This report describes a new data warehouse model developed for integrating Road Weather Information System (R/WIS) and traffic data and the prototype implemented. The building blocks of the prototype include data aggregation methods from sensors, a data archiving system, and multi-user data access and retrieval environments through a network. This new data warehouse model seamlessly integrates the heterogeneous nature of R/WIS and traffic data. The key to this data model was utilization of a network storage model referred to as a parallel First-In-First-Out (FIFO) data storage where various sensor data are deposited as they are aggregated while different types of data-consuming modules obtain data without an explicit protocol requirement. For the prototype implementation, four different data aggregation methods from traffic and R/WIS sources were used to demonstrate that diverse data types and collection methods could be seamlessly integrated together. As an application of this data warehouse, weather impact on traffic flow was studied by retrieving traffic data under various atmospheric and pavement conditions, and the results are included. It was noticed that R/WIS provides a significant advantage over the traditional National Weather Service data in learning detailed location specific weather and pavement conditions from which weather impact on traffic flow could be accurately analyzed.
The Minnesota Department of Transportation (Mn/DOT) has been responsible for collecting, analyzing and publishing traffic counts from the various roadway systems throughout the state. The traffic reporting system mainly developed by the Traffic Forecasting and Analysis Section (TFAS) of Mn/DOT has been used in several federal programs, internal Mn/DOT applications and many private sectors. The objective of this project was to continue the TFAS automation efforts by automating the Traffic Managment Center (TMC) portion of traffic data (intelligent transportation system (ITS) generated data) contributed to the Mn/DOT's Traffic Monitoring System. The focus was given to develop an Internet-based system that produces computerized reports on continuous and short-duration count data. One of the challenges of utilizing ITS generated traffic data for computing continuous and short-duration count was in dealing with missing and incorrect data produced by faulty conditions of traffic data collection systems including detectors and communication links. This study found that data imputation techniques based on spatial and temporal inferences of traffic flow can overcome the difficulties and produce accurate statistical data. This report describes the details on actual implementation of the algorithms developed, analysis utilities and practical system integration examples. One unresolved issue in this project was dealing with the stations in which nearly no data is available for the entire year, which was observed from 2-3% of the short-duration count stations. This problem is left for future work.
Travel-time data provides vital information for traffic monitoring, management, and planning. The objective of this research was to develop a new computational approach that could accurately measure travel time from two sets of spatially separated loop detectors using re-identification of vehicle inductance signatures generated by the loops. Although measuring travel time using loop inductance signatures is not new, all past approaches essentially relied on pattern matching of raw inductance waveforms without restoring the loss of detailed features caused by a large detection zone of inductive loops. The main effort in this research was to develop a new computational algorithm that restores the lost details from the raw inductance waveforms by modeling the output of loop detectors as a convolution of the original vehicle signature and the loop system function. This restoration problem was formulated as a blind deconvolution problem since we know neither the impulse response of the loop detectors, nor the original vehicle signature. To solve this blind problem, two basic blind deconvolution approaches were used, Godard deconvolution and constrained least squares. Experimental results showed that both methods performed well and significantly exposed the original signature information with unique vehicle characteristics.
The ALERT-2 system was redesigned to mitigate increased roll-throughs. With respect to technological advances, the ALERT-2 system improves many aspects of the basic technologies, providing higher system reliability, easier installation and maintainability, and better self-sustainability through redesign of the renewable energy application. To assess the driver behaviors at the test site, 13 months worth of video data and a survey of local residents were collected. This report describes the system development, implementation, and analysis of the video and survey data.
Installing permanent in-pavement weigh-in-motion (WIM) stations on local roads is very expensive and requires recurring costs of maintenance trips, electricity, and communication. For county roads with limited average daily traffic (ADT) volume, such a high cost of installation and maintenance is rarely justifiable. One solution to bring WIM technologies to local roads is to utilize a portable WIM system, much like pneumatic tube counters used in short-duration traffic counts. That is, a single unit is reused in multiple locations for few days at a time. This way, WIM data is obtained without the cost of permanent in-pavement WIM stations. This report describes the results of a two-year research project sponsored by the Minnesota Department of Transportation (MnDOT) to develop a portable WIM system that can be readily deployed on local roads. The objective of this project was to develop a portable WIM system that would be used much like a pneumatic tube counter. The developed system is battery operated, low cost, portable, and easily installable on both rigid and flexible pavements. The report includes a sideby- side comparison of data between the developed on-pavement portable WIM system and an in-pavement permanent WIM system.
