This paper provides formulations of traffic operational capacity in mixed traffic, consisting of automated vehicles (AVs) and regular vehicles, when traffic is in equilibrium. The capacity formulations take into account (1) AV penetration rate, (2) micro/mesoscopic characteristics of regular and automated vehicles (e.g., platoon size, spacing characteristics), and (3) different lane policies to accommodate AVs such as exclusive AV and/or RV lanes and mixed-use lanes. A general formulation is developed to determine the valid domains of different lane policies and more generally, AV distributions across lanes with respect to demand, as well as optimal solutions to accommodate AVs.
The use of autonomous vehicles is attracting more and more attention as a promising approach to improving both highway safety and efficiency. Most previous studies on autonomous intersection management relied heavily on custom-built simulation tools to implement and evaluate their control algorithms, but the use of nonstandard simulation platforms makes the comparison of systems almost impossible. Furthermore, without support from standard simulation platforms, reliable and trustworthy simulation results are hard to obtain. In this context, this paper explores a way to model autonomous intersections through the use of VISSIM, a standard microscopic simulation platform. A reservation-based intersection control system named autonomous control of urban traffic (ACUTA) was introduced and implemented in VISSIM through the use of VISSIM's external driver model. The operational and safety performance characteristics of ACUTA were evaluated with VISSIM's easy-to-use evaluation tools. In comparison with the results obtained with optimized signalized control, significantly reduced delays, along with a higher intersection capacity and lower volume-to-capacity ratios under various traffic demand conditions, resulted from the use of ACUTA. The safety performance of ACUTA was evaluated by use of the surrogate safety measure model, and few conflicts between vehicles within the intersection were detected. Moreover, the key steps and elements for implementation of ACUTA in VISSIM were introduced. These steps and elements can be useful for other researchers and practitioners implementing their autonomous intersection control algorithms in a standard simulation platform. By use of a standard simulation platform, the performance characteristics of autonomous intersection control algorithms can eventually be compared.
Roadway horizontal alignment has long been recognized as one of the most significant contributing factors to lane departure crashes. Knowledge of the location and geometric information of horizontal curves can greatly facilitate the development of appropriate countermeasures. When curve information is unavailable, obtaining curve data in a cost-effective way is of great interest to practitioners and researchers. To date, many approaches have been developed to extract curve information from commercial satellite imagery, Global Positioning System survey data, laser-scanning data, and AutoCAD digital maps. As geographic information system (GIS) roadway maps become more accessible and more widely used, they become another cost-effective source for extraction of curve data. This paper presents a fully automated method for the extraction of horizontal curve data from GIS roadway maps. A specific curve data–extraction algorithm was developed and implemented as a customized add-in tool in ArcMap. With this tool, horizontal curves could be automatically identified from GIS roadway maps. The length, radius, and central angle of the curves were also computed automatically. The only input parameter of the proposed algorithm was calibrated to have the least curve identification errors. Finally, algorithm validation was conducted through a comparison of the algorithm-extracted curve data with the ground truth curve data for 76 curves that were obtained from Bing aerial maps. The validation results indicated that the proposed algorithm was very effective and that it identified completely 96.7% of curves and computed accurately their geometric information.
The effects of automated speed photo–radar enforcement (SPE) and traditional speed reduction treatments (speed feedback trailer, presence of police vehicles with emergency lights on and off, and combinations of the speed feedback trailer and police presence) on speed were studied at a location 1.5 mi downstream of the actual treatment (spatial effects). Three data sets from two Interstate highway work zones were used. Field data consistently showed significant spatial (downstream) effects for SPE. The combination of speed feedback trailer and police vehicle with emergency lights off had downstream effects in some cases but to a lesser degree than SPE. Other treatments showed no significant downstream effects. For free-flowing traffic, SPE reduced the average downstream speed by 2 to 3.8 mph for cars and by 0.8 to 5.3 mph for trucks. Also, SPE reduced speeding cars by 7.1% to 23.4% (except for cars in median in Data Set 1), and speeding trucks by 4.2% to 48.3% (except for trucks in shoulder in Data Set 3). For the general traffic stream, SPE reduced the average downstream speed by 1.1 to 2.9 mph on cars and by 0.9 to 3.3 mph on trucks. When SPE was used, the percentage of speeding cars and trucks in the general traffic stream was reduced by 2.9% to 28.6%, and by 7.5% to 36.1%, respectively. SPE also reduced the percentage of cars in the general traffic stream exceeding the speed limit by more than 10 mph in virtually all cases, and eliminated such trucks in all but one case.
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