Liao, Chen-Fu

One of the key measures of freight performance along interstate corridors in the United States is the average speed of travel. This report documents the findings and analysis of the ATRI Freight Performance Measure (FPM) database systems and investigates a potential FPM system design that can efficiently and effectively processes more and larger Automatic Vehicle Location (AVL) datasets collected from various trucking companies. The current FPM system at ATRI was evolved from its previous system based on GIS software. The averaged speed calculations resulting from the data process of each FPM system are somewhat different. Analysis of the average speed calculation and investigation of speed differences are discussed in chapter one. FPM database system analysis and comparison are included in chapter two. The final chapter presents an ideal FPM system and requirements needed for migration.

An ongoing research project examines guidance systems, which can take over control of a vehicle if the driver becomes incapacitated. Part of this project includes an evaluation of a Differential Global Positioning System (DGPS) for vehicle-based lane sensing. This report documents the results of tests of the 5 Hz NovAtel RT20 DGPS receiver. A series of 32 static tests found the overall mean and standard deviation for the offset errors within specifications. In a series of dynamic tests, in which the vehicle was driven around the track at speeds of 20-35 miles per hour, after removing the effect of the GPS receiver's latency, the DGPS determined position exhibited a mean offset error of -17.3 cm (-6.82 in) and a mean standard deviation of 25.5 cm (10.1 in) in the direction of vehicle motion. In the direction perpendicular to vehicle motion, the mean offset was 4.57 cm (1.8 in) with a mean standard deviation of 39.6 cm (15.6 in). With no overhead obstructions in these tests, continuous satellite lock was possible. Tests at higher speeds based on a more accurate methodology are planned for the future.

This presentation pertains to report 2015-18, Implementation of Traffic Data Quality Verification for WIM Sites.

Transportation agencies in the U.S. use devices such as loop detectors, automatic traffic recorders (ATR), or weigh-in- motion (WIM) sensors to monitor the performance of traffic network for planning, forecasting, and traffic operations. With a limited number of ATR and WIM sensors deployed throughout the state roadways, temporary double tubes are often deployed to get axle-based vehicle classification counts. An inductive loop signature technology previously developed by a Small Business Innovation Research (SBIR) program sponsored by the US Department of Transportation is used to classify vehicles using existing loops. This technology has the potential to save time and money while providing the state, counties or cities more data especially in the metro area where loop detectors have already been installed. This research leveraged the outcomes from previous development to validate the classification accuracy with video data. A loop signature system was initially installed at a traffic station in Jordan, MN, to evaluate its performance. The system was later moved to another location on US-52 near Coates, MN, to validate its classification accuracy with more heavy- vehicle traffic. Individual vehicle records were manually verified and validated with ground-truth video data using both the 13 and 7-bin classification schemes from the Federal Highway Administration (FHWA) and the Highway Performance Monitoring System (HPMS). The combined results from both test sites indicated that the loop signature technology had an overall classification accuracy of 93% and 96% using the FHWA and HPMS schemes, respectively. The classification performance can be further improved by including additional vehicle signatures from the state to the classification library.

An increasing number of Accessible Pedestrian Signals (APS) have been installed at new or upgraded intersections to assist people with vision impairment to navigate streets. For un-signalized intersections and intersections without APS; people with vision impairment have to rely on their own orientation and mobility skills to gather necessary information to navigate to their destinations. Previously; a smartphone-based accessible pedestrian system was developed to support wayfinding and navigation for people with vision impairment at both signalized and un-signalized intersections. A digital map was also created to support the wayfinding app. This system allows a visually impaired pedestrian to receive signal timing and intersection geometry information from a smartphone app for wayfinding assistance. A beacon using Bluetooth Low Energy (BLE) technology helps to identify a pedestrian's location when he or she travels in a GPS-unfriendly environment. A network of Bluetooth beacons ensures that correct traffic information is provided to the visually impaired at the right location. This project leverages the previous work by installing the system at a number of intersections in downtown Stillwater; Minnesota; where MnDOT operates the signalized intersections. In this study; researchers interface with the traffic controllers to broadcast traffic signal phasing and timing (SPaT) information through a secured and private wireless network for visually impaired users. The aim is to test the smartphone-based accessible system and evaluate the effectiveness and usefulness of the system in supporting wayfinding and navigation while the visually impaired travel through signalized and un-signalized intersections.