Bull-Converter/Reporter is a software stack for Weigh-In-Motion (WIM) data analysis and reporting tools developed by the University of Minnesota Duluth for the Minnesota Department of Transportation (MnDOT) to resolve problems associated with deployment of multi-vendor WIM systems in a statewide network. These data tools have been used by the MnDOT Office of Transportation System Management (OTSM) since their initial delivery in 2009. The objective of this project was to expand the current conversion capabilities of BullConverter to include more raw data formats from different companies and the current BullReporter functions to include new analysis and reporting capabilities. Data analysis needs change over time; and the members of the OTSM WIM section identified several new functions that would increase efficiency and improve quality of WIM data. This report describes the new reporting and conversion functions implemented in this project.
A complete portable weigh-in-motion (PWIM) system consists of a pair of weigh-pads (one for upstream and the other for downstream), a controller which translates raw load signals to WIM data, and an optional external battery pack. The weigh-pad dimensions are one foot wide and 24 feet long, covering two lanes. This document describes how to install and remove weigh-pads using the recommended tools and setup of the controller. The operation of controller that includes initial setup and calibration is described step-by-step. The controller stores WIM data in the controller hard disk using a comma separated values (CSV) format; the details of the CSV file naming convention and column formats are described.
Building a WIM system around polymer piezoelectric film sensors, called BL sensors, costs only a fraction of the traditional WIM system built around crystalline-quartz piezoelectric sensors called Lineas sensors. However, BL sensors are highly sensitive to temperature, which limits the accuracy of weight measurements. The objective of this research was to investigate the performance of BL sensors head-to-head with Lineas sensors by installing a BL WIM system and collecting data from the same highway. After the test site installation, pavement temperatures were recoded as part of each vehicle record from both Lineas and BL sensor-based WIM stations. The analysis of data collected over 10 months showed that temperature dependency of BL sensors can be removed in terms of average but not variance. More specifically, the average of axle weights after temperature-based calibration was about the same for both BL and Lineas sensors, but the variance was much higher for BL sensors. In conclusion, if BL sensors are used, pavement temperatures must be recorded as part of vehicle records. Then, the weights calibrated based temperature would be as accurate as Lineas sensors in terms of the average but not variance.
A majority of intersection-related fatal crashes occur at rural, through/stop intersections. At these intersections, sight restrictions caused by vertical and horizontal curves negatively affect a driver's ability to safely accept a gap in the traffic stream. Static advanced warning signs are sometimes installed at these intersections to warn drivers on the main, through approaches that an intersection is ahead. These warning signs appear to be ineffective. A new Advanced Light-Emitting Diode (LED) Warning System was developed and deployed at a rural, through/stop intersection with limited intersection sight distance due to a severe vertical curve. This warning system actively detects vehicles on all approaches and activates LED blinker warning signs for the conflicting movements. The research project included analysis of driver behavior obtained through video data and a survey of local residents and frequent users of the intersection. This report describes the development, implementation, and evaluation results of this new warning system.
Presently, the Office of Transportation Data & Analysis (TDA) at the Minnesota Department of Transportation (Mn/DOT) manages 29 Vehicle Classification (VC) sites and 12 Weigh-in-Motion (WIM) sites installed on various Minnesota roadways. The data is collected 24/7 from all sites, resulting in a large amount of data. The total amount of data is expected to substantially grow with time due to the continuous accumulation of data from the present sites and future expansion of sites. Therefore, there is an urgent need to develop an efficient data management strategy for dealing with the present needs and future growth of this data. The solution proposed in this research project is to develop a centralized data warehouse from which all applications can acquire the data. The objective of this project was to develop software for creating a VC/WIM data warehouse and example applications that utilize it. This project was successfully completed by developing the software necessary to build the VC/WIM data warehouse and the application software packages that utilize the data. The main contribution of this project is that it provides a single access point for querying all of the Mn/DOT's WIM and VC data, from which many more applications can be developed without concerns of proprietary binary formats.
