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Speed Advisory System Using Real-Time Actuated Traffic Light

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Speed Advisory System Using Real-Time Actuated Traffic Light This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 1 Speed Advisory System Using Real-Time Actuated Traffic Light Phase Length Prediction Mikhail Burov, Murat Arcak and Alexander Kurzhanskiy Abstract—Speed advisory systems for connected vehicles rely 12.4%. The phase length estimation, based on the five previous on the estimation of green (or red) light duration at signalized green-red/red-green transitions is also inefficient: according to intersections. A particular challenge is to predict the signal the algorithm, the best prediction is just slightly better than phases of semi- and fully-actuated traffic lights. In this paper, we introduce an algorithm that processes traffic measurement estimating the current phase length to be the same as the data collected from advanced detectors on road links and previous phase length. assigns “PASS”/“WAIT” labels to connected vehicles according A follow-on study [3] addresses the problem of finding to their predicted ability to go through the upcoming signalized optimal speed trajectories to minimize fuel consumption. How- intersection within the current phase. Additional computations ever, the work considers only pre-timed signals, leaving behind provide an estimate for the duration of the current green phase that can be used by the Speed Advisory System to minimize fuel issues with adaptive phase duration. The studies [4] and [5] consumption. Simulation results show 95% prediction accuracy, also focus on pre-timed traffic lights. which yields up to 30% reduction in fuel consumption when References [2] and [6] use noisy measurements of a signal used in a driver-assistance system. Traffic progression quality phase to process SPaT estimation. The study analyzes a large also benefits from our algorithm demonstrating an improvement number of GPS position and speed samples from 4300 buses of 20% at peak for medium traffic demand, reducing delays and idling at intersections. within a period of one month to estimate phase duration, cycle length and cycle start time. However, a large percentage of the Index Terms—Speed Advisory System, connected vehicles, recorded data was not suitable for the analysis, which limited actuated traffic light, phase prediction, real-time algorithm the accuracy of the algorithm (6s error for 36s phase duration). The later study [7] estimates the waiting time spent by buses I. INTRODUCTION in queues and presents significantly better results for the SPaT EHICLES equipped with the Speed Advisory System estimate. V (SAS) [1] use traffic light information and traffic data to All of the noisy measurement-based algorithms are imple- obtain the near-optimal speed trajectories to reduce fuel con- mented only for pre-timed traffic lights. In addition, collecting sumption. These trajectories also benefit progression quality by and processing noisy measurements may be computationally minimizing idling at intersections. One of the key parameters inefficient, since most of the signal data are available from required by the SAS is the estimated remaining time until the transportation authorities. end of the current traffic light (TL) phase. In the case of static The study [8] makes probabilistic SPaT predictions based traffic light (constant phase length) this information can be on the intersection traffic data. At the beginning of each cycle, easily obtained directly from the traffic light via Signal Phase the empirical frequency distribution is computed. Furthermore, and Timing (SPaT) messages or any other signaling system. for every second within the cycle, the algorithm tries to predict However, if an intersection is equipped with an actuated whether a certain phase is G(green), R(red) or M(uncertain) TL, the phase length depends on the traffic demand (vehicle with some level of confidence. Further estimation of the phase flow) and, therefore, may vary from “minimum duration” residual time requires the knowledge of within-cycle time, which even if available does not guarantee the accuracy of arXiv:2107.10372v1 [eess.SY] 21 Jul 2021 to “maximum duration” - specific variables characterizing a particular traffic light. Since the exact phase length value the prediction. In addition to the fact that “80% confidence” is unavailable, a prediction algorithm is required to provide predictions may be incorrect, the algorithm does not provide an estimation to be used in near-optimal speed trajectory firm guarantees and uncertainty may grow with the increase derivation. of confidence level. The software presented in [2] encourages drivers to use An algorithm presented in [9] relies on the historical data smartphone cameras to identify the traffic light color at the from several intersections in Munich and uses a Kalman Filter upcoming intersection and estimate the remaining time within to predict future probability distributions of phase durations. the current phase. The study reports error