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.     

A data-driven approach to manpower planning at U.S.–Canada border crossings

We investigate the staffing problem at Peace Arch, one of the major U.S.–Canada border crossings, with the goal of reducing time delay without compromising the effectiveness of security screening. Our data analytics show how the arrival rates of vehicles vary by time of day and day of week, and that the service rate per booth varies considerably by the time of day and the number of active booths. We propose a time-varying queueing model to capture these dynamics and use empirical data to estimate the model parameters using a multiple linear regression. We then formulate the staffing task as an integer programming problem and derive a near-optimal workforce schedule. Simulations reveal that our proposed workforce policy improves on the existing schedule by about 18% in terms of average delay without increasing the total work hours of the border staff.     

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.     

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.     

Quantifying uncertainty in short-term traffic prediction and its application to optimal staffing plan development

In this paper, we aim to quantify uncertainty in short-term traffic volume prediction by enhancing a hybrid machine learning model based on Particle Swarm Optimization (PSO) and Extreme Learning Machine (ELM) neural network. Different from the previous studies, the PSO-ELM models require no statistical inference nor distribution assumption of the model parameters, but rather focus on generating the prediction intervals (PIs) that can minimize a multi-objective function which considers two criteria, reliability and interval sharpness. The improved PSO-ELM models are developed for an hourly border crossing traffic dataset and compared to: (1) the original PSO-ELMs; (2) two state of the art models proposed by Zhang et al. (2014) and Guo et al. (2014) separately; and (3) the traditional ARMA and Kalman filter models. The results show that the improved PSO-ELM can always keep the mean PI length the lowest, and guarantee that the PI coverage probability is higher than the corresponding PI nominal confidence, regardless of the confidence level assumed. The study also probes the reasons that led to a few points being not covered by the PIs of PSO-ELMs. Finally, the study proposes a comprehensive optimization framework to make staffing plans for border crossing authority based on bounds of PIs and point predictions. The results show that for holidays, the staffing plans based on PI upper bounds generated much lower total system costs, and that those plans derived from PI upper bounds of the improved PSO-ELM models, are capable of producing the lowest average waiting times at the border. For a weekday or a typical Monday, the workforce plans based on point predictions from Zhang et al. (2014) and Guo et al. (2014) models generated the smallest system costs with low border crossing delays. Moreover, for both holiday and normal Monday scenarios, if the border crossing authority lacked the required staff to implement the plans based on PI upper bounds or point predictions, the staffing plans based on PI lower bounds from the improved PSO-ELMs performed the best, with an acceptable level of service and total system costs close to the point prediction plans.     

Optimization of pedestrian phase patterns at signalized intersections: a multi‐objective approach

This paper presents a multi‐objective optimization model and its solution algorithm for optimization of pedestrian phase patterns, including the exclusive pedestrian phase (EPP) and the conventional two‐way crossing (TWC) at an intersection. The proposed model will determine the optimal pedestrian phase pattern and the corresponding signal timings at an intersection to best accommodate both vehicular traffic and pedestrian movements. The proposed model is unique with respect to the following three critical features: (1) proposing an unbiased performance index for comparison of EPP and TWC by explicitly modeling the pedestrian delay under the control of TWC and EPP; (2) developing a multi‐objective model to maximize the utilization of the available green time by vehicular traffic and pedestrian under both EPP or TWC; and (3) designing a genetic algorithm based heuristic algorithm to solve the model. Case study and sensitivity analysis results have shown the promising property of the proposed model to assist traffic practitioners, researchers, and authorities in properly selecting pedestrian phase patterns at signalized intersections. Copyright © 2013 John Wiley & Sons, Ltd.     

Level crossing safety on East Japan Railway Company: Application of probabilistic risk assessment techniques

This paper describes the application of probabilistic risk assessment techniques to level crossing safety on JR East, the largest of the six private railroads in Japan. The risk of a level crossing accident was defined as the product of the accident rate and the expected consequences per accident. Rail traffic volume, road traffic volume, visibility of the crossing from the road, road gradient, width of the crossing and the type of safety devices at the crossing were shown to influence the accident rate and the collective risk. The mean accident rate at all crossings was 0.74 per million trains. The accident rate was 0.59 per million trains at crossings equipped with barriers, 1.25 at crossings equipped with warning bells and 0.76 at pedestrian crossings. Crossings equipped with obstacle detectors had a lower accident rate (0.12 per million trains) than crossings without detectors (0.43 per million trains). Crossings with visibility less than 20 m had a 50% higher mean accident rate than crossings with visibility greater than 20 m. As the number of tracks increased, the accident rate monotonically increased due to the increased accident exposure. Risk assessment techniques were applied to determine the efficacy of the various level crossing safety devices. In addition to upgrading the safety of crossings, the management techniques stressed the importance of education campaigns in warning the public about the dangers of illegal crossings.     

