Forecasting Freight Flows

Trade patterns and transport markets are changing as a result of the growth and globalization of international trade, and forecasting future freight flow has to rely on trade forecasts. Forecasting freight flows is critical for matching infrastructure supply to demand and for assessing investment. This article models long-term dynamic physical trade flows and estimates a dynamic panel data model for foreign trade for the EU15 and two countries from the EFTA (European Free Trade Association) 1967–2002. The analysis suggests that a dynamic three-way-effects gravity equation is the best-fitted econometric model. The analysis uses a structural relationship to explain the structure of the exchange of the goods—a relationship that can be used in the year of forecast. This article also provides a new methodology for converting monetary aggregates into quantity aggregates. The resulting commodity growth rates constitute a valuable input to freight models for forecasting future capacity problems.     

Quantifying errors in micro-scale emissions models using a case-study approach

To quantify the level of uncertainty attached to forecasts of CO₂ emissions, an analysis of errors is undertaken; looking at both errors inherent in the model structure and the uncertainties in the input data. Both error types are treated in relation to CO₂ emissions modelling using a case-study from Brisbane, Australia. To estimate input data uncertainty, an analysis of traffic conditions using Monte Carlo simulation is used. Model structure induced uncertainties are also quantified by statistical analysis for a number of traffic scenarios. To arrive at an optimal overall CO₂ prediction, the interaction between the two components is taken into account. Since a more complex model does not necessarily yield higher overall accuracy, a compromise solution is found. The results suggest that the CO₂ model used in the analysis produces low overall uncertainty under free flow traffic conditions. When average traffic speeds approach congested conditions, however, there are significant errors associated with emissions estimates.     

Quantification of motorway capacity variation: influence of day type specific variation and capacity drop

Many road authorities work with static values for road capacities, while it has been proven that capacity is not a fixed quantity. At the same time, there is an increasing need for accurate stochastic input for traffic models, such as the variation in road capacity. In this paper, a methodological framework with a conceptual model for practical stochastic capacity estimation is presented, and a quantification of motorway capacity variation is given for the influence of day‐type specific variations in capacity values. The results of the analysis show that there is a reduction in motorway breakdown capacity of 4% on weekend days in comparison with workdays. Furthermore, a capacity decrease of 8% was found for the discharge capacity in comparison with workdays. The analysis further shows that the breakdown capacity on holidays is not significantly lower than on workdays. Discharge capacity and capacity drops are also derived in each case. The results show that the capacity is significantly different depending on the type of day. A quantification of these differences is given in the form of a Weibull capacity estimation fit for each type‐of‐day scenario. Further consideration of the implications and applications of the framework is also given. Copyright © 2015 John Wiley & Sons, Ltd.     

An econometric analysis of motorway renewal costs in Germany

The analysis of motorway renewal costs presented in this paper was driven by two research questions: First, to analyse the economic process of motorway renewal work and to identify whether there exist economies of scale; and second to identify the influence of traffic volume on renewal costs and to derive an estimate of marginal infrastructure costs as part of optimal road user charges. The analysis is based on cross-sectional data for motorway renewal costs and traffic volume in Germany during the period 1980–1999. Two translog models were estimated, each of them including the factor input prices for labour, material and capital, and a set of regional dummy variables as well as dummy variables for the type of material used for renewal. The first model includes in addition to these variables the sqm of renewed road as explanatory variable. The second model was constructed to analyse the relationship between traffic volume and renewal costs and contains the average annual daily traffic volume of trucks and passenger cars as independent variables. Two main results were derived from the models: First, motorway renewal work is characterised by substantial economies of scale. Second, the relationship between renewal costs and traffic volume is expressed by a cost elasticity, i.e., the ratio between marginal and average costs, which ranges from 0.05 up to 1.17 with a digressive increase of marginal costs.     

The reliability of the motorway transport system

This paper presents an analysis of motorway circulation under congested traffic conditions, with the aim of arriving at a measurement of the reliability of the motorway transport system. The author suggests a model of the speed process on a motorway lane under conditions of congestion, which was tested experimentally employing a great number of data collected on two motorways, varying considerably in terms of environmental and traffic conditions. It is shown that the relationships between the parameters of this process and the traffic density define in sufficiently complete fashion the behaviour of the motorway transport system under conditions of congestion. Using this speed process model, a simulation procedure was developed which permits calculation of the reliability of a traffic stream. Finally a method was determined for calculating the reliability in real time, which can be usefully employed in motorway traffic control.     