rates from 7.8% to Although a high level of accuracy was achieved (95%), the practical applicability is limited since the availability level is Manuscript received ... only 71% on average. M. Burov and M. Arcak are with the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, 94704 USA Another study [10] suggests using both the historical phase (e-mails: [email protected]; [email protected]). measurements and the real-time information that locates the A. Kurzhanskiy is with PATH, Berkeley, CA, 94720, USA (e-mail: current time within the current phase to predict all future phase [email protected]). This project was sponsored by the NSF CPS grant CNS-1545116 - Traffic transitions. Two approaches are considered: “conditional ex- Operating System for Smart Cities. pectation based prediction” and “confidence based prediction”. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 2 These methods greatly improve the prediction of the residual time for the current phase as well as for the subsequent phase; however, as stated in the paper, the proposed algorithms “pose a challenge to the design of speed profiles that reduce fuel consumption”. Since both algorithms update their predictions every second, SAS-equipped vehicles are forced to reevaluate the speed trajectories every second as well, causing jerky motion and fuel consumption increase. Several other papers study vehicle flow estimation at arterial roads using adaptive Figure 1: Simple Network. signal control predictions [11], [12], [13]. Most of the algorithms discussed above try to estimate or predict the phase length/residual time of the phase using historical data and statistical methods. In contrast, our primary objective is to determine whether or not a vehicle can pass the upcoming intersection during the current green phase. Using real-time traffic data from advanced detectors and the upcoming actuated TL, the algorithm analyzes downstream traffic, estimates the time vehicles can reach the TL and Figure 2: North Bethesda, Montgomery County, MD. assigns labels (“PASS”/ “WAIT”) depending on the vehicle’s passing capability. According to the obtained labels, the algo- rithm derives an estimated phase residual time for near-optimal upstream from actuators. When a vehicle crosses a counter, the speed trajectory computation for every participating vehicle. infrastructure records and stores the vehicle’s speed and time of passing for one cycle. At the end of every cycle, the data II. THE ALGORITHM are erased, since it is no longer relevant to the current phase We consider traffic lights with fixed cycle length and one actuation. We assume Tth seconds is enough to travel from actuated axis (later in the paper “green / red / yellow phase” the counter to the intersection in moderate traffic, therefore, refers to the phase of the actuated axis). Therefore, knowing counter data corresponding to the previous cycle has no impact the time within the cycle allows us to compute the precise on the current one. The case when the vehicle fails to reach the remaining time until the next cycle (i.e. next green phase, intersection within Tth seconds implies congestion that forces assuming that every cycle starts with the green phase). In other the vehicle to switch to the car-following model. words, for a non-green phase the remaining time until the next green is known and no predictions are required. However, if B. Traffic Light Properties the current phase is green, the algorithm is used to estimate As stated earlier, the cycle length of the TL j is fixed and the phase length. equals cycLen 2 [90; 120] seconds. The cycle consists of 4 All the computations and simulations were conducted in an j phases in the simple case (Tab. I) and up to 8 phases in the open source simulator SUMO (Simulation of Urban Mobility). complex set-up. A. Simple and Complex Network Architecture Table I: Traffic light states: groups of three from left to right - The project consists of two parts. First, we analyze a simple North ! South, West ! East, South ! North, East ! West; symmetric signalized intersection (Fig. 1) with East $ West r - red, G - green, y - yellow actuated axis to test an idealistic set-up and obtain a bench- Phase TL States Min Duration Max Duration mark for further evaluation. The second part is a simulation 0 rrrGGGrrrGGG 39 s 48 s of North Bethesda, Montgomery County, Maryland network 1 rrryyyrrryyy 6 s 6 s (around the intersections of Montrose Rd and Montrose Pkwy), 2 GGGrrrGGGrrr 30 s 39 s a complex system of 9 actuated traffic lights with different 3 yyyrrryyyrrr 6 s 6 s geometries and signal schedules (Fig. 2). This set-up allows us to test the algorithm in realistic conditions in the presence Moreover, we assume that traffic lights are time-gap ac- of various uncertainties. tuated, i.e. a vehicle can activate it only if the previous Every incoming link on the actuated axis is equipped with actuation was at most minGap seconds ago (3 seconds for our a stop-bar detector and two advanced detectors: an “actuator” simulations) and the maximum phase duration is not exceeded.
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