This paper describes the development of a systematic methodology for identifying and ranking bottlenecks using probe data collected by commercial global positioning system fleet management devices mounted on trucks. These data are processed in a geographic information system and assigned to a roadway network to provide performance measures for individual segments. The authors hypothesized that truck speed distributions on these segments can be represented by either a unimodal or bimodal probability density function and proposed a new reliability measure for evaluating roadway performance. Travel performance was classified into three categories: unreliable, reliably fast, and reliably slow. A mixture of two Gaussian distributions was identified as the best fit for the overall distribution of truck speed data. Roadway bottlenecks were ranked on the basis of both the reliability and congestion measurements. The method was used to evaluate the performance of Washington state roadway segments, and proved efficient at identifying and ranking truck bottlenecks.

Analytical macroscopic models are presented to quantify delay at signalized intersections for extended control periods (multiple cycles) attributable to downstream time-variable congestion. Each of the models consists of an upstream demand estimation algorithm, a downstream queue build-up prediction algorithm, a shockwave propagation and dissipation tracking algorithm, and an algorithm that carries traffic aftereffect of a cycle’s control into the following cycle. Model inputs are basic traffic flow properties, signal control parameters, and link geometry. The models are modular and can be incorporated into any size system for one or multiple cycles. The models were tested on a hypothetical system of closely spaced intersections and under different traffic flow, control, and geometric conditions. Results show that delay induced by downstream traffic operations on an upstream intersection can be significant and that it may change with time and reach steadiness once traffic flow, downstream queues, and signal control measures stabilize and start replicating over time. Green ratios of neighboring intersections exert a significant influence on delay. For certain green ratios and geometric combinations, offsets can exert appreciable influence as well. The delay values and their temporal variation compared well with those from a microscopic simulation model.

A cost estimating model is developed for light rail transit (LRT) and bus rapid transit (BRT) systems to better aid transportation planners and decision makers in the selection process. In evaluating transit systems, cost estimating has always been a major consideration. Without an applicable cost estimating model or methodology, the decision on the choice between LRT and BRT would cause significant controversy and difficulty in the early corridor planning stage. The model developed in this paper can be applied to estimate costs and compare the LRT and BRT systems that operate on various right-of-way categories, alignment configurations, and different given transit demand volumes. An example and comparative analyses illustrate how the model can be applied in real-world situations.

This study presents an approach to jointly optimize temporal service headway and differential fare for an intercity transit system considering heterogeneous demand elasticity. The research optimization problem is combinatorial and difficult to solve analytically. A genetic algorithm is developed to search for the optimal solution, including temporal headway and differential fare, which maximizes the daily profit considering service capacity sufficiency and operable fleet size. A real world case study, the Taiwan High Speed Rail Line, is applied to demonstrate the applicability of the developed model and the efficiency of the solution algorithm to search for the optimal solution that yields the maximum profit operation. Results from the sensitivity analyses indicate that the optimized number of partitions of travel distance and headway decreases as the demand increases, which results in the increase of profit. The developed model can be applied to evaluate and plan an intercity rail system.

Non-recurrent traffic congestion due to incidents, highway construction zones, and special events has contributed up to 60% of the total freeway corridor delay in the United States. Under most scenarios, if proper diversion plans can be implemented in time, motorists can circumvent the congested segments and best use the available corridor capacity. Nevertheless, before the implementation of any detour strategy, traffic managers must ensure the resulting benefits, because implementing those advanced control strategies usually demand substantial amount of resources and manpower. This study, proposed in response to such needs, presents a detour decision methodology for urban freeway non-recurrent congestion management, based on the results of extensive simulation experiments and operational guidelines from highway agencies. The proposed model offers a reliable and effective tool for responsible traffic management personnel to make consistent detour decisions in response to a detected incident. Numerical results clearly indicate that a timely and well-justified detour operation can yield substantial benefits to both the driving population and the entire community.