Rail-road crossing impacts: an international synthesis

Rail-road crossings generate a range of transport, economic, social and environmental impacts. While much research has focused on selected impacts such as safety, little consideration has been given to wider impacts. The aim of this research was to therefore develop a holistic understanding of the impacts of rail-road crossings (both at-grade and grade-separated) and to identify key gaps in knowledge in this field. An international synthesis of rail-road crossing impacts was developed through a detailed literature review, which revealed a total of 18 different types of impacts associated with rail-road crossings. The review found that most research to date has focused on quantifying transport and economic impacts, particularly safety and road vehicle delay, with little consideration given to social and environmental impacts. A number of research gaps have been identified which provide a clear agenda for future research. These gaps include the use of empirical evidence to support impact assessments, and the need to better understand the impacts of grade-separating rail-road crossings in terms of safety, travel time variability, land use, traffic flow, rail vehicle delay, disability access, and crime.     

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.     

Development of a delay model for unsignalized intersections applicable to traffic assignment

Abstract

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models.

Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods.

A comparison between delay models performed using different time intervals shows that the coefficients of determination, R ², increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.     

Eco-system optimal time-dependent flow assignment in a congested network

This research addresses the eco-system optimal dynamic traffic assignment (ESODTA) problem which aims to find system optimal eco-routing or green routing flows that minimize total vehicular emission in a congested network. We propose a generic agent-based ESODTA model and a simplified queueing model (SQM) that is able to clearly distinguish vehicles’ speed in free-flow and congested conditions for multi-scale emission analysis, and facilitates analyzing the relationship between link emission and delay. Based on the SQM, an expanded space-time network is constructed to formulate the ESODTA with constant bottleneck discharge capacities. The resulting integer linear model of the ESODTA is solved by a Lagrangian relaxation-based algorithm. For the simulation-based ESODTA, we present the column-generation-based heuristic, which requires link and path marginal emissions in the embedded time-dependent least-cost path algorithm and the gradient-projection-based descent direction method. We derive a formula of marginal emission which encompasses the marginal travel time as a special case, and develop an algorithm for evaluating path marginal emissions in a congested network. Numerical experiments are conducted to demonstrate that the proposed algorithm is able to effectively obtain coordinated route flows that minimize the system-wide vehicular emission for large-scale networks.     

A model of pedestrians’ intended waiting times for street crossings at signalized intersections

For the purposes of both traffic-light control and the design of roadway layouts, it is important to understand pedestrian street-crossing behavior because it is not only crucial for improving pedestrian safety but also helps to optimize vehicle flow. This paper explores the mechanism of pedestrian street crossings during the red-man phase of traffic light signals and proposes a model for pedestrians’ waiting times at signalized intersections. We start from a simplified scenario for a particular pedestrian under specific traffic conditions. Then we take into account the interaction between vehicles and pedestrians via statistical unconditioning. We show that this in general leads to a U-shaped distribution of the pedestrians’ intended waiting time. This U-shaped distribution characterizes the nature of pedestrian street-crossing behavior, showing that in general there are a large proportion of pedestrians who cross the street immediately after arriving at the crossing point, and a large proportion of pedestrians who are willing to wait for the entire red-man phase. The U-shaped distribution is shown to reduce to a J-shaped or L-shaped distribution for certain traffic scenarios. The proposed statistical model was applied to analyze real field data.     

Bridge and tunnel toll elasticities in New York

In 1992, the authors carried out a statistical analysis of Triborough Bridge and Tunnel Authority (TBTA) crossings in New York City, to determine the impact of toll increases on traffic volumes and revenue. Using twelve years of monthly time-series data, we developed a set of multiple regression models that estimated traffic volumes on each TBTA bridge and tunnel as a function of the toll level and other explanatory variables. In most cases, the estimated toll elasticities were negative and much less than 1.0 in absolute value; the median toll elasticity for automobiles was found to be –0.10. Our finding that automobile travel demand is highly inelastic with respect to toll rates is consistent with most previous travel demand studies.     

A queueing based traffic flow model

The assessment of uninterrupted traffic flow is traditionally based on empirical methods. We develop some analytic queueing models based on traffic counts and we model the behavior of traffic flows as a function of some of the most relevant determinants. These analytic models allow for parameterized experiments, which pave the way towards our research objectives: assessing what-if scenario’s and sensitivity analysis for traffic management, congestion control, traffic design and the environmental impact of road traffic (e.g. emission models). The impact of some crucial modeling parameters is studied in detail and links with the broader research objectives are given. We illustrate our results for a highway, based on counted traffic flows in Flanders.     

