Border crossing delay prediction using transient multi-server queueing models

As a result of the continued increase in travel demand coupled with the need for tighter security and inspection procedures after September 11, border crossing delay has recently become a critical issue with tremendous economic and social costs. The current paper develops multi-server queuing models to estimate border crossing delay in support of a predictive traveler information system for the crossings. Two classes of multi-server models are considered: (1) models with exponential inter-arrival times and Erlang service times; and (2) a more generic model with a Batch Markovian Arrival Process (BMAP) and phase types (PH) services. As a case study, the models are developed based on real-time traffic volume and inspection time data collected at one of the major US–Canada border crossings, the Peace Bridge, and their transient solution is obtained using heuristic methods. For validation, the queueing models’ estimates are compared to the results from a detailed microscopic traffic simulation model of the Peace Bridge border crossing. The comparison shows that the transient queueing model, along its heuristic solution algorithm, is capable of predicting border crossing delay. Finally, a set of sensitivity analysis tests are conducted, and the developed models are incorporated within an optimization framework to help inform border crossing management strategies.     

Competitiveness and macroeconomic impacts of reduced wait times at U.S. land freight border crossings

We analyze the macroeconomic and trade impacts of reducing wait times by adding one customs officer at each of the twelve major land freight crossings of the U.S. The change in wait time stemming from staffing changes is first estimated on the basis of primary data and then translated into changes in freight costs through a logistical model. The transportation cost changes are then fed into a multi-country computable general equilibrium model. We find that adding one customs officer at each land border crossing would, on average per crossing, generate an increase in U.S. GDP of $350 thousand and 3.58 additional jobs.     

Adaptive road border crossing system management: the role of priority programs

Land border crossings in North America, such as those between Canada and U.S.A., are expected to experience severe imbalance of travel demand and capacity of processors. During peak travel periods, this is already the case at high traffic locations. The land border crossing authorities have to address problems of congestion, national security and environmental impacts in the operation of the existing systems and to continue to address these problems as a part of infrastructure expansion plans. There is a need to adapt the crossing system management in order to accommodate efficiency and productivity‐oriented priority crossing measures. From a methodological perspective, it is a challenge to evaluate the role of priority crossing measures within the complex border crossing system. This paper reports research on modelling priority crossing initiatives. A microsimulation approach was used to model and analyse integrated processors of the Peace Bridge crossing system between Fort Erie (Ontario) and Buffalo (New York) under different scenarios of travel demand, customs processing times, priority crossing and queue jump lanes for automobile and truck traffic. Findings show the extent to which a border crossing system with priority crossing and queue jump lanes is more efficient and productive than one without these innovations. Copyright © 2011 John Wiley & Sons, Ltd.     

Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation

Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is the possibility of reducing discretionary fuel loading by dispatchers. In this study, we propose a novel discretionary fuel estimation approach that can assist dispatchers with better discretionary fuel loading decisions. Based on the analysis on our study airline, our approach is found to substantially reduce unnecessary discretionary fuel loading while maintaining the same safety level compared to the current fuel loading practice. The idea is that by providing dispatchers with more accurate information and better recommendations derived from flight records, unnecessary fuel loading and corresponding cost-to-carry could both be reduced. We apply ensemble learning techniques to improve fuel burn prediction and construct prediction intervals (PIs) to capture the uncertainty of model predictions. The upper bound of a PI can then be used for discretionary fuel loading. The potential benefit of this approach is estimated to be $61.5 million in fuel savings and 428 million kg of CO₂ reduction per year for our study airline. This study also builds a link between discretionary fuel estimation and aviation system predictability in which the proposed models can also be used to predict benefits from reduced fuel loading enabled by improved Air Traffic Management (ATM) targeting on improved system predictability.     

Use of duration models for predicting vehicular delay at a US/Canadian border crossing

Delays caused by congestion at the US/Canadian border crossing between Washington state and British Columbia have underscored the need for some sort of intervention. One obvious congestion-mitigation measure would be to estimate delay times and then relay this information to motorists so that they could select among alternative border crossing sites, or delay their trips. This paper applies duration models to estimate vehicular delay and demonstrates the usefulness of such models as a basis for a fully automated motorist information system. The paper also explores the flexibility of duration models, in providing estimates of vehicle delay, by using alternate parametric forms and assessing prediction accuracy.     

