基于船舶大数据的港口装卸效率值计算方法

集装箱港口的装卸效率是衡量港口竞争力和吸引船公司前来挂靠的关键指标之一。为准确估计港口的装卸效率值，基于船舶自动识别系统(AIS)数据，利用 Greatmaps(GMap)可视化技术，提出一种计算港口装卸效率值的方法。利用该方法估算上海港、新加坡港、深圳港和宁波-舟山港的月度装卸效率值，4 个港口 2017 年上半年的装卸效率月度均值分别为 2.85、1.87、2.17 和 2.10。基于上半年估计的装卸效率值，对4个港口下半年的月度吞吐量进行估计，估算误差均值分别为2.77%、2.06%、2.93%和2.46%。结果表明，该方法能够较为准确地反映港口的装卸效率，可应用于推断和实时监控港口的吞吐量，为港口提高绩效和船公司选择港口策略提供理论参考，提升港口数字化管理水平。     

Real-time bus route state forecasting using particle filter and mesoscopic modeling

In the absence of system control strategies, it is common to observe bus bunching in transit operations. A transit operator would benefit from an accurate forecast of bus operations in order to control the system before it becomes too disrupted to be restored to a stable condition. To accomplish this, we present a general bus prediction framework. This framework relies on a stochastic and event-based bus operation model that provides sets of possible bus trajectories based on the observation of current bus positions, available via global positioning system (GPS) data. The median of the set of possible trajectories, called a particle, is used as the prediction. In particular, this enables the anticipation of irregularities between buses. Several bus models are proposed depending on the dwell and inter-stop running time representations. These models are calibrated and applied to a real case study thanks to the high quality data provided by TriMet (the Portland, Oregon, USA transit district). Predictions are finally evaluated by an a posteriori comparison with the real trajectories. The results highlight that only bus models accounting for the bus load can provide valid forecasts of a bus route over a large prediction horizon, especially for headway variations. Accounting for traffic signal timings and actual traffic flows does not significantly improves the prediction. Such a framework paves the way for further development of refined dynamic control strategies for bus operations.     

Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns

This paper investigates punctuality at bus stops. Although it is typically evaluated from the point of view of bus operators, it must also account for users, as required in recent service quality norms. Therefore, evaluating punctuality at bus stops is highly important, but may also be a complex task, because data on both bus arrivals (or departures) and users must be taken into account and processed. Data on buses can be collected by Automatic Vehicle Location (AVL) systems, but several challenges must be addressed in order to use them effectively. Passengers data at bus stops cannot be derived from AVL, but they can be used to derive passenger patterns and need to be integrated into processed AVL data. This paper proposes a new punctuality measure defined as the fraction of passengers who will be served within an acceptably short interval after they arrive. A method is proposed to determine this measure: it provides (i) several rules to handle AVL collected data, (ii) a procedure integrating processed AVL data and potential passengers’ patterns and (iii) a hierarchical process to perform the punctuality measure on each bus route direction of a transit network, as well as for every bus stop and time period. The paper illustrates the experimentation of this method on more than 4,000,000 data of a real bus operator and represents outcomes by easy-to-read control dashboards.     

论公路的网络化管理及实践

网络化管理在公路管理中的实现是城市管理信息化的必要手段,有明确的实施范围和技术方案.在实际工作中,要配合公路建设做好各项工作,提高公路服务能力.     

基于交通大数据的移动模式分析综述

交通大数据可为揭示交通主体显性出行行为背后的深层规律（即移动模式）提供重要基础。精确掌握大数据驱动下交通主体的移动模式，可为需求预测、客流组织、土地利用、事件管理等应用提供理论依据。交通大数据主体繁多、时空多态、关联性复杂的特性迫使小数据时代下的移动模式分析方法转型和升级，但仍可能遇到移动模式一致性表达难、异常类型检测难、复杂关联性表达难、时空多态性建模难、一体可视化分析难等普遍问题。针对这些问题，利用科学知识图谱，对2010~2020年期间3 747篇文献的热点关键词分布、发表趋势分布、出版刊物分布等特点进行归纳总结，结合常见的移动模式分析数据集，系统综述现有研究在移动模式常态分析、非常态分析、关联分析、预测分析和可视化分析方向上的阶段性进展。其中，移动模式常态分析综述了个体活动特性分析、出行类别划分、特定群体分析等应用的研究进展。移动模式非常态分析综述了基于模板匹配和数据驱动的非常态事件检测方法的发展脉络。移动模式关联分析综述了面向不同数据源的关联特性检测方法的发展近况。移动模式预测分析综述了基于数理统计和数据驱动的交通属性预测方法的发展状况。移动模式可视化分析综述了用户交互设计、移动模式宏观可视化、微观可视化和整体可视化的发展近况。最后，系统总结各个分支方向面临的主要问题与挑战，并从数据整合、模型创新、机制变革等角度提炼移动模式分析研究的未来发展趋势，期望为后续研究采用新理论技术开展移动模式分析提供参考。     

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.     

提高桥梁设计的安全性和耐久性的对策

通过介绍桥梁安全性、耐久性不足的严重危害,分析目前我国桥梁安全性、耐久性不足的原因,有针对性地提出提高桥梁设计的安全和耐久性的对策,对于指导实际工作有一定的意义.     

手机数据在交通调查和交通规划中的应用

手机作为一种理想的交通探测器,为居民出行信息分析提供了很好的技术选择。将手机数据映射至交通分析单元,并经信息预处理、匹配分析、交通模型分析处理、数据去噪、扩样等一系列海量数据运算处理,最终可获得居民出行特征数据。利用长期历史手机话单数据,可分析常住人口和就业人口分布、通勤出行特征、大区间OD、特定区域出行特征、流动人口出行特征等。手机信令数据能够较完整地识别手机用户的出行轨迹,可进一步应用于分析城市人口时空动态分布、特定区域客流集散、查核线断面或关键通道客流、轨道交通客流特征、出行时耗、出行距离、出行强度、道路交通状态等。根据天津手机话单数据应用案例及上海手机信令数据应用案例,验证了技术可行性。     

数据存储和GPS定位在冷藏机组控制器中的应用

本文研究了一种基于ATMEGA644PA-AU单片机的具有数据存储和GPS定位功能的冷藏机组控制器,其将冷藏运输车辆在运行过程中冷藏箱体内温度、冷藏制冷机组运行时各参数、GPS定位信息以逗号分隔文档的形式存储于USB闪存盘中,用以实现冷藏机组运行时的全过程参数的存储与查询,为冷藏机组的维修、运输货物的保存、品质的查询提供详实、可靠数据。