Effect of Adaptive and Cooperative Adaptive Cruise Control on Throughput of Signalized Arterials

TL;DR

This study uses simulation to assess how adaptive cruise control (ACC) and cooperative ACC (CACC) vehicles affect intersection throughput and urban mobility, showing significant benefits with minimal infrastructure changes.

Contribution

It provides new insights into the impact of ACC/CACC vehicles on traffic flow and throughput at signalized intersections through detailed simulation analysis.

Findings

CACC vehicles increase intersection throughput.

Higher ACC/CACC proportions reduce travel time variability.

Significant mobility benefits observed with minimal infrastructure changes.

Abstract

The paper evaluates the influence of the maximum vehicle acceleration and variable proportions of ACC/CACC vehicles on the throughput of an intersection. Two cases are studied: (1) free road downstream of the intersection; and (2) red light at some distance downstream of the intersection. Simulation of a 4-mile stretch of an arterial with 13 signalized intersections is used to evaluate the impact of (C)ACC vehicles on the mean and standard deviation of travel time as the proportion of (C)ACC vehicles is increased. The results suggest a very high urban mobility benefit of (C)ACC vehicles at little or no cost in infrastructure.

Equations

$$v_{safe}=v_l(t)+\frac{g(t)-v_l(t)\ast \tau }{\frac{\bar{v}}{b(\bar{v})}+\tau }$$

$$v_{1}=min⁡[v(t)+b,v_{max},v_{safe]\\ v_{0}=v_{1}-eps*(v_{1}-(v(t)-b))\\ v(t+1)=min⁡(v_{1},v_{0})\\ x(t+1)=x(t)+v(t+1)*dt\end{equation}}$$

Full text

SUMO-Based Traffic Simulation of Platoons

Daniel Albarnaz Farias, Armin Askari, Alexander A. Kurzhanskiy, Kannan Ramchandran, Pravin Varaiya

(April 2016)

{Abstract}

1 Introduction

With the onset of vehicles augmented with autonomous features, there is a push to utilize these capabilities to form platoons of vehicles acting as a single unit. Vehicles within platoons maintain mutual communication, allowing uniform acceleration, smaller inter-vehicle gaps, re-routing, among other functions. Platoons may be formed based on destination, maintaining the same grouping and unified directions for longer periods. Forming and directing platoons has been shown to significantly improve highway throughput in theoretical calculations [1].

Platoons can also be used in arterial networks with traffic signals. Early theoretical work shows an approximately 15 percent increase in throughput by improving route choice and path planning decisions within platoons. The performance increase is still apparent in purely arterial networks, which are investigated in this paper [2]. In urban roads, throughput may increase by as much as 100 percent [6]. However, these calculations have not been tested under traffic dynamics.

We test and develop these previous calculations through the Simulation for Urban Mobility (SUMO) traffic simulator [3] using an arterial network. SUMO is a microscopic simulation tool that utilizes a car following model defined by ordinary differential equations. The SUMO model accounts is able to account for metastable states of very high throughput and hysteresis effects are accounted for in traffic flow.

Arterial networks involve frequent signaled intersections and junctions. Vehicle sequencing is highly dynamic with cars continuously leaving from and merging into the primary arterial lane. This requires strict platoon maintenance to ensure uniform groupings throughout the simulation. Platoons may be explicitly broken up if only part of it is projected to cross an intersection depending on remaining green time.

The goals were twofold; 1) Comparing flow efficiency as a function of vehicle class parameters and individual class share within the total traffic volume. 2) Implementing platoons and evaluating throughput totals.

2 Simulation Set-Up

The simulation network was created using real world data from the Huntington Drive-North Colorado Boulevard arterial segment (HCS) in northern Los Angeles. The network consists of 13 intersections, 11 of which are signaled. Traffic is generated on 23 sources nodes based on collected flow information and a combination of collected and estimated turning ratios. Specifically, possible vehicles routes are first calculated through Dijkstra’s algorithm. Cars are then generated as a uniform random variable corresponding to the rate of flow through the given lane. When created, cars are assigned a route based on the cumulative probability of routes as a function of each turn at intersections.

Figure 1. Top. Satellite image of HCS. Bottom. Equivalent SUMO network. The main intersection studied is to the top-left corner, as circled.

