A methodology for predicting regional freight traffic in developing countries

The prime concern of transportation planners in developing countries is how to collect and transfer data into models as simple and as effective as possible and obtain solutions in the shortest time.

In this study, a methodology which utilizes multivariate statistical analysis techniques for travel estimation is presented. The two simple and convenient techniques namely Cluster Analysis and Principal Component Analysis, are used for the evaluation of available data. The traffic demand is expressed as a function of principal components which are determined independently as a small set of variables to represent total system variability. A stepwise regression analysis is carried out to derive the traffic demand‐principal component relationship. The proposed methodology is then applied to Southeastern Anatolia Regional Development Project in Turkey.     

Performance assessment of solute transport upscaling methods in the context of nuclear waste disposal

Stochastic simulations of solute transport in heterogeneous log₁₀ K random fields were conducted at two different support scales to assess solute transport upscaling methods in the context of nuclear waste disposal. A very fine grid-scale is used to obtain a reference solution of the real problem, which is based on data from the Sellafield site. A coarse-scale model is obtained by upscaling the heterogeneous grid-blocks onto equivalent homogeneous hydraulic conductivity tensors calculated using the Simple Laplacian Technique. Random fields were designed with different degrees of heterogeneity such that the standard deviation of log₁₀ K ranged between 0 and 1. It is shown that the early arrival time of particles at a control location, reflected in the lower limit of the 95% confidence interval of the mass flux cumulative density function stochastic process, associated with the upscaled model is strikingly similar to the one associated with the real solution for all heterogeneities. This is encouraging for the application of upscaled stochastic models to the design of nuclear waste repositories where the design of a nuclear waste disposal facility relies on the estimation of the early travel time of radionuclides arriving at a control location. On the contrary, the late arrival time of particles at the control location is largely underestimated by the upscaled model.     

A Bayesian mixture model for short-term average link travel time estimation using large-scale limited information trip-based data

Accurate estimation and prediction of urban link travel times are important for urban traffic operations and management. This paper develops a Bayesian mixture model to estimate short-term average urban link travel times using large-scale trip-based data with partial information. Unlike typical GPS trajectory data, trip-based data from taxies or other sources provide limited trip level information, which only contains the trip origin and destination locations, trip travel times and distances, etc. The focus of this study is to develop a robust probabilistic short-term average link travel time estimation model and demonstrate the feasibility of estimating network conditions using large-scale trip level information. In the model, the path taken by each trip is considered as latent and modeled using a multinomial logit distribution. The observed trip data given the possible path set and the mean and variance of the average link travel times can thus be characterized using a finite mixture distribution. A transition model is also introduced to serve as an informative prior that captures the temporal and spatial dependencies of link travel times. A solution approach based on the expectation–maximization (EM) algorithm is proposed to solve the problem. The model is tested on estimating the mean and variance of the average link travel times for 30 min time intervals using a large-scale taxi trip dataset from New York City. More robust estimation results are obtained owing to the adoption of the Bayesian framework.     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

A Refined Methodology for Durability‐Based Service Life Estimation of Reinforced Concrete Structural Elements Considering Fuzzy and Random Uncertainties

Abstract: A reliable method for service life estimation of the structural element is a prerequisite for service life design. A new methodology for durability‐based service life estimation of reinforced concrete flexural elements with respect to chloride‐induced corrosion of reinforcement is proposed. The methodology takes into consideration the fuzzy and random uncertainties associated with the variables involved in service life estimation by using a hybrid method combining the vertex method of fuzzy set theory with Monte Carlo simulation technique. It is also shown how to determine the bounds for characteristic value of failure probability from the resulting fuzzy set for failure probability with minimal computational effort. Using the methodology, the bounds for the characteristic value of failure probability for a reinforced concrete T‐beam bridge girder has been determined. The service life of the structural element is determined by comparing the upper bound of characteristic value of failure probability with the target failure probability. The methodology will be useful for durability‐based service life design and also for making decisions regarding in‐service inspections.     

Development of an Estimation Procedure for an Activity-Based Travel Demand Model

Abstract:   In this article, we implement an estimation procedure for a particular mathematical programming activity-based model to estimate the relative importance of factors associated with spatial and temporal interrelationships among the out-of-home activities that motivate a household's need or desire to travel. The method uses a genetic algorithm to estimate coefficient values of the utility function, based on a particular multidimensional sequence alignment method to deal with the nominal, discrete attributes of the activity/travel pattern (e.g., which household member performs which activity, which vehicle is used, sequencing of activities), and a time sequence alignment method to handle temporal attributes of the activity pattern (e.g., starting and ending time of each activity and/or travel). The estimation procedure is tested on data drawn from a well-known activity/travel survey.     