Short-term prediction of border crossing time and traffic volume for commercial trucks: A case study for the Ambassador Bridge

Short-term forecasting of traffic characteristics, such as traffic flow, speed, travel time, and queue length, has gained considerable attention from transportation researchers and practitioners over past three decades. While past studies primarily focused on traffic characteristics on freeways or urban arterials this study places particular emphasis on modeling the crossing time over one of the busiest US–Canada bridges, the Ambassador Bridge. Using a month-long volume data from Remote Traffic Microwave Sensors and a yearlong Global Positioning System data for crossing time two sets of ANN models are designed, trained, and validated to perform short-term predictions of (1) the volume of trucks crossing the Ambassador Bridge and (2) the time it takes for the trucks to cross the bridge from one side to the other. The prediction of crossing time is contingent on truck volume on the bridge and therefore separate ANN models were trained to predict the volume. A multilayer feedforward neural network with backpropagation approach was used to train the ANN models. Predicted crossing times from the ANNs have a high correlation with the observed values. Evaluation indicators further confirmed the high forecasting capability of the trained ANN models. The ANN models from this study could be used for short-term forecasting of crossing time that would support operations of ITS technologies.     

Existence of simultaneous route and departure choice dynamic user equilibrium

This paper is concerned with the existence of the simultaneous route-and-departure choice dynamic user equilibrium (SRDC-DUE) in continuous time, first formulated as an infinite-dimensional variational inequality in Friesz et al. (1993). In deriving our existence result, we employ the generalized Vickrey model (GVM) introduced in Han et al., 2013a, Han et al., 2013b to formulate the underlying network loading problem. As we explain, the GVM corresponds to a path delay operator that is provably strongly continuous on the Hilbert space of interest. Finally, we provide the desired SRDC-DUE existence result for general constraints relating path flows to a table of fixed trip volumes without invocation of a priori bounds on the path flows.     

Bike route choice modeling using GPS data without choice sets of paths

Concerned by the nuisances of motorized travel on urban life, policy makers are faced with the challenge of making cycling a more attractive alternative for everyday transportation. Route choice models can help achieve this objective by gaining insights into the trade-offs cyclists make when choosing their routes and by allowing the effect of infrastructure improvements to be analyzed. We estimate a link-based bike route choice model from a sample of GPS observations in the city of Eugene on a network comprising over 40,000 links. The so-called recursive logit (RL) model (Fosgerau et al., 2013) does not require to sample any choice set of paths. We show the advantages of this approach in the context of prediction by focusing on two applications of the model: link flows and accessibility measures. Compared to the path-based approach which requires to generate choice sets, the RL model proves to make significant gains in computational time and to avoid paradoxical accessibility measure results discussed in previous works, e.g. Nassir et al. (2014).     

Price of anarchy for non-atomic congestion games with stochastic demands

We generalize the notions of user equilibrium, system optimum and price of anarchy to non-atomic congestion games with stochastic demands. In this generalized model, we extend the two bounding methods from Roughgarden and Tardos (2004) and Correa et al. (2008) to bound the price of anarchy, and compare the upper bounds we have obtained. Our results show that the price of anarchy depends not only on the class of cost functions but also demand distributions and, to some extent, the network topology. The upper bounds are tight in some special cases, including the case of deterministic demands.     

Why are work travel plans effective? Comparing conclusions from narrative and meta-analytical research synthesis

Reliable research synthesis is of high significance for a transport policy which tries to base its decisions on available empirical evidence. There is growing doubt whether the frequently used narrative approach provides a scientifically defensible way of synthesising a body of quantitative research results. The present paper demonstrates meta-analysis as a more objective and powerful alternative for this task. For this purpose evaluation data synthesised in two earlier narrative reviews on the effectiveness of work travel plans [Cairns et al. Making travel plans work: research report (former), Department for Transport, Local Government and the Regions (DTLR), London, 2002; Smarter choices—changing the way we travel. (Final report of the research project: The influence of soft factor interventions on travel demand. Research report for the Department for Transport.) London. Retrieved 1 December 2005, from , 2004] are meta-analytically re-analysed and compared with the conclusions drawn from the narrative synthesis of these data. Our meta-analyis provides only limited support for the conclusion that addressing parking is the hallmark of successful work travel plans. Our meta-analysis indicates that site and organisational factors as well as characteristics of the monitoring process are significant predictors of effective work travel plans, whereas Cairns et al. view little evidence for the impact of these factors.     

A partial differential equation formulation of Vickrey’s bottleneck model,part II: Numerical analysis and computation

