Pavement improvement and capacity expansion traditionally fall in two different decision-making processes. Pavement improvement decisions are typically made at the maintenance level and focus on maintaining, rehabilitating, and reconstructing the existing pavements with respect to the physical conditions, such as poor riding quality, severe cracking, or rutting. In contrast, capacity expansion decisions are normally made at the planning level in regard to the operational conditions, such as levels of service, travel speeds, or delays. The recently adopted asset management approach calls for integrated decision-making that balances both types of decisions. In this context, this paper introduces a nested Markov decision process (NMDP) framework that can be used to obtain the optimal policy for joint pavement improvement and capacity expansion decisions. The applicability of the proposed NMDP framework is demonstrated through a numerical example showing how a special capacity expansion decision, road widening, can be integrated with conventional pavement improvement decisions for upgrading roadway facilities.

The conventional nonpermeable asphalt pavement interface significantly modifies relationships between rainfall and runoff while also altering the coupled transport of particulate matter (PM), heat, and solutes. This study examines an original drainage design method for commonly utilized porous friction courses (PFC) promoting roadway system drainage, improved skid control, and in situ passive control of PM through infiltration-exfiltration of surface water by a pavement system PFC. This study presents a generalized mathematical model on the basis of physical modeling, which is utilized in the proposed design method. Specifically, for the geometric characteristics of a roadway, the rainfall intensity, and the mix design porosity characteristics, the method is utilized to evaluate the hydraulic response and thickness of the PFC to minimize ephemeral surface water films on the pavement. The method proves to be sufficiently robust to design the required spacing of pavement subsurface drains to minimize surface water films. Model results are synthesized into design nomographs to facilitate design solutions. The study illustrates applications of the design model and method for PFC construction. As with any drainage design model and method, field validation of the hydraulic response, for example through field permeameter testing, is required.

Rubblization is considered one of the sustainable surface preparation techniques before placing a hot mix asphalt (HMA) overlay that involves breaking the concrete pavement into pieces. The design of the structural overlay thickness for rubblized projects is difficult as the resulting structure is neither a true rigid pavement nor a true flexible pavement. The most important aspect of the design procedure is to characterize the rubblized concrete layer in terms of a layer coefficient and modulus for the empirical and the mechanistic-empirical (M-E) pavement design methods. This paper describes a field study undertaken to investigate the performance of rubblized pavements in Iowa, which includes characterization of the rubblized Portland cement concrete (PCC) layer (layer coefficient and modulus) using a neural networks-based layer moduli backcalculation program. The average rubblized PCC layer coefficient and backcalculated modulus of the rubblized layer in Iowa were found to be 0.19 and 539 MPa (78 ksi), respectively. The M-E design program developed in-house seems to estimate the HMA overlay thickness reasonably well to achieve long-lasting performance of HMA-overlaid rubblized PCC pavements.

The Rolling Wheel Deflectometer (RWD) offers the benefit to measure pavement deflection without causing any traffic interruption or compromising safety along the tested road segments. This paper presents a detailed field evaluation of the RWD system in Louisiana, in which 16 sites representing a wide array of pavement conditions were tested. Measurements were used to assess the repeatability of RWD measurements, the effect of truck speeds, and the relationship between RWD and (FWD) deflection measurements and pavement conditions. On the basis of the results of the experimental program, it was determined that the repeatability of RWD measurements was acceptable, with an average coefficient of variation at all test speeds of 15%. In addition, the influence of the testing speed on the measured deflections was minimal. The scattering and uniformity of the FWD and RWD data appear to follow closely the conditions of the roadway. Both test methods appear to properly reflect pavement conditions and structural integrity of the road network by providing for a greater average deflection and scattering for sites in poor conditions. The RWD deflection measurements were in general agreement with the FWD deflections measurements; however, the mean center deflections from RWD and FWD were statistically different for 15 of the 16 sites.

