Calibration of large-scale transport planning models: a structured approach

Transportation - Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be...     

A novel approach for systematically calibrating transport planning model systems

Calibration of a transport planning model system is a complex process. While trial-and-error methods and modelling expertise are still the backbone of calibration of transport models, analytical approaches automating the calibration process can improve the accuracy of the models. Introducing a model to guide modellers in the calibration process of large-scale transport planning model systems is the core of this study, where a systematic model for choosing the most appropriate models and parameters is discussed. The effectiveness of the proposed model is investigated by comparing three scenarios which are built on the Travel/Activity Scheduler for Household Agents model as a large-scale agent-based model system.

     

Testing and evaluation of a multi-commodity multi-modal network flow model for disaster relief management

The results of the testing of an optimization model in disaster relief management are presented. The problem is a large-scale multi-commodity, multi-modal network flow problem with time windows. Due to the nature of this problem, the size of the optimization model grows extremely rapidly as the number of modes and/or commodities increase. The formulation is based on the concept of a time-space network. Two heuristic algorithms are developed. One exploits an inherent network structure of the problem with a set of side constraints and the other is an interactive fix-and-run heuristic. The findings of the model-testing and a wide range of sensitivity analyses using an artificially generated data set are presented. Both solution procedures prove to be efficient and effective in providing close to optimal solutions.     

Tangential force variation due to the bogie direction reversal procedure

A range of tangential forces is generated within the contact patch when a wheelset moves on the rail. These forces are intensified when incorporating curved tracks and motored axle rail vehicles [Arrus, P., de Pater, A.D. and Meyers, P., 2002, The stationary motion of a one-axle vehicle along a circular curve with real rail and wheel profiles. Vehicle System Dynamics, 37(1), 29-58]. The wheelset is subject to flange contact if an unbalanced force remains in a curve towards the high rail gauge face. The resultant force in the transverse direction includes the lateral force, the radial force, and the creep forces in addition to the effect of the frequent wheelset displacement due to the kinematic oscillation [Iwnicki, S., 2003, Simulation of wheel-rail contact forces. Fatigue Fracture Engineering Material Structure, 26, 887-900]. This article has focused on a potential variation in some of the forces cited when the wheelset is subject to backward and forward movements. A severe wear rate observed within the wheel flange region in Iranian Railways was investigated by operating a test bogie on a curvaceous track. An obvious improvement in the wear rate and wear pattern of the wheels was attained when the second test bogie encountered a bogie direction reversal procedure. This enhancement is considered in this article from the force analysis standpoint.     

Emission analysis of a compressed natural gas directinjection engine with a homogenous mixture

In an era in which environmental pollution and depletion of world oil reserves are of major concern, emissions produced by automotive vehicles need to be controlled and reduced. An ideal solution is to switch to a cleaner fuel such as natural gas, which generates cleaner emissions. In addition, control over the in-cylinder air-fuel mixture can be best achieved through a direct-injection mechanism, which can further improve combustion efficiency. This need for cleaner automobiles provides the motivation for this paper’s examination of the use of computational fluid dynamic (CFD) simulations to analyze the concentrations of the exhaust gases produced by a compressed natural gas engine with a direct-fuel-injection system. In this work, a compressed natural gas direct-injection engine has been designed and developed through a numerical simulation using computational fluid dynamics (CFD) to provide an insight into complex in-cylinder behavior. The emissions analyzed in this study were carbon monoxide (CO), nitric oxide (NO) and carbon dioxide (CO₂), i.e. the main pollutants produced by natural gas combustion. Based on a stoichiometric mixture, the concentrations of CO and NO were computed using the dissociation of carbon dioxide and the extended Zeldovich mechanism. CO₂ was calculated using a mass balance of the species involved in the combustion process. The simulation results were then compared with the experimental data generated by a single-cylinder research engine test rig. A good agreement was obtained with the experimental data for the engine speeds considered for all emissions concentrations.     