Near-Term Travel Speed Prediction Utilizing Hilbert–Huang Transform

Abstract:  Accurate short-term prediction of travel speed as a proxy for time is central to many Intelligent Transportation Systems, especially for Advanced Traveler Information Systems and Advanced Traffic Management Systems. In this study, we propose an innovative methodology for such prediction. Because of the inherently direct derivation of travel time from speed data, the study was limited to the use of speed only as a single predictor. The proposed method is a hybrid one that combines the use of the empirical mode decomposition (EMD) and a multilayer feedforward neural network with backpropagation. The EMD is the key part of the Hilbert–Huang transform, which is a newly developed method at NASA for the analysis of nonstationary, nonlinear time series. The rationale for using the EMD is that because of the highly nonlinear and nonstationary nature of link speed series, by decomposing the time series into its basic components, more accurate forecasts would be obtained. We demonstrated the effectiveness of the proposed method by applying it to real-life loop detector data obtained from I-66 in Fairfax, Virginia. The prediction performance of the proposed method was found to be superior to previous forecasting techniques. Rigorous testing of the distribution of prediction errors revealed that the model produced unbiased predictions of speeds. The superiority of the proposed model was also verified during peak periods, midday, and night. In general, the method was accurate, computationally efficient, easy to implement in a field environment, and applicable to forecasting other traffic parameters.     

Modelling concentration fluctuations and individual exposure in complex urban environments

One of the key problems in coping with deliberate or accidental atmospheric releases, which in many cases are short or/and result in high concentrations, is the ability to reliably predict the individual exposure during the event. Furthermore, for consequence assessment and countermeasures application, it is more realistic to rely on the maximum expected dosage rather than on the actual dosage. Recently, Bartzis et al. (2008) have introduced an approach relating maximum dosage to parameters such as concentration variance and turbulence integral time scale. The need for an estimation of these parameters poses new challenges to CFD models. In the CFD RANS model ADREA, new approaches have been implemented recently, where the parameterization of the dispersion of a pollutant emitted from a point source depends not only on the parameters of turbulence, but also on the pollutant travel times. In this study the new methodology is tested against MUST and FLADIS field experimental data, which consist of high resolution concentration time series enabling the production of short term dosage data. The present comparisons further strengthen the evidence that the applied methodology is capable of dealing properly with complex transient dispersion phenomena.     

A Methodology for Assessing Transportation Network Terrorism Risk with Attacker and Defender Interactions

Abstract: In an adversarial setting, a transportation network's capacity is influenced by both the defender's protective measures and the attacker's actions, which include substituting targets and attack methods in response to security measures. Decision makers need a methodology that can capture the complex attacker–defender interactions and help them understand the overall effects on the transportation system, as well as the consequences of asset failure. This article presents such a methodology, which uses probabilities of target–attack method combinations that are degree of belief based and updated using Bayes' Theorem after evidence of the attack is obtained. Monte Carlo simulation generates the probability of link capacity effects by sampling from distributions of capacity reductions due to preevent security measures, substitutions, target failure, and postevent security measures. The average capacity reduction for a particular target–attack method combination serves as an input to the traffic assignment–simulation package DYNASMART‐P to determine travel time effects. The methodology is applied to a sample network based on the northern Virginia area. Results based on notional data indicated that preevent security measures reduced attack probabilities, but in some cases increased the mobility consequences. Thus, decision makers must carefully evaluate the effects of their decisions.     

Speed Estimation from Single Loop Data Using an Unscented Particle Filter

Abstract: This article presents a hybrid method, the Unscented Particle Filter (UPF), for traffic flow speed estimation using single loop outputs. The Kalman filters used in past speed estimation studies employ a Gaussian assumption that is hardly satisfied. The hybrid method that combines a parametric filter (Unscented Kalman Filter) and a nonparametric filter (Particle Filter) is thus proposed to overcome the limitations of the existing methods. To illustrate the advantage of the proposed approach, two data sets collected from field detectors along with a simulated data set are utilized for performance evaluation and comparison with the Extended Kalman Filter and the Unscented Kalman Filter. It is found that the proposed method outperforms the evaluated Kalman filter methods. The UPF method produces accurate speed estimation even for congested flow conditions in which many other methods have significant accuracy problems.     

Reliability‐Based Transit Assignment for Congested Stochastic Transit Networks

Abstract: This article proposes a nonlinear complementarity problem (NCP) formulation for the risk‐aversive stochastic transit assignment problem in which in‐vehicle travel time, waiting time, capacity, and the effect of congestion are considered as stochastic variables simultaneously and both their means and variances are incorporated into the formulation. A new congestion model is developed and captured in the proposed NCP formulation to account for different effects of on‐board passengers and passengers waiting at stops. A reliability‐based user equilibrium condition is also defined based on the proposed generalized concept of travel time budget referred to as effective travel cost, and is captured in the formulation. A column generation based algorithm is proposed to solve the NCP formulation. A survey was conducted to validate that the degree of risk aversion of transit passengers affects their route choices. Numerical studies were performed to demonstrate the problem and the effectiveness of the proposed algorithm. The results obtained show that underestimating the congestion effect and ignoring the risk aversion behavior can overestimate the patronage of transit service, which have profound implications on the profit of the operators involved and the development of transit network design models.     