This paper examines participants who are blind, with and without normal hearing, regarding detection of forward approaching and backing vehicles operating in electric mode (identified in this paper as quiet vehicles) under low speed conditions. Testing under low ambient sound conditions involved evaluation of internal combustion engine vehicles, hybrid vehicles operating in electric mode (EM), and the same hybrid vehicles operating in EM but with five different artificially generated sounds. Three of the five artificial sounds improved detection relative to the internal combustion engine condition for both forward and backward detection tasks. Regression analysis indicated that significant predictors of forward detection performance include average wind speed, amplitude modulation of the signal, hearing loss in the 500 Hz range, vehicle velocity, minimum ambient sound level, and overall vehicle sound level in units of A-weighted decibels. The corresponding analysis for backward detection indicated significant predictors, including a hearing loss in the 4 or 500 Hz ranges, vehicle velocity, minimum ambient sound level, vehicle sound level at the 250 Hz octave band, and the overall vehicle sound level in A-weighted decibels.

Bicycle passing maneuvers represent interferences between bicycle travelers and are important operational attributes of bicycle traffic. The number of bicycle passing maneuvers has been used to evaluate the level of service (LOS) of off-street bicycle facilities. The primary objectives of this paper are to propose a method to model bicycle passing maneuvers on multilane bicycle paths with heavy bicycle traffic and explore the characteristics of those passes. The authors classified bicycle passing maneuvers into free, adjacent, and delayed passes according to the lateral distance between bicyclists during the passing. Models were developed to estimate the number of each type of passing maneuver on unidirectional two-, three- and four-lane bicycle paths. The authors used field observations of bicycle traffic on bicycle paths in Nanjing, China to calibrate and validate these models. The model predictions on bicycle passing maneuvers were consistent with the observations. The model sensitivity analyses showed that all passing maneuvers increase as bicycle flow rate increases. The faster a bicycle runs, the more passes the rider encounters. All types of passing maneuvers linearly increase as the standard deviation of bicycle speeds increases. On wider bicycle paths, the probability of free passes remarkably increases, whereas the probabilities of adjacent and delayed passes significantly decrease.

Given the increasing cost of designing and building new highway pavements, reliability analysis has become vital to ensure that a given pavement performs as expected in the field. Recognizing the importance of failure analysis to safety, reliability, performance, and economy, back analysis has been employed in various engineering applications to evaluate the inherent uncertainties of the design and analysis. The probabilistic back analysis method formulated on Bayes’ theorem and solved using the Markov chain Monte Carlo simulation method with a Metropolis-Hastings algorithm has proved to be highly efficient to address this issue. It is also quite flexible and is applicable to any type of prior information. In this paper, this method has been used to back-analyze the parameters that influence the pavement life and to consider the uncertainty of the mechanistic-empirical pavement design model. The load-induced pavement structural responses (e.g., stresses, strains, and deflections) used to predict the pavement life are estimated using the response surface methodology model developed based on the results of linear elastic analysis. The failure criteria adopted for the analysis were based on the factor of safety (FOS), and the study was carried out for different sample sizes and jumping distributions to estimate the most robust posterior statistics. From the posterior statistics of the case considered, it was observed that after approximately 150 million standard axle load repetitions, the mean values of the pavement properties decrease as expected, with a significant decrease in the values of the elastic moduli of the expected layers. An analysis of the posterior statistics indicated that the parameters that contribute significantly to the pavement failure were the moduli of the base and surface layer, which is consistent with the findings from other studies. After the back analysis, the base modulus parameters show a significant decrease of 15.8% and the surface layer modulus a decrease of 3.12% in the mean value. The usefulness of the back analysis methodology is further highlighted by estimating the design parameters for specified values of the factor of safety. The analysis revealed that for the pavement section considered, a reliability of 89% and 94% can be achieved by adopting FOS values of 1.5 and 2, respectively. The methodology proposed can therefore be effectively used to identify the parameters that are critical to pavement failure in the design of pavements for specified levels of reliability.

