Managing Oversaturated Arterials: from Measurements to Control Dr. Henry Liu Professor, Dept. of Civil and Environmental Engr. Research Professor, Transportation Research Institute University of Michigan, Ann Arbor Seminar at University at Buffalo February 6, 2015 Around 1920, William Potts a Detroit policeman, invented (unpatented) several automatic electric traffic light systems including an overhanging four-way, red, green, and yellow light system. The first to use a yellow light.

National Traffic Signal Report Cards Around 1920, William Potts a Detroit policeman, invented (unpatented) several automatic electric traffic light systems including an overhanging four-way, red, green, and yellow light system. The first to use a yellow light. Produced by  the National Transportation Operations Coalition (NTOC) The majority of transportation agencies DO NOT monitor or archive traffic signal data. What even bothers me further is that all these grades are given in a subjective manner. Usually through a survey of traffic engineer. NO OBJECIVE MEASUREMENTS here. 2

Presentation Outline Performance Measurement Quantifying Oversaturation Oversaturation Severity Index (OSI) Managing Oversaturation A Maximum Flow Based Approach Simulation Results 3

Performance Measurement Using the High-resolution Data SMART-Signal: Systematic Monitoring of Arterial Road Traffic Signals

1st Generation Data Collection Terminal Box 1st Generation Data Collection DAC

2nd Generation Data Collection TS-1 type cabinets TS-2 type cabinets

Installation at Pasadena, CA (02/04/15) 170 type cabinets

Event-Based Data 8

Queue Length Estimation Instead of traditional input-output approach, we estimate queue length by taking advantage of queue discharge process Based on LWR shockwave theory

Queue Length Estimation Utilize the data collected by advance detector Identify Critical Points: A, B, C Point A: Shockwave v1 arrives to detector, indicating traffic state changes from (qa,ka) to (0,kj) Point B: Shockwave v2 arrives to detector, indicating traffic state changes from (0,kj) to (qm,km) Point C: Shockwave v3 arrives to detector, indicating traffic state changes from (qm,km) to (qa,ka) Identify from high-resolution event-based data Vehicle Gap Detector occupied time Point A: Long occupy time (>3 sec) or occupancy > 1 for 3 sec Point B: Occupy time changes to normal value (< 1 sec) or occupancy changes to less than 1 Point C: Large gap time (>2.5) or occupancy = 0 for about 3 sec

Break Point Identification from High-Resolution Detector Data

Field Tests on TH55 in Minneapolis

Independent Evaluation of Performance Measures on TH55 By Alliant Engr. Inc Queue length Manually count the vehicles (Two persons per approach) Four peak hours (July 22nd and 23rd, 2009)

Results – Maximum Queue Length 14

MnDOT Implementation The system has been installed on more than 80 intersections in Minnesota. MnDOT Website: http://dotapp7.dot.state.mn.us/smartsignal/

Quantifying Oversaturation Using the High-resolution Data

What is oversaturation? Gazis (1963): An oversaturated intersection is defined as one in which the demand exceeds the capacity. Little research has been conducted on the identification and quantification of oversaturated conditions Mostly qualitative and incomplete However, traffic arrivals are usually hard to predict or measure. Therefore, a network of intersections would become oversaturated when the system is overloaded with heavy demand which exceeds the total capacity of the network

Definition of oversaturation Gazis (1964) “a stopped queue cannot be completely dissipated during a green cycle” Abu-Lebdeh & Benekohal (2003) “traffic queues persist from cycle to cycle either due to insufficient green splits or because of blockage” Roess et al. (2004) in Traffic Engineering “the oversaturated environment is characterized by unstable queues that tend to expand over time with potential of physically blocking intersections (spillback)” The problem is that queue length is difficult to measure with the existing approach.

Detrimental Effects Temporally, characterized by a residual queue at the end of cycle. Residual vehicles cannot be discharged due to insufficient green splits Creating detrimental effects on the following cycle by occupying a portion of green time. Spatially, characterized by a spill-over from a downstream intersection. Vehicles cannot be discharged even in green phase due to spill-over Creating detrimental effects by reducing useable green time for upstream movements

Oversaturation Severity Index (OSI) OSI: the ratio between unusable green time and total available green time in a cycle. Further differentiate OSI into T-OSI and S-OSI. Temporal dimension (T-OSI) The “unusable” green: because of the residual queue from the last cycle Spatial dimension (S-OSI) The “unusable” green: because of the downstream blockage

Measure T-OSI & S-OSI T-OSI: S-OSI: Estimate the length of residual queue at the end of cycle S-OSI: Identify spillover Calculate the reduction of green time of upstream intersections

