An augmented Lagrangian origin‐based algorithm for link‐capacitated traffic assignment problem

The origin‐based algorithm is embedded into the augmented Lagrangian method for the link‐capacitated traffic assignment problem. In order to solve the “nonexistence” problem due to the second partial derivatives of the augmented Lagrangian function at some specific points, the approximate expressions of the second partial derivatives are amended in the origin‐based algorithm. The graph of last common nodes is developed on the basis of the restricted single‐origin network. A method is proposed for finding n–1 last common nodes of the restricted single‐origin network, resulting in computational complexity of O(n²) in finding last common nodes. Numerical analysis on the Sioux Falls network and Chicago Sketch network demonstrated the effectiveness and characteristics of the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Norm approximation method for handling traffic count inconsistencies in path flow estimator

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin–destination (O–D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three L_p-PFE models for estimating consistent path flows and O–D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three L_p-PFE models. In addition, the proposed L_p-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed.     

Comparison of pedestrian trip generation models

Using Poisson regression and negative binomial regression, this paper presents an empirical comparison of four different regression models for the estimation of pedestrian demand at the regional level and finds the most appropriate model with reference to the National Household Travel Survey (NHTS) 2001 data for the Baltimore (USA) region. The results show that Poisson regression seems to be more appropriate for pedestrian trip generation modeling in terms of χ² ratio test, Pseudo R², and Akaike's information criterion (AIC). However, R² based on deviance residuals and estimated log‐likelihood value at convergence confirmed the empirical studies that negative binomial regression is more appropriate for the over‐dispersed dependent variable than Poisson regression. Copyright © 2011 John Wiley & Sons, Ltd.     

Short-term traffic flow prediction using time-varying Vasicek model

This paper provides a two-step approach based on the stochastic differential equations (SDEs) to improve short-term prediction. In the first step of this framework, a Hull-White (HW) model is applied to obtain a baseline prediction model from previous days. Then, the extended Vasicek model (EV) is employed for modeling the difference between observations and baseline predictions (residuals) during an individual day. The parameters of this time-varying model are estimated at each sample using the residuals in a short duration of time before the time point of prediction; so it provides a real time prediction. The extracted model recovers the valuable local variation information during each day. The performance of our method in comparison with other methods improves significantly in terms of root mean squared error (RMSE), mean absolute error (MAE) and mean relative error (MRE) for real data from Tehran’s highways and the open-access PeMS database. We also demonstrate that the proposed model is appropriate for imputing the missing data in traffic dataset and it is more efficient than the probabilistic principal component analysis (PPCA) and k-Nearest neighbors (k-NN) methods.     

A hybrid scheme for real‐time prediction of bus trajectories

The uncertainty associated with public transport services can be partially counteracted by developing real‐time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model combines schedule, instantaneous and historical data. The contribution of each predictor as well as values of respective parameters is estimated by minimizing the prediction error using a linear regression heuristic. The hybrid method was applied to five bus routes in Stockholm, Sweden, and Brisbane, Australia. The results indicate that the hybrid method consistently outperforms the timetable and delay conservation prediction method for different route layouts, passenger demands and operation practices. Model validation confirms model transferability and real‐time applicability. Generating more accurate predictions can help service users adjust their travel plans and service providers to deploy proactive management and control strategies to mitigate the negative effects of service disturbances. Copyright © 2017 John Wiley & Sons, Ltd.     

A G/G(n)/C/C state‐dependent simulation model for metro station corridor width design

Metro station corridor and passengers are described as a G/G(n)/C/C state‐dependent queuing system with a general random arrival interval G and a general random and state‐dependent service time G(n) to offset the shortcomings in existing design methods. The corresponding G/G(n)/C/C state‐dependent discrete event simulation model is developed, and its high‐fidelity is tested. Then the optimization algorithm based on the simulation model is designed to determine corridor width. The proposed simulation optimization method and the existing analytical optimization methods, based on M/G(n)/C/C and D/D/1/C queuing models, are applied to design corridor width in a numerical example of 48 combinations of passenger flow rates and level of service (LOS). The designed corridor widths are tested in a micro‐simulation model, and the performance measure is compared. The result shows that the corridor widths obtained by the new method are 0.357 m (7.4%) larger than that of the other two methods on average; the area per passenger of the new method increases 10.53% and 11.63%, respectively, compared with that of the other two methods; the widths designed by the new method satisfy the requirement of LOS under various passenger flows, whereas 93% of the corridor widths obtained by the other two methods fail to meet the requirement of LOS, and the corridor widths designed by the new method have high elasticity coefficients of LOS‐width. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparing prediction methods for maritime accidents

