Congestion and emissions mitigation: A comparison of capacity,demand, and vehicle based strategies

Capacity, demand, and vehicle based emissions reduction strategies are compared for several pollutants employing aggregate US congestion and vehicle fleet condition data. We find that congestion mitigation does not inevitably lead to reduced emissions; the net effect of mitigation depends on the balance of induced travel demand and increased vehicle efficiency that in turn depend on the pollutant, congestion level, and fleet composition. In the long run, capacity-based congestion improvements within certain speed intervals can reasonably be expected to increase emissions of CO₂e, CO, and NO_x through increased vehicle travel volume. Better opportunities for emissions reductions exist for HC and PM_2.5 emissions, and on more heavily congested arterials. Advanced-efficiency vehicles with emissions rates that are less sensitive to congestion than conventional vehicles generate less emissions co-benefits from congestion mitigation.     

An improved formulation for bulk cargo ship scheduling with a single loading port

This paper presents an improved, significantly more efficient formulation of an existing model for bulk cargo or semi-bulk cargo ship scheduling problems with a single loading port. The original model, published by Ronen in 1986, was formulated as a non-linear, mixed integer program. In this work, the authors were able to re-formulate it into a linear one, by eliminating all the non-linearities of the original model. In addition, this model has far fewer integer variables than the original one. A numerical example has been given to illustrate the elimination of non-linearities and how 40 integer variables, in the original model, are reduced to just eight. This example also shows that this model is better at finding exact optimal solutions than the original one. It is also worth observing that the resulting model is a generalization of the 'capacitated facility location problem'.     

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.     

The Increasing Role of Latent Variables in Modelling Bicycle Mode Choice

The growing interest in promoting non-motorised active transport has led to an increase in the number of studies to identify the key variables associated with bicycle use, and especially those related to the bicycle mode choice problem. This paper presents a comprehensive survey of the modelling literature on the choice of the bicycle for utilitarian purposes, and summarises and assesses the evolution of the explanatory variables and methodologies used. We review both the evolution of the incorporation of latent variables in bicycle mode choice models and the critical role they play. The chronological evolution of the studies is divided into three stages —initial, intermediate and late — according to the different ways of introducing attitudinal or perceptual indicators and latent variables into the models. Our review shows that the incorporation of latent variables in bicycle choice models has increased in the last decade, with a progressive use of more sophisticated methodologies until the arrival of complex models that explicitly and properly deal with psychological latent variables. In fact, with the use of hybrid choice models, latent variables have nowadays become the core of bicycle mode choice models. Based on our review, a set of questions is proposed as a uniform measurement scale to identify attitudes towards bicycling. Recommendations for future research are also presented.     

基于SVM的隧道衬砌空洞填充物雷达图像识别研究北大核心CSCD

衬砌背后空洞及其填充物对隧道结构安全具有重要影响,开展空洞探测识别对于结构安全评估和病害处置具有重要意义。首先采用室内试验和FDTD正演模拟相结合的方法,获得了空洞内填充空气、水、干砂、湿砂条件下的雷达图谱数据,并对不同填充物波形规律进行对比分析;然后,基于支持向量机算法对波形特征进行提取和分类识别,建立了一种空洞填充物的人工智能辨识方法。研究结果表明,采用傅里叶变换前的平均值、方差、平均绝对离差和傅里叶变换后的最大幅度值max(fft(X))四个统计量作为支持向量机的识别特征,可以有效区分出衬砌背后填充物的六种类型;当采取单一倾向数据时,识别准确率较好,六种物质二分类问题准确率均可以达到90%以上。     

Revenue management in the parking industry: a multiple garage intelligent reservation model

This paper explores how advanced reservations, coupled with dynamic pricing (based on booking limits) can be used to maximize parking revenue. An integer programing formulation that maximizes parking revenue over a system of garages is presented. Furthermore, an intelligent parking reservation model is developed that uses an artificial neural network procedure for online reservation decision-making. Finally, the paper provides some strategic and managerial implications of multi-garage revenue management systems, and discusses techniques for identifying and implementing micro-market segmentation in the parking industry.     

An alternative model in traffic flow equations

The kinetic theory for traffic flow equations can be approached using the Grad’s method. This method, which is derived from the kinetic gas theory, was developed for the Paveri-Fontana equation when a special desired velocity model is assumed. A closure relation for the set of macroscopic equations is found when the density, the average velocity and the velocity variance are the relevant variables chosen to describe the system. Simulation results are also shown and a qualitative comparison with other models in the literature is presented.     

Identifying a behaviourally relevant choice set from stated choice data

Stated choice experiments are designed optimally in a statistical sense but not necessarily in a behavioural choice making sense. Statistical designs, and consequently model estimation, assume that the set of alternatives offered in the experiment are processed by respondents with a specific processing strategy. Much has been studied about attribute processing using discrete choice methods in travel choice studies, but this paper focuses more broadly on processing of alternatives in the choice set offered in the experiment. This paper is motivated by the primary idea that the distribution of predicted choice probabilities associated with a set of alternatives defining a given choice set might provide strong evidence on the strategies that agents appear to use when choosing a preferred alternative. In an empirical setting of a choice set of size three, four model specifications are considered including a model for the selection of the best alternative in the full choice set and three variants of a best–worst regime. Using state choice data on road pricing reform, the empirical analysis examines which model specification delivers the most accurate prediction of the chosen alternative. The results suggest which alternatives really matter in choice making and hence the alternatives that might be included in a choice set for model specification.