huang. An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity. Journal of Advanced Transportation. 2021; 2021 ():1-12.

Chicago/Turabian Style

Lei Zhang; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Yan Huang. 2021. "An Improved Car-Following Model considering Desired Safety Distance and Heterogeneity of Driver’s Sensitivity." Journal of Advanced Transportation 2021, no. : 1-12.

Journal article
Published: 02 November 2020 in Physica A: Statistical Mechanics and its Applications
Reads 0
Downloads 0

Car-following behavior is strongly related to the dynamic performance and safety of vehicles, and these behaviors must be studied more comprehensively. This study first quantitatively analyzes the relationship between time to collision and car-following behavior, and then proposes the concept of collision sensitivity coefficient. Based on the full velocity difference model, we introduce the collision sensitivity coefficient into the car-following model to explore its effect on vehicle dynamic performance and safety from a different perspective. The stability of traffic flow is investigated using linear stability theory and obtain the neutral stability condition of the proposed model. Numerical simulation results show that the proposed model can improve the stability of traffic flow and successfully suppress traffic jams. The computation results using various safety evaluation indicators reveal that the proposed model not only has better dynamic performance than the full velocity difference model but also can effectively improve the safety of vehicles. We conclude that collision sensitivity substantially affects car-following behavior.

ACS Style

Shuaiyang Jiao; Shengrui Zhang; Bei Zhou; Lei Zhang; Liyuan Xue. Dynamic performance and safety analysis of car-following models considering collision sensitivity. Physica A: Statistical Mechanics and its Applications 2020, 564, 125504 .

AMA Style

Shuaiyang Jiao, Shengrui Zhang, Bei Zhou, Lei Zhang, Liyuan Xue. Dynamic performance and safety analysis of car-following models considering collision sensitivity. Physica A: Statistical Mechanics and its Applications. 2020; 564 ():125504.

Chicago/Turabian Style

Shuaiyang Jiao; Shengrui Zhang; Bei Zhou; Lei Zhang; Liyuan Xue. 2020. "Dynamic performance and safety analysis of car-following models considering collision sensitivity." Physica A: Statistical Mechanics and its Applications 564, no. : 125504.

Conference paper
Published: 12 August 2020 in CICTP 2020
Reads 0
Downloads 0

The instantaneous speed prediction plays a crucial role in autonomous driving, which directly affects the safety of the autonomous vehicle. It is necessary to study instantaneous speed prediction approaches in the car-following. In this study, different machine learning approaches are used to predict the instantaneous speed in the car-following (i.e., support vector regression, random forest, and XGBoost and AdaBoost regression models). And then different model evaluation criteria are selected to assess the model’s prediction power, including mean absolute error, mean absolute percentage error, root mean square error, and variance of absolute percentage error. The denoising trajectory data of the next generation simulation (NGSIM) project is used, and the grey relational analysis is used to extract the feature variables. The results indicate that XGBoost model can effectively improve the accuracy of instantaneous speed prediction in the car-following.

ACS Style

Shuaiyang Jiao; Shengrui Zhang; Zixuan Zhang; Dan Zhao; Bei Zhou. Prediction of Vehicle Instantaneous Speed in the Car-Following Based on Machine Learning Approaches. CICTP 2020 2020, 3401 -3412.

AMA Style

Shuaiyang Jiao, Shengrui Zhang, Zixuan Zhang, Dan Zhao, Bei Zhou. Prediction of Vehicle Instantaneous Speed in the Car-Following Based on Machine Learning Approaches. CICTP 2020. 2020; ():3401-3412.

Chicago/Turabian Style

Shuaiyang Jiao; Shengrui Zhang; Zixuan Zhang; Dan Zhao; Bei Zhou. 2020. "Prediction of Vehicle Instantaneous Speed in the Car-Following Based on Machine Learning Approaches." CICTP 2020 , no. : 3401-3412.

Conference paper
Published: 12 August 2020 in CICTP 2020
Reads 0
Downloads 0

The application of connected vehicles technology makes it easy for vehicles to obtain the information of other vehicles’ status, such as speed, acceleration, and position. This is different from the traditional diver’s self-determination information to change lanes; this technology improves the accuracy and timeliness of information, and is of great significance for improving driving safety and road utilization efficiency. First, we analyzed connected vehicle technology and built an optimal vehicle networking environment for the lane-changing model. Then we proposed the lane-changing model in connected vehicles and analyzed the different lane-changing situations. Finally, we performed simulation verification of the two models and compared the results. The results show that the frequency of lane-change and the average speed of the vehicles are improved 8.17% and 20.56%, respectively, because the lane-changing model in connected vehicles can increase speed and change lanes instantly while ensuring minimum safety distance.

ACS Style

Zixuan Zhang; Shengrui Zhang; Shuaiyang Jiao. A Vehicle Lane-Changing Model Based on Connected Vehicles. CICTP 2020 2020, 3027 -3038.