Uncertainty in traffic forecasts: literature review and new results for The Netherlands

This paper provides a review of transport model applications that not only provide a central traffic forecast (or forecasts for a few scenarios), but also quantify the uncertainty in the traffic forecasts in the form of a confidence interval or related measures. Both uncertainty that results from using uncertain inputs (e.g. on income) and uncertainty in the model itself are treated. The paper goes on to describe the methods used and the results obtained for a case study in quantifying uncertainty in traffic forecasts in The Netherlands.

Modelling major traffic incident impacts and estimation of their associated costs

Network area-wide impacts due to major traffic incidents can be assessed using a microsimulation approach. A VISSIM microsimulation model for a motorway network has been developed and is used to quantify impacts of a major incident in terms of associated costs. The modelled results reveal that a 65% capacity reduction results in 36% more incident-induced delay when compared with the application of a 50% capacity reduction assumption for a two-hour incident clearance duration that blocked one lane of a two-lane motorway. Additionally, an incident which caused a full blockage incurred 40 times more associated impact costs when compared with a major incident which caused a one lane blockage. A 23% cost saving can be achieved by clearing one lane of a fully blocked two-hour major traffic incident after 90 minutes, while a 37% cost saving can be achieved by clearing all blockages after 90 minutes.     

Control Concepts for Facilitating Motorway On-ramp Merging Using Intelligent Vehicles

Abstract

Congestion at motorway junctions is a traffic phenomenon that degrades operation of infrastructure and can lead to breakdown of traffic flow and associated reduction in capacity. Advanced communication technologies open new possibilities to prevent or at least delay this phenomenon, and innovative active traffic management systems have been developed in the recent years for better control of motorway traffic. This paper presents a review of control strategies for facilitating motorway on-ramp merging using intelligent vehicles. First, the concepts of the control algorithms are reviewed chronologically divided into three types of intelligent vehicle: completely automated, equipped with cooperative adaptive cruise control and equipped with on-board display. Then, a common structure is identified, and the algorithms are presented based on their characteristics in order to identify similarities, dissimilarities, trends and possible future research directions. Finally, using a similar approach, a review of the methods used to evaluate these control strategies identifies important aspects that should be considered by further research on this topic.     

Predicting and planning airport acceptance rates in metroplex systems for improved traffic flow management decision support

Efficient planning of Airport Acceptance Rates (AARs) is key for the overall efficiency of Traffic Management Initiatives such as Ground Delay Programs (GDPs). Yet, precisely estimating future flow rates is a challenge for traffic managers during daily operations as capacity depends on a number of factors/decisions with very dynamic and uncertain profiles. This paper presents a data-driven framework for AAR prediction and planning towards improved traffic flow management decision support. A unique feature of this framework is to account for operational interdependency aspects that exist in metroplex systems and affect throughput performance. Gaussian Process regression is used to create an airport capacity prediction model capable of translating weather and metroplex configuration forecasts into probabilistic arrival capacity forecasts for strategic time horizons. To process the capacity forecasts and assist the design of traffic flow management strategies, an optimization model for capacity allocation is developed. The proposed models are found to outperform currently used methods in predicting throughput performance at the New York airports. Moreover, when used to prescribe optimal AARs in GDPs, an overall delay reduction of up to 9.7% is achieved. The results also reveal that incorporating robustness in the design of the traffic flow management plan can contribute to decrease delay costs while increasing predictability.     

Optimal mainstream traffic flow control of large-scale motorway networks

The continuously increasing daily traffic congestions on motorway networks around the world call for innovative control measures that would drastically improve the current traffic conditions. Mainstream traffic flow control (MTFC) is proposed as a novel and efficient motorway traffic management tool, and its possible implementation and principal impact on traffic flow efficiency is analysed. Variable speed limits, suitably operated and enforced, is considered as one (out of several possible) way(s) for MTFC realisation, either as a stand-alone measure or in combination with ramp metering. A previously developed, computationally efficient software tool for optimal integrated motorway network traffic control including MTFC is applied to a large-scale motorway ring-road. It is demonstrated via several investigated control scenarios that traffic flow can be substantially improved via MTFC with or without integration with coordinated ramp metering actions.     

Uncertainty in the context of highway appraisal

Transport planning is based on traffic forecasts which are subject to great uncertainties. These uncertainties have generally been ignored or, at least, not explicitly included in the planning process. This paper describes the principles behind the estimation of the uncertainty (or range of error) of the forecasts of a traffic model and discusses the means by which this information can be absorbed into the decision-making in transport planning projects. These principles have been applied to the appraisal of a British highway project and throughout the paper reference is made to this project in order to illustrate the results of the practical application of an explicit treatment of uncertainty. It is believed that this approach can and should be developed for application in most areas of transport planning, leading inevitably to an improvement in the quality of the decisions taken.     

