Resilient modulus $\left(M_R\right)$ value of a subgrade soil is the primary property needed for pavement design and analysis. It is the elastic modulus based on the recoverable strain under repeated loads, and depends on several factors including soil properties, soil type, and state of stresses. This paper presents prediction equations developed using regression analysis for six AASHTO soil types (A-1-b, A-3, A-2-4, A-4, A-6, and A-7-6) for estimating $M_R$ . Data extracted from the Long-Term Pavement Performance Information Management System (LTPP IMS) database for 258 test specimens (approximately 3,870 $M_R$ values) collected in 19 states in New England and the nearby regions in the United States and two provinces in Canada were used in this study. A generalized constitutive model that captures the effect of both bulk stress and octahedral shear stress was used to predict the $M_R$ of subgrade soils by developing regression equations that relate the $k$ coefficients to the soil physical properties. The prediction models developed were verified for four types of soils collected in the New England region by independent laboratory $M_R$ tests. It was observed that $M_R$ values from the prediction models compared well with the laboratory values for the majority of the soil types.

Video detection has become an increasingly popular technology for vehicle detection at signalized intersections. Among the potential disadvantages of this technology is the tendency of video detectors to activate early at night due to headlight reflection on the pavement. This early activation results in a dramatic increase in the length of the effective vehicle detection zone. This observed variation in the effective length of the vehicle detection zone that varies by ambient lighting condition and camera placement presents a very serious impediment for traffic engineers to design vehicle extension intervals that operate consistently during day, night, and transition periods. Further, the stochastic variation in the length of the vehicle detection zone length has the potential to create driver expectancy issues. Tables are included that report the observed average and range of detection zone length variations for 16 observed video cameras that were extensively calibrated by the manufacturer at the test site. The paper concludes by recommending near-side placement of video detection devices to reduce the stochastic variation in detection zone length.

Future deployments of transit signal priority (TSP) in the United States depend largely on improving TSP strategies to better accommodate transit vehicles while at the same time minimizing the negative impacts on the vehicles of the nonprioritized approaches. Advanced vehicle location (AVL) technology holds great potential in this regard. This paper develops a methodology that incorporates the predicted bus arrival time information into an AVL based TSP system to improve its performance. It is demonstrated analytically that the time to trigger the traffic signal for priority operation, especially Early Green, is of particular importance to both transit and passenger vehicles. A theoretical model is developed to identify the optimal time to place a priority call based on the predicted bus arrival time information. A simulation analysis is conducted to verify the theoretical approach and further identify the optimal call-time points for general cases. The research is focused on operation of TSP under moderately congested and congested traffic conditions wherein the concern about the adverse impact of TSP exists. It shows that in general, starting the priority operation when the bus is about $20–30\mathrm{s}$ away from the intersection produces good results for both bus and general traffic. The findings of the research can be easily integrated into an AVL based TSP system and may potentially enhance the performance of such a system.

High-speed intersections pose a safety and operational challenge. Currently, no specific guidance for treating high-speed intersections exists, particularly in the case of intersections with substantial truck traffic. To fill in this gap, this paper presents a synthesis of findings from a literature review and a survey of practitioners. Results indicate that the primary safety concerns at high-speed intersections relate to red-light running and rear-end collisions. Further, operational and safety concerns at high-speed intersections were identified as being related to the different characteristics of trucks (e.g., accelerating/decelerating capabilities). The survey indicates no consensus among practitioners on how best to improve operations and safety at high-speed signalized intersections. Specific concerns over the liability and system/equipment reliability issues were raised. This note summarizes the findings from the literature review and survey and offers recommendations to increase awareness and acceptance for high-speed intersection treatments.

We propose an intelligent tool for terrestrial transportation design based on the underlying concept of adaptive meshing. Vehicle routes are deformable visual patterns which cover the geographic area adapting shapes to distributed demands or specific places. In this way the system allows dynamic interaction and continuous visual feedback for the designer constructing and evaluating a transportation bus network. To assist the designer, an optimization framework merging neural networks and evolutionary algorithms permits one to position services and create routes among them automatically. The transport system optimizer built on a geographic information system is illustrated by application to a real life case of clustering and routing for the transportation of the 780 employees of an enterprise.

The “single file” and “side-by-side” travel characteristics of bicyclists and pedestrians should be considered for the determination of stopping sight distances, horizontal sightline offsets, and minimum lengths of vertical curves for bike paths. This paper describes these unique travel behaviors and classifies them into two cases (Case I and Case II) for geometric design. It presents a set of new design equations and charts that consider these two design cases and introduces design methods to supplement current AASHTO standards and guidelines. The paper then provides a practical procedure for using the new charts and equations for design of bike paths. It is recommended that the new analytical means of evaluating the effect of side-by-side riding on geometric design of bike paths should be included in the revision of the AASHTO Bike Guide for the Development of Bicycle Facilities.