How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach

In this paper, we reformulate conventional data envelopment analysis (DEA) models and propose a novel method for evaluating sustainability of suppliers in the presence of interval volume discount offers, fuzzy data, and ordinal data. To this end, we convert all data into interval data. To convert fuzzy data into interval data, we use nearest weighted interval approximation by applying weighting function and we convert each ordinal data into interval one. Then, using enhanced Russell model, interval efficiencies are obtained. After that, using preference degree approach, we rank suppliers. Finally, a case study is presented to illustrate our proposed approach.     

Determinants of airports’ environmental effects

Aviation is a fast growing sector with increasing environmental concerns linked to aircraft emissions at airports and noise nuisance. This paper investigates the factors affecting the annual environmental effects produced by a national aviation system. The environmental effects are computed using certification data for each aircraft-engine combination. Moreover, we also take into account for the amount of environmental effects that is internalized at the airport, mainly through noise regulation. We study a dataset covering information on Italian airports during the period 1999–2008. We show that a 1% increase in airport’s yearly movements yields a 1.05% increase in environmental effects, a 1% in aircraft size (measured in MTOW) gives rise to a 1.8% increase and a 1% increase in aircraft age generates a 0.69% increase in environmental effects. Similar results but with smaller magnitudes are observed if airport internalization is considered. Our policy implications are that the tariff internalizing the total amount of externality is about euro 180 per flight, while the tariff limiting only pollution is about euro 60 and the one reducing noise is about euro 110. Moreover, our airport examples show that managers should prefer to address additional capacity by increasing frequency rather than aircraft size, since the former strategy is more environmental friendly.     

Predicting travel time reliability using mobile phone GPS data

Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.     

Impacts of weather on public transport ridership: Results from mining data from different sources

The existing studies concerning the influence of weather on public transport have mainly focused on the impacts of average weather conditions on the aggregate ridership of public transit. Not much research has examined these impacts at disaggregate levels. This study aims to fill this gap by accounting for intra-day variations in weather as well as public transport ridership and investigating the effect of weather on the travel behavior of individual public transit users. We have collected smart card data for public transit and meteorological records from Shenzhen, China for the entire month of September 2014. The data allow us to establish association between the system-wide public transit ridership and weather condition on not only daily, but also hourly basis and for each metro station. In addition, with the detailed trip records of individual card holders, the travel pattern by public transit are constructed for card holders and this pattern is linked to the weather conditions he/she has experienced. Multivariate modeling approach is applied to analyze the influence of weather on public transit ridership and the travel behavior of regular transit users. Results show that some weather elements have more influence than others on public transportation. Metro stations located in urban areas are more vulnerable to outdoor weather in regard to ridership. Regular transit users are found to be rather resilient to changes in weather conditions. Findings contribute to a more in-depth understanding of the relationship between everyday weather and public transit travels and also provide valuable information for short-term scheduling in transit management.     

A discounted recursive logit model for dynamic gridlock network analysis

Emerging sensing technologies such as probe vehicles equipped with Global Positioning System (GPS) devices on board provide us real-time vehicle trajectories. They are helpful for the understanding of the cases that are significant but difficult to observe because of the infrequency, such as gridlock networks. On the premise of this type of emerging technology, this paper propose a sequential route choice model that describes route choice behavior, both in ordinary networks, where drivers acquire spatial knowledge of networks through their experiences, and in extraordinary networks, which are situations that drivers rarely experience, and applicable to real-time traffic simulations. In extraordinary networks, drivers do not have any experience or appropriate information. In such a context, drivers have little spatial knowledge of networks and choose routes based on dynamic decision making, which is sequential and somewhat forward-looking. In order to model these decision-making dynamics, we propose a discounted recursive logit model, which is a sequential route choice model with the discount factor of expected future utility. Through illustrative examples, we show that the discount factor reflects drivers’ decision-making dynamics, and myopic decisions can confound the network congestion level. We also estimate the parameters of the proposed model using a probe taxis’ trajectory data collected on March 4, 2011 and on March 11, 2011, when the Great East Japan Earthquake occurred in the Tokyo Metropolitan area. The results show that the discount factor has a lower value in gridlock networks than in ordinary networks.     