Development of Recurrent Neural Network Considering Temporal‐Spatial Input Dynamics for Freeway Travel Time Modeling

Abstract: The artificial neural network (ANN) is one advance approach to freeway travel time prediction. Various studies using different inputs have come to no consensus on the effects of input selections. In addition, very little discussion has been made on the temporal–spatial aspect of the ANN travel time prediction process. In this study, we employ an ANN ensemble technique to analyze the effects of various input settings on the ANN prediction performances. Volume, occupancy, and speed are used as inputs to predict travel times. The predictions are then compared against the travel times collected from the toll collection system in Houston. The results show speed or occupancy measured at the segment of interest may be used as sole input to produce acceptable predictions, but all three variables together tend to yield the best prediction results. The inclusion of inputs from both upstream and downstream segments is statistically better than using only the inputs from current segment. It also appears that the magnitude of prevailing segment travel time can be used as a guideline to set up temporal input delays for better prediction accuracies. The evaluation of spatiotemporal input interactions reveals that past information on downstream and current segments is useful in improving prediction accuracy whereas past inputs from the upstream location do not provide as much constructive information. Finally, a variant of the state‐space model (SSNN), namely time‐delayed state‐space neural network (TDSSNN), is proposed and compared against other popular ANN models. The comparison shows that the TDSSNN outperforms other networks and remains very comparable with the SSNN. Future research is needed to analyze TDSSNN's ability in corridor prediction settings.     

Life-cycle cost optimal design of passive dissipative devices

The cost-effective performance of structures under natural hazards such as earthquakes and hurricanes has long been recognized to be an important topic in the design of civil engineering systems. A realistic comprehensive treatment of such a design requires proper integration of (i) methodologies for treating the uncertainties related to natural hazards and to the structural behavior over the entire life-cycle of the building, (ii) tools for evaluating the performance using socioeconomic criteria, as well as (iii) algorithms appropriate for stochastic analysis and optimization. A systematic probabilistic framework is presented here for detailed estimation and optimization of the life-cycle cost of engineering systems. This framework is a general one but the application of interest here is the design of passive dissipative devices for seismic risk mitigation. A comprehensive methodology is initially presented for earthquake loss estimation; this methodology uses the nonlinear time-history response of the structure under a given excitation to estimate the damage in a detailed, component level. A realistic probabilistic model is then presented for describing the ground motion time history for future earthquake excitations. In this setting, the life-cycle cost is uncertain and can be quantified by its expected value over the space of the uncertain parameters for the structural and excitation models. Because of the complexity of these models, calculation of this expected value is performed using stochastic simulation techniques. This approach, though, involves an unavoidable estimation error and significant computational cost, features which make efficient design optimization challenging. A highly efficient framework, consisting of two stages, is discussed for this stochastic optimization. An illustrative example is presented that shows the efficiency of the proposed methodology; it considers the seismic retrofitting of a four-story non-ductile reinforced-concrete building with viscous dampers.     

A Knowledge Formalization and Aggregation‐Based Method for the Assessment of Dam Performance

Abstract: A computational methodology dedicated to embankment dam performance assessment has been designed and implemented. The model's inputs are the whole set of available information and data: visual observations, monitoring measurements, calculated data, and documents related to design and construction processes. First, a formal grid is proposed to structure the inputs. It is composed of six fields: name, definition, scale, references as anchorage points on the scale, and spatial and temporal characteristics. Structured inputs are called indicators. Second, an indicator aggregation method is proposed that allows obtaining not only the dam performance but also the assessment of its design and construction practices. The methodology is illustrated mainly with the internal erosion mechanism through the embankment, but results concerning other failure modes are also provided. An application of the method for monitoring dams through time is given.     

