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国际学术期刊
Do cooperative systems make drivers’ car-following behavior safer?
发布时间:2014-3-2110:47:31来源:作者:Haneen Farah, Haris N. Koutsopoulos   

Haneen Farah
Haris N. Koutsopoulos



Highlights


•This study investigates the Impact of I2V system on driver car-following behavior.
•Instrumented vehicle was used to collect real data from the field.
•Car-following models were estimated with and without the system.
•Calibrated models’ comparison showed that the system harmonizes drivers’ behavior.
•The calibrated models can be implemented in simulation programs.



Keywords

Co-operative systems; Infrastructure-to-vehicle; Driver behavior; Car-following; Socio-demographic



Abstract

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.



Article Outline

1. Introduction
2. System evaluation
3. Methodology
3.1. Field test
3.2. Experiment
3.3. Model formulation

4. Estimation results
5. Conclusion
Acknowledgments
References



Figures

   

Fig. 1.

System evaluation framework.


Fig. 2.

Location of VMS Gantries/IR-transceivers (13).


Fig. 3.

Illustration of the in-vehicle unit interface (AustraiTech).


Fig. 4.

Distributions of the variables for system ON/OFF.


Fig. 5.

Impact of subject speed on drivers’ acceleration and deceleration while holding ΔDn = 10 m and ΔVn = 5 m/s.


Fig. 6.

Impact of spacing on drivers’ acceleration and deceleration while holding Vn = 20 m/s and ΔVn = 5 m/s.


Fig. 7.

Impact of relative speed on drivers’ acceleration and deceleration while holding Vn = 20 m/s and ΔDn = 10 m.


Fig. 8.

Probability density function of the reaction time for system OFF/ON.


Fig. 9.

Impact of relative speed on the stimulus (a) acceleration and (b) deceleration.


Fig. 10.

Sensitivity term as a function of the driving speed.



Tables

   
Table 1. Simulated COOPERS service messages.

Table 2. Drivers’ gender and age frequency.

Table 3. Estimation Results of Car-Following Models for System OFF/ ON.

Table 4. Estimation results for the reaction time distribution, mean and standard deviation.

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