ABSTRACT

The purpose of maritime accident prediction is to reasonably forecast an accident occurring in the future. In determining the level of maritime traffic management safety, it is important to analyze development trends of existing traffic conditions. Common prediction methods for maritime accidents include regression analysis, grey system models (GM) and exponential smoothing. In this study, a brief introduction is provided that discusses the aforementioned prediction models, including the associated methods and characteristics of each analysis, which form the basis for an attempt to apply a residual error correction model designed to optimize the grey system model. Based on the results, in which the model is verified using two different types of maritime accident data (linear smooth type and random-fluctuation type, respectively), the prediction accuracy and the applicability were validated. A discussion is then presented on how to apply the Markov model as a way to optimize the grey system model. This method, which proved to be correct in terms of prediction accuracy and applicability, is explored through empirical analysis. Although the accuracy of the residual error correction model is usually higher than the accuracy of the original GM (1,1), the effect of the Markov correction model is not always superior to the original GM (1,1). In addition, the accuracy of the former model depends on the characteristics of the original data, the status partition and the determination method for the status transition matrix.     

Capacity-constrained traffic forecasting model

Projecting future traffic is an important stage in any traffic and transportation planning study. Accurate traffic forecasting is vital for transportation planning, highway safety evaluation, traffic operations analysis, and geometric and pavement design among others. In view of its importance, this paper introduces a regression-based traffic forecasting methodology for a one dimensional capacity-constrained highway. Five different prediction functions are tested; the best was selected according to the accuracy of projections against historical traffic data. The three-parameter logistic function produced more accurate projections compared to other functions tested when highway capacity constraints were taken into consideration. The R ² values at various test locations ranged from 88% to 98%, indicating good prediction capability. Using the Fisher's information matrix approach, the t-statistic test showed all parameters in the logistic function were highly statistically significant. To evaluate reliability of projections, predictive intervals were calculated at a 95% level of confidence. Predictions using the logistic function were also compared to those predicted using the compound growth rate and linear regression methods. The results show that the proposed methodology generates much more reasonable projections than current practices.     

Train movement simulation by element increment method

The article presents an element increment method that is developed by current time increment method of train traction calculation. A railway route was divided, breaking it down into elements of different lengths. A whole train movement simulation curve (v–t curve and v–S curve) was formed by splitting the joints of each of the elements' individual simulation curves. During this process, the train velocity variance was calculated by time increment method with assistance of polynomial fitting technology. Additionally, a step‐by‐step method with iteration was used to combine each element and makes the whole simulation curve continuous. Meanwhile, the energy‐saving issue was also taken into account to optimize the simulation curve. This article gives more details about the modeling by providing an example of a railway route based on moving block control. The element increment method is a more effective way to calculate train traction of high‐speed railway, and it is an alternative method to train movement simulation for aiding macroscopic railway transportation planning. Copyright © 2017 John Wiley & Sons, Ltd.     

Route structures in air transportation—the situation in the federal republic of Germany

This paper deals with route structures in air transportation in general and describes the derivation of such structures. Based on an extensive analysis of scheduled air traffic in Germany, an overview of the situation in domestic and international air travel is given. In particular, relationships were found which permit—in connection with a number of influencing factors—to derive from the present situation route structures, which are also valid for a future year.

This approach was used for the assignment of origin‐destination‐passenger flows to air network routes in a forecast of demand and services in commercial air transportation of the Federal Republic of Germany for the year 1995.     

Constrained H∞ control of urban transportation network

Because transportation systems involve massive complex human activities, there exist substantial unpredictable uncertainties of the traffic demands. This paper aims at presenting an H_∞ control method for transportation network that can enhance the tolerance of the system due to these uncertainties. In particular, the store‐and‐forward approach is applied to model the system into a linear form. Then, a detailed controllability analysis shows that the system is not completely controllable by taking the constraints on the green times into account. This makes difficult to apply directly the H_∞ method. To overcome this difficulty, this paper isolates the fully controllable part of the transportation system, and the problem of disturbance attenuation is then solved by means of a convex optimization with linear matrix inequality. Finally, the simulation of a large‐scale hypothetical network is carried out to illustrate the results. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimal routing of a single vehicle with loading and unloading constraints

A real‐life situation in which a trucker has to collect a cargo of similar size from n different customers spread out in a given region and to deliver them to n locations spread out in another far‐away region has been formulated as a route‐design problem for a single vehicle. The minimal total time of loading, shipping and unloading is considered for different reshuffling methods, and the optimal method is determined. A solution procedure by enumeration is suggested to solve an actual small size problem, and an illustration is provided.     