Stochastic dynamic itinerary interception refueling location problem with queue delay for electric taxi charging stations

A new facility location model and a solution algorithm are proposed that feature (1) itinerary-interception instead of flow-interception; (2) stochastic demand as dynamic service requests; and (3) queueing delay. These features are essential to analyze battery-powered electric shared-ride taxis operating in a connected, centralized dispatch manner. The model and solution method are based on a bi-level, simulation–optimization framework that combines an upper level multiple-server allocation model with queueing delay and a lower level dispatch simulation based on earlier work by Jung and Jayakrishnan. The solution algorithm is tested on a fleet of 600 shared-taxis in Seoul, Korea, spanning 603 km², a budget of 100 charging stations, and up to 22 candidate charging locations, against a benchmark “naïve” genetic algorithm that does not consider cyclic interactions between the taxi charging demand and the charger allocations with queue delay. Results show not only that the proposed model is capable of locating charging stations with stochastic dynamic itinerary-interception and queue delay, but that the bi-level solution method improves upon the benchmark algorithm in terms of realized queue delay, total time of operation of taxi service, and service request rejections. Furthermore, we show how much additional benefit in level of service is possible in the upper-bound scenario when the number of charging stations is unbounded.     

The construction and use of continuous autoregressive models for traffic indices

This article discusses the use of continuous autoregressive models to describe the behavior of traffic indices. From discretely sampled data, second-order differential equation models are constructed to represent dynamic traffic fluctuations as the response of a linear system to a stochastic forcing function. The results are compared to the more common M/G/∞ queueing model approach, and the analysis is demonstrated on time series of aircraft concentration in thirty-one enroute air traffic control sectors.     

Delay processes at unsignalised junctions: The interrelation between geometric and queueing delay

The interrelation between geometric delay—the delay caused by the need for isolated vehicles to slow down to negotiate a junction—and the queueing delay due to vehicle-vehicle interactions is considered. The delay elements present in measurements of geometric delay are identified, and some overlap is seen with those normally included in the service mechanisms in queueing models. The total delay per vehicle is not, therefore, the sum of the measured geometric delay and the queueing delay. Although the geometric delay can be redefined so as to eliminate the overlap, it is not then measurable. A framework is developed in which the relationships between the “pure” and measured geometric delay and the queueing delay are expressed for the population mean values. Approximate expressions are developed for the elements of delay. The framework is extended to allow queueing delays to be represented by time-dependent functions allowing approximately for non-randomness in arrival and departure patterns and service time differences between queueing and non-queueing vehicles. Numerically, geometric delay elements constitute a significant proportion of total delay except at traffic intensities approaching unity. In this region time-dependent effects dominate the queueing process.     

The effects of lighting on driver's injury severity at highway‐rail grade crossings

This study estimates the safety effect of illumination on accidents at highway‐rail grade crossings in the United States, using data from exhaustive data from Federal Railroad Administration database covering the period 2002–2011. Using mixed logit modeling approach, the study explores the determinants of driver injury severity at unlighted highway‐rail grade crossings compared with lighted highway‐rail grade crossings in the United States. Several key issues are explored including availability of relevant highway‐rail grade crossing accident inventory data; relevant data element structures; specification and estimation of models to estimate driver's injury severity with lighting and without lighting; and techniques to interpret model parameters. Overall, highway‐rail grade crossing lighting improves safety by reducing the probability of high‐level injury severity through improvements in driver's visibility compared with unlighted intersections. Copyright © 2015 John Wiley & Sons, Ltd.     

黄浦江首座公轨双层桥——闵浦二桥引桥工程总体方案研究

上海市轨道交通5号线南延伸工程跨越黄浦江节点与沪闵路一沪杭公路地方交通越江工程结合（即闵浦二桥新建工程），采用上下双层一体化结构形式。结合一体化区间引桥段桥梁型式，对各种方案的结构合理性、交通功能、对景观与环境的影响、工程量以及车站布置等方面进行了研究和论证，最终推荐“干”字形的独柱桥墩为实施方案。     

Quasi-dynamic traffic assignment with residual point queues incorporating a first order node model

Static traffic assignment models are still widely applied for strategic transport planning purposes in spite of the fact that such models produce implausible traffic flows that exceed link capacities and predict incorrect congestion locations. There have been numerous attempts to constrain link flows to capacity. Capacity constrained models with residual queues are often referred to as quasi-dynamic traffic assignment models. After reviewing the literature, we come to the conclusion that an important piece of the puzzle has been missing so far, namely the inclusion of a first order node model. In this paper we propose a novel path-based static traffic assignment model for finding a stochastic user equilibrium in general transportation networks. This model includes a first order (steady-state) node model that yields more realistic turn capacities, which are then used to determine consistent capacity constrained traffic flows, residual point (vertical) queues (upstream bottleneck links), and path travel times consistent with queuing theory. The route choice part of the model is specified as a variational inequality problem, while the network loading part is formulated as a fixed point problem. Both problems are solved using existing techniques to find a solution. We illustrate the model using hypothetical examples, and also demonstrate feasibility on large-scale networks.