Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment
Abstract
:1. Introduction
2. Model Development
2.1. Road Model Based on VISSIM
2.2. Optimization Speed Model Based on MATLAB
2.2.1. Optimal Speed Control Strategy
- i = vehicle number (i = 1, 2, 3…)
- n = total number of vehicles in Segment B
- t = the tth time step
- ai,t = acceleration of vehicle i at time step t
- vi,t = velocity of vehicle i at time step t
- xi,t = distance of vehicle i at time step t to the merging point
- vmax = speed limit (here vmax = 20 m/s; the design speed is 50 km/h)
- Gmin = minimum distance gap (here Gmin = 10 m, considering driving safety)
- amin = minimum acceleration rate (here amin = −5 m/s2, estimated based on the measured data)
- amax = maximum acceleration rate (here amax = −5 m/s2, estimated based on the measured data)
- C = the average total acceleration of all the situations, which is calculated in advance
2.2.2. Using MATLAB to Implement Optimization Speed Model
2.3. Simulation Framework
- 1
- The road model and the traffic environment were established in VISSIM for micro simulation.
- 2
- The experiment results were read in the VISSIM’s COM (Cluster Communication Port) interface. Specifically, the speed, acceleration, and position of the vehicles in Segment A were read per second. Such an advanced simulation was handled through the VISSIM–COM interface and implemented in MATLAB.
- 3
- Excel was adopted as the intermediate program to realize data transmission of VISSIM and MATLAB. MATLAB was called through the Excel link interface, and the simulation data was transferred between VISSIM and MATLAB.
- 4
- The MATLAB optimization speed model was adopted with the Fmincon algorithm to optimize the average running speed. Then MATLAB returned the output value per second (i.e., the acceleration), and stored it in Excel via Excel link [22].
- 5
- The optimization decision was read in Excel through the COM interface, and the COM interface was connected with Excel. A C++ program was written to control the vehicles as the kernel of the model [23].
- 6
- The instant simulation of VISSIM was based on the optimized decision, by which the vehicle exchanged information instantly under the connected state and determines its own speed and acceleration. Then VISSIM output the simulation evaluation data.
3. Results
3.1. Model Verification
3.1.1. Case 1
3.1.2. Case 2
3.2. Speed Optimization Results
3.2.1. Analysis on Data Collection Points
3.2.2. Analysis on Travel Time and Delay Time
3.2.3. Analysis of the Mixed Traffic Flow in the Normal and the Connected States
4. Discussion
- 1.
- Most vehicles’ speed in the crowded traffic flow tended to be zero, which brought negative effects on the accuracy of speed analysis.
- 2.
- The vehicle distance was limited to 10 m in this model, but it was not true when the traffic was blocked. As a result, it is not conductive to analyzing queue length and vehicles’ passing time.
5. Conclusions
- (1)
- The running speed in the connected state is 4 km/h larger than that in the normal state. It proves that the fully-connected environment can improve running speed significantly.
- (2)
- The average total travel time and delay time of changing lanes or diverging decrease by 5.34% and 16.76% in the connected state, respectively. It shows that the fully-connected environment can make good use of the existing road infrastructure and improve traffic efficiency.
- (3)
- The optimal CV market penetrating rate is 0.6. At such a rate, the running speed, travel time, and delay time is 56.21 km/h, 14.77 s, and 0.76 s, respectively.
Author Contributions
Funding
Conflicts of Interest
References
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Time(s) | Number | Position | Speed (km/h) | Speed (m/s) |
---|---|---|---|---|
500 | 321 | 200.13 | 52.56 | 14.6 |
500 | 322 | 172.16 | 52.56 | 14.6 |
500 | 323 | 120.68 | 54 | 15 |
500 | 324 | 80.36 | 56.52 | 15.7 |
500 | 325 | 55.23 | 50.76 | 14.1 |
500 | 326 | 33.12 | 57.6 | 16 |
Time(s) | Number | Position (m) | Speed (km/h) | Speed (m/s) |
---|---|---|---|---|
568 | 299 | 174.89 | 53.1 | 14.75 |
568 | 301 | 155.63 | 55.8 | 15.50 |
568 | 303 | 139.87 | 54.5 | 15.14 |
568 | 304 | 118.87 | 53.2 | 14.78 |
568 | 306 | 100.13 | 55.0 | 15.28 |
568 | 308 | 73.65 | 49.6 | 13.78 |
568 | 310 | 45.27 | 52.4 | 14.56 |
568 | 312 | 23.52 | 50.0 | 13.89 |
568 | 314 | 3.00 | 48.9 | 13.58 |
Collection Point | The Connected State | The Normal State | ||
---|---|---|---|---|
Speed (km/h) | Acceleration (m/s2) | Speed (km/h) | Acceleration (m/s2) | |
1 | 55.412 | −0.078 | 51.298 | −0.102 |
2 | 55.035 | −0.025 | 50.529 | −0.029 |
3 | 55.4 | 0.255 | 51.387 | 0.26 |
4 | 55.659 | 0.26 | 51.608 | 0.246 |
Collection Points | Average | Maximum | Minimum | Standard Deviation | ||
---|---|---|---|---|---|---|
The normal state | 2 | Speed (km/h) | 50.529 | 60.074 | 43.573 | 4.093 |
Acceleration (m/s2) | −0.029 | 2.016 | −6.933 | 0.689 | ||
4 | Speed (km/h) | 51.608 | 59.619 | 28.177 | 4.927 | |
Acceleration (m/s2) | 0.246 | 2.398 | −0.322 | 0.559 | ||
The connected state | 2 | Speed (km/h) | 55.035 | 62.426 | 47.188 | 3.908 |
Acceleration (m/s2) | −0.025 | 2.009 | −1.921 | 0.874 | ||
4 | Speed (km/h) | 55.659 | 62.744 | 33.155 | 4.227 | |
Acceleration (m/s2) | 0.26 | 2.408 | −0.314 | 0.564 |
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Share and Cite
Yu, B.; Wu, M.; Wang, S.; Zhou, W. Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment. Int. J. Environ. Res. Public Health 2019, 16, 4373. https://doi.org/10.3390/ijerph16224373
Yu B, Wu M, Wang S, Zhou W. Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment. International Journal of Environmental Research and Public Health. 2019; 16(22):4373. https://doi.org/10.3390/ijerph16224373
Chicago/Turabian StyleYu, Bin, Miyi Wu, Shuyi Wang, and Wen Zhou. 2019. "Traffic Simulation Analysis on Running Speed in a Connected Vehicles Environment" International Journal of Environmental Research and Public Health 16, no. 22: 4373. https://doi.org/10.3390/ijerph16224373