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SMART-Signal

 
 

Systematic Monitoring of Arterial Road Traffic Signals



 
 

- Summary

Although measuring and archiving freeway traffic performance using commonly available loop detector data has become a norm for many transportation agencies, similar approaches for urban arterials do not exist. In practice, operational data from traffic signal systems are neither stored nor analyzed, which prevents proactive management of arterial streets. The development of the SMART-Signal (Systematic Monitoring of Arterial Road Traffic Signals) system fills in this gap. The SMART-Signal system simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time arterial performance measures including intersection queue length and arterial travel time. The development of the system has laid the groundwork for better traffic models and control strategies and opens up entirely new opportunities for managing traffic on congested roads.

In the SMART-Signal system, a complete history of traffic signal control, including all vehicle actuation events and signal phase change events, are archived and stored. At each intersection, an industrial PC with a data acquisition card is installed inside the controller cabinet, and event data collected at each intersection are transmitted to the data server in real-time using an Ethernet connection. Using the event-based data, a set of arterial performance measures, especially intersection queue length and arterial travel time, can be estimated. SMART-Signal uses a newly developed algorithmic approach to queue length estimation based on traffic shockwave theory. Cyclic traffic shockwaves at an intersection can be reconstructed using event-based data, allowing for queue length estimation even when the queue of cars extends beyond the upstream vehicle detector. To measure travel time, SMART-Signal simulates the movements of a virtual “probe vehicle” along the arterial road. As the virtual probe moves, it can modify its own state in response to the state of traffic around it by accelerating, decelerating, or maintaining a constant speed at each time step as it encounters queues, traffic signals, and changes in traffic density. SMART-Signal can also optimize traffic signal parameters using the collected high-resolution data. Instead of relying on traditional offset optimization approaches, which are based on manually collected volume data on a typical day, SMART-Signal can account for traffic flow variations by using archived traffic signal data and the derived performance measures.

The SMART-Signal system has been field-tested on three major arterial corridors in Minnesota including six intersections on Trunk Highway 55 in Golden Valley, eleven intersections on France Avenue in Bloomington, and three intersections on Prairie Center Drive in Eden Prairie. A demonstration project is also being carried out on Orange Grove Boulevard in Pasadena, California. A large-scale implementation project currently under discussion with the Minnesota Department of Transportation will monitor 100 intersections in the Twin Cities area using the SMART-Signal system.

 
 

- System Architecture

Although measuring and archiving freeway traffic performance using commonly available loop detector data has become a norm for many transportation agencies, similar approaches for urban arterials do not exist. In practice, operational data from traffic signal systems are neither stored nor analyzed, which prevents proactive management of arterial streets. The development of the SMART-Signal (Systematic Monitoring of Arterial Road Traffic Signals) system fills in this gap. The SMART-Signal system simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time arterial performance measures including intersection queue length and arterial travel time. The development of the system has laid the groundwork for better traffic models and control strategies and opens up entirely new opportunities for managing traffic on congested roads.


SMART-Signal System Architecture
 
 

- Data Collection Unit

Although measuring and archiving freeway traffic performance using commonly available loop detector data has become a norm for many transportation agencies, similar approaches for urban arterials do not exist. In practice, operational data from traffic signal systems are neither stored nor analyzed, which prevents proactive management of arterial streets. The development of the SMART-Signal (Systematic Monitoring of Arterial Road Traffic Signals) system fills in this gap. The SMART-Signal system simultaneously collects event-based high-resolution traffic data from multiple intersections and generates real-time arterial performance measures including intersection queue length and arterial travel time. The development of the system has laid the groundwork for better traffic models and control strategies and opens up entirely new opportunities for managing traffic on congested roads.


Data Acquisition Card


Terminal Box​


Traffic Cabinet​

Data communication between two controller cabinets is done using the existing twisted pair communication lines. A protocol of RS-485 is used to transmit data and synchronize time between cabinets. After the data in the local cabinets is transferred to the master cabinet, DSL or a wireless unit installed in the master cabinet is used to send the data back to the database located at the University of Minnesota.

A sample of data is shown in the following figure. Each logged event starts with a time stamp that includes the date, hour, minute, second and millisecond based on the computer system time, followed by different types of event data including phase changes, detector actuation, and pedestrian calls. A complete history of traffic signal events is thus recorded.


Sample Data​
 
 

- Queue Length Estimation

Using the event-based data, a set of arterial performance measures, especially intersection queue length and arterial travel time, can be estimated. Queue length is unarguably the most important performance measure at a signalized intersection, since other performance measures such as average delay and level of services can be easily derived from queue length information. A major shortcoming of traditional input-output models for estimating queue length has been their inability to determine queue length under saturated conditions—i.e., when the queue of cars waiting to pass through an intersection extends beyond the upstream vehicle detector. Under saturated conditions, data on incoming traffic flow are no longer available and the input side of the input-output model breaks down. The SMART-Signal developers overcame this limitation by developing a new algorithmic approach to queue length estimation based on traffic shockwave theory.