AMA Style

Zixuan Zhang, Shengrui Zhang, Shuaiyang Jiao. A Vehicle Lane-Changing Model Based on Connected Vehicles. CICTP 2020. 2020; ():3027-3038.

Chicago/Turabian Style

Zixuan Zhang; Shengrui Zhang; Shuaiyang Jiao. 2020. "A Vehicle Lane-Changing Model Based on Connected Vehicles." CICTP 2020 , no. : 3027-3038.

Journal article
Published: 25 March 2020 in Sustainability
Reads 0
Downloads 0

To promote the sustainable development and safety of bicycle traffic, survival analysis of the risk perception sensitivity of cyclists is proposed. The cumulative probability of survival serves as an index of risk perception sensitivity, and a Cox regression model is established. The proposed method is applied to middle school cyclists, and the factors of their risk perception are analyzed. Data are collected by questionnaire and traffic conflict survey and are quantified by factor analysis. The model results show that active and extroverted personality, negative peer influence, unsafe riding behavior intention, non-motor vehicle flow and speed, and a lack of separation facilities have negative correlations with risk perception sensitivity. Positive attitude towards traffic rules, good family education, heightened traffic safety awareness, motor vehicle flow and speed, pedestrian flow, and non-motorized lane width have positive correlations with risk perception sensitivity. The conflict type has no correlation with risk perception sensitivity. This study aims to improve the sensitivity of risk perception, prevent traffic conflicts and provide a theoretical basis for risk perception research on vulnerable traffic participants.

ACS Style

Dan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability 2020, 12, 2613 .

AMA Style

Dan Zhao, Shengrui Zhang, Bei Zhou, Shuaiyang Jiao, Ling Yang. Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model. Sustainability. 2020; 12 (7):2613.

Chicago/Turabian Style

Dan Zhao; Shengrui Zhang; Bei Zhou; Shuaiyang Jiao; Ling Yang. 2020. "Risk Perception Sensitivity of Cyclists Based on the Cox Risk Perception Model." Sustainability 12, no. 7: 2613.

Journal article
Published: 19 February 2020 in Sustainability
Reads 0
Downloads 0

In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers’ characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers’ characteristics and the following car’s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers’ characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers’ characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles’ mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers’ characteristics into account under the V2V communication situation for car-following theory.

ACS Style

Shuaiyang Jiao; Shengrui Zhang; Bei Zhou; Zixuan Zhang; Liyuan Xue. An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment. Sustainability 2020, 12, 1552 .

AMA Style

Shuaiyang Jiao, Shengrui Zhang, Bei Zhou, Zixuan Zhang, Liyuan Xue. An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment. Sustainability. 2020; 12 (4):1552.

Chicago/Turabian Style

Shuaiyang Jiao; Shengrui Zhang; Bei Zhou; Zixuan Zhang; Liyuan Xue. 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment." Sustainability 12, no. 4: 1552.

Research article
Published: 14 January 2020 in Journal of Advanced Transportation
Reads 0
Downloads 0

This paper introduces an improved car-following speed (CFS) model that simultaneously considers speed of the lead vehicle, vehicle spacing, and driver’s sensitivity to them. Specifically, the proposed model extends the Helbing-Tilch model and Yang et al. model developed based on the principle of grey relational analysis where vehicle spacing is considered as the primary factor contributing to car-following speed choices. A computational experiment is conducted for model calibration using vehicle spacing, speed, and acceleration data derived from vehicle trajectory data of the Next Generation Simulation (NGSIM) project sponsored by the Federal Highway Administration (FHWA). It shows that speed of the lead vehicle and vehicle spacing significantly affect speed of the lag vehicle. Further, model validation is carried out using an independent NGSIM dataset by comparing vehicle speed predictions made by the calibrated CFS model with Helbing-Tilch model and Yang et al. model as benchmarks. Compared with speed prediction results of the benchmark models, mean relative errors, root mean square errors, and equal coefficient of speed predictions of the CFS model have reduced by 72.41% and 61.85%, 70.14% and 57.99%, and 33.15% and 14.48%, respectively. The findings of model validation reveal that the CFS model could improve the accuracy of speed predictions in the car-following process.

ACS Style

Shuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation 2020, 2020, 1 -13.

AMA Style

Shuaiyang Jiao, Shengrui Zhang, Zongzhi Li, Bei Zhou, Dan Zhao. An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them. Journal of Advanced Transportation. 2020; 2020 ():1-13.

Chicago/Turabian Style

Shuaiyang Jiao; Shengrui Zhang; Zongzhi Li; Bei Zhou; Dan Zhao. 2020. "An Improved Car-Following Speed Model considering Speed of the Lead Vehicle, Vehicle Spacing, and Driver’s Sensitivity to Them." Journal of Advanced Transportation 2020, no. : 1-13.