Shipment forecasting for supply chain collaborative transportation management using grey models with grey numbers

The sharing of forecasts is vital to supply chain collaborative transportation management (CTM). Shipment forecasting is fundamental to CTM, and is essential to carrier tactical and operational planning processes such as network planning, routing, scheduling, and fleet planning and assignment. By applying and extending grey forecasting theory, this paper develops a series of shipment forecasting models for supply chain CTM. Grey time-series forecasting and grey systematic forecasting models are developed for shipment forecasting under different collaborative frameworks. This paper also integrates grey numbers with grey models for analyzing shipment forecasting under partial information sharing in CTM frameworks. An example of an integrated circuit (IC) supply chain and relevant data are provided. The proposed models yield more accurate prediction results than regression, autoregressive integrated moving average (ARIMA), and neural network models. Finally, numerical results indicate that as the degree of information sharing increases under CTM, carrier prediction accuracy increases. This paper demonstrates how the proposed forecasting models can be applied to the CTM system and provides the theoretical basis for the forecasting module developed for supply chain CTM.     

Estimation of missing traffic counts using factor,genetic, neural,and regression techniques

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be less accurate. ARIMA models only use the historical data. In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed. It was found that genetically designed regression models based on data from before and after the failure had the most accurate results. Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns. Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases.     

Comparison of parametric and nonparametric models for traffic flow forecasting

Single point short-term traffic flow forecasting will play a key role in supporting demand forecasts needed by operational network models. Seasonal autoregressive integrated moving average (ARIMA), a classic parametric modeling approach to time series, and nonparametric regression models have been proposed as well suited for application to single point short-term traffic flow forecasting. Past research has shown seasonal ARIMA models to deliver results that are statistically superior to basic implementations of nonparametric regression. However, the advantages associated with a data-driven nonparametric forecasting approach motivate further investigation of refined nonparametric forecasting methods. Following this motivation, this research effort seeks to examine the theoretical foundation of nonparametric regression and to answer the question of whether nonparametric regression based on heuristically improved forecast generation methods approach the single interval traffic flow prediction performance of seasonal ARIMA models.     

Forecasting automobile petrol demand in Australia: An evaluation of empirical models

Transport fuel consumption and its determinants have received a great deal of attention since the early 1970s. In the literature, different types of modelling methods have been used to estimate petrol demand, each having methodological strengths and weaknesses. This paper is motivated by an ongoing need to review the effectiveness of empirical fuel demand forecasting models, with a focus on theoretical as well as practical considerations in the model-building processes of different model forms. We consider a linear trend model, a quadratic trend model, an exponential trend model, a single exponential smoothing model, Holt’s linear model, Holt–Winters’ model, a partial adjustment model (PAM), and an autoregressive integrated moving average (ARIMA) model. More importantly, the study identifies the difference between forecasts and actual observations of petrol demand in order to identify forecasting accuracy. Given the identified best-forecasting model, Australia’s automobile petrol demand from 2007 through to 2020 is presented under the “business-as-usual” scenario.     

Estimation of bus dwell time using univariate time series models

A significant proportion of bus travel time is contributed by dwell time for passenger boarding and alighting. More accurate estimation of bus dwell time (BDT) can enhance efficiency and reliability of public transportation system. Regression and probabilistic models are commonly used in literatures where a set of independent variables are used to define the statistical relationship between BDT and its contributing factors. However, due to technical and monetary constraints, it is not always feasible to collect all the data required for the models to work. More importantly, the contributing factors may vary from one bus route to another. Time series based methods can be of great interest as they require only historical time series data, which can be collected using a facility known as automatic vehicle location (AVL) system. This paper assesses four different time series based methods namely random walk, exponential smoothing, moving average (MA), and autoregressive integrated moving average to model and estimate BDT based on AVL data collected from Auckland. The performances of the proposed methods are ranked based on three important factors namely prediction accuracy, simplicity, and robustness. The models showed promising results and performed differently for central business district (CBD) and non‐CBD bus stops. For CBD bus stops, MA model performed the best, whereas for non‐CBD bus stops, ARIMA model performed the best compared with other time series based models. Copyright © 2014 John Wiley & Sons, Ltd.     

Prediction of ambient carbon monoxide concentration using nonlinear time series analysis technique

This study evaluates the potential of nonlinear time series analysis based methods in predicting the carbon monoxide concentration in an urban area. To establish the functional relationship between current and future observations, two models based on local approximations and neural network approximations are used. To compare the performance of the models, an autoregressive integrated moving average model is also applied. The multi-step forecasting capabilities of the models are evaluated.     