This paper discusses the impacts of heavier and longer combination vehicles (LCVs) on the pavements of five major Texas corridors. These corridors were divided into 152 segments presenting uniform pavement properties and truck volumes. For each segment, axle load spectra were estimated for the baseline (existing) scenario and an LCV scenario was developed in concert with industry stakeholders and transportation officials. Axle weight data came from 10 weigh-in-motion stations. Pavement lives under both scenarios were estimated using elastic theory and classical fatigue equations. Non-significant impacts happened when both lives were 30 years or longer, or when the difference between lives was under one year. The LCV scenario had no impact on rigid pavements, decreased the life of some flexible pavements, and was less detrimental than the baseline in segments carrying overweight axles. Annualized overlay cost differences were calculated for all segments with significant LCV impacts. Total cost decreased. This paper’s primary contribution is a viable methodology to verify whether specific increases in vehicle axle or gross or weights have undue impacts on pavement costs.

This paper constructs a train routing model combined with a train scheduling problem, which is a 0–1 mixed-integer nonlinear programming problem. Except for train route choice, the model considers a system of complicated constraints on headway, trip time, meeting-crossing and overtaking between trains, capacity of siding, and so on. Based on the delay information of each train, a route adjustment algorithm is designed to obtain satisfactory route schemes of trains. Moreover, a tabu search procedure is designed to further improve the route schemes. The simulation results show that, relative to the optimal solution, the solutions obtained by the current method exhibit small relative error. The tabu search algorithm exhibits unstable performance because of dependence on the initial solution. Combined with the route adjust algorithm, the tabu search technique can improve the quality and stability of solutions. In addition, the departure order of heterogeneous trains exerts important influences on train route choice.

System-level infrastructure management involves determining optimal maintenance, rehabilitation, and reconstruction activities for a system of facilities over a planning horizon to maximize the level of service subject to system constraints. In railway networks, different types of facilities (such as tracks, and bridges) comprise the system, and maintenance of one facility may affect the maintenance of other facilities, both economically and functionally. Therefore, when developing an asset management system for railway networks, it is essential to take into account heterogeneity and interdependency in the network. In this paper, a two-stage, bottom-up methodology is formulated to capture these interdependencies and heterogeneities in railroad infrastructure management. Numerical experiments are presented to examine the effects of economic and functional interdependencies on network optimization and to demonstrate the validity of the proposed model. The results show that accounting for heterogeneity and interdependencies at the system level can greatly influence the choice of activities and the estimated life-cycle cost.

Directional midblock median openings with left-turn lanes and loons were installed on major highways in Tehran, Iran, to prohibit direct left turns from major and minor roadways and to prevent through movements of minor roadways. Therefore, traffic lights were removed and intersections were converted to U-turns. The safety effect of this treatment was estimated by an empirical Bayesian method that indicated there was an increase of approximately 13.22% in crashes. Moreover, accident prediction models for crash types and frequency were developed to examine the geometry and traffic factors contributing to the crashes at U-turn conversions and to explain the reason some sites showed a decrease in the number of crashes while others showed a large increase. The results of data analysis showed that the increase in weaving length and median opening length resulted in the decrease in angle crashes. Also, the increase in the turn radius resulted in the decrease in angle and total crashes. Total crashes and crash types increased with the average daily traffic (ADT) increase on major roadways and U-turns. Consequently, the result of safety analysis with close examination of each site demonstrated that it is necessary to mention geometric specifications in order to improve the efficiency of the treatment. Therefore, it can be concluded with no reasonable doubt that with a certain range of traffic volume and right geometric specification, the treatment may be safe.

A pavement temperature profile is indispensable for analyzing and interpreting falling weight deflectometer (FWD) testing data. This paper presents a simple analytical approach to rapidly predicting the transient temperature profile within the asphalt layer of a flexible pavement on the basis of measured surface temperatures during FWD testing. The proposed analytical approach consists of the classical solution of a one-dimensional heat equation in a homogeneous half-space subjected to specified surface temperatures and a Gaussian-quadrature numerical scheme to approximate the classical solution. Model validation was conducted using a large temperature database. Comparisons between predicted and measured pavement subsurface temperatures suggest that the proposed simplified analytical method generated a reasonable asphalt temperature profile during the short period of FWD testing. The main advantage of the proposed method is that it can be easily applied to assist field engineers in rapidly estimating the temperature profile within the asphalt layer of a multilayered flexible pavement based on measured pavement surface temperatures during FWD testing.