Optimisation of dynamic motorway traffic via a parsimonious and decentralised approach

This paper presents an optimisation framework for motorway management via ramp metering and variable speed limit. We start with presenting a centralised global optimal control problem aiming to minimise the total travel delay in a motorway system. Given the centralised global optimal control solutions, we propose a set of decentralised ramp metering and speed control strategies which operate on a novel parsimonious dynamic platform without needing an underlying traffic model. The control strategies are applied to a case on UK M25 motorway. The results show that the proposed set of decentralised control is able to deliver a performance that is close to the global optimal ones with significantly less computational and implementation effort. This study provides new insights to motorway management.     

Motorway crash duration and its determinants: do durations vary across motorways?

Traffic congestion has been a growing issue in many metropolitan areas during recent years, which necessitates the identification of its key contributors and development of sustainable strategies to help decrease its adverse impacts on traffic networks. Road incidents generally and crashes specifically have been acknowledged as the cause of a large proportion of travel delays in urban areas and account for 25% to 60% of traffic congestion on motorways. Identifying the critical determinants of travel delays has been of significant importance to the incident management systems, which constantly collect and store the incident duration data. This study investigates the individual and simultaneous differential effects of the relevant determinants on motorway crash duration probabilities. In particular, it applies parametric Accelerated Failure Time (AFT) hazard‐based models to develop in‐depth insights into how the crash‐specific characteristic and the associated temporal and infrastructural determinants impact the duration. AFT models with both fixed and random parameters have been calibrated on one year of traffic crash records from two major Australian motorways in South East Queensland, and the differential effects of determinants on crash survival functions have been studied on these two motorways individually. A comprehensive spectrum of commonly used parametric fixed parameter AFT models, including generalized gamma and generalized F families, has been compared with random parameter AFT structures in terms of goodness of fit to the duration data, and as a result, the random parameter Weibull AFT model has been selected as the most appropriate model. Significant determinants of motorway crash duration included traffic diversion requirement, crash injury type, number and type of vehicles involved in a crash, day of week and time of day, towing support requirement and damage to the infrastructure. A major finding of this research is that the motorways under study are significantly different in terms of crash durations; such that motorway 1 exhibits durations that are on average 19% shorter compared with the durations on motorway 2. The differential effects of explanatory variables on crash durations are also different on the two motorways. The detailed presented analysis confirms that looking at the motorway network as a whole, neglecting the individual differences between roads, can lead to erroneous interpretations of duration and inefficient strategies for mitigating travel delays along a particular motorway.     

A methodology to control urban traffic noise under the constraint of environmental capacity: A case study of a double-decision optimization model

This paper presents a novel methodology to control urban traffic noise under the constraint of environmental capacity. Considering the upper limits of noise control zones as the major bottleneck to control the maximum traffic flow is a new idea. The urban road network traffic is the mutual or joint behavior of public self-selection and management decisions, so is a typical double decision optimization problem.The proposed methodology incorporates theoretically model specifications. Traffic noise calculation model and traffic assignment model for O–D matrix are integrated based on bi-level programming method which follows an iterated process to obtain the optimal solution. The upper level resolves the question of how to sustain the maximum traffic flow with noise capacity threshold in a feasible road network. The user equilibrium method is adopted in the lower layer to resolve the O–D traffic assignment.The methodology has been applied to study area of QingDao, China. In this illustrative case, the noise pollution level values of optimal solution could satisfy the urban environmental noise capacity constraints. Moreover, the optimal solution was intelligently adjusted rather than simply reducing the value below a certain threshold. The results indicate that the proposed methodology is feasible and effective, and it can provide a reference for a sustainable development and noise control management of the urban traffic.     

Geoinformatic prediction of motorway noise on buildings in 3D GIS

This study predicts and displays the impacts of motorway traffic noise on a nearby building, utilizing a motorway traffic noise model in combination with geoinformatic technique. Two- and three-dimensional geographical information systems are used to visualize noise impacts in the form of three-dimensional noise contour on the building and ground surface. The results offer a clear, visual display of impact levels from roadway noise in the form of noise contour overlay on vicinity area and building blocks along the roadway in three-dimensional form. The area can be turned and investigated at different angles.     