This approach first utilizes the high-resolution data collected by the SMART-Signal to identify the changes of traffic states, and then applies Lighthill-Whitham-Richards (LWR) shockwave theory to construct shockwave profiles. The figure below demonstrates the shockwave profile within a cycle. This profile consists of four shockwaves and the shockwave motion will repeat from cycle to cycle. Using the high-resolution data, the changes of traffic states, i.e. the “break points” (A, B, and C), can be identified. The time-dependent queue length including the maximum and minimum queue (if existing) can then be easily derived from the constructed shockwave profile.


Shockwave Profile


Break Points Identification​

A Minneapolis-based Transportation Consulting firm, Alliant Engineering, Inc., conducted an independent evaluation of the queue length estimation algorithm. To observe the queue length, Alliant sent observers to the field (the Rhode Island intersection on TH55) during the morning peaks (7:00am-9:00am) on three randomly selected days in 2008: Jul. 23rd, Occ. 29th, and Dec. 10th. These observers manually counted the vehicles as they entered the queue (they were instructed to count a stopped vehicle as one that was traveling at less than 5 mph), and recorded the time when queue was maximum. The following figure compares the measured and estimated times and lengths of maximum queues. As indicated in the figure, the proposed model tracks the trend of cycle-based queue dynamics successfully.


Cycle-based Queue Comparison​​

Reference:
Liu, H., Wu, X., Ma, W., and Hu, H. (2009). Real-Time Queue Length Estimation for Congested Signalized Intersections. Transportation Research-Part C. 17(4), pp. 412-427.

 
 

- Travel Time Estimation

For measuring the performance of a corridor or network rather than an individual intersection, the key metric is travel time. Measuring travel time on signalized arterials is difficult because instead of the smooth traffic flow observed on highways under normal traffic conditions, traffic on arterials is repeatedly interrupted by traffic signals. SMART-Signal approaches this problem using the same sources of event-based data to simulate the movements of a “virtual” probe vehicle along an arterial road. As a virtual probe moves, it can modify its own state in response to the state of traffic around it by accelerating, decelerating, or maintaining a constant speed at each time step as it encounters queues, traffic signals, and changes in traffic density. The virtual probe approach to travel-time estimation actually benefits from traffic flow interruptions caused by traffic signals, because differences between the trajectories of a virtual probe vehicle and a hypothetical real vehicle—for example, if the virtual probe moves slightly faster than real-world traffic—are corrected by stopping at a red traffic signal. In this way, even an error that has increased in size as the virtual probe passed through several intersections will be eliminated at a single blocked intersection. This error-correction property increases the robustness of the travel-time estimation model and makes the result less sensitive to model parameters.


Maneuver Decision Tree of the Virtual Probe

Alliant Engineering, Inc. also conducted an independent evaluation of the travel time estimation algorithm using six intersections on TH55 in the Twin Cities area (from Boone Ave. to TH100). Alliant used GPS to collect travel time data. The following figure compares the measured and estimated travel times. The results indicate that the proposal model can accurately estimate arterial travel time.


Travel Time Comparison​

Reference:
Liu, H., and Ma, W. (2009). A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials, Transportation Research-Part C. 17(1), pp. 11-26.

 
 

- Optimization of Signal Parameters

SMART-Signal can also optimize traffic signal parameters using the high-resolution data. The figure below demonstrates how a stochastic optimization model can be formulated to fine-tune signal offsets. Instead of relying on traditional offset optimization approaches, which are based on manually collected volume data on a typical day, SMART-Signal can account for traffic flow variations by using archived traffic signal data and the derived performance measures.


Queue Process between Two Intersections​

The inputs to the optimization model are statistical distributions of signal phase changes and intersection turning volumes, obtained from SMART-Signal system. Since side-street queues and main-street residual queues are critical to the determination of signal offsets, they are being considered explicitly in the optimization model. Different objective functions, including maximization of throughput, maximization of green bandwidth, and minimization of total delay and number of stops, can be easily incorporated into the model.

The optimized offset values were implemented to the 6 intersections on TH55 for a morning peak (7:00 ~9:00 am) on 9/3/2009. The resulting delay (estimated by a virtual probe approach) was compared to the delay from a day (9/11/2009) with normal offset values. As shown in the figure, the average travel delay changes from 19.38 seconds with the original offset (9/11/2009) to 14.97 seconds with the optimized offset (9/3/2009), a 22.73% reduction. This result indicates a significant improvement by implementing the proposed model.


