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Select link analysis provides information of where traffic comes from and goes to at selected links. This disaggregate information has wide applications in practice. The state-of-the-art planning software packages often adopt the user equilibrium (UE) model for select link analysis. However, empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model. This paper proposes an alternative select link analysis method by making use of the recently developed logit-weibit hybrid model, to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression. To enhance the applicability in large-scale networks, Bell's stochastic loading method originally developed for logit model is adapted to the hybrid model. The features of the proposed method are twofold: (1) unique O-D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and (2) applicability in large-scale networks due to the link-based stochastic loading method. An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O-D-specific link flow information. A visualization method is also proposed to enhance the understanding of O-D-specific link flow originally in the form of a matrix. [ABSTRACT FROM AUTHOR]

Traffic equilibrium models under uncertainty characterize travelers’ route choice behaviors under travel time variability. In this paper, we develop a link -based mean-excess traffic equilibrium (L-METE) model by integrating the sub-additivity property and complete travel time variability characterization of mean-excess travel time (METT), and the computationally tractable additive route cost structure of the conventional user equilibrium (UE) problem. Compared to the majority of relevant models formulated in the route domain, the link -based modeling has two desirable features on modeling flexibility and algorithmic development. First, it avoids the normal route travel time distribution assumption (uniformly imposed for all routes) that inherits from the Central Limit Theorem in most route -based models, permitting the use of any suitable link travel time distributions from empirical studies. Second, the additive route cost structure makes the L-METE model solvable by readily adapting existing UE algorithms without the need of storing/enumerating routes while avoiding the computationally demanding nonadditive shortest path problem and route flow allocations in route-based models, which is a significant benefit for large-scale network applications under uncertainty. [ABSTRACT FROM AUTHOR]

Recent empirical studies have found widespread inaccuracies in traffic forecasts despite the fact that travel demand forecasting models have been significantly improved over the past few decades. We suspect that an intrinsic selection bias may exist in the competitive project appraisal process, in addition to the many other factors that contribute to inaccurate traffic forecasts. In this paper, we examine the potential for selection bias in the governmental process of Build-Operate-Transfer (BOT) transportation project appraisals. Although the simultaneous consideration of multiple criteria is typically used in practice, traffic flow estimate is usually a key criterion in these appraisals. For the purposes of this paper, we focus on the selection bias associated with the highest flow estimate criterion. We develop two approaches to quantify the level and chance of inaccuracy caused by selection bias: the expected value approach and the probability approach. The expected value approach addresses the question “ to what extent is inaccuracy caused by selection bias? ”. The probability approach addresses the question “ what is the chance of inaccuracy due to selection bias? ”. The results of this analysis confirm the existence of selection bias when a government uses the highest traffic forecast estimate as the priority criterion for BOT project selection. In addition, we offer some insights into the relationship between the extent/chance of inaccuracy and other related factors. We do not argue that selection bias is the only reason for inaccurate traffic forecasts in BOT projects; however, it does appear that it could be an intrinsic factor worthy of further attention and investigation. [ABSTRACT FROM AUTHOR]

Highlights: [•] Quantitative uncertainty analysis of a combined travel demand model. [•] The combined model overcomes limitations of the sequential four-step procedure. [•] Analytical sensitivity-based method requires less computation efforts than sampling method. [•] Uncertainties stemming from inputs and parameters can be treated separately. [•] Individual and collective effects of uncertainty on outputs can be quantified. [Copyright &y& Elsevier]

Highlights: [•] A path-size weibit (PSW) stochastic user equilibrium (SUE) model is developed. [•] The PSW-SUE model accounts for both route overlapping and route-specific perception variance. [•] Equivalency and uniqueness of the PSW-SUE model are provided. [•] A path-based algorithm is developed for solving the PSW-SUE model. [•] Numerical results using a real network are presented. [Copyright &y& Elsevier]

