La influencia de la confianza y la percepción en el fin último

We now present the simulation results from our test corridor. First, we discuss the characteristics of platoons. Then, we discuss the mobility impacts of platooning.

7.3.1 Platoon Formation

In this section, we explore the formation and evolu- tion of a platoon in an ad-hoc formation setting.

7.3.1.1 Trajectory of a Platoon. Figure 7.6 is a plot of the time-space diagram of a platoon, which could attain maximum size 12. Different colors represent different vehicle trajectories. Jerks in vehicle trajectory represents change in platoon. Diverging trajectories represent breaking of platoon while merging trajectories indicate platoon expansion.

Let us evaluate the three snapshots shown in the plot. Snapshot 1 represents the situation when vehicles have just entered the network. We see higher spacing between vehicles initially. Snapshot 2 represents the case when the vehicles have spent some time in the network and have had the opportunity to platoon. Thus, we see vehicle trajectories closer to each other than snapshot 1. Snapshot 3 is when the vehicles are leaving the network. Therefore, platoons break, and we see that trajectories diverge. We also see some vehicles still moving close to each other. This maybe because they were going to the same destination and did not have to break apart.

TABLE 7.1

Multinomial Distribution for Gap Strategy

Gap Strategy Low Medium High Probability 0.5 0.3 0.2

TABLE 7.2

Multinomial Distribution for Time Gap Based on Gap Strategy

ACC Time Gaps (s) 1.1 1.5 1.7 2.2 CACC Time Gaps (s) 0.6 0.7 0.9 1.1 Probability Low 0.5 0.25 0.15 0.1 Medium 0.2 0.3 0.3 0.2 High 0.1 0.15 0.25 0.5

TABLE 7.3

Vehicle Colors Based on Type and Status

Vehicle color Black Blue Red Green

7.3.1.2 Desired Time Gap. Figure 7.7 shows the varia- tion of desired time gap with different penetration levels for flow rate of 800 veh/h/lane. The desired time gap is stochastically chosen as discussed in section 7.2.2.1. We observe that as the percentage of CVs in the traffic composition increases, the overall average time gap between vehicles in the network keeps decreasing. This is so because with more CVs, more platoons are formed which move at closer time gaps as compared to non-CV vehicles. The average time gap of non-CV vehicles is almost the same since they cannot platoon.

7.3.1.3 Number of Platoons. Figure 7.8 shows the number of platoons formed in the simulation. As expec- ted, the number of platoons formed is higher with higher

percentage composition of CVs. In addition, higher flow rate implies higher number of platoons are formed since the chance of vehicles being within 100 m of each other is higher for higher flow rate. Interestingly, at higher penetration levels, the slope of the curve is increasing for lower flow rates while it is almost constant for higher flow. We can infer from here that when the chance of platooning is low (whether due to low penetration or low flow), a slight increase in number of CVs would have a higher increase in the number of platoons as compared to when the flow rate and saturation are high.

7.3.1.4 Platoon Size. Figure 7.9 illustrates the variation of platoon sizes with saturation of CVs and flow rate. Lighter color implies that the platoon size is more fre- quent. We see that the most common platoon size is two

Figure 7.6 Time space diagram of a platoon.

for both the flow rates, especially when more CVs are present. This observation can be attributed to our assump- tion that platoons grow only at the rear. At higher penetration, the chances of two vehicles being near each other are higher and thus, the chance of platoons being formed is higher. Also, higher concentration of CVs implies that the chance of two vehicles coming together to form two-sized platoons at the same time is higher. Since we do not allow two small platoons to merge and form one big platoon, size 2 remains dominant in the system.

We also observe that the maximum platoon size, which can be, achieved increases with increase in both flow and CV concentration. When the system is fully saturated with CVs, the maximum platoon size

that is observed with 400veh/h/lane is eight while that with 800 veh/h/lane is 13. Interestingly, this maxi- mum corresponds to 90% penetration of CVs for flow of 800 veh/h/lane as opposed to 400 veh/h/lane where the maximum is obtained at 100% CVs. This suggests that as flow rate increase, the maximum platoon size that can be achieved occurs at a penetration level lower than 100% penetration since the chance of small platoons being formed increases with higher percentage of CVs.

7.3.2 Impact Assessment

Now we discuss the impacts of platooning on average travel time and average speed in the network.

7.3.2.1 Average Travel Time. Figure 7.10 shows the variation of average network travel time with different flow and penetration. We observe a 3% decrease in average travel time with 100% CACC equipped vehicles

as compared to 0%, when the flow rate is 800 veh/h/lane. With a flow rate of 400 veh/h/lane, about 1% decrease in average travel time is observed. At a flow rate of 800 veh/h/lane, the average travel time at 50% penetration

decreases by only 0.5%. However, we observe steeper decrease in average travel time at higher percentages. This implies that a certain saturation of CVs would be needed before benefits seem significant. Improvements may not seem significant with the test corridor, but are expected to accumulate over a network.

7.3.2.2 Average Speed. Figure 7.11 describes the variation of average speed in the network with differ- ent CV composition and flow rate. We see improve- ments in average speed with higher CV composition. However, the improvement is marginal when the flow rate is 400 veh/h/lane. On the other hand, we observe

1% and 3% increase in average speed with 50% and 100% saturation respectively when flow rate is 800 veh/h/lane. We also see that the speed of non- CV vehicles drops significantly with higher saturation

of CVs when flow rate is lower. Therefore, plato- oning of CACC-enabled vehicles negatively affects unequipped vehicles when flow is low or penetration of CVs is low.

8. SIGNAL CONTROL WITH CONNECTED