Comparison of Virtual Probe Travel Delay based on Different Offset Setting​

Reference:
Liu, H., and Hu, H. (2011, Jan.). A Data-Driven Approach to Arterial Offset Optimization, Poster session presented at the 90th Transportation Research Board (TRB) Annual Meeting, Washington, D.C.

 
 

- SMART-Signal Implementation & Potential Impact

The SMART-Signal system has been field-tested on three major arterial corridors in Minnesota including six intersections on Trunk Highway 55 in Golden Valley, eleven intersections on France Avenue in Bloomington, and three intersections on Prairie Center Drive in Eden Prairie. A demonstration project is also being carried out on Orange Grove Boulevard in Pasadena, California.

In the current phase of research, the research team is developing the next-generation SMART-Signal hardware that will be easier to deploy and maintain. The prototype system was constructed using Commercial Off-The-Shelf (COTS) hardware and was not optimized for use by agency personnel. The next generation is a plug-and-play device; therefore, it would be much easier to install. A large-scale implementation project currently under discussion with the Minnesota Department of Transportation will monitor 100 intersections in the Twin Cities area using the new generation of the SMART-Signal system.

This project will benefit both transportation industry and society as a whole. The development of the SMART-Signal system has clearly made significant impacts on the state-of-the-practice for traffic signal control because SMART-Signal provides practicing engineers with a new toolbox for battling urban congestion. With the SMART-Signal system, the onset and reasons for traffic congestion at signalized intersections can be easily identified and diagnosed; therefore, system efficiency can be improved. In addition, the SMART-Signal system contributes to the improvement of arterial traffic flow models and data-driven-based signal parameters fine-tuning and optimization, which will in turn significantly improve signal performance and reduce dependency on an expensive signal re-timing process.

 
 

- Resources

US Patent:
Liu, H., Ma, W., and Wu, X., (2009) Traffic Flow Monitoring for Intersections with Signal Control, U.S. Patent Application No. US-2010-0079306. Filed by the University of Minnesota on Sept. 25, 2009.
Accessible Online: http://www.faqs.org/patents/app/20100079306

Papers in Refereed Journals:
1.   Lu, G., Wang, Y., Wu, X., and Liu, H. (2015) Analysis of Yellow-Light Running at Signalized Intersections Using High-Resolution Traffic Data, Transportation Research Part A, 73, 39-52.
2.   Zheng, J., Liu, H., Misgen, S., Schwartz, K., Green, B., Anderson, M. (2014), Generating Time-Space Diagram Using Event-based Traffic Data for Evaluation of Signal Coordination, Transportation Research Record, 2439, 94-104.
3.   Wu, X. and Liu, H.* (2014) Using High-Resolution Event-based Data for Traffic Modeling and Control: An Overview, Transportation Research Part C, 42, 28-43.
4.   Liu, H. * and Sun, J. (2014) Length-based vehicle classification using event-based loop detector data Transportation Research Part C, 38, 2014, 156-166.
5.   Hu, H., Wu, X., and Liu, H.* (2013) Managing oversaturated signalized arterials: A maximum flow based approach, Transportation Research Part C, 36, 196-211.
6.   Hu, H. and Liu, H.* (2013) Arterial offset optimization using archived high-resolution traffic signal data, Transportation Research Part C, 37, 131-144.
7.   Wu, X.*, Vall, N.D., Liu, H., Cheng, W., and Jia, X. (2013) Analysis of Drivers’ Stop-or-Run Behaviorat Signalized Intersections Using High-Resolution Traffic and Signal Event Data, Transportation Research Record, 2365, 99-108.
8.   Zheng, J., Liu, H.*, Misgen, S., and Yu, G. (2013) A Performance Diagnosis Tool for Arterial Traffic Signals, Transportation Research Record, 2356, 109-116.
9.   Wu, X., and Liu, H. (2011). A Shockwave Profile Model for Traffic Flow on Congested Urban Arterials. Transportation Research - Part B.
10.   Wu, X., Liu, H., and Geroliminis, N. (2011).An Empirical Analysis on the Arterial Fundamental Diagram. Transportation Research-Part B. 45(1), pp. 255-266.
11.   Liu, H., Ma, W., Wu, X., and Hu, H. (2010). Real-Time Estimation of Arterial Travel Time under Congested Conditions. Transportmetrica, 1944-0987. DOI: 10.1080/1812860090350229.
12.   Wu, X., Liu, H., and Getttman, D. (2010). Identification of Oversaturated Intersections Using High-Resolution Traffic Signal Data. Transportation Research-Part C. 18(4), pp. 626-638.
13.   Liu, H., Wu, X., Ma, W., and Hu, H. (2009). Real-Time Queue Length Estimation for Congested Signalized Intersections. Transportation Research-Part C. 17(4), pp. 412-427.
14.   Liu, H., and Ma, W. (2009). A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials, Transportation Research-Part C. 17(1), pp. 11-26.