The Vickrey model, originally introduced in Vickrey (1969), is one of the most widely used link-based models in the current literature in dynamic traffic assignment (DTA). One popular formulation of this model is an ordinary differential equation (ODE) that is discontinuous with respect to its state variable. As explained in Ban et al., 2011, Han et al., 2013, such an irregularity induces difficulties in both continuous-time analysis and discrete-time computation. In Han et al. (2013), the authors proposed a reformulation of the Vickrey model as a partial differential equation (PDE) and derived a closed-form solution to the aforementioned ODE. This reformulation enables us to rigorously prove analytical properties of the Vickrey model and related DTA models.In this paper, we present the second of a two-part exploration regarding the PDE formulation of the Vickrey model. As proposed by Han et al. (2013), we continue research on the generalized Vickrey model (GVM) in a discrete-time framework and in the context of DTA by presenting a highly computable solution methodology. Our new computational scheme for the GVM is based on the closed-form solution mentioned above. Unlike finite-difference discretization schemes which could yield non-physical solutions (Ban et al., 2011), the proposed numerical scheme guarantees non-negativity of the queue size and the exit flow as well as first-in-first-out (FIFO). Numerical errors and convergence of the computed solutions are investigated in full mathematical rigor. As an application of the GVM, a class of network system optimal dynamic traffic assignment (SO-DTA) problems is analyzed. We show existence of a continuous-time optimal solution and propose a discrete-time mixed integer linear program (MILP) as an approximation to the original SO-DTA. We also provide convergence results for the proposed MILP approximation.     

Recent success stories on integrated optimization of railway systems

Planning and operating railway transportation systems is an extremely hard task due to the combinatorial complexity of the underlying discrete optimization problems, the technical intricacies, and the immense size of the problem instances. Because of that, however, mathematical models and optimization techniques can result in large gains for both railway customers and operators, e.g., in terms of cost reductions or service quality improvements. In the last years a large and growing group of researchers in the OR community have devoted their attention to this domain developing mathematical models and optimization approaches to tackle many of the relevant problems in the railway planning process. However, there is still a gap to bridge between theory and practice (e.g. Cacchiani et al., 2014; Borndörfer et al., 2010), with a few notable exceptions. In this paper we address three individual success stories, namely, long-term freight train routing (part I), mid-term rolling stock rotation planning (part II), and real-time train dispatching (part III). In each case, we describe real-life, successful implementations. We will discuss the individual problem setting, survey the optimization literature, and focus on particular aspects addressed by the mathematical models. We demonstrate on concrete applications how mathematical optimization can support railway planning and operations. This gives proof that mathematical optimization can support the planning of railway resources. Thus, mathematical models and optimization can lead to a greater efficiency of railway operations and will serve as a powerful and innovative tool to meet recent challenges of the railway industry.     

Capturing correlation with a mixed recursive logit model for activity-travel scheduling

Representing activity-travel scheduling decisions as path choices in a time–space network is an emerging approach in the literature. In this paper, we model choices of activity, location, timing and transport mode using such an approach and seek to estimate utility parameters of recursive logit models. Relaxing the independence from irrelevant alternatives (IIA) property of the logit model in this setting raises a number of challenges. First, overlap in the network may not fully characterize perceptual correlation between paths, due to their interpretation as activity schedules. Second, the large number of states that are needed to represent all possible locations, times and activity combinations imposes major computational challenges to estimate the model. We combine recent methodological developments to build on previous work by Blom Västberg et al. (2016) and allow to model complex and realistic correlation patterns in this type of network. We use sampled choices sets in order to estimate a mixed recursive logit model in reasonable time for large-scale, dense time-space networks. Importantly, the model retains the advantage of fast predictions without sampling choice sets. In addition to estimation results, we present an extensive empirical analysis which highlights the different substitution patterns when the IIA property is relaxed, and a cross-validation study which confirms improved out-of-sample fit.     

Exploring the feasibility of classification trees versus ordinal discrete choice models for analyzing crash severity

A cross-median crash (CMC) is one of the most severe types of crashes in which a vehicle crosses the median and sometimes collides with opposing traffic. A study of severity of CMCs in the state of Wisconsin was conducted by Lu et al. in 2010. Discrete choice models, namely ordinal logit and probit models were used to analyze factors related to the severity of CMCs. Separate models were developed for single and multi-vehicle CMCs. Although 25 different crash, roadway, and geometric variables were used, only 3 variables were found to be statistically significant which were alcohol usage, posted speed, and road conditions. The objective of this research was to explore the feasibility of GUIDE Classification Tree method to analyze the severity of CMCs to discover if any additional information could be revealed.A dataset of CMCs in the state of Wisconsin between 2001 and 2007, used in the study by Lu et al. was used to develop three different GUIDE Classification Trees. Additionally, the effects of variable types (continuous or discrete), misclassification costs, and tree pruning characteristics on models results were also explored. The results were directly compared with discrete choice models developed in the study by Lu et al. showing that the GUIDE Classification Trees revealed new variables (median width and traffic volume) that affect CMC severity and provided useful insight on the data. The results of this research suggest that the use of Classification Tree analysis should at least be considered in conjunction with regression-based crash models to better understand factors affecting crashes. Classification Tree models were able to reveal additional information about the dependent variable and offer advantages with respect to multicollinearity and variable redundancy issues.     

