Video enforcement for HOV lanes: field test results for the I-30 HOV lane in Dallas.

Texas Department of Transportation, Austin

Evaluation of freeway high occupancy vehicle lanes and ramp metering. Volume I. Final report.

Transportation Department, Office of the Assistant Secretary for Policy and International Affairs, Washington, D.C.

Evaluation of freeway high occupancy vehicle lanes and ramp metering. Volume II: bibliography and abstracts.

Transportation Department, Office of the Assistant Secretary for Policy and International Affairs, Washington, D.C.

Passing lanes and other operational improvements on two-lane highway. Phase I - technical report.

Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, Washington, D.C.

Geometric design considerations for separate truck lanes. Interim report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Operational and geometric evaluation of exclusive truck lanes. Final report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Guidelines for estimating the cost effectiveness of high-occupancy vehicle lanes. Interim report.

Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin

Optimal detector locations for HOV lane operations.

Texas Department of Transportation, Austin

The effect of motorcycle travel on the safety and operations of HOV facilities in Virginia. Final report.

Federal Highway Administration, Washington, D.C.

High occupancy vehicle treatments, impacts and parameters. Volume I - procedures and conclusions. Final report.

Mode of access: Internet.

High occupancy vehicle treatments, impacts and parameters. Volume II - bibliography and data. Final report.

Mode of access: Internet.

Truck accident countermeasures on urban freeways. Final report.

Federal Highway Administration, Office of Safety and Traffic Operations Research and Development, McLean, Va.

Extraction of road lanes from high-resolution stereo aerial imagery based on maximum likelihood segmentation and texture enhancement

Accurate road lane information is crucial for advanced vehicle navigation and safety applications. With the increasing of very high resolution (VHR) imagery of astonishing quality provided by digital airborne sources, it will greatly facilitate the data acquisition and also significantly reduce the cost of data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lanes from aerial images with employment of the image analysis procedures. This algorithm starts with constructing the (Digital Surface Model) DSM and true orthophotos from the stereo images. Next, a maximum likelihood clustering algorithm is used to separate road from other ground objects. After the detection of road surface, the road traffic and lane lines are further detected using texture enhancement and morphological operations. Finally, the generated road network is evaluated to test the performance of the proposed approach, in which the datasets provided by Queensland department of Main Roads are used. The experiment result proves the effectiveness of our approach.

Changing lanes : an exploration of the journey from dance through choreography to directing and the spoken word

This paper reveals a journey of theatrical exploration. It is a journey of enquiry and investigation backed by a vigorous, direct and dense professional history of creative work.

The significance of endogeneity problems in crash models: An examination of left-turn lanes in intersection crash models

Crash prediction models are used for a variety of purposes including forecasting the expected future performance of various transportation system segments with similar traits. The influence of intersection features on safety have been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes compared to other segments in the transportation system. The effects of left-turn lanes at intersections in particular have seen mixed results in the literature. Some researchers have found that left-turn lanes are beneficial to safety while others have reported detrimental effects on safety. This inconsistency is not surprising given that the installation of left-turn lanes is often endogenous, that is, influenced by crash counts and/or traffic volumes. Endogeneity creates problems in econometric and statistical models and is likely to account for the inconsistencies reported in the literature. This paper reports on a limited-information maximum likelihood (LIML) estimation approach to compensate for endogeneity between left-turn lane presence and angle crashes. The effects of endogeneity are mitigated using the approach, revealing the unbiased effect of left-turn lanes on crash frequency for a dataset of Georgia intersections. The research shows that without accounting for endogeneity, left-turn lanes ‘appear’ to contribute to crashes; however, when endogeneity is accounted for in the model, left-turn lanes reduce angle crash frequencies as expected by engineering judgment. Other endogenous variables may lurk in crash models as well, suggesting that the method may be used to correct simultaneity problems with other variables and in other transportation modeling contexts.