Queue Length Estimation Utilize the data collected by advance detector Identify Critical Points: A, B, C Point A: Shockwave v1 arrives to detector, indicating traffic state changes from (qa,ka) to (0,kj) Point B: Shockwave v2 arrives to detector, indicating traffic state changes from (0,kj) to (qm,km) Point C: Shockwave v3 arrives to detector, indicating traffic state changes from (qm,km) to (qa,ka) Identify from high-resolution event-based data Vehicle Gap Detector occupied time Point A: Long occupy time (>3 sec) or occupancy > 1 for 3 sec Point B: Occupy time changes to normal value (< 1 sec) or occupancy changes to less than 1 Point C: Large gap time (>2.5) or occupancy = 0 for about 3 sec

Identify Queue-over-detector (QOD) Caused by Spillover Due to cyclic signal timing, vehicles slow down and stop for red phase or joining the queue, and then resume travel as the light turns green or queue is clear. Such deceleration-stop-acceleration process rapidly changes the occupancy at some locations. In other words, if a vehicle stays on the detector during the queuing process, the occupancy is significantly changed because of the relatively prolonged detector occupation time. We call this phenomenon as “Queue-Over-Detector” (QOD). This phenomenon is clearly indicated in the high-resolution data by large occupation time (2 sec, for example) or occupancy being 100% for several seconds. Generally, there are two kinds of QOD in signalized arterials: the first is caused by cyclic signal timing, i.e. the red phase; the second is because the queue spills back from downstream intersections, i.e. spillover. If the second QOD has been identified, the oversaturation at an arterial/route can be diagnosed.

Queue Estimation with Spillover

Managing Oversaturation: A Maximum Flow Based Approach

Problem Setting N intersections along an oversaturated path At control period t, decisions are made according to the average TOSI and SOSI values at the control period t-1, i.e.,

Decision Variables Red time changes Green time changes Different control variables are designed for different tasks is responsible for spillover elimination is responsible for residual queue elimination Positive SOSI indicates the spill-back of downstream queue Positive TOSI indicates that the available green time is insufficient for queue discharge The values of the two control variables can be directly transformed into the values of offset and green duration Cycle length is unchanged

TOSI > 0, SOSI = 0 Extending green

SOSI > 0 Reducing red at the downstream intersection

SOSI > 0 Gating (Reducing traffic arrivals & giving green to other approaches)

Handling Spillover

Handling Residual Queue The total increase of discharging capacity (i.e., increase of effective green time) at intersection n To eliminate residual queue at control period t

Available Green Constraints The green time increase is constrained by the available green can be defined in many ways, e.g.,

Model formulation Control Objective Eliminate spillovers and residual queues Moving the vehicles out of the congested area as soon as possible, i.e., maximizing the discharging capacities

Graphical Illustration A Multi-commodity Maximum Flow Problem

Simulation Test 5 intersections, Pasadena, CA

Traffic Flow Conditions Simulation Settings Traffic Flow Conditions Control Strategies Simulation time (Sec) Traffic Flow Conditions 0~1800 Normal flow condition (a) 1800~5400 Increased flow condition (b) 5400~7200 Control Strategy Description Cycle Length 1 Actuated-coordinated 80 2 FBP

Network Performance Comparison Strategy #1 Strategy #2 Value (%) Average Delay (Seconds/per veh.) 81.37 64.28 -21.00 Average # of stops (per veh.) 2.05 1.60 -21.96 Average Speed 10.95 12.92 +17.96

Network Throughput Comparison Strategy #1 Strategy #3 Value (%) Southbound 3021 3762 +24.52 Northbound 1248 1242 - 0.50 Int. 1 minor 1490 1539 +3.27 Int. 2 minor 647 772 +19.28 Int. 3 minor 1120 1180 +5.32 Int. 4 minor 1795 1815 +1.11 Int. 5 minor 1555 1613 +3.73 TOTAL 10880 11925 +9.6

Simulation Results Side Street Queue Length Side street queue decreases due to green time increase on side streets

Summary A quantifiable measure of oversaturation (TOSI and SOSI) is developed. We can measure TOSI and SOSI using high-resolution signal data. A simple and effective control strategy is developed to manage oversaturation.

Concluding Remarks Although traffic is traditionally modeled as “continuous flow”, traffic, after all, is discrete. Measuring traffic flow parameters using the data collected at the individual vehicle level Embrace the era of “BIG DATA”, but DATA is not everything. Measurements and Models, you cannot have one without the other. Technological advances support such data collection at affordable prices, so there is no technical barriers. The individual vehicle level means that we measure each vehicle’s response to a detector. For example, for a loop detector, we want the information related to when a vehicle touches the detector and when the vehicle leaves the detector. Here I don’t mean vehicle signature, where the detection resolution is even higher.

Acknowledgements

THANK YOU! Prof. Henry Liu 1-734-764-4354 henryliu@umich.edu