A model of complex travel behavior: Part II—An operational model

Based on the theoretical model of complex travel behavior developed in a companion paper (Recker et al., 1986), an operational system of models, STARCHILD (Simulation of Travel/Activity Responses to Complex Household Interactive Logistic Decisions), has been developed to examine the formation of household travel/activity patterns. The system employs a simulation approach in combination with techniques of pattern recognition, multiobjective optimization and disaggregate choice models. Initial empirical verification of the system of models is presented based on results obtained from a sample data set.     

Adaptive traffic parameter prediction: Effect of number of states and transferability of models

Traffic parameters can show shifts due to factors such as weather, accidents, and driving characteristics. This study develops a model for predicting traffic speeds under these abrupt changes within regime switching framework. The proposed approach utilizes Hidden Markov, Expectation Maximization, Recursive Least Squares Filtering, and ARIMA methods for an adaptive forecasting method. The method is compared with naive and mean updating linear and nonlinear time series models. The model is fitted and tested extensively using 1993 I-880 loop data from California and January 2014 INRIX data from Virginia. Analysis for number of states, impact of number of states on forecasting, prediction scope, and transferability of the model to different locations are investigated. A 5-state model is found to be providing best results. Developed model is tested for 1-step to 45-step forecasts. The accuracy of predictions are improved until 15-step over nonadaptive and mean adaptive models. Except 1-step predictions, the model is found to be transferable to different locations. Even if the developed model is not retrained on different datasets, it is able to provide better or close results with nonadaptive and adaptive models that are retrained on the corresponding dataset.     

The role of congestion of transportation networks in urban location and travel choices

Congestion of urban transportation systems results from an equilibrium of location and travel choices with generalized travel costs which increase with vehicle flows as well as other factors. The use of network equilibrium concepts in analyzing urban policies and evaluating alternative plans is examined. Issues arising in the use of network equilibrium models are described, and formulations of urban network prediction and design models are explored.     

Improving fuel consumption and CO2 emissions calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model

Τhis study demonstrates the combination of a microscopic traffic simulator (AIMSUN) with an instantaneous emissions model (AVL CRUISE) to investigate the impact of traffic congestion on fuel consumption on an urban arterial road. The micro traffic model was enhanced by an improved car-following law according to Morello et al. (2014) and was calibrated to replicate measured driving patterns over an urban corridor in Turin, Italy, operating under adaptive urban traffic control (UTC). The method was implemented to study the impact of congestion on fuel consumption for the category of Euro 5 diesel <1.4 l passenger cars. Free flow and congested conditions led to respective consumption differences of −25.8% and 20.9% over normal traffic. COPERT 5 rather well predicted the impact of congestion but resulted to a much lower relative reduction in free flow conditions. Start and stop system was estimated to reduce consumption by 6% and 11.9% under normal and congested conditions, respectively. Using the same modelling approach, UTC was found to have a positive impact on CO₂ emissions of 8.1% and 4.5% for normal and congested conditions, respectively, considering the Turin vehicle fleet mix for the year 2013. Overall, the study demonstrates that the combination of detailed and validated micro traffic and emissions models offers a powerful combination to study traffic and powertrain impacts on greenhouse gas and fuel consumption of on road vehicles over a city network.     

Estimation of the instantaneous air count of general aviation aircraft flying under visual flight rules

This paper presents preliminary estimates of the instantaneous aircraft count (IAC) of general aviation (GA) aircraft flying under visual flight rules (VFR). Separate estimates for each of the following sub-categories of VFR flights are made from available data. (1) Itinerant GA flying under formal VFR flight plans. (2) Itinerant GA with no flight plans of any type. (3) Local GA. An itinerant aircraft is defined to be one whose origin and destination airports are different. A local GA is primarily one doing touch and go runway operations at an airfield. All estimates are generated by new mathematical models which utilize sampled field data. The IAC data of all GA come from towered and non-towered air fields and are tabulated by Air Route Traffic Control Center (ARTCC), and by type of flight plan (IFR, VFR, no flight plan), although this paper willdeal only with last two categories of flight plan. General aviation with no flight plans constitute most of the aircraft in the air at any one time. The basic approach has been: (1) data sampling instead of census; (2) statistical estimates to infer data not sampled; and (3) data validation. The IAC of itinerant GA-VFR over the continental United States is estimated to be roughly 13,000, which is about 8% of all registered GA aircraft. The IAC of local GA is about 7% of the itinerant count. Comparison of these estimates with other sources of information indicate that they are fairly reasonable. A point of major importance in the overall approach is that aircraft activity and flight characteristics are similar in similar regions, thereby drastically reducing data collection costs and data processing. Predictive techniques are also facilitated.     

A fuzzy-stochastic optimization model for the intermodal fleet management problem of an international transportation company

ABSTRACT

In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO₂ emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model.