The Influences of Preceding Vehicle Taillights on Macro Traffic Flow

2019 ◽  
pp. 125-133
Author(s):  
Jian Zhang ◽  
Meng-Tian Li ◽  
Tie-Qiao Tang ◽  
Yun Zou
Keyword(s):  
Traffic Flow ◽  
Preceding Vehicle

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

scholarly journals Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

2021 ◽  
Vol 13 (4) ◽  
pp. 88
Author(s):  
Xiaoyuan Wang ◽  
Junyan Han ◽  
Chenglin Bai ◽  
Huili Shi ◽  
Jinglei Zhang ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Free Flow ◽  
Traffic Information ◽  
Flow State ◽  
Simulation Framework ◽  
Car Following ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
Vehicle Information

With the application of vehicles to everything (V2X) technologies, drivers can obtain massive traffic information and adjust their car-following behavior according to the information. The macro-characteristics of traffic flow are essentially the overall expression of the micro-behavior of drivers. There are some shortcomings in the previous researches on traffic flow in the V2X environment, which result in difficulties to employ the related models or methods in exploring the characteristics of traffic flow affected by the information of generalized preceding vehicles (GPV). Aiming at this, a simulation framework based on the car-following model and the cellular automata (CA) is proposed in this work, then the traffic flow affected by the information of GPV is simulated and analyzed utilizing this framework. The research results suggest that the traffic flow, which is affected by the information of GPV in the V2X environment, would operate with a higher value of velocity, volume as well as jamming density and can maintain the free flow state with a much higher density of vehicles. The simulation framework constructed in this work can provide a reference for further research on the characteristics of traffic flow affected by various information in the V2X environment.

Multi-Lane Lane-Changing Model under the Influence of Heavy Vehicles

2013 ◽  
Vol 838-841 ◽  
pp. 2117-2120
Author(s):  
Xiao Fang Yang ◽  
Jian Rong Wang ◽  
Xin Zhu Wang
Keyword(s):  
Traffic Flow ◽  
Empirical Data ◽  
Mixed Traffic ◽  
Heavy Vehicles ◽  
Lane Changing ◽  
Mixed Traffic Flow ◽  
Traffic Conditions ◽  
Preceding Vehicle

This paper presents a new lane-changing model of multi-lane mixed traffic flow. The influences of heavy vehicles on lane-changing are analyzed. An improved accumulated speed benefit model is proposed in which drivers generate lane-changing intentions based on accumulated speed benefit of preceding vehicle in target lane over the preceding vehicle in current lane, not just relative to the speed and desired speed of subject vehicle. Drivers may accelerate or decelerate during lane-changing due to different traffic conditions. Simulations show that with the increase in the proportion of heavy vehicles, lane changing frequency first increases and then decreases. The model is validated with empirical data.

New full velocity difference model considering the driver’s heterogeneity of the disturbance risk preference for car-following theory

2016 ◽  
Vol 30 (01) ◽  
pp. 1550241 ◽  
Author(s):  
You-Zhi Zeng ◽  
Ning Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Flow Instability ◽  
Risk Preference ◽  
Difference Model ◽  
Velocity Difference ◽  
Absolute Size ◽  
Car Following ◽  
Preceding Vehicle ◽  
Preference Coefficient

This paper proposes a new full velocity difference model considering the driver’s heterogeneity of the disturbance risk preference for car-following theory to investigate the effects of the driver’s heterogeneity of the disturbance risk preference on traffic flow instability when the driver reacts to the relative velocity. We obtain traffic flow instability condition and the calculation method of the unstable region headway range and the probability of traffic congestion caused by a small disturbance. The analysis shows that has important effects the driver’s heterogeneity of the disturbance risk preference on traffic flow instability: (1) traffic flow instability is independent of the absolute size of the driver’s disturbance risk preference coefficient and depends on the ratio of the preceding vehicle driver’s disturbance risk preference coefficient to the following vehicle driver’s disturbance risk preference coefficient; (2) the smaller the ratio of the preceding vehicle driver’s disturbance risk preference coefficient to the following vehicle driver’s disturbance risk preference coefficient, the smaller traffic flow instability and vice versa. It provides some viable ideas to suppress traffic congestion.

2202 Effect of Driving Assistant System to Anticipate The Behavior of Pre-Preceding Vehicle on Traffic Flow

2013 ◽  
Vol 2013.22 (0) ◽  
pp. 203-206
Author(s):  
Hironori Suzuki ◽  
Yoshitaka Marumo ◽  
Takashi Nakano ◽  
Akio Tanaka
Keyword(s):  
Traffic Flow ◽  
Preceding Vehicle

scholarly journals A New Car-Following Model considering Driving Characteristics and Preceding Vehicle’s Acceleration

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yong Zhang ◽  
Ping Ni ◽  
Minwei Li ◽  
Hao Liu ◽  
Baocai Yin
Keyword(s):  
Traffic Flow ◽  
Clustering Algorithm ◽  
Traffic Data ◽  
Velocity Difference ◽  
Car Following ◽  
The Past ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
The Stability ◽  
Anticipation Effects

In the past decades, many improved car-following models based on the full velocity difference (FVD) model have been developed. But these models do not consider the acceleration of leading vehicle. Some of them consider individual anticipation behavior of drivers, but they either do not quantitatively determine the types of driving or artificially divide the driving types rather than deriving them from actual traffic data. In this paper, driver’s driving styles are firstly categorized based on actual traffic data via data mining and clustering algorithm. Secondly, a new car-following model based on FVD model is developed, taking into account individual anticipation effects and the acceleration of leading vehicle. The effect of driving characteristics and leading vehicle’s acceleration on car-following behavior is further analyzed via numerical simulation. The results show that considering the acceleration of preceding vehicle in the model improves the stability of traffic flow and different driving characteristics have different influence on the stability of traffic flow.

The Analysis of Traffic Flow State on Foggy Expressway Based on Fuzzy Clustering

ICCTP 2009 ◽  
2009 ◽  
Author(s):  
Jianjun Wang ◽  
Chenfeng Xie ◽  
Zhenwen Chang ◽  
Jingjing Zhang
Keyword(s):  
Traffic Flow ◽  
Fuzzy Clustering ◽  
Flow State

Study on Discrete Autonomous Traffic Flow Model with Zero-Order Hold

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lidong Zhang ◽  
Wenxing Zhu ◽  
Mengmeng Zhang ◽  
Cuijiao Chen
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Zero Order ◽  
Traffic Flow Model
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