A Dynamic Route Choice Model Considering Uncertain Capacities

Abstract: The standard assumption in (dynamic) traffic assignment models is that route choice is solely determined by a (perceived) deterministic travel time. However, recently, there is a growing interest in (dynamic) equilibrium route choice models in which travelers not only select their paths based on an estimated deterministic travel time, but also based on travel time reliability, in this article defined as the probability that the actual travel time deviates from the anticipated value. We extend the linear programming cell transmission model‐based dynamic traffic assignment (LP CTM‐DTA) model to account for travelers’ consideration of uncertainty regarding saturation flow rates (in this article referred to as capacities). It is shown that these reliability considerations can be accounted for by simply reducing the road capacities appearing in the constraint set of the classical LP CTM‐DTA model. More importantly, we provide results on the amount of capacity reduction necessary to ensure a certain reliability level. Although in the proposed model any probability distribution can be used to model the uncertainty, the selection of a specific probability distribution can potentially be burdensome for the modeler. To this end, we also present results on the class of symmetric probability distributions that has been particularly popular in the robust optimization literature. Properties for this broad class of distributions will be derived within the context of the introduced model. In numerical case studies, the model predicts that travel patterns can be significantly different when accounting for travelers’ reliability considerations.     

An empirical analysis of household choices on housing and travel mode in Boston

We study household choices on the housing type and travel mode in Boston in the year 1991. We first develop a theoretical model for an integrated analysis of housing and travel mode choices, which implies that people with higher income level tend to live farther away from the Central Business District, enjoy more spacious housing and travel with a speedier mode. Logit estimation methods are employed to analyze the 1991 Boston Household Survey data. Our empirical results provide indirect confirmation of the theoretical model. “The outcome of the city will depend on the race between the automobile and the elevator, and anyone who bets on the elevator is crazy.” Frank Lloyd Wright, Architect.      

Cohesion of unsaturated residual soils as a function of volumetric water content

Soil cohesion changes with the moisture state of a soil. This paper presents an empirical equation to predict the cohesive component in the shear strength of unsaturated residual soils as an exponential function of volumetric water content. The formulation originated from a multiple linear-regression analysis for data sets obtained from shear tests using undisturbed soils with varying moisture contents. The empirical equations can realistically predict the reduction in soil cohesion due to wetting (R ²=0.88, 0.93). The methodology described in this paper provides a convenient alternative to the quantitative estimation of unsaturated shear strength, especially in an engineering practice such as a slope stability analysis as no matrix suction data are required.      

A Generic Approach to Estimate Freeway Traffic Time Using Vehicle ID‐Matching Technologies

New technologies have emerged to estimate the travel time on freeways by matching certain unique identifications of passing vehicles at different locations. These types of technologies share many similarities despite having different mechanisms. In this article, a generic method is presented to estimate freeway travel times using vehicle ID‐matching technologies. In particular, the new method addresses two long‐standing challenges: outlier screening and travel time estimation. Innovations include (1) using both statistical methods and traffic flow theory to screen outliers; and (2) accounting for mechanisms of various equipment measurement errors. The effectiveness of the proposed method is demonstrated using simulation and shown to be more accurate and responsive to travel time changes than methods based on the use of traditional inductive loops.     

A methodology for estimating occupant CO2 source generation rates from measurements in small commercial buildings

It is necessary to know CO₂ source generation rates and system flow parameters, such as supply flow rate and overall room ventilation effectiveness, in order to evaluate cost savings for demand-controlled ventilation applied to commercial buildings. This paper presents a methodology for estimating schedules for generation rates and flow parameters using short-term testing. These parameters are used within a model that predicts return air CO₂ concentrations as part of an overall energy analysis model. As a first step in developing the methodology, two different parameter estimation techniques were evaluated using simulated data. Each method gave models that provide good predictions of return air CO₂ concentrations, but differed in terms of the identified parameters. The preferred parameter estimation method provides estimates of both average hourly source generation rates and day-to-day variations. This technique was applied to three different types of commercial buildings using field monitored data. The sites are small commercial buildings with packaged HVAC equipment and included modular schoolrooms, children's play areas in fast food restaurants and a pharmacy retail store. The impact of the length of model training data period on estimated CO₂ generation rates was investigated. Eight weeks of data is sufficient for training. Expressed in terms of the coefficient of variation, the errors in predicted CO₂ concentrations ranged from 4% to 15% depending on the sites. The predicted frequency of time that CO₂ concentrations were within a given range agreed well with the field measured data.     

A Heuristic Approach to the Railroad Track Maintenance Scheduling Problem

Abstract: Every year, billions of dollars are spent on rail track maintenance to keep the serviceability of the railroad network. These maintenance projects (of different types) must be performed by suitable maintenance teams within a planning horizon. This article presents a time‐space network model to solve the track maintenance scheduling problem (TMSP). The objective is to minimize the total travel costs of the maintenance teams as well as the impact of maintenance projects on railroad operation, which are formulated by three types of side constraints: mutually exclusive, time window, and precedence constraints. An iterative heuristic solution approach is proposed to solve the large‐scale TMSP model with a large number of side constraints. The proposed model and solution approach are applied to a large‐scale real‐world problem. Compared to the current industry practice the model outcome eliminated all hard side‐constraint violations and reduced the total objective value (travel costs and soft side‐constraint violation penalties) by 66.8%.