Hubris or humility? Accuracy issues for the next 50 years of travel demand modeling

This study reviews the 50-year history of travel demand forecasting models, concentrating on their accuracy and relevance for public decision-making. Only a few studies of model accuracy have been performed, but they find that the likely inaccuracy in the 20-year forecast of major road projects is ±30 % at minimum, with some estimates as high as ±40–50 % over even shorter time horizons. There is a significant tendency to over-estimate traffic and underestimate costs, particularly for toll roads. Forecasts of transit costs and ridership are even more uncertain and also significantly optimistic. The greatest knowledge gap in US travel demand modeling is the unknown accuracy of US urban road traffic forecasts. Modeling weaknesses leading to these problems (non-behavioral content, inaccuracy of inputs and key assumptions, policy insensitivity, and excessive complexity) are identified. In addition, the institutional and political environments that encourage optimism bias and low risk assessment in forecasts are also reviewed. Major institutional factors, particularly low local funding matches and competitive grants, confound scenario modeling efforts and dampen the hope that technical modeling improvements alone can improve forecasting accuracy. The fundamental problems are not technical but institutional: high non-local funding shares for large projects warp local perceptions of project benefit versus costs, leading to both input errors and political pressure to fund projects. To deal with these issues, the paper outlines two different approaches. The first, termed ‘hubris’, proposes a multi-decade effort to substantially improve model forecasting accuracy over time by monitoring performance and improving data, methods and understanding of travel, but also by deliberately modifying the institutional arrangements that lead to optimism bias. The second, termed ‘humility’, proposes to openly quantify and recognize the inherent uncertainty in travel demand forecasts and deliberately reduce their influence on project decision-making. However to be successful either approach would require monitoring and reporting accuracy, standards for modeling and forecasting, greater model transparency, educational initiatives, coordinated research, strengthened ethics and reduction of non-local funding ratios so that localities have more at stake.     

Use of sequential learning for short-term traffic flow forecasting

Accurate short-term traffic flow forecasting has become a crucial step in the overall goal of better road network management. Previous research [H. Kirby, M. Dougherty, S. Watson, Should we use neural networks or statistical models for short term motorway traffic forecasting, International Journal of Forecasting 13 (1997) 43–50.] has demonstrated that a straightforward application of neural networks can be used to forecast traffic flows along a motorway link. The objective of this paper is to report on the application and performance of an alternative neural computing algorithm which involves ‘sequential or dynamic learning’ of the traffic flow process. Our initial work [H. Chen, S. Clark, M.S. Dougherty, S.M. Grant-Muller, Investigation of network performance prediction, Report on Dynamic Neural Network and Performance Indicator development, Institute for Transport Studies, University of Leeds Technical Note 418, 1998 (unpublished)] was based on simulated data (generated using a Hermite polynomial with random noise) that had a profile similar to that of traffic flows in real data. This indicated the potential suitability of dynamic neural networks with traffic flow data. Using the Kalman filter type network an initial application with M25 motorway flow data suggested that a percentage absolute error (PAE) of approximately 9.5% could be achieved for a network with five hidden units (compared with 11% for the static neural network model). Three different neural networks were trained with all the data (containing an unknown number of incidents) and secondly using data wholly obtained around incidents. Results showed that from the three different models, the ‘simple dynamic model’ with the first five units fixed (and subsequent hidden units distributed amongst these) had the best forecasting performance. Comparisons were also made of the networks’ performance on data obtained around incidents. More detailed analysis of how the performance of the three networks changed through a single day (including an incident) showed that the simple dynamic model again outperformed the other two networks in all time periods. The use of ‘piecewise’ models (i.e. where a different model is selected according to traffic flow conditions) for data obtained around incidents highlighted good performance again by the simple dynamic network. This outperformed the standard Kalman filter neural network for a medium-sized network and is our overall recommendation for any future application.     

Sea space capacity and operation strategy analysis system

Sea space planning and congestion management is receiving more attention. However, little work on sea space capacity and strategy analyses can be found in the literature. Compared to other transportation systems, a sea space system has some special features that require consideration. The system capacity also depends on the pattern of traffic using the system. In this paper, we model a sea space as a directional network and capacity models for berthing areas, anchorage areas, fairways and their intersections, as well as the entire sea space system are developed. These models can be used to compute capacity for any given traffic pattern which can be extracted from vessel trip records or from traffic forecasts. To implement these models, a software system called Sea Space Capacity and Strategy Analysis System (SCSAS) has been developed in Visual C + + and is now being used in Singapore.     

Study on road section environmental traffic capacity model and algorithm under double constraints

The coordinated development of city traffic and environment is a key research content in traffic field in twenty-first Century. Among them, road section environmental traffic capacity analysis is one of the important research issues. It can provide solid theoretical basis and reliable data support for road network traffic optimization control, road traffic pollution control and city traffic structure optimization. This paper analyzed main factors which impacted environmental traffic capacity from two aspects, including road capacity constraint conditions and road traffic pollution control constraint conditions. Then, road section environmental traffic capacity optimization model was established, and method of improved augmented Lagrange function was used to solve the model. Case study showed that, (1) The environmental traffic capacity optimal model and methodology were effective; (2) In order to ensure road section environmental traffic capacity greater than (or equal to) road capacity, some measures could be taken including adjusting motor vehicle type proportion as well as improving emission characteristics of motor vehicles exhausting pollutants.