An inductive loop signature technology was previously developed by a US Department of Transportation (DOT) Small Business Innovation Research (SBIR) program to classify vehicles along a section of the roadway using existing inductive loop detectors installed under the pavement. It was tested and demonstrated in California that the loop signature system could obtain more accurate; reliable and comprehensive traffic performance measures for transportation agencies. Results from the studies in California indicated that inductive loop signature technology was able to re-identify and classify vehicles along a section of roadway and provide reliable performance measures for assessing progress; at the local; State; or national level. This study aimed to take advantage of the outcomes from the loop signature development to validate the performance with ground truth vehicle classification data in the Twin Cities Metropolitan Area (TCMA). Based on the results from individual vehicle class verification; class 2 vehicles had the highest match rate of 90%. Possible causes of classification accuracy for other vehicle classes may include types of loops; sensitivity of inductive loops that generates a shadow loop signal on neighboring lanes; and classification library that was built based on California data. To further understand the causes of loop signature performance and improve the classification accuracy; the author suggests performing additional data verification at a permanent Automatic Traffic Recorder (ATR) site. There is also an opportunity to investigate the classification algorithm and develop an enhanced pattern recognition methodology based on the raw loop signature profile of various types of vehicles in Minnesota.

Freight transportation provides a significant contribution to our nation's economy. A reliable and accessible freight network enables business in the Twin Cities to be more competitive in the Upper Midwest region. Accurate and reliable freight data on freight activity is essential for freight planning; forecasting and decision making on infrastructure investment. A report entitled "Twin Cities Metropolitan Region Freight Study" published by MnDOT and the Metropolitan Council in 2013; suggested a need to understand where and when trucks are most affected by congestion. A framework for truck data collection and analysis was recommended to better understand the relationships between truck traffic and congestion in rush hours. Building upon our previous study to measure freight mobility and reliability along 38 key freight corridors in the Twin Cities Metropolitan Area (TCMA); this study leveraged our previous effort to implement the performance measures using the National Performance Measurement Research Dataset (NPMRDS) from the USDOT. The researcher team first worked with stakeholders to prioritize a list of key freight corridors with recurring congestion in peak periods in the TCMA. We used 24 months of NPMRDS data to measure travel time reliability and estimate truck delay at the corridor level and to identify system impediments during the peak hours. The objective is to use performance measures for assessing impact of truck congestions and identifying operational bottlenecks or physical constraints. Trucking activity nearby a congested area is examined to analyze traffic pattern and investigate possible causes of recurring congestions.

In order to reduce risky behavior around workzones, this project examines the effectiveness of using in-vehicle messages to heighten drivers' awareness of safety-critical and pertinent workzone information. This investigation centers around an inexpensive technology based on Bluetooth low-energy (BLE) tags that can be deployed in or ahead of the workzone. A smartphone app was developed to trigger nondistracting, auditory-visual messages in a smartphone mounted in a vehicle within range of the BLE workzone tags. Messages associated with BLE tags around the workzone can be updated remotely in real time and as such may provide significantly improved situational awareness about dynamic conditions at workzones such as: awareness of workers on site, changing traffic conditions, or hazards in the environment. Experiment results indicate that while travelling at 70 mph (113 km/h), the smartphone app is able to successfully detect a long-range BLE tag placed over 410 feet (125 meters) away on a traffic barrel on a roadway shoulder. Additional experiments are being conducted to validate the system performance under different roadway geometry, traffic, and weather conditions.

Weigh-In-Motion (WIM) system tends to go out of calibration from time to time, as a result generate biased and inaccurate measurements. Several external factors such as vehicle speed, weather, pavement conditions, etc. can be attributed to such anomaly. To overcome this problem, a statistical quality control technique is warranted that would provide the WIM operator with some guidelines whenever the system tends to go out of calibration. A mixture modeling technique using Expectation Maximization (EM) algorithm was implemented to divide the Gross Vehicle Weight (GVW) measurements of vehicle class 9 into three components, (unloaded, partially loaded, and fully loaded). Cumulative Sum (CUSUM) statistical process technique was used to identify any abrupt change in mean level of GVW measurements. Special attention was given to the presence of auto-correlation in the data by fitting an auto-regressive time series model and then performing CUSUM analysis on the fitted residuals. A data analysis software tool was developed to perform EM Fitting and CUSUM analyses. The EM analysis takes monthly WIM raw data and estimates the mean and deviations of GVW of class 9 fully loaded trucks. Results of the EM analyses are stored in a file directory for CUSUM analysis. Output from the CUSUM analysis will indicate whether there is any sensor drift during the analysis period. Results from the analysis suggest that the proposed methodology is able to estimate a shift in the WIM sensor accurately and also indicate the time point when the WIM system went out-of-calibration. A data analysis software tool, WIM Data Analyst, was developed using the Microsoft Visual Studio software development package based on the Microsoft Windows® .NET framework. An open source software tool called R.NET was integrated into the Microsoft .NET framework to interface with the R software which is another open source software package for statistical computing and analysis.

This report builds on past efforts to analyze freight mobility and reliability by developing an analysis methodology using GPS data to study freight performance of heavy commercial trucks along 38 key freight corridors in the Twin Cities metropolitan area.