基于虚拟仪器的城市生活用水回用系统水质监测体系的构建

虚拟仪器技术是一个新兴的软件应用技术,它开辟了仪器仪表行业的新局面。在简要介绍虚拟仪器技术的基本思想及其软件开发平台的功能与特点的基础上,结合当前水质监测仪器的特点阐述了在城市生活用水回用系统水质监测体系中开发研制虚拟仪器的硬件和软件系统设计框架。开发研制基于虚拟仪器技术的测试设备在智能化程度、性能价格比、灵活性、可操作性等方面具有明显的优势。最后对虚拟仪器技术在水工业中应用的发展前景进行了展望。     

星型数据网络通信仿真分析

就专网中的星形数据网推导出数学排队模型,并进行计算机仿真,得到相应的网络性能参数,对星形数据网的运行具有指导性意义。     

基于GPS的实时机车运行信息传输系统

利用铁路既有的列车无线调度通信系统和铁路分组交换网络, 结合GPS技术, 提出了一种实现机车运行信息实时传输的方案。采用话音压缩/扩展和数据同传, 使无线列调在原来只传输话音的基础上增加了数据传送, 数据仓库又使得这些信息能在铁路相关部门实现共享, 可实现机车中低速率的数据传输。     

Analysis of yellow-light running at signalized intersections using high-resolution traffic data

Many accidents occurring at signalized intersections are closely related to drivers’ decisions of running through intersections during yellow light, i.e., yellow-light running (YLR). Therefore it is important to understand the relationships between YLR and the factors which contribute to drivers’ decision of YLR. This requires collecting a large amount of YLR cases. However, existing data collection method, which mainly relies on video cameras, has difficulties to collect a large amount of YLR data. In this research, we propose a method to study drivers’ YLR behaviors using high-resolution event-based data from signal control systems. We used 8 months’ high-resolution data collected by two stop-bar detectors at a signalized intersection located in Minnesota and identified over 30,000 YLR cases. To identify the possible reasons for drivers’ decision of YLR, this research further categorized the YLR cases into four types: “in should-go zone”, “in should-stop zone”, “in dilemma zone”, and “in optional zone” according to the driver’s location when signal turns to yellow. Statistical analysis indicates that the mean values of approaching speed and acceleration rate are significantly different for different types of YLR. We also show that there were about 10% of YLR drivers who cannot run through intersection before traffic light turns to red. Furthermore, based on a strong correlation between hourly traffic volume and number of YLR events, this research developed a regression model that can be used to predict the number of YLR events based on hourly flow rate. This research also showed that snowing weather conditions cause more YLR events.     

Transportation habits: Evidence from time diary data

The interdisciplinary Time Use Observatory workshops learned that transportation research and social sciences strive for the same multi-day time-diary data in order to make interferences about human habitual (travel) behavior. It also is learned that when it comes to the mathematics and analytics involved both disciplines are miles apart, though both with founded reasons to do so. In brief, transportation research relies on modeling to make predictions whereas social sciences apply statistics to their data to draw conclusions. In line with the interdisciplinary philosophy of the Time Use Observatory workshops, this contribution aims to communicate 30 years of experience in analyzing time-diary data. To do so, it demonstrates the latter by calculation transportation habits and aims to illustrate that multi-day time-diary data might have some additional benefits for computing temporal regularities. It shows that including a flexible notion of both regular tempo (or recurrence) of activities (e.g. every day) and regular timing of activities (e.g. always at 6 am) produces different results for different kind of transportation purposes. It also shows that these calculations using multi-day time-diary data result in an indicator at the individual level that can be analyzed in terms of socio-demographic and socio-economic characteristics. This work concludes that partitioning temporal regularities in regular reoccurrence and regular timing is a crucial element of (transportation) habits.