Development of a standardized vehicle identification number. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Bus and car travel time on urban networks : integrating Bluetooth and bus vehicle identification data

Travel time in an important transport performance indicator. Different modes of transport (buses and cars) have different mechanical and operational characteristics, resulting in significantly different travel behaviours and complexities in multimodal travel time estimation on urban networks. This paper explores the relationship between bus and car travel time on urban networks by utilising the empirical Bluetooth and Bus Vehicle Identification data from Brisbane. The technologies and issues behind the two datasets are studied. After cleaning the data to remove outliers, the relationship between not-in-service bus and car travel time and the relationship between in-service bus and car travel time are discussed. The travel time estimation models reveal that the not-in-service bus travel time are similar to the car travel time and the in-service bus travel time could be used to estimate car travel time during off-peak hours

Developing molecular identification numbers by an all-paths method

A new topological index is devised from an all-paths method. This molecular topological index has highly discriminating power for various kinds of organic compounds such as alkane trees, complex cyclic or polycyclic graphs, and structures containing heteroatoms and thus can be used as a Molecular IDentification number (MID) for chemical documentation. Some published MIDs derived from an all-paths method and their structural selectivity for alkane trees are also reviewed.

Motor vehicle titling and registration; a bibliography.

Mode of access: Internet.

Gray market vehicle program: extension warranted, but improvements in vehicle identification are needed.

Mode of access: Internet.

A manual of anti-theft guidelines for state motor vehicle titling programs.

Mode of access: Internet.

Does my business need generator identification numbers and manifests?

Because you generate waste, it is your responsibility to determine how to properly manage and dispose of your waste. This fact sheet discusses special waste, who must obtain a generator identification number, and who must use uniform hazardous waste manifests, which are required for both nonhazardous and hazardous special waste. ... Depending on the types of waste you generate, you may need a U.S. Environmental Protection Agency (U.S. EPA) and/or Illinois Environmental Protection Agency (Illinois EPA) generator identification number. The first step in determining whether you need an identification number is to identify the types and amounts of waste you generate.

Does my business need generator identification numbers and manifests?

Overview: Information presented in this publication is intended to provide a general understanding of the statutory and regulatory requirements governing generator identification numbers and manifests. This information is not intended to replace, limit, or expand upon the complete statutory and regulatory requirements found in the Illinois Environmental Protection Act and Title 35 of the Illinois Administrative Code of Regulations.

Assessment of the application of automatic vehicle identification technology to traffic management : Appendix C, Evaluation of potential applications of automatic vehicle monitoring to traffic management /

Mode of access: Internet.

Assessment of the application of automatic vehicle identification technology to traffic management : Appendix B, Evaluation of potential applications of automatic vehicle identification to traffic management /

Mode of access: Internet.

Test and evaluation of the AMF, Inc. and Fairchild Industries experimental safety vehicles. Volume II. ESV program summary. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

The extent of use of Social Security numbers.

"OAI-06-88-00800."

Do perceptions mirror reality? Student perceptions of learning versus grade outcomes

This research addresses whether educators should consider measuring if students have learned what was intended, as recommended by education researchers. Students in an Introductory Marketing subject were asked to complete a voluntary survey rating their own progress on the intended learning outcomes for the course. One hundred and one surveys were completed by students in the second-last teaching week of the semester. Student identification numbers were used to link student perceptions with their grade outcomes. Regression analysis was used to ascertain whether student perceptions of their progress on the intended learning outcomes for the course could be used to predict their grades. While the results were significant, student perceptions of their progress on learning outcomes were a poor predictor of grade outcomes. The results of this study suggest that student perceptions may not mirror the reality. These results are somewhat surprising and future research examining the degree of change in the learning outcomes perceived by students is warranted. This will further contribute to decisions surrounding whether educators should measure if students have learned what was intended.

Benefits and issues for bus travel time estimation and prediction

Bus travel time estimation and prediction are two important modelling approaches which could facilitate transit users in using and transit providers in managing the public transport network. Bus travel time estimation could assist transit operators in understanding and improving the reliability of their systems and attracting more public transport users. On the other hand, bus travel time prediction is an important component of a traveller information system which could reduce the anxiety and stress for the travellers. This paper provides an insight into the characteristic of bus in traffic and the factors that influence bus travel time. A critical overview of the state-of-the-art in bus travel time estimation and prediction is provided and the needs for research in this important area are highlighted. The possibility of using Vehicle Identification Data (VID) for studying the relationship between bus and cars travel time is also explored.

Retrieving dynamic origin-destination matrices from Bluetooth data

The Bluetooth technology is being increasingly used, among the Automated Vehicle Identification Systems, to retrieve important information about urban networks. Because the movement of Bluetooth-equipped vehicles can be monitored, throughout the network of Bluetooth sensors, this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. Some of the main challenges inherent to Bluetooth data are, first, that Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be estimated. Second, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold: to give an overview of the issues inherent to the Bluetooth technology, through the analysis of the data available from the Bluetooth sensors in Brisbane; and to propose a method for retrieving the itineraries of the individual Bluetooth vehicles. We argue that estimating these latent itineraries, accurately, is a crucial step toward the retrieval of accurate dynamic Origin Destination Matrices.