Long-memory characteristics of urban roadside air quality

Carbon monoxide is a major contributor to air pollution in urban cities, particularly at the roadside. Hourly, monthly and seasonal mean carbon monoxide concentration data are collected from a roadside air monitoring station in Hong Kong over 7-years. The station is a few metres from a major intersection surrounded by tall buildings. In particular, hourly patterns of concentrations on different days of the week are investigated. The data show that hourly carbon monoxide concentrations resemble the traffic pattern of the area and tend to be lower in the summer. Using a seasonal autoregressive integrated moving average models shows that the daily traffic cycle strongly influences concentrations. Further, it is found that urban roadside carbon monoxide monitoring data exhibits a long-memory process, suggesting that a model incorporating long memory and seasonality effects is needed simulate urban roadside air quality.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Discrete dynamic models for freeway incident detection systems

This paper considers the problem of freeway incident detection within the general framework of computer‐based freeway surveillance and control. A new approach to the detection of freeway traffic incidents is presented based on a discrete‐time stochastic model of the form ARIMA (0, 1, 3) that describes the dynamics of traffic occupancy observations. This approach utilizes real‐time estimates of the variability in traffic occupancies as detection thresholds, thus eliminating the need for threshold calibration and lessening the problem of false‐alarms. Because the moving average parameters of the ARIMA (0, 1, 3) model change over time, these parameters can be updated occasionally. The performance of the developed detection algorithm has been evaluated in terms of detection rate, false‐alarm rate, and average time‐lag to detection, using a total of 1692 minutes of occupancy observations recorded during 50 representative traffic incidents.     

Forecasting ridership for a metropolitan transit authority

The recent volatility in gasoline prices and the economic downturn have made the management of public transportation systems particularly challenging. Accurate forecasts of ridership are necessary for the planning and operation of transit services. In this paper, monthly ridership of the Metropolitan Tulsa Transit Authority is analyzed to identify the relevant factors that influence transit use. Alternative forecasting models are also developed and evaluated based on these factors—using regression analysis (with autoregressive error correction), neural networks, and ARIMA models—to predict transit ridership. It is found that a simple combination of these forecasting methodologies yields greater forecast accuracy than the individual models separately. Finally, a scenario analysis is conducted to assess the impact of transit policies on long-term ridership.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.     

Estimation of infrastructure performance models using state-space specifications of time series models

We consider state-space specifications of autoregressive moving average models (ARMA) and structural time series models as a framework to formulate and estimate inspection and deterioration models for transportation infrastructure facilities. The framework provides a rigorous approach to exploit the abundance and breadth of condition data generated by advanced inspection technologies. From a managerial perspective, the framework is attractive because the ensuing models can be used to forecast infrastructure condition in a manner that is useful to support maintenance and repair optimization, and thus they constitute an alternative to Markovian transition probabilities. To illustrate the methodology, we develop performance models for asphalt pavements. Pressure and deflection measurements generated by pressure sensors and a falling weight deflectometer, respectively, are represented as manifestations of the pavement’s elasticity/load-bearing capacity. The numerical results highlight the advantages of the two classes of models; that is, ARMA models have superior data-fitting capabilities, while structural time series models are parsimonious and provide a framework to identify components, such as trend, seasonality and random errors. We use the numerical examples to show how the framework can accommodate missing values, and also to discuss how the results can be used to evaluate and select between inspection technologies.     

Spatiotemporal traffic forecasting: review and proposed directions

This paper systematically reviews studies that forecast short-term traffic conditions using spatial dependence between links. We extract and synthesise 130 research papers, considering two perspectives: (1) methodological framework and (2) methods for capturing spatial information. Spatial information boosts the accuracy of prediction, particularly in congested traffic regimes and for longer horizons. Machine learning methods, which have attracted more attention in recent years, outperform the naïve statistical methods such as historical average and exponential smoothing. However, there is no guarantee of superiority when machine learning methods are compared with advanced statistical methods such as spatiotemporal autoregressive integrated moving average. As for the spatial dependency detection, a large gulf exists between the realistic spatial dependence of traffic links on a real network and the studied networks as follows: (1) studies capture spatial dependency of either adjacent or distant upstream and downstream links with the study link, (2) the spatially relevant links are selected either by prejudgment or by correlation-coefficient analysis, and (3) studies develop forecasting methods in a corridor test sample, where all links are connected sequentially together, assume a similarity between the behaviour of both parallel and adjacent links, and overlook the competitive nature of traffic links.     

Information metrics for improved traffic model fidelity through sensitivity analysis and data assimilation

We develop theoretical and computational tools which can appraise traffic flow models and optimize their performance against current time-series traffic data and prevailing conditions. The proposed methodology perturbs the parameter space and undertakes path-wise analysis of the resulting time series. Most importantly the approach is valid even under non-equilibrium conditions and is based on procuring path-space (time-series) information. More generally we propose a mathematical methodology which quantifies traffic information loss.In particular the method undertakes sensitivity analysis on available traffic data and optimizes the traffic flow model based on two information theoretic tools which we develop. One of them, the relative entropy rate, can adjust and optimize model parameter values in order to reduce the information loss. More precisely, we use the relative entropy rate as an information metric between time-series data and parameterized stochastic dynamics describing a microscopic traffic model. On the other hand, the path-space Fisher Information Matrix, (pFIM) reduces model complexity and can even be used to control fidelity. This is achieved by eliminating unimportant model parameters or their combinations. This results in easier regression of parametric models with a smaller number of parameters.The method reconstructs the Markov Chain and emulates the traffic dynamics through Monte Carlo simulations. We use the microscopic interaction model from Sopasakis and Katsoulakis (2006) as a representative traffic flow model to illustrate this parameterization methodology. During the comparisons we use both synthetic and real, rush-hour, traffic data from highway US-101 in Los Angeles, California.     

