Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Department of Automation, Tsinghua University, Beijing 100084, China
Shenzhen Urban Transport Planning Center Co., Ltd., Shenzhen 518000, China
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Abstract
Cooperative driving is widely viewed as a promising method to better utilize limited road resources and alleviate traffic congestion. In recent years, several cooperative driving approaches for idealized traffic scenarios (i.e., uniform vehicle arrivals, lengths, and speeds) have been proposed. However, theoretical analyses and comparisons of these approaches are lacking. In this study, we propose a unified group-by-group zipper-style movement model to describe different approaches synthetically and evaluate their performance. We derive the maximum throughput for cooperative driving plans of idealized unsignalized intersections and discuss how to minimize the delay of vehicles. The obtained conclusions shed light on future cooperative driving studies.
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Li S, Zhang J, Chen Z, et al. Theoretical Analysis of Cooperative Driving at Idealized Unsignalized Intersections. Tsinghua Science and Technology, 2024, 29(1): 257-270. https://doi.org/10.26599/TST.2022.9010069
The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
10.26599/TST.2022.9010069.F001
Illustration of different control methods at the simplest conflict point, where vehicles run in only two directions: (a) rhythmic control, where vehicles pass with the one-by-one zipper-style movement; (b) modular vehicles control, where vehicles condense the car-following gap or even diminish such gap to increase the intersection capacity; (c) platoon-based control, where vehicles in the same direction are grouped to pass the intersection, but there is a gap between vehicles in the same group.
10.26599/TST.2022.9010069.F002
(a) Illustration of cooperative driving at the simplest conflict point, where vehicles run in only two directions; (b) the corresponding right-of-way assignments for the conflict point illustrated using the virtual vehicle mapping technique.
10.26599/TST.2022.9010069.F003
Illustration of the condensing process before cooperative driving at the core area of an unsignalized intersection.
Delay of vehicles and feasible cooperative driving plan search algorithm
The previously presented analysis indicates that we can have different cooperative driving plans that “serve” the same inflow rates and but the resulting delays for vehicles in different flows can vary significantly. Let us consider an extreme example: we have vehicles of the west-to-east flow and vehicles of the south-to-north flow. Cooperative driving vehicle plan A first dispatches vehicles of the west-to-east flow to pass the core area and then dispatches only one vehicle of the south-to-north flow (i.e., we set and ). Cooperative driving vehicle plan B dispatches the first vehicles of the west-to-east flow to pass the core area, then dispatches only one vehicle of the south-to-north flow, and finally dispatches the first vehicles of the west-to-east flow (i.e., we set and ). We determine that the maximum delays of the south-to-north flow obtained by the two plans are quite different.
To retain the equity of different travelers, we need to distribute the benefits and costs to travelers fairly and appropriately[
49
,
50
,
51
,
52
]. Thus, we are interested in the following problem:
Problem 3 Suppose the upstream inflow rates and can be served by a certain number of cooperative driving plans. Determine the plan that provides the lowest maximum delay for vehicles.
Suppose a cooperative driving plan that is characterized by a 9-tuple (i.e., , , , , , , , , and ) can “serve” the upstream inflow rates and . We assume uniform arrival of vehicles. Without any delay, the -th vehicle of the west-to-east flow should arrive at the core area at time
When the cooperative driving plan is applied, the -th vehicle of the west-to-east flow should arrive at the core area at time
where function yields the remainder of divided by ; function .
The delay of the -th vehicle of the west-to-east flow can then be written as follows:
Similarly, we determine the delay of the -th vehicle of the south-to-north flow and formulate our decision problem as follows:
The programming problem expressed in Formula (
20
) is difficult to solve because of the complicated round-off operations of integer decision variables. However, we can derive a simple upper bound for the maximum delay.
Theorem 5 The upper bound for the maximum delay of any vehicle is derived as follows:
If we can further apply an asymmetric cooperative driving plan (i.e., , , , , , , and ), then we should select the minimum .
Proof Based on Formula (
14
), we derive for the west-to-east flow. Thus, the -th vehicle of the west-to-east flow should arrive at the core area at time
Considering the periodic feature of the cooperative driving plans, we roughly derive the following expression:
By combining Eqs. (
22
) and (
23
), we derive the following expression:
Further considering Eq. (
18
) and Formula (
1
), we derive the following expression:
Such a condition also holds for the delay of the -th vehicle of the south-to-north flow. This concludes the proof.
■
Further considering Formula (
11
), we derive the following expression:
Based on Eq. (
22
) and Formula (
25
), we select the smallest possible and that “serve” the inflow rates and to reduce the lowest maximum delay of every individual vehicle. This indicates that when the condition in Formula (
7
) is satisfied, the rhythmic control method leads to the lowest maximum delay and is suggested. However, when the condition in Formula (
7
) is not satisfied, but the condition in Formula (
11
) is satisfied, we should select the following cooperative driving plan.
Because we aim to keep and as small as possible, given and , we can identify the ideal asymmetric cooperative driving plan using Algorithm 1 assuming .
Theorem 4 guarantees that we can finally obtain a satisfactory solution if Formula (
12
) holds. Let us use a simple test case to illustrate the algorithm. Suppose s, veh/h, and veh/h. The rhythmic control method cannot “serve” such inflow rates. When , we obtain s, , s, , and . At that point, we derive . Thus, we increment to . We obtain s, , s, , , and . This cooperative driving plan can “serve” such inflow rates. Indeed, such a cooperative driving plan (i.e., , , , , , , and ) can at most “serve” the upstream inflow rates veh/h and veh/h. If this plan is applied to the upstream inflow rates veh/h and veh/h, then some positions in the groups of the west-to-east flow (or the equivalent planned time slots[
31
,
33
,
35
,
36
]) will be left unoccupied.
10.26599/TST.2022.9010069.F004
Three-lane intersection with two west-to-east flow lanes and one south-to-north flow lane.
10.26599/TST.2022.9010069.F005
Three-lane intersection with different flow lanes and the right-of-way assignments along the time axis. (a) Three-lane intersection with one west-to-east flow lane, one east-to-north flow lane, and one south-to-north flow lane; (b) the corresponding right-of-way assignments along the time axis.
10.26599/TST.2022.9010069.F006
Four-lane intersection with different flow lanes and the right-of-way assignments along the time axis. (a) Four-lane intersection with lanes labeled as counterclockwise; (b) the corresponding right-of-way assignments along the time axis.
10.26599/TST.2022.9010069.F007
Eight-lane intersection with four through lanes first labeled as counterclockwise and four left-turn lanes then labeled as counterclockwise.