Default traffic signal programming follows a fixed-length cycle stemming from real network data with re-optimized offset values. Other methods used utilize real time queue lengths to optimize their cycle allocations. Queue length was estimated using imperfect sensors to simulate a realistic scenario. The queue is defined as the number of vehicles in between the two sensors, typically 75 meters apart. Two to three sensors were used per lane at each intersection. No assumptions was made on queue clearance rate or detection error distribution, so simple checks were implemented using the sensors to detect empty and full queues to bound error.

Performance between all scenarios is evaluated by intersection throughput. Given HCS is a strictly arterial network, there is a sole path between any source and drain nodes. Thus, intersection flow is estimated at a given intersection by temporarily maximizing vehicle creation in all directions and averaging the throughput over four simulated hours after a stabilization period of one hour.

3 Two Vehicle-Class Simulation

For simplicity, only two types of vehicle classes are used: smart/autonomous and non-smart vehicles. Each class had different values for minimum time gap (tau), corresponding to reaction time, and distance gap (delta) between cars, the former relevant when vehicles are in flow and latter relevant when stopped in traffic. The table below shows the values used for each. Some additional minor SUMO parameters are adjusted slightly, including driver imperfection and impatience.

[TABLE]

Table 1. Corresponding values in each vehicle class for each gap parameter. [Potentially non-final Tau]

All other vehicle parameters are identical between classes. The acceleration is 2.6 m/s2, the deceleration is 4.5 m/s2, and maximum speed is 70 m/s. SUMO has internal imperfection and impatience parameters which were set to zero unless otherwise noted. SUMO has a stepping rate parameter corresponding to frequency of updating vehicle state, which is set to the simulation dt rate of 0.2 seconds.

SUMO’s use of microscopic modeling allows vehicle dynamics to consider the positions and speed of surrounding vehicles. SUMO’s car-following model only considers vehicles within the same lane.

SUMO uses τ to calculate safe vehicle speed through the following formula:

[TABLE]

Where vl is the speed of the leading car, and v ̵̅ is the mean vehicle speed. g(t) is the current gap and b is the deceleration, as a function of velocity, which is constant and predefined by vehicle class in the simulations. Vmax corresponds to the maximum speed for the given class.

At every step the vehicle updates its target speed and accelerates or decelerates at the rate corresponding to the parameters previously listed. The specific rules for parameter update are given below:

[TABLE]

The values for the manual class stem from approximate median values of human driving [add source- SUMO default], whereas smart class properties are conservatives estimates on the space needed in a partially autonomous system with sensors and faster reactions. We increased the share of smart vehicles from 0 to 100 percent of total traffic, which yields a roughly linear relationship in increased intersection flow as a function of the share of vehicles which are smart. Total volume of traffic flow through the main intersection increased from 3207 to 5150 vehicles per hour (over 8 total lanes), roughly a 50

Under a control scheme of 100 percent non-smart vehicles, the east-west lanes in the northeast intersection of the network demonstrate queue accumulation, and eventually spillback using collected flow data from the real network. As the share of smart vehicles increased as a percent of total volume, this was resolved, with no accumulation at any lane.

Figure 2. Simulation flow lines with best fits. The simulation was run with a granularity of one hour. Left. Total flow through intersection as a function of percent of vehicles in smart class. Right. Total flow for each direction. Each has two lanes. The blue line corresponds to simulation results and black line corresponds to best fit.

However, further gain can be achieved by grouping the vehicles in a specific class. If a smart vehicle has another in the same class leading it, it can further decrease both gap properties under the assumption that the car in front is able to communicate before having to slowing down. Other behaviors can also be determined for a whole platoon, such as when to accelerate to reach an intersection in time, further improving overall flow.

Figure 3. Top. East-lane queue accumulation under control scheme, over one hour. Bottom. Queue length in all four directions with 75 percent autonomous vehicles, over one hour.

Figure 4. Time-series plot for a typical example of both manual vehicle (left) and autonomous vehicle (right) starting stopped at an intersection while trailing another vehicle. Clearly, the autonomous vehicle consistently maintains a larger gap from the vehicle in front.

4 Organization of Platoons

Platoon management entails three general phases: 1) Identifying vehicles that can be grouped into platoons. 2) Setting individual platoon speed advisories to maximize flow. 3) Performing maintenance on the current platoon.