Rutting is a major form of pavement distress in asphalt pavements. The main concern with rutting has been related to driving safety. Many highway agencies and researchers suggested that pavement rutting could lead to vehicle hydroplaning and loss of skid resistance in wet weather. However, to date no theoretical basis has been established for an analytical assessment of the severity of rutting for the purpose of pavement maintenance and rehabilitation. Most highway agencies classify rut severity on the basis of engineering judgment or field experience. This paper presents an analytical procedure to assess the severity of rutting based on vehicle skidding and hydroplaning analysis. It considers the worst-case scenario where a rut is filled with water and analyzes (1) if a car will hydroplane at a given speed; and (2) the length of braking distance required for the car traveling at the given speed. A finite-element simulation model is adopted to perform the analysis. For a given rut depth filled with water, the computer model computes the hydroplaning speed for a typical passenger car, and the required braking distance for the car traveling at a known speed. It was found that depending on the rut depth and the surface frictional property of a pavement, the severity classification of a rut may be governed by either hydroplaning risk or safety requirement of braking distance. The traditional method of using the same set of critical rut depths for all pavement sections in a road network is not ideal for effective handling of rutting maintenance.

A thin concrete overlay bonded to asphalt pavement design approach has been developed by extending an existing ultrathin whitetopping (UTW) design method. A significant contribution to this proposed design method is the incorporation of fiber reinforcement into the structural design and concrete material specification, through the implementation of a residual strength ratio requirement. Climate and traffic inputs were simplified through the use of an equivalent temperature gradient and equivalent single-axle loads (ESALs). A probabilistic concrete fatigue algorithm was also used to allow engineers to select the appropriate level of reliability and the amount of cracking for the UTW project. The new concrete overlay design method is most influenced by the concrete flexural strength, fiber-reinforcement residual strength, slab size, traffic, and the underlying asphalt condition and thickness.

The accurate prediction of transit travel times has a range of applications to benefit operators and passengers. Transit travel time is affected by several factors such as traffic flow and passenger demand, which have to be considered to make accurate predictions. However, previous studies have not considered real world traffic flow variables in their prediction models. This paper develops artificial neural network (ANN) models to predict bus travel time on the basis of a range of input variables including traffic flow data collected from a bus route in Melbourne, Australia. To overcome the drawback of ANNs in determining the effect of each input variable on the independent variable, the paper adopts a regression analysis to determine the important input variables for prediction. The paper examines the value that traffic flow data would make to the prediction accuracy. To this end, two alternative models are developed and the results are compared with those obtained from the traffic flow data–based models. A historical data–based ANN in which temporal variables are substituted with the traffic flow variable and a timetable-based model that traditionally utilizes scheduled travel times are developed. Although the use of scheduled travel times results in the poorest prediction performance, incorporating traffic flow data yields minor improvements in prediction accuracy compared with when temporal variables are used.

In December 2009, ramp meters along US 95 in Las Vegas between Rancho Drive and Craig Road were activated. A study was performed to quantify the benefits of ramp meters on the freeway mainline by using archived ITS data. Traditional performance measures such as average travel speed, speed standard deviation, interquartile speed range, travel time index (TTI), and buffer index (BI) were employed to conduct the evaluation. In addition, two new performance measures—delay volume (DV) and average vehicle delay (AVD)—developed by the Freeway and Arterial System of Transportation (FAST) Division of the Regional Transportation Commission (RTC) of Southern Nevada were used to enhance the evaluation. Specifically, DV quantifies the congestion with temporal, spatial, and congestion intensity values, and AVD measures the average delay per vehicle with traffic volume as a weighting factor. Comparing both the freeway and ramp performance with and without ramp meters clearly demonstrates that ramp meters are very effective in mitigating the recurring heavy congestion for southbound US 95 during weekday peak periods. The improvement is comparatively little on the more lightly congested northbound section of US 95. The evaluation results also demonstrate that the DV and AVD metrics are useful in evaluating operational strategies such as ramp metering. Finally, informative results are achieved regarding the influence of ramp meters on the bottleneck, and the comparative effects metering has on the general purpose and high occupancy vehicle (HOV) lanes.