Time-series model for vehicle speeds

This paper presents a time-series model for the spot speeds of vehicles on a road section. Based on time-series analysis techniques, the model incorporates information on the extent of existing dependency between the speeds of successive vehicles. The model for the data is chosen while relying heavily on the data, and thus emphasis is given to their special characteristics. The advantages of using the model are examined with regard to the relative speed of two successive vehicles along a road section. The results are compared with those obtained by using a model of independent observations; fewer errors are obtained with the time-series model. Therefore, it is concluded that the sequence of speed observations contains valuable information which should be incorporated into speed models.     

Short‐term travel speed prediction models in car navigation systems

The objective of this study is the development of the short‐term prediction models to predict average spot speeds of the subject location in the short‐term periods of 5, 10 and 15 minutes respectively. In this study, field data were used to see the comparison of the predictability of Regression Analysis, ARIMA, Kalman Filtering and Neural Network models. These field data were collected from image processing detectors at the urban expressway for 17 hours including both peak and non‐peak hours. Most of the results were reliable, but the results of models using Kalman Filtering and Neural Networks are more accurate and realistic than those of the others.     

Incorporating spatial interactions in zero-inflated negative binomial models for freight trip generation

This paper formulates a spatial autoregressive zero-inflated negative binomial model for freight trip productions and attractions. The model captures the following freight trip characteristics: count data type, positive trip rates, overdispersion, zero-inflation, and spatial autocorrelation. The spatial autoregressive structure is applied in the negative binomial part of the models to obtain unbiased estimates of the effects of different regressors. Further, we estimate parameters using the full information maximum likelihood estimator. We perform empirical analysis with an establishment based freight survey conducted in Chennai. Separate models are estimated for trips generated by motorised two-wheelers and three-wheelers, and pickups besides an aggregate model. Spatial variables such as road density and indicator of geolocation are insignificant in all the models. In contrast, the spatial autocorrelation is significant in all of the models except for the freight trips attracted and produced by pickups. From a policy standpoint, the elasticity results show the importance of considering spatial autocorrelation. We also highlight the bias due to aggregation of vehicle classes, based on the elasticities.

     

New insights on random regret minimization models

This paper develops new methodological insights on Random Regret Minimization (RRM) models. It starts by showing that the classical RRM model is not scale-invariant, and that – as a result – the degree of regret minimization behavior imposed by the classical RRM model depends crucially on the sizes of the estimated taste parameters in combination with the distribution of attribute-values in the data. Motivated by this insight, this paper makes three methodological contributions: (1) it clarifies how the estimated taste parameters and the decision rule are related to one another; (2) it introduces the notion of “profundity of regret”, and presents a formal measure of this concept; and (3) it proposes two new family members of random regret minimization models: the μRRM model, and the Pure-RRM model. These new methodological insights are illustrated by re-analyzing 10 datasets which have been used to compare linear-additive RUM and classical RRM models in recently published papers. Our re-analyses reveal that the degree of regret minimizing behavior imposed by the classical RRM model is generally very limited. This insight explains the small differences in model fit that have previously been reported in the literature between the classical RRM model and the linear-additive RUM model. Furthermore, we find that on 4 out of 10 datasets the μRRM model improves model fit very substantially as compared to the RUM and the classical RRM model.     

An empirical analysis of freight transport traffic modes in Brazil, 1996–2012

This paper analyses the performance of freight transportation modes in Brazil – namely air, water, rail and road – from February 1996 to August 2012 by investigating their long memory properties using fractional integration and autoregressive models on monthly tonnage data. Two important features are analysed: the degree of dependence of transportation traffic across time and its seasonal structure over the period. Furthermore, the stability of parameters across the sample period is investigated, incorporating potential structural breaks in the data, which describe discontinuity in freight transportation traffic. Some policy implications are derived.     

Short-term,real-time prediction of the extreme ambient carbon monoxide concentrations due to vehicular exhaust emissions using transfer function-noise model

The Box–Jenkins transfer function-noise (TFN) models (Box, G.E.P., Jenkins, G.M., Reinsel, G.C., 1994. Time Series Analysis: Forecasting and Control, 3rd ed. Prentice-Hall, Englewood Cliffs, NJ.) have been used to provide short-term, real-time forecast of the extreme carbon monoxide for an air quality control region (AQCR) comprising a major traffic intersection in the centre of the capital city of Delhi. The time series of the surface wind speed and ambient temperature have been used as “explaining” exogenous variables in the TFN models. When compared with the results of univariate ARIMA model of the endogenous series, the forecast performance is found to improve with the inclusion of the wind speed as input series; however, no significant improvement is observed in the forecast with the inclusion of temperature as input series.