Hybrid artificial neural network and locally weighted regression models for lane-based short-term urban traffic flow forecasting

ABSTRACT

In recent years, there has been considerable research interest in short-term traffic flow forecasting. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g. 1 or 5?min) and lane level are still rare. In this study, a combination of genetic algorithm, neural network and locally weighted regression is used to achieve optimal prediction under various input and traffic settings. The genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) models are developed and tested, with the former forecasting traffic flow every 5-min within a 30-min period and the latter for forecasting traffic flow of a particular 5-min period of each for four lanes of an urban arterial road in Beijing, China. In particular, for morning peak and off-peak traffic flow prediction, the GA-ANN 5-min traffic flow model results in average errors of 3–5% and most 95th percentile errors of 7–14% for each of the four lanes; for the peak and off-peak time traffic flow predictions, the GA-LWR 5-min traffic flow model results in average errors of 2–4% and most 95th percentile errors are lower than 10% for each of the four lanes. When compared to previous models that usually offer average errors greater than 6–15%, such empirical findings should be of interest to and instrumental for transportation authorities to incorporate in their city- or state-wide Advanced Traveller Information Systems (ATIS).     

High temperature indentation test to improve constitutive model for welding simulation

The high temperature gradients experienced during fusion welding lead to a local dilatation and metallurgical transformations that generate inhomogeneous plastic deformation, residual stresses and distortions in the welded parts which can affect the service life of the structures. To predict such residual stresses and distortions, finite element analysis is nowadays widely used. This work reports a 3D finite element model for welding simulation. The proposed model is based on semi coupled thermo-mechanical analysis using a double ellipsoidal model of heat source. Firstly, a disk heated in its central zone is investigated. Secondly the validation concerns the simulation of a fully 3D two pass butt weld. The idea in this work concerns the calibration of the heat source parameters with the help of the inverse analysis to improve the heat flow predictions. For the stress analysis, an annealing temperature is introduced to force the material to lose its hardening memory above a given temperature. The predicted residual stresses as well as the predicted distortions are found to be sensitive to the annealing temperature.     

Analogy of fixed route shared taxi (taxi khattee) and bus services under various demand density and economical conditions

Taxi khattee is a fixed route unregulated shared taxi. It is a very common mode of transportation in Iran. Fixed route, unscheduled operation, open, unlimited pick‐up and drop‐off locations, and share ride are common features of taxi khattees. Low passenger capacity and working in high demand corridors provides for the possibility of high service frequencies any time of the day. Taxi khattees are similar to jitneys, which are obsolete or illegal in many countries. The aim of this research is to design transit network of an area using taxi khattees in addition to buses. The methodology employed in this paper simultaneously considers the costs to the users and operators on the one hand, and those of the public non‐users on the other hand. Taxi khattees are used in the design of a multimodal network along with buses to characterize the appropriate economic domain for their use. Moreover, their operation indices are compared against those of buses. A sensitivity analysis is carried out on various performance measures. Results show taxi khattees should be used in areas where population density is low, work force is inexpensive, social costs are not considered in fare calculation, and users' value of time is high. The study contradicts the common belief that since taxi khattees provide a high frequency compared to buses, they are economically plausible to use in a transit fleet. Copyright © 2011 John Wiley & Sons, Ltd.     

A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure

Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative, as the SVM methodology has not previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM's prediction performance in terms of minimization of the number of false positive prediction errors. Experiment data generated by VIRTTEX, a hydraulically powered, 6-degrees-of-freedom moving base driving simulator at Ford Motor Company, were used. All the vehicle variables were sampled at 50 Hz and there were 16 drowsy drivers (about 3 hours of driving per subject) and six control drivers (approximately 20 minutes f driving each). In total, 3,508 unintentional lane departures occurred for the drowsy drivers and 23 for the control drivers. Our study involving these 22 drivers with a total of more than 7.5 million prediction decisions demonstrates that (a) excellent SVM prediction performance, measured by numbers of false positives (i.e., falsely predicted lane departures) and false negatives (i.e., lane departures failed to be predicted), was achieved when the prediction horizon was 0.6 seconds or less, (b) lateral position and lateral velocity worked the best as SVM input variables among the nine variable sets that we explored, and (c) the radial basis function performed the best as the SVM kernel function.     

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.