Reducing the cost of operations is one of the most important considerations for urban transport systems. Deadhead kilometer costs are an important aspect of operating costs and are associated with buses traveling empty to and from depots; minimizing these costs is referred to as the bus-to-depot allocation problem and addressing it may be important in reducing overall costs. We present a model and an associated decision support system (DSS) for optimally allocating buses to depots while minimizing deadhead costs and keeping depot occupancy at “ideal” operational levels. The model and DSS are tested in the Athens, Greece, large urban transit system and results indicate that considerable cost savings can be realized compared to current allocation schemes.

This paper addresses the problem of modeling and predicting urban traffic flow variability, which involves considerable implications for the deployment of dynamic transportation management systems. Traffic variability is described in terms of a volatility metric, i.e., the conditional variance of traffic flow level, as a latent stochastic (low-order Markov) process. A discrete-time parametric stochastic model, referred to as stochastic volatility (SV) model is employed to provide short-term adaptive forecasts of traffic (speed) variability by using real-time detector measurements of volumes and occupancies in an urban arterial. The predictive performance of the SV model is compared to that of the generalized autoregressive conditional heteroscedasticity (GARCH) model, which has been recently used for the traffic variability forecasting, with regard to different measurement locations, forms of data input, lengths of forecasting horizon and performance measures. The results indicate the potential of the SV model to produce out-of-sample forecasts of speed variability with significantly higher accuracy, in comparison to the GARCH model.

The number and arrangement of lanes on freeways are important considerations in freeway geometric design. The objective of this study is to evaluate the safety impacts of lane arrangements on freeway segments with closely spaced entrance and exit ramps. Three different types of lane arrangements were considered. They were designated as Type A, Type B, and Type C arrangements. The research team compared crash frequency, crash rate, crash severity, and collision types for freeway segments with different types of lane arrangements. Crash prediction models were developed to relate crash counts to various explanatory variables such as traffic conditions and geometric characteristics. The crash data analysis results show that the Type C arrangement reported the lowest average crash frequency and crash rate. The Type C arrangement uses a continuous auxiliary lane to connect the entrance and exit ramps; and the auxiliary is dropped in a one-lane exit. Freeway segments with the Type B arrangement reported the highest average crash frequency, crash rate, and percentage of fatal plus severe injury crashes. Based on the crash prediction models, if other factors remain constant, a Type B arrangement results in 113% more total crashes and 102% more severe crashes than does a Type C arrangement. Crash data analysis results suggest that the Type B arrangement should be used cautiously when entrance and exit ramps are closely spaced.

Transit service reliability is an important determinant of service quality, which has been mainly studied from the perspective of passengers waiting at stops. Day-to-day variability of travel time also deteriorates service reliability, but is not a well-researched area in the literature partly due to the lack of comprehensive data sets on bus travel times. While this problem is now being addressed through the uptake of global positioning system (GPS)-based tracking systems, methodologies to analyze these data sets are limited. This paper addresses this issue by investigating day-to-day variability in public transport travel time using a GPS data set for a bus route in Melbourne, Australia. It explores the nature and shape of travel time distributions for different departure time windows at different times of the day. Factors causing travel time variability of public transport are also explored using a linear regression analysis. The results show that in narrower departure time windows, travel time distributions are best characterized by normal distributions. For wider departure time windows, peak-hour travel times follow normal distributions, while off-peak travel times follow lognormal distributions. The factors contributing to the variability of travel times are found to be land use, route length, number of traffic signals, number of bus stops, and departure delay relative to the scheduled departure time. Travel time variability is higher in the AM peak and lower in the off-peak. The impact of rainfall on travel time variability is only found significant in the AM peak. While the paper presents new methods for analyzing GPS-based data, there is much scope for expanding knowledge through wider applications to new data sets and using a wider range of explanatory variables.

Traffic simulation modeling has been applied to many transport planning and traffic engineering situations. The further development of traffic simulation modeling to study the impact of safety measures in a systematic, rigorous, and transparent fashion is becoming increasingly viable as the models improve and the understanding of driver behavior is improving. This paper presents an application of traffic simulation to the study the safety problem of heavy vehicle red-light running at a vehicle actuated highway intersection in a metropolitan area in Australia. The processes of data collection, model calibration and validation, and evaluation are described. Modeling driver stop-or-go behavior of drivers of heavy and light vehicle in the “dilemma zone” of the vehicle actuated signal is also illustrated, along with the modeling of surrogate safety measures. Five alternative signal treatments intended to reduce heavy vehicle crash risk were considered. The results of a comparison against the existing situation showed that an extension of amber time was the most effective short-term treatment. Over the long term, however, this treatment is likely to be subject to behavioral adaptation. A green extension for heavy vehicles detected in the dilemma zone and an all-red extension for potential red-light runners were found to be treatments likely to provide a sustainable safety improvement with little impact on operational efficiency. This type of safety modeling is considered to have great potential in the future.