Predictions of fundamental traffic variables in the short-term or near-term future are vital for any successful dynamic traffic management application. Univariate short-term traffic flow prediction algorithms are popular in literature. However, to facilitate the operationalities of advanced adaptive traffic management systems, there is a necessity of developing multivariate traffic condition prediction algorithms. A new multivariate short-term traffic flow and speed prediction methodology is proposed in this paper where the traffic flow and speed observations from uncongested (or linear) and congested (or nonlinear) regimes are regime-adjusted to ensure consistent system dynamics. The prediction methodology is developed by using artificial neural networks (ANN) algorithms in conjunction with adaptive learning rules. These learning rules demonstrate significantly improved accuracy and simultaneous reduction in computation times. Additionally, the paper attempts to identify the most suitable adaptive learning rule from a chosen pool of rules. The validation of the prediction methodology is performed by using traffic data from multiple locations in the United Kingdom (U.K.). The results indicate that the proposed multivariate forecasting algorithm is effective and computationally parsimonious to simultaneously predict traffic flow and speed in freeway or highway networks.

Signalized intersections are provided in traffic networks to improve the safety and efficiency of vehicular and pedestrian movement. Various measures under education, enforcement, and engineering headings being attempted to improve the safety and efficiency of operations at signalized intersections. Provision of signal countdown timer, a timer showing the remaining red and green time in a phase, is one such measure and is commonly adopted in India. However, studies on the effects of a countdown timer under Indian traffic conditions are very scarce. Traffic heterogeneity and lack of lane discipline make the transferability of models developed in other countries (with more organized traffic) infeasible. The present study is an attempt to analyze the changes in queue-discharge characteristics and red-light violations (RLVs) under Indian traffic conditions due to the presence of a timer. A before-and-after analysis was carried out using the data collected from a selected intersection in Chennai, India. The analysis was carried out for different vehicle types in the presence and absence of timers separately for the start and end of red/green. Results showed that the information provided at the start of green (end of red) enhances efficiency, the start-up lost time is reduced, and there is an increase in RLVs. Two-wheelers present at the start of the queue are found to be the category that is mostly affected by this information. However, the information provided at the end of green (start of red) was found to reduce the incidence of RLVs. In the presence of information, it was found that the propensity of RLV (proportion of cycles having RLV) decreased from 59 to 31% at the end of green (start of red) and increased from 12 to 75% at the start of green (end of red) with a statistically significant drop in the headways (indicating an increased efficiency). Also, in the presence of information, the intensity of RLVs (mean RLVs per RLV cycle) for both the start of red and end of red decreased from 3.32 to 2.30 vehicles and 8.52 to 5.60 vehicles, respectively. The impacts varied based on vehicle type with major impacts on two-wheelers. The queue-discharge models show a significant change in trend, implying a need to update the signal timings when timers are installed. These results also bring to light the trade-off between safety and efficiency and the choices drivers make in the presence of phase-change information. These trade-offs should be carefully considered as the technology advances and drivers are provided with more and more information. For example, with the advent of intellidrive technology (vehicle-to-infrastructure communications), the extent of information provided to drivers should be tailored to achieve system optimality. The results of studies such as this one can help in decision making.

In urban areas, conventional transit priority systems (e.g., passive priority) present some problems, such as causing too many traffic delays and degradation of on line performance due to the lack of real-time data. The purpose of this study is to develop an on line adaptive priority strategy to optimize traffic operation at a series of consecutive grade crossings in urban areas. The proposed strategy takes into account the impact on cross-street traffic as well. A mathematical model of non linear programming with linear constraints is formulated to minimize overall passenger delays at grade crossings for light rail transit and to minimize the impact on cross-street traffic. The optimization is enabled by real-time arrival-time prediction with GPS-equipped vehicles. To exhibit the anticipatory performance of the proposed strategy, both a numerical analysis and a simulation test are conducted with trolley operation along a stretch of C Street in San Diego. Compared with current signal timings, those obtained from the proposed strategy demonstrate promising results.

Airport terminal facilities continue to increase in size due to the trend of increasing demand at our nation’s regional and international airports. Airport operators have introduced various passenger conveyances including moving walkways, escalators, and elevators to reduce passenger walking distance and provide an acceptable level of service. This research is based on a data collection effort in various airports and depicts an empirical study on the use of passenger conveyance systems in airport terminals. A logistic regression approach was applied to estimate a passenger’s mode choice probability and to examine the relationship between passenger characteristics and their choice of automated conveyance systems in airport terminals.