Conference Presentations:
1.   Zheng, J., Liu, H., Misgen, S., and Yu, G. (2013) A Performance Diagnosis Tool for Arterial Traffic Signal, To be presented at the 92nd Annual Meeting of Transportation Research Board (TRB), January 13-17, 2013, Washington DC.
2.   Wu, X., Liu, H., and Cheng, W. (2013) Analysis of Drivers' Stop-or-Run Behavior at Signalized Intersections Using High-Resolution Traffic and Signal Event Data, To be presented at the 92nd Annual Meeting of Transportation Research Board (TRB), January 13-17, 2013, Washington DC.
3.   Wu, X., and Liu, H. (2013) Using High-Resolution Event-Based Detector Data for Traffic Modeling and Control: Overview, To be presented at the 92nd Annual Meeting of Transportation Research Board (TRB), January 13-17, 2013, Washington DC.
4.   Sun, J. and Liu, H. (2012) Estimation of Vehicle Emissions and Fuel Consumption Using High-Resolution Traffic Data, Presented at the 91st Annual Meeting of Transportation Research Board (TRB), January 22-26, 2012, Washington DC.
5.   Hu, H., Wu, X., and Liu, H. (2012) Simple Forward-Backward Procedure for Real-Time Signal Timing Adjustment on Oversaturated Arterial Networks, Presented at the 91st Annual Meeting of Transportation Research Board (TRB), January 22-26, 2012, Washington DC.
6.   Liu, H. and Sun, J. (2011) Length-Based Vehicle Classification Using Event-Based Loop Detector Data, Poster Presentation at the 90th Annual Meeting of Transportation Research Board (TRB), January 23-27, Washington DC.
7.   Liu, H. and Sun, J. (2011) Approximating Queue Size Dynamics at Actuated Signalized Intersections, Poster Presentation at the 90th Annual Meeting of Transportation Research Board (TRB), January 23-27, Washington DC.
8.   Liu, H., and Hu, H. (2011, Jan.). A Data-Driven Approach to Arterial Offset Optimization, Poster session presented at the 90th Transportation Research Board (TRB) Annual Meeting, Washington, D.C.
9.   Liu, H. and Sun, J.(2011, Jan.). Length-Based Vehicle Classification Using Event-Based Lopp Detector Data, Poster session presented at the 90th Transportation Research Board (TRB) Annual Meeting, Washington, D.C.
10.   Wu, X., Liu, H., and Getttman, D. (2010, Jan.). Identification of Oversaturated Intersections Using High-Resolution Traffic Signal Data. Oral presentation at the 89th Transportation Research Board (TRB) Annual Meeting, Washington, D.C.
11.   Wu, X., and Liu, H. (2010, Jan.). A Shockwave-based Traffic Flow Model for Oversaturated Arterial Arterials. Poster session presented at the 89th Transportation Research Board (TRB) Annual Meeting, Washington, D.C.
12.   A Simplified Traffic Flow Model for Congested Signalized Arterials. Oral presentation at the INFORMS Annual Meeting, San Diego, CA.
13.   Liu, H., Ma, W., Wu, X., and Hu, H. (2009, Jul.). Real-Time Estimation of Arterial Travel Time under Congested Conditions. Poster session presented at the 18th International Symposium on Transportation and Traffic Theory (ISTTT 18), Hongkong, China.
14.   Liu, H., Wu, X., Ma, W., and Hu, H. (2009, Jan.). Time-Dependent Queue Length Estimation for Arterial Links under Congestion. Oral presentation at the 88th Transportation Research Board (TRB) Annual Meeting, Washington, D.C.
15.   Wu, X., oliminis, N. (2008, Jul.). Fundamental Diagram for Signalized Arterials: An Empirical Analysis using High-Resolution Traffic Data. Oral presentation at the Summer Meeting of the TRB Traffic Flow Theory Committee, Woods Hole, MA. the Summer Meeting of the TRB Traffic Flow Theory Committee, Woods Hole, MA.
16.   Liu, H., Ma, W., Hu, H., Wu, X., and Yu, G. (2008, Oct.). SMART-SIGNAL: Systematic Monitoring of Arterial Road Traffic Signals. Oral presentation at the 11th International IEEE Conference on Intelligent Transportation System (ITSC), Beijing, China.
17.   Liu, H. and Ma, W. (2008, Jan.). A Real-Time Performance Measurement System for Arterial Traffic Signals. Poster session presented at the 87th Transportation Research Board (TRB) Annual Meeting, Washington, D.C.
18.   Liu, H. and Ma, W. (2007, Jan.). Arterial Travel Time Estimation using Event-Based Traffic Data from Signalized Intersections. Poster session presented at the 86th Transportation Research Board (TRB) Annual Meeting,, Washington, D.C.