Embedded Files
System Architecture
Introduction
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.
Traffic Cabinet
Sample Data
Data Collection Unit
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 Left-hand side 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.
Shockwave Profile
Selected Application 1: 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.
Queue Process between Two Intersections
Selected Application 2: 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.
Patents
Liu, H., Ma, W., and Wu, X. (2012) Traffic Flow Monitoring for Intersections with Signal Controls, U.S. Patent No. 8,279,086. Issued on October 2, 2012. [PDF]
Selected publications
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.
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