Connected-vehicles network provides opportunities and conditions for improving traffic signal control, and macroscopic fundamental diagrams (MFD) can control the road network at the macrolevel effectively. This paper integrated proposed real-time access to the number of mobile vehicles and the maximum road queuing length in the Connected-vehicles network. Moreover, when implementing a simple control strategy to limit the boundary flow of a road network based on MFD, we determined whether the maximum queuing length of each boundary section exceeds the road-safety queuing length in real-time calculations and timely adjusted the road-network influx rate to avoid the overflow phenomenon in the boundary section. We established a road-network microtraffic simulation model in VISSIM software taking a district as the experimental area, determined MFD of the region based on the number of mobile vehicles, and weighted traffic volume of the road network. When the road network was tending to saturate, we implemented a simple control strategy and our algorithm limits the boundary flow. Finally, we compared the traffic signal control indicators with three strategies:
With the rapid development of the social economy, vehicle ownership has surged, causing urban traffic congestion problems and bottlenecks in urban development. To reduce vehicle-delay time and queuing length and ease traffic congestion, advanced traffic-control technologies are being used, and intelligent traffic signal control systems are implemented in largest cities [
Daganzo et al. [
Some other scholars have proposed to control the traffic in oversaturated areas using MFD theory to improve the traffic congestion in oversaturated areas. For example, Yan et al. [
Recently, more and more developed and developing countries have been vigorously promoting vehicle network technologies because of their great potentials in the intelligent transportation systems. In the vehicle network, vehicle location, speed, and other information can be uploaded in real-time to the command centre through the vehicle terminal and roadside unit to provide a reliable means to determine the number of vehicles and queuing length of the road network and to provide opportunities and conditions for improving the traffic signal controllers.
In this new study, we proposed a control strategy to limit road-network boundary traffic based on MFD and queuing length that real-time accesses the number of vehicles in road network and the maximum queuing length in the road section based on the MFD. Moreover, when implementing the road traffic-control strategy in the road network, our strategy based on the MFD helps to determine whether the maximum queuing length of each boundary section exceeds the road safety queue length or not and timely adjust the influx rate of road network to avoid the overflow phenomenon of the boundary section.
For the study, the following assumptions are made: All the vehicles are equipped with GPS in connected-vehicle network, which can achieve the location, speed, and other information to the signal control device of the intersection. The phase of the road boundary intersections used the mode of individual release of each import. The traffic flow in the studied road network is oversaturated. The studied road network with relatively uniform traffic density (homogeneous network) has macroscopic fundamental diagram.
According to MFD theory, the formula of relevant parameters is the following:
According to the MFD theory, the relationship between the weighted traffic (
Macroscopic fundamental diagrams, MFD.
In the connected-vehicles network, the vehicle is equipped with GPS (vehicle equipment), which can send latitude, longitude, speed, and other information to the roadside unit in real-time. Determining whether a mobile vehicle (vehicles with average speed greater than 5 km/h) falls within the network area is equivalent to determining whether the point falls within the polygon area. The ray method [
We have previously discussed calculating the distance between each vehicle and the entrance stop line of each road section in the connected-vehicles network [
Therefore, the distance set
The instantaneous velocity set
The vehicle with the instantaneous speed
Therefore, the maximum queuing length of the vehicle in the
Take 95% of the road section length
We previously proposed to determine the road network traffic status based on the MFD. When the road network tends to be congested, we can apply a simple control strategy to limit the boundary flow on the road network [
According to the allowable influx flow rate
After further study, we found that after implementing the simple strategy to control boundary flow, because individual boundary intersections fail to provide enough space for the vehicle queue, the queue overflows to the upstream intersections. To avoid queue overflow, we should implement the control strategy to limit boundary flow while considering the boundary section queuing space. Specific ideas are as follows:
If
The entrance flow limit that is suitable for limiting the flow at the road-network boundary intersections is calculated as follows:
The new influx volume of each entrance suitable for limiting flow at the road-network boundary intersections is expressed as follows:
The new influx volume of all the entrances suitable for limiting flow at the road-network boundary intersections is calculated as follows:
The actual influx volume
The new influx rate
The details of above control process are shown in Figure
Flow chart of a control strategy to limit the boundary flow that takes queuing space into account.
In this paper, we also chose to study the Guangzhou Tianhe District Sports Center business district, as we did in a previous report [
Tianhe District Sports Center business district diagram.
In the VISSIM traffic simulation software, we established the simulation model for the road-network traffic, which can effectively simulate the connected-vehicles network, as shown in Figure
Tianhe business district road-network simulation model.
From the fitting curve of Figure
MFD graph of the simulated road network.
We used the C# programming language to perform secondary development on the com programming interface provided by VISSIM and implemented the control strategy to limit the boundary flow while considering the boundary section queuing space. To obtain the maximum boundary section queuing length, a queuing detector was established in the road safety queuing position. When the road network was simulated to 133 cycles (simulation time was about 15960 s), the road network entered the congestion state. According to the control strategy to limit boundary flow, the initial road-network influx rate was 92% and 8% of the traffic rate needed to be limited. Therefore, to simplify the calculation, we reduced 8% of the green light time in the drive-in direction of boundary section for resimulation analysis. After running about 156 cycles, due to the limited queuing space of road sections CR, ED, and FG (as shown in Figure
We analyzed simulation data of oversaturated road network (15960–27000 s were simulation time period for oversaturation) with three control strategies: (
Average queuing lengths at each intersection in oversaturated road network.
Average delay time at each intersection in oversaturated road network.
Average stop times at each intersection in oversaturated road network.
Table
Comparison of traffic signal control indicators for oversaturated road networks with three strategies.
No control strategy | Boundary control | Boundary control with limiting queue strategy | |
---|---|---|---|
Average delay time (s) | 19.9 | 17.4 | 16.5 |
Average number of stops (times) | 0.45 | 0.42 | 0.39 |
Average queuing length (m) | 23.1 | 19.0 | 18.8 |
The connected-vehicles network and MFD present new ideas for studying traffic signal control methods. We implemented the control strategy to limit the boundary while considering boundary section queuing space on the road network at the macrolevel in this paper by using the MFD theory in the connected-vehicles network environment. However, the road-network boundary mentioned in this paper is predefined, and we will continue to study the dynamic division method of road-network boundaries based on MFD in the next stage.
The authors declare that they have no conflicts of interest.
This paper is jointly funded by Guangdong Province Science and Technology Development Special Funds (2016 Basic and Applied Basic Research, 2016A030313786) and Guangdong Province Higher Education Outstanding Young Teachers Training Program in 2015 (YQ2015184).