Estimation of intersection turning movements is one of the key inputs required for a variety of transportation analysis, including intersection geometric design, signal timing design, traffic impact assessment, and transportation planning. Conventional approaches that use manual techniques for estimation of turning movements are insensitive to congestion. The drawbacks of the manual techniques can be amended by integrating a network traffic model with a computation procedure capable of estimating turning movements from a set of link traffic counts and intersection turning movement counts. This study proposes using the path flow estimator, originally used to estimate path flows (hence origin-destination flows), to derive not only complete link flows, but also turning movements for the whole road network given some counts at selected roads and intersections. Two case studies using actual traffic counts are used to demonstrate the proposed intersection turning movement estimation procedure. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: The uncertainty of an origin–destination (O–D) trip table estimate is affected by two factors: (i) the multiplicity of solutions due to the underspecified nature of the problem, and (ii) the errors of traffic counts. In this paper, a confidence interval estimation procedure for path flow estimator (PFE) is developed for assessing the quality of O–D trip tables estimated from traffic counts. The confidence interval estimation consists of two parts: (i) a generalized demand scale (GDS) measure for quantifying the intrinsic underspecified nature of the O–D estimation problem at various spatial levels, and (ii) an error bound to quantify the contribution of input errors (traffic counts) to the estimation results. Numerical results using PFE as the O–D estimator show that the proposed confidence interval estimation procedure is able to separate the two sources of uncertainty in constructing the confidence intervals at various spatial levels. Simulation results also confirm that the proposed quality measure indeed contain the true estimates within the defined confidence intervals. [Copyright &y& Elsevier]

Abstract: Computing traffic equilibria with a general nonadditive route cost disutility function is considered in this paper. Following the user equilibrium (UE) condition, that is, no driver can unilaterally change route to achieve less travel costs, the traffic equilibrium problem (TEP) can be formulated as a nonlinear complementary problem (NCP). In this paper, we propose a semismooth Newton method with a penalized Fischer–Burmeister (PFB) NCP function to solve the NCP formulation of the TEP, and also, we investigate the properties of the proposed method. Numerical results are provided and compared with the classical TEP with additive route cost functions. The results show the algorithm can achieved substantially better performance than the existing approaches. A sensitivity analysis is also conducted to examine the parameter of the proposed nonadditive route cost function. [Copyright &y& Elsevier]

Abstract: Flexibility of the transportation system is one of the important performance measures needed to deal with demand changes. In this paper, we provide a quantitative assessment of capacity flexibility for the passenger transportation network using bi-level network capacity models. Two approaches for assessing the value of capacity flexibility are proposed. One approach is based on the concept of reserve capacity, which reflects the flexibility with respect to changes in terms of demand volume only. The second approach allows for variations in the demand pattern in addition to changes in demand volume in order to more fully capture demand changes. Two models are developed in the second approach to consider two types of capacity flexibility. The total capacity flexibility allows all users to have both route choice and destination choice when estimating capacity flexibility. The limited capacity flexibility estimates how much more demand volume could be added to a fixed demand pattern by allowing the additional demand to deviate from the fixed demand pattern. Numerical examples are provided to demonstrate the different concepts of capacity flexibility for a passenger transportation system under demand changes. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present an algorithm for solving the bicriteria traffic equilibrium problem with variable demand and nonlinear path costs. The path cost function considered is comprised of two attributes, travel time and toll, that are combined into a nonlinear generalized cost. Travel demand is determined endogenously according to a travel disutility function. Travelers choose routes with the minimum overall generalized costs. The algorithm involves two components: a bicriteria shortest path routine to implicitly generate the set of non-dominated paths and a projection and contraction method to solve the nonlinear complementarity problem (NCP) describing the traffic equilibrium problem. Numerical experiments are conducted to demonstrate the feasibility of the algorithm to this class of traffic equilibrium problems. [Copyright &y& Elsevier]

