scholarly journals Investigating the Impact of Connected and Automated Vehicles on Signalized and Unsignalized Intersections Safety in Mixed Traffic

2022 ◽  
Vol 2 (1) ◽  
pp. 24-40
Author(s):  
Amirhosein Karbasi ◽  
Steve O’Hern
Keyword(s):  
Case Studies ◽  
Human Error ◽  
Road Traffic ◽  
Driver Model ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Road Traffic Crashes ◽  
Car Following ◽  
Car Following Model ◽  
The Impact

Road traffic crashes are a major safety problem, with one of the leading factors in crashes being human error. Automated and connected vehicles (CAVs) that are equipped with Advanced Driver Assistance Systems (ADAS) are expected to reduce human error. In this paper, the Simulation of Urban MObility (SUMO) traffic simulator is used to investigate how CAVs impact road safety. In order to define the longitudinal behavior of Human Drive Vehicles (HDVs) and CAVs, car-following models, including the Krauss, the Intelligent Driver Model (IDM), and Cooperative Adaptive Cruise Control (CACC) car-following models were used to simulate CAVs. Surrogate safety measures were utilized to analyze CAVs’ safety impact using time-to-collision. Two case studies were evaluated: a signalized grid network that included nine intersections, and a second network consisting of an unsignalized intersection. The results demonstrate that CAVs could potentially reduce the number of conflicts based on each of the car following model simulations and the two case studies. A secondary finding of the research identified additional safety benefits of vehicles equipped with collision avoidance control, through the reduction in rear-end conflicts observed for the CACC car-following model.

scholarly journals The Research of Car-Following Model Based on Real-Time Maximum Deceleration

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Longhai Yang ◽  
Xiqiao Zhang ◽  
Jiekun Gong ◽  
Juntao Liu
Keyword(s):  
Real Time ◽  
Vehicle Dynamics ◽  
Driver Model ◽  
Cruise Control ◽  
Maximum Acceleration ◽  
Car Following ◽  
Time Headway ◽  
Asphalt Road ◽  
Car Following Model ◽  
Maximum Deceleration

This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM.

scholarly journals Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity

2010 ◽  
Vol 368 (1928) ◽  
pp. 4585-4605 ◽  
Author(s):  
Arne Kesting ◽  
Martin Treiber ◽  
Dirk Helbing
Keyword(s):  
Traffic Flow ◽  
Flow Characteristics ◽  
Driver Model ◽  
Cruise Control ◽  
Lane Changes ◽  
Congested Traffic ◽  
Car Following Model ◽  
Traffic Flow Characteristics ◽  
Bottleneck Capacity ◽  
The Impact

With an increasing number of vehicles equipped with adaptive cruise control (ACC), the impact of such vehicles on the collective dynamics of traffic flow becomes relevant. By means of simulation, we investigate the influence of variable percentages of ACC vehicles on traffic flow characteristics. For simulating the ACC vehicles, we propose a new car-following model that also serves as the basis of an ACC implementation in real cars. The model is based on the intelligent driver model (IDM) and inherits its intuitive behavioural parameters: desired velocity, acceleration, comfortable deceleration and desired minimum time headway. It eliminates, however, the sometimes unrealistic behaviour of the IDM in cut-in situations with ensuing small gaps that regularly are caused by lane changes of other vehicles in dense or congested traffic. We simulate the influence of different ACC strategies on the maximum capacity before breakdown and the (dynamic) bottleneck capacity after breakdown. With a suitable strategy, we find sensitivities of the order of 0.3, i.e. 1 per cent more ACC vehicles will lead to an increase in the capacities by about 0.3 per cent. This sensitivity multiplies when considering travel times at actual breakdowns.

Car-following model of multispecies systems of road traffic

1997 ◽  
Vol 55 (3) ◽  
pp. 2203-2214 ◽  
Author(s):  
Anthony D. Mason ◽  
Andrew W. Woods
Keyword(s):  
Road Traffic ◽  
Car Following ◽  
Car Following Model ◽  
Multispecies Systems

Effects of adaptive and cooperative adaptive cruise control on the fuel consumption and emissions at the signalized intersection

2018 ◽  
Vol 32 (32) ◽  
pp. 1850396 ◽  
Author(s):  
Hongjun Cui ◽  
Jiangke Xing ◽  
Xia Li ◽  
Minqing Zhu
Keyword(s):  
Fuel Consumption ◽  
Red Light ◽  
Signalized Intersection ◽  
Environmental Benefit ◽  
Cruise Control ◽  
Car Following ◽  
Vehicle Type ◽  
Cooperative Adaptive Cruise Control ◽  
Car Following Model ◽  
Fuel Consumption And Emissions