Platoons require a common destination, so at a minimum, they must share the next turn in the next intersection. Though extra care could be given to instructing vehicle movement when forming platoons, this is a costly operation in a two lane network with high number of non-smart vehicles, and thus platoons are only formed if the vehicles are already in sequential position to immediately identify the grouping. Thus, at each intersection immediately before the phase changes, smart cars in adjacent positions within their lane that share the same turn at this intersection are grouped and recorded as part of a new platoon.

All non-leading vehicles have both their gap parameters reduced, and the leading vehicle is instructed to immediately accelerate. In order to maintain safe distances within platoons, the leading car constantly monitors the headway in front of it. As soon as it is forced to decelerate due to a decreasing headway, it propagates the message to the rest of the vehicles in the platoons to also decelerate.

As soon as the leading vehicle passes the sensors for the queue in the next intersection, it checks which vehicles within the platoon share the same turn at this intersection. The procedure breaks up the platoon accordingly and resets the gap values for the vehicles which are now leading their own platoon. Accordingly, the front platoon check if there are new vehicles immediately ahead which may join the platoon and become the new leader, and the rear platoon performs the same check on the trailing vehicles. This allows for a seamless update of platoon states without causing any deceleration in the platoon overall. Figure 5. Finite Automata describing platoon behavior. [illustrator version in folder]

Figure 5. Finite Automata describing platoon behavior.

After performing maintenance, the platoon performs a conservative estimate on what portion of the group is able to pass through the intersection if their lane is currently in the green phase. If only a portion is able to get through, even when accelerating up to 10 percent, the platoon is further broken up for the cars that are able to get through. Once the phase turns red for this lane, all platoons are dissolved and gap parameters restored to previous values, while the procedure returns to the first step of platoon creation at the new intersection.

This procedure allowed for a further increase in throughput of approximately 8 percent along the main arterial roads; Huntington and Colorado. [Variable]

Platoons are also able to communicate with signal controls if desirable. When approaching a signaled intersection, a platoon is able to request an extension of its current phase if it is ending, and the signal control can either reject or accept the extension depending on the size of the platoon and the queue-pressure in other directions. This mechanism is not possible in the standard fixed-length control method since it does not maintain offsets. However, two alternative signal control methods are used which allow for it: 1) actuated control and 2) full max-pressure.

[Here I can go into more detail instead on the other methods since you didn’t think we should discuss it earlier, however, it seems necessary if discussing this last topic.]

[Simulation? – I discuss implementation in the previous paragraphs, unless I should further do so]

5 Conclusion

SUMO-based simulations demonstrate the potential increase in throughput by increasing autonomous vehicles and introducing communication. Simply introducing new intelligent vehicles with faster reaction times can greatly increase intersection flow at busy intersections by approximately 60 percent. More importantly, this overall improvement grows linearly with the share of intelligent vehicles currently active in traffic.

Implementing platoons further significantly increased throughput to a value comparable to the 15 percent estimated through theoretical calculations. Introducing small scale lane changing in order to increase size of platoons to vehicles that are in the wrong lane but could otherwise join could push the improvement in intersection flow closer to the theoretical estimate.

A key next step is to conduct simulations on more complicated grid networks where platoon rerouting can be considered as a factor. Changing the distribution of flow among roads has the potential for further improvement which is not possible to test with an arterial network such as HCS. However, the framework for building, directing, and maintaining platoons described in the previous section should extend well to such a network. Such a network would also more accurately test the performance of the platoon system by having a greater rate of platoon updating due to increased number of possible near turn destinations.

The framework also allows for further control of traffic signals depending on traffic state. For example, a phase could be extended if it detects a large platoon entering its queue without enough opposing pressure from the other queues.

6 Citations

[1] Vehicle sorting for platoon formation: Impacts on highway entry and throughput [2] Smart Cars on Smart Roads: Problems of Control [3] D. Krajzewicz, M. Bonert, and P. Wagner, “The open source traffic simulation package SUMO,” in Proc. Infrastructure Simul. Competition, RoboCup, Bremen, Germany, 2006, pp.1–5. [4] Microscopic Modeling of Traffic Flow: Investigation of Collision Free Vehicle Dynamics [5] Lane-Changing Model in SUMO [6] Platoons of connected vehicles can double throughput in urban roads