Multi-Lane Freeway with an Interchange Model

Fig 4.5 A multi-lane freeway with an interchange

For the realistic traffic model, we study a multi-lane freeway with an interchange, as shown in Fig 4.5. We simulate a 9-km, 3-lane freeway with a single-lane interchange at 4 km post. There is a 200-m long acceleration lane at the intersection of the freeway and interchange. The speed limit on the freeway is 90 km/h, and the desired speed of each vehicle is uniformly distributed between 85 km/h and 120 km/h. The traffic flow on the freeway before the interchange is 3000 vehicle/hour. By controlling the traffic flow input from the interchange, we can create traffic jams on the freeway. Table 4-4 lists the traffic flow input from the interchange as the simulation goes on. Initially, there is no traffic input from the interchange. At simulation time 900 sec., we generate a heavy traffic flow input (3000 veh./h ) from the interchange to cause a traffic jam on the freeway, and gradually decrease the traffic flow input from the interchange to remove the traffic jam. The total simulation time is 1 hour, and the status of simulated vehicles recorded by VISSIM is input to a simulation program which performs the functions of both the TIC and the probes.

Table 4-4 The variation of traffic flow from interchange

Simulation Time(second) Traffic Flow(veh/h)

0~900 0

900~1800 3000

1800~2400 2000

2400~3000 1000

Based on the status of simulation vehicles, we depict the ground truth and the traffic conditions with respect to the simulation time and the freeway location in

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Fig 4.6. The X-axis is the simulation time and the Y-axis is the highway location starting from 2 km post to 6 km post. In the green areas, the traffic is a free flow, and in the red areas, the traffic is congested. We can observe that when a heavy traffic flow starts to input from the interchange at simulation time 900 sec., the interchange becomes the bottleneck of the freeway, and a traffic jam builds up before the

Fig 4.6 The ground truth and the detected start and end positions of traffic jams

(b) (a)

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interchange. While the traffic flow input to the interchange decreases gracefully, the traffic jam reduces its area, separates to small traffic jams and disappears. Unlike the single-lane freeway with an interchange model, vehicles are able to change lane while the front vehicles slow down. So, in this simulation model, besides the bottleneck of the road network, there are few traffic jams in the road network. Fig. 4.6(b) depicts the start and end position of traffic jams detected by the vehicles in the road network. From the ground truth of the simulated road, we found that the traffic jams in the road network can be bounded by the start and end position detected by the probes. The start position of the traffic jam travel upstream, while the end position remain almost fixed at the km post of the interchange.

Fig. 4.3 depicts the message broadcasted by the TIC. It shows that the report policy roughly depicts the traffic jams in the road network. During the period of traffic jams starting generation and disappearance, the number of broadcasts increases. In addition, the setup of distance and time thresholds efficiently reduce the number of

Fig 4.7 the message the system broadcasted

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broadcasts.

Table 4-5 lists the performance metrics of the system we proposed and the effects of GPS position errors. When the GPS positioning is assumed to be no error, there are 72 broadcast messages from the TIC, i.e., 72 reports sent from the TIC. The 72 reports include 27 start position reports, 30 end position reports and 23 segment removal reports. The results indicate that these report thresholds reduce over 80% of the start position and end position reports sent by the probes, while providing a high AWC of 96%, a low location of less than 55m, and a small travel time error of 12.3%. In addition, when GPS positioning errors is 20 m in average, the number of broadcast messages increases by about 95%, the average location error increases by about 4 m, the travel time error decreases by about 2.5%, and the AWC decreases by 0.5%. When the GPS positioning errors are 50 m, the number of broadcast messages increases by about 90%, the average location error increases by about 25 m, the travel time error increases by about 2.5%, and the AWC decreases by 1.5%. Compared with single-lane model, the AWC is higher. The report thresholds reduce the probe cars reports more efficiently than that for the single-lane model. In addition, the location errors of start position of

Table 4-5 The effects of GPS position errors

GPS

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traffic jams are a little slighter than that of single-lane model. The reason caused the above three feature is highly related to the difference of the simulation models which bring about the distribution of the traffic jam.

We also observe the impact of different penetration rates of the probe cars. Fig 4.8 shows the impact of the probe penetration rate. As the penetration rate increases, the number of broadcasts increases at a lower rate than that in single-lane freeway model.

In addition, Location error is lower than that of the single-lane model.

Fig 4.8 The effects of the penetration rate of probe cars