A GIS‐based traffic analysis zone design: implementation and evaluation

The purpose of this paper is to implement an efficient method for GIS‐based traffic analysis zone (TAZ) design in order to evaluate and validate such a method. The method was developed by the authors.

Moran's I spatial autocorrelation coefficient and sample variance are used for evaluating the generated TAZs using the Champaign‐Urbana, IL region as a case study. Sensitivity analysis is also conducted to explore the fluctuations in TAZ generation outcomes. The evaluation, the validation as well as the TAZ design have been implemented with ARC/INFO GIS software on a UNIX workstation platform.     

Integrating multi-source maritime information to estimate ship exhaust emissions under wind,wave and current conditions

How to accurately calculate ship exhaust emissions has become urgent needs. In this paper, multi-source maritime information is integrated to estimate ship exhaust emissions under ocean environment. Influences of wind, wave and current on ship speed are firstly analyzed and mathematically modeled. Based on the influences, ocean environment information and ship trajectories are integrated to identify ship activities exactly. After that, ship activity based calculation method is present to obtain exhaust emissions from ship in various activities. Contribution ratios of different ship type and ship activities have been further discussed. In a case study of Ningbo-Zhoushan port in China, greenhouse gas (CO₂, CO, SO_x, NO_x and PM) emissions from ships in 2014 calculated by the proposed method are 8.72 × 10⁵ ton, 2.07 × 10³ ton, 1.47 × 10⁴ ton, 2.60 × 10⁴ ton and 1.40 × 10³ ton respectively. The maximum error is under 10%. Experimental results illustrate that the proposed method can produce more accurate ship exhaust emissions than traditional method under ocean environment conditions.     

A queueing system for airport buses

The use of remote terminals to relieve airport congestion leads rather naturally to queueing systems with batch arrivals occuring at fixed time intervals to a multiple server service facility. In this paper arrival point steady-state solutions to the D^[X]/M/c queueing system are presented. Solution of the steady-state equation WP = W and Neuts' method of solving the GI^[X]/M/c system are used to obtain steady-state system size densities. Results obtained using the two methods are compared with each other as well as with simulation results.     

The convergence of diagonalization algorithms for asymmetric network equilibrium problems

We provide a sufficient condition for the convergence of diagonalization algorithms for equilibrium traffic assignment problems with asymmetric Jacobian matrix B(v) of the link user cost mapping s(v) of the flow v. When

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, where D(v^*) > 0 is the diagonal of B(v^*) and v^* is the equilibrium flow, we demonstrate a local convergence theorem for nonlinear cost functions. The implication of this result for practical applications of the model are outlined.     

Estimating intersection O-D matrices from observed time-series of traffic counts: The zellner estimator

The problem of estimating intersection O-D matrices from input and output time-series of traffic counts is considered in this paper. Because of possible existence of significant correlation between the error terms across structural equations forming the O-D matrices, the seemingly unrelated estimator (Zellner estimator) was suggested. Estimation results showed evidence of strong correlation between error terms across-equations. Generally, the Zellner estimator produced more efficient estimates than did the ordinary least-squares estimator. Furthermore, the Zellner estimator satisfied all constraints and reproduced turning movements comparable to the actual ones.     

Use of Bayesian inference method to model vehicular air pollution in local urban areas

Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO₂, NO₂ and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system.     

Behavior of the cell transmission model and effectiveness of ramp metering

The paper characterizes the behavior of the cell transmission model of a freeway, divided into N sections or cells, each with one on-ramp and one off-ramp. The state of the dynamical system is the N-dimensional vector n of vehicle densities in the N sections. A feasible stationary demand pattern induces a unique equilibrium flow in each section. However, there is an infinite set—in fact a continuum—of equilibrium states, including a unique uncongested equilibrium n^u in which free flow speed prevails in all sections, and a unique most congested equilibrium n^con. In every other equilibrium n^e one or more sections are congested, and n^u  n^e  n^con. Every equilibrium is stable and every trajectory converges to some equilibrium state.Two implications for ramp metering are explored. First, if the demand exceeds capacity and the ramps are not metered, every trajectory converges to the most congested equilibrium. Moreover, there is a ramp metering strategy that increases discharge flows and reduces total travel time compared with the no-metering strategy. Second, even when the demand is feasible but the freeway is initially congested, there is a ramp metering strategy that moves the system to the uncongested equilibrium and reduces total travel time. The two conclusions show that congestion invariably indicates wastefulness of freeway resources that ramp metering can eliminate.