In this paper, the HDM car-following model, the IIDM car-following model and the IDM car-following model with a constant-acceleration heuristic is utilized to explore the effects of ACC/CACC on the fuel consumption and emissionsat the signalized intersection. Two simulation experiments are studied: (i) one with free road ahead and (ii) the second with a red light 300 m downstream at the second intersection. The numerical results show that CACC vehicle is the best vehicle type among the three vehicle types from the perspective of vehicle’s cumulative fuel consumptions and cumulative exhaust emissions. The results of this paper also suggest a very high environmental benefit of ACC/CACC at little or no cost in infrastructure.

scholarly journals A New Car-Following Model with Consideration of Dynamic Safety Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tao Wang ◽  
Jing Zhang ◽  
Guangyao Li ◽  
Keyu Xu ◽  
Shubin Li
Keyword(s):  
Traffic Flow ◽  
Velocity Model ◽  
Safe Distance ◽  
Optimal Velocity ◽  
New Model ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Safety Distance ◽  
Car Following Model ◽  
The Impact

In the traditional optimal velocity model, safe distance is usually a constant, which, however, is not representative of actual traffic conditions. This paper attempts to study the impact of dynamic safety distance on vehicular stream through a car-following model. Firstly, a new car-following model is proposed, in which the traditional safety distance is replaced by a dynamic term. Then, the phase diagram in the headway, speed, and sensitivity spaces is given to illustrate the impact of a variable safe distance on traffic flow. Finally, numerical methods are conducted to examine the performance of the proposed model with regard to two aspects: compared with the optimal velocity model, the new model can suppress traffic congestion effectively and, for different safety distances, the dynamic safety distance can improve the stability of vehicular stream. Simulation results suggest that the new model is able to enhance traffic flow stability.

An improved car-following model considering the impact of safety messages

2018 ◽  
Vol 32 (32) ◽  
pp. 1850398 ◽  
Author(s):  
Tenglong Li ◽  
Fei Hui ◽  
Xiangmo Zhao
Keyword(s):  
Traffic Congestion ◽  
Dominant Factor ◽  
Velocity Difference ◽  
Car Following ◽  
Safety Message ◽  
Warning Messages ◽  
Gray Correlation ◽  
Car Following Model ◽  
The Stability ◽  
The Impact

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

Linear Acceleration Car-Following Model Development and Validation

1998 ◽  
Vol 1644 (1) ◽  
pp. 10-19 ◽  
Author(s):  
M.F. Aycin ◽  
R.F. Benekohal
Keyword(s):  
Intelligent Transportation Systems ◽  
Field Data ◽  
Statistical Tests ◽  
Reaction Times ◽  
Linear Acceleration ◽  
Transportation Systems ◽  
Cruise Control ◽  
Vehicle Simulation ◽  
Car Following ◽  
Car Following Model

A linear acceleration car-following model has been developed for realistic simulation of traffic flow in intelligent transportation systems (ITS) applications. The new model provides continuous acceleration profiles instead of the stepwise profiles that are currently used. The brake reaction times of the drivers are simulated effectively and are independent of the simulation time steps. Chain-reaction times of the drivers are also simulated and perception thresholds are incorporated in the model. The preferred time headways are utilized to determine the simulated drivers’ separation during car-following. The features of the model and the realistic vehicle simulation in car-following and in stop-and-go conditions make this model suitable to ITS, especially to autonomous intelligent cruise-control systems. The car-following algorithm is validated at microscopic and macroscopic levels by using field data. Simulated versus field trajectories and statistical tests show very strong agreement between simulation results and field data.

Assessing the Impact of a Cooperative Merging System on Highway Traffic Using a Microscopic Flow Simulator

2014 ◽  
Author(s):  
Ioannis A. Ntousakis ◽  
Kallirroi Porfyri ◽  
Ioannis K. Nikolos ◽  
Markos Papageorgiou
Keyword(s):  
Road Traffic ◽  
Highway Traffic ◽  
Cruise Control ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control ◽  
Low Efficiency ◽  
Critical Traffic ◽  
Vehicle Technologies ◽  
The Impact

Vehicle merging on highways has always been an important aspect, which directly affects the capacity of the highway. Under critical traffic conditions, the merging of main road traffic and on-ramp traffic is known to trigger speed breakdown and congestion. Additionally, merging is one of the most stressful tasks for the driver, since it requires a synchronized set of observations and actions. Consequently, drivers often perform merging maneuvers with low efficiency. Emerging vehicle technologies, such as cooperative adaptive cruise control and/or merging-assistance systems, are expected to enable the so-called “cooperative merging”. The purpose of this work is to propose a cooperative merging system and evaluate its performance and its impact on highway capacity. The modeling and simulation of the proposed methodology is performed within the framework of a microscopic traffic simulator. The proposed model allows for the vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication, which enables the effective handling of the available gaps between vehicles. Different cases are examined through simulations, in order to assess the impact of the system on traffic flow, under various traffic conditions. Useful conclusions are derived from the simulation results, which can form the basis for more complex merging algorithms and/or strategies that adapt to traffic conditions.

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