Roughness of a pavement surface is commonly correlated to its serviceability. On the other hand, on many occasions, investigators have attributed pavement roughness to inadequate skid resistance (friction) as well. However, current pavement friction evaluation and standardization models have yet to incorporate effects of pavement roughness. Hence a study was conducted to investigate and quantify the effects of pavement roughness on the skid number (SN) (or $100$ coefficient of friction). First, an experimental program was executed to evaluate SN measured from a locked wheel tester (LWT) on pavement sections with similar micro- and macrotexture conditions but different levels of roughness. The measured average SN was seen to be significantly lower on relatively rougher pavement sections. To explain the above observations, a second set of experiments was conducted to study the effect of the normal load on the LWT tire on SN. Statistical analysis including regression and ANOVA was used to validate the nonlinear reciprocal relationship found between SN and the normal load which contradicts the general perception of constant SN with respect to the normal load. Then, a one-dimensional two-degrees-of-freedom vibration model was formulated to incorporate the significant dynamic fluctuations of the normal load of the LWT induced by pavement roughness and the vehicle speed. The variation of the normal load and its nonlinear relation to SN was used to explain lower SN values measured on relatively rougher surfaces. The feasibility of using the international roughness index and the dynamic load coefficient as predictors of the reduction in SN due to pavement roughness was also investigated. Assurance of adequate skid resistance is a vital factor considered in allocating pavement rehabilitation funds at the network level. Since excessively rough pavements also create skid hazards, it is concluded that roughness effects must be considered in pavement management systems not only for serviceability purposes, but also in safety evaluations.

The assumption of a linear temperature change through the slab depth has been overwhelmingly used in pavement analysis since Westergaard proposed a curling solution for rigid pavements. However, the actual temperature profiles through the slab thickness are primarily nonlinear. These nonlinear temperature profiles produce stresses that can be divided into three components: a uniform temperature stress, an equivalent linear curling stress, and a nonlinear self-equilibrating stress. It is the self-equilibrating stress component that often goes unaccounted for in concrete pavement stress prediction and can significantly affect the tensile stress magnitude and critical location. This paper presents a solution for a piecewise method and proposes a simplified method termed NOLA, or nonlinear area, that easily captures the effect of temperature nonlinearity on rigid pavement responses. The proposed NOLA method enables the use of a three-dimensional temperature frequency distribution that allows simple postprocessing of rigid pavement curling stress solutions derived from a linear temperature assumption. The impact of accounting for self-equilibrating stresses in terms of projected fatigue damage levels and critical cracking locations is also explored using a mechanistic-based rigid pavement analysis program called RadiCAL.

Two specific three-dimensional finite-element models are presented for making an evaluation of the maximum stresses at critical locations in concrete slabs (i.e., at the interior and edge) that is superior to the classic approach of the approximate methods. Concrete airport pavements with square-shaped slabs are studied. A homogeneous, isotropic elastic half-space is assumed as a subgrade (foundation) model so that the influence of variation in Young’s modulus is examined in contrast with Winkler’s reaction modulus. The effects of two aircrafts (A380 and B747), different subgrade bearing capabilities and temperature gradients $\left(\Delta t\right)$ , are investigated. Combined stresses due to aircraft load and temperature gradient $\left(\Delta t\right)$ are accurately estimated by means of a finite-element software and compared to their separate actions. As a consequence the incorrect applicability of the principle of effects superposition, which is due to the support soil that is nonresistant to tensile stress, is quantified. Moreover, the difference in the values of the stresses due to the two different aforementioned aircrafts is calculated. A simplified method for predicting maximum stresses is also derived from the results of the finite-element software. This predictive method represents a useful tool for a faster estimation of stresses at a preliminarily design level. The proposed equations make adjustments to Westergaard’s load stress model and Eisenmann’s temperature stress model necessary, as they also do for the separate actions of load and $\Delta t$ . The procedure makes use of dimensionless variables so that it may also be extended to different square-slab dimensions.

The mechanistic-empirical study of pavement performance requires that immediate pavement responses due to tire loading be mechanistically computed for pavement structures, and the long-term pavement performance be related to the computed pavement responses. The problem becomes very complicated when variability is considered for loading, pavement and environmental conditions. A Monte Carlo simulation-based mechanistic-empirical pavement design/analysis procedure was verified in this study. The complex tire-pavement interaction was more realistically handled and computed using finite element models and measured tire-pavement contact stress data. The computation time problem involved in pavement response computations was resolved by using a computationally efficient method that relates critical pavement responses to tire loading and pavement structural conditions. In the Monte Carlo simulation, different truck classes were drawn from the truck population empirically based on actual traffic volume data. Axle load spectra were characterized by actual axle load data collected at a weigh-in-motion site. Results from a survey of truck configurations were used to describe the distribution of truck tire pressure. Pavement structural parameters and relationships between material moduli and environmental conditions were obtained from the Long-Term Pavement Performance data. The distress models developed in National Cooperative Highway Research Program Project 1–37A were employed to predict pavement performance. The simulation study estimates the effects of increased tire pressure and steering axle load on a typical pavement structure for 2 million truck passes. The Monte Carlo simulation method and models used in this study can provide useful experience for the development of future flexible pavement design and analysis procedures.

Flow-speed-density plots are widely used by transportation professionals. The traditional presentation of these plots can be misleading, especially when large data sets are used. An improved presentation is suggested and its advantages are discussed.