Lai, Leong, Ortuoste, Yu and Ong. Vision-based Intelligent System for Traffic Analysis (VISTA).

Lai, Leong, Ortuoste, Yu and Ong. Vision-based Intelligent System for Traffic Analysis (VISTA). Proceedings of the 5th ERDT Conference, Manila, Phils. 10 Sept 2010.

Abstract taken directly from texts and as such, quoted below:

“Vision-based Intelligent System for Traffic Analysis (VISTA) is a computer-based vision system that captures video footages of roads for analysis of traffic parameters. Problems with existing technology have led researchers to venture into the use of computer vision. VISTA consists of a dadta acquisition, data processing and result generation modules. The input of the system is a video footage of a roadway taken with a still digital video camera. The input is then converted into an image sequence. Each image is then processed to segregate entities on the road that contribute to traffic from those that do not. Once these entities are identified, pertinent traffic parameters are then computed based on the image sequence. The output of the system can be used as the necessary traffic parameters for traffic management and information systems.

This paper presents the design and implementation of the Codebook sub-module, which is responsible for segregating foreground and background segments of an image. It also outlines performance analysis and evaluation of the sub-module.”

This is one of the good researches that will surely help improve traffic data collection through the use of video cameras and computers. A lot of video image processors for traffic analysis are already being used in other countries. This, however, is locally developed and thus, local application would be much easier, cheaper, and appropriate.

On a note, errors are inherent from devices or personnel performing the data collection. Thus, the system errors present for VISTA is normal. In the conducted on-road object tests, the obtained error can be factored in the traffic flow computations such that more accurate estimates can be achieved.

In the advent of increasing use of surveillance cameras in most cities in Metro Manila, it would be much more wise to include the capabilities of VISTA to maximize the benefits that can be obtained from these. Giving attention to the critical issues of VISTA also would further ensure the accuracy of traffic flow computations. As a transport expert, I highly commend the researchers for producing a practical and valuable tool for traffic analysis to address traffic congestion in the metropolis. This tool should be explored by the MMDA for their real-time traffic surveillance in order to improve the agency’s traffic management and operations.

Lessons Learned: Monitoring Highway Congestion and Reliability Using Archived Traffic Detector Data by Turner, Margiotta and Lomax

Technical reports, research studies, and publications about transportation engineering fill my list of must-be-read-now notes. There is so much to learn and take note from all the written articles. If you ask WHY on earth would I read these boring stuff? My answer is because I need to for my NEW thesis topic. Apart from this main reason is that I love transportation engineering, I like doing research in this field, I enjoy everything that goes with it. (possing with two hands up! =)

Anyway, so much for intros.. I came upon these Lessons Learned: Monitoring Highway Congestion and Reliability Using Archived Traffic Detector Data by Shawn Turner, Rich Margiotta, and Tim Lomax. This FHWA report gives important insights on three general areas: analytical methods, data quality, and institutional issues. The following texts were directly taken from the report. Here are the top 10 lessons:

Analytical Methods

1) Don't wait for a "silver bullet."
The lesson learned is that transportation agencies should not wait idly for a “silver bullet” dataset or collection technique. More often, change in transportation is evolutionary rather than revolutionary, and agencies may find that what seemed like an ideal data source also has problems. Of course, agencies must become comfortable with available data resources and their features and limitations. In a limited number of instances, available data may be so poor as to not be considered for performance monitoring. Data of such poor quality should be obvious to even the casual observer.

2) Travel time modelling and estimation will always be necessary.
The lesson learned is that travel time modeling and estimation techniques will always be necessary (even with widespread availability of collected link travel times), particularly in a performance-based planning process. One of the challenges will be to ensure that estimation techniques produce roughly compatible travel time estimates as those from direct measurement.

3) Visualize the data, pictures are cool!
The lesson learned is that simple charts and graphics are more easily interpreted by this diverse audience than complex data tables and lengthy text descriptions. Data collectors and analysts may be adept at interpreting complex technical data because that is their primary job function; however, other non-technical audiences may only be able to devote 30 to 60 seconds to understanding key report elements.

4) Whatever affects traffic should be part of tperformance monitoring.
The lesson learned is that, to be effective, performance monitoring must also gather information on activities and events that can affect system performance. Examples include:
• System usage;
• Traffic incidents;
• Work zones;
• Severe or inclement weather;
• Special events;
• Economic conditions; and,
• Data quality.


Data Quality

5) Use can improve quality.
The lesson learned is that, in these instances, the agency or workgroup collecting data should be encouraged to use the data to improve their own agency functions or decision-making.

6) Support for operations can be built with quality archives.
The lesson learned here is that data
collected and archived while managing the transportation system can be easily reformulated to demonstrate the benefits of operations and management activities. However, the reuse of operations data for analytical purposes requires at least two things: 1) foresight to develop information systems that support real-time traffic management activities as well as historical analyses; and 2) commitment to collect and maintain quality data that can be used to demonstrate the benefits of operations... ...Archived operations data can help to “level the playing field”.

7) The devil is in the details.
The lesson learned is that the devil is in the details; that is, there are several seemingly minor data management practices that could have significant consequences when using archived data for performance monitoring.


Institutional Issues

8) Find and fix the barriers that hinder performance monitoring.
Some of the barriers to the development of archived data systems are similar to those experienced in further developing ransportation operations and management functions:
• Lack of financial resources for building and maintaining systems;
• Professional capacity to manage and analyze large data archives and warehouses;
• Widely ranging costs and benefits of implementation; and
• Uncertainty about data quality.
It will be vitally important to identify and remove these and other barriers to performance monitoring.

9) Performance monitoring may be a "killer app" for archived data.
Current trends and anecdotal evidence indicate that more traffic managers have taken an interest in developing and maintaining data archives. There appear to be at least two applications that
provide tangible benefits to traffic managers:
• Performance monitoring – helps traffic managers preserve or expand funding for operations; and
• Detector status/health reporting – helps traffic managers diagnose and troubleshoot extensive data collection systems.
Of these two applications, performance monitoring appears to be the most compelling application that is likely to strengthen traffic managers’ interest in developing data archiving systems.

10) Local knowledge contributes to national interpretation.
The lesson learned is that capturing local knowledge is desirable for interpreting system performance at a national level. State and local agencies are likely to be more familiar with highways in their jurisdiction and significant activities or events that have affected system performance. Some State and local agencies may be monitoring performance using other methods or techniques that could confirm or differ from national congestion monitoring results.
Because of their experience with local issues, State and local agency staff may also serve as a “reality check” for data collected in national congestion monitoring. However, this capture of local knowledge is currently, at best, an informal process that involves sporadic communication with State and local agencies.

**Note that this FHWA report can be downloaded in their website.