Recently, a multi-objective model for locating the automatic vehicle identification (AVI) readers in a transportation network was proposed by the authors along with its solution procedure. The model locates the AVI readers by simultaneously considering three objectives: the equipment and installation cost (e.g. number of AVI readers), the coverage of the AVI system (e.g. number of origin-destination (O-D) pairs), and the amount/quality of travel information obtained (e.g. number of AVI readings). However, only a single travel demand pattern for a certain time period, such as the evening peak hour, was considered in determining the AVI reader locations. Therefore, the recommended AVI system may not be able to guarantee the amount of travel information gathered during other time periods. This study extends our previously proposed model by developing three scenario-based models to accommodate different travel demand patterns observed during the whole day. A case study is provided to illustrate the applicability of the new models and the robustness of the AVI system designed by considering different travel demand patterns. [ABSTRACT FROM AUTHOR]

A procedure is developed to model network traffic for planning applications in small and/or medium-sized communities with limited planning resources. The proposed method is based on the theories and assumptions of conventional four-step travel demand models, but the baseline trip table is estimated from existing traffic counts using path flow estimator (PFE) to render a quick response approach that requires less data than the conventional approach. The proposed modeling approach is suitable for short-range, small area planning applications, such as the evaluation of alternative roadway networks that does not involve significant growth in trip generation patterns. A case study is set up with data from a small community (St. Helena, Calif.) to demonstrate the proposed approach. An O-D trip table is estimated with PFE from traffic counts. The estimation process is shown to be feasible as the results matched observed data with a satisfactory error bound. The traffic impacts of various scenarios of land use and network changes can be effectively evaluated with the proposed modeling approach. [ABSTRACT FROM AUTHOR]

Describes a method to accelerate the performance of the Franke-Wolfe (FW) algorithm, which is used for solving traffic assignment problems. Development of a FW implementation with an origin-destination (OD) flow update; Computational storage OD-based FW algorithm; Line search computational effort when using OD-based FW.

A capacity related reliability for transportation networks with random link capacity is introduced. It is defined as the probability that the road network can accommodate a certain level of traffic demand, and is built on the concept of network reserve capacity. Network reserve capacity is defined as the largest multiplier applied to an existing origin-destination demand matrix that can be allocated to a transportation network in a user-optimal way without violating the link capacities. Due to large variability associated with link capacities, a probabilistic approach is adopted to model the different physical and operational factors that often degrade the capacity of roadways. A Monte Carlo simulation procedure is developed to estimate the capacity related reliability measure. Numerical results are provided to demonstrate the feasibility of the approach. [ABSTRACT FROM AUTHOR]

Highlights: [•] We illustrate shortcomings of existing travel time reliability UE models. [•] We introduce bi-objective user equilibrium models to overcome the shortcomings. [•] We formulate bi-objective versions of two travel time reliability UE models. [•] We prove that the UE models are special cases of the bi-objective formulations. [•] We propose a general bi-objective formulation as a general modelling framework. [Copyright &y& Elsevier]

This paper proposes a generalized model to estimate the peak hour origin–destination (OD) traffic demand variation from day-to-day hourly traffic counts throughout the whole year. Different from the conventional OD estimation methods, the proposed modeling approach aims to estimate not only the mean but also the variation (in terms of covariance matrix) of the OD demands during the same peak hour periods due to day-to-day fluctuation over the whole year. For this purpose, this paper fully considers the first- and second-order statistical properties of the day-to-day hourly traffic count data so as to capture the stochastic characteristics of the OD demands. The proposed model is formulated as a bi-level optimization problem. In the upper-level problem, a weighted least squares method is used to estimate the mean and covariance matrix of the OD demands. In the lower-level problem, a reliability-based traffic assignment model is adopted to take account of travelers’ risk-taking path choice behaviors under OD demand variation. A heuristic iterative estimation-assignment algorithm is proposed for solving the bi-level optimization problem. Numerical examples are presented to illustrate the applications of the proposed model for assessment of network performance over the whole year. [ABSTRACT FROM AUTHOR]