Real-time trip purpose prediction using online location-based search and discovery services

The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

隧道工程建设地质预报及信息化技术的主要进展及发展方向

复杂的不良地质条件是制约隧道安全高效建设的主要因素,要实现隧道工程的安全高效建设,首先要提高地质预测预报技术水平及其信息化程度。1)介绍我国复杂不良地质隧道超前预报的方法进展及其应用,包括突水突泥灾害源超前探测方法与设备、断层破碎带超前预报、城市地铁溶洞和孤石等探测的进展及应用等;2)介绍我国隧道岩爆监测预警方法及其应用,预报清楚之后就要加强安全风险过程监控;3)介绍基于BIM技术的建筑物(隧道工程)安全风险监控最新进展,包括安全风险实时感知系统和实时预警系统;4)指出隧道工程建设信息化技术的发展方向,包括开展基于大数据技术的TBM/盾构施工的分析与控制研究以及数字隧道向智慧隧道(建设和运营维护)的发展。     

高黎贡山隧道复杂地质条件下敞开式TBM施工关键技术研究

高黎贡山隧道地质条件极其复杂,可总结为"三高四活跃"。其中,高地热、高地应力、多断层破碎带、高压突涌水是制约TBM施工质量和安全的主要地质因素,将给TBM施工带来不可预估的风险。为了减少以上地质问题带来的TBM施工风险,通过资料查阅、调研国内外现有的TBM施工案例、专家咨询研讨、设计高适应性的TBM,并结合工程目前的施工状况,提出了TBM超前地质预报、钢筋排和钢拱架联合喷射混凝土及时支护、合理调整掘进参数等一系列确保TBM连续施工的方案与措施。研究结果可为即将进场施工的TBM提供理论参考。     

多元评价体系组合模型在铁路隧道变形预测中的应用

为克服传统预测模型结构单一、预测精度及稳定性不足等缺陷,提出多元体系组合预测模型的建模思路。首先,基于支持向量机、BP神经网络及ARMA模型3种单一预测模型,构建铁路隧道变形预测体系;再以均方根误差、误差平方和及平均绝对误差等为评价准则或指标,构建各预测结果的误差评价体系,求解各单项预测模型的权值贡献指数,得到最优组合权值;然后利用后验差检验、残差检验和关联度检验构建预测精度校验体系,对组合预测结果进行检验,评价预测模型的有效性;最后,结合工程实例,对多元体系组合预测模型在特大断面隧道中的变形预测效果进行检验。结果表明:多元评价体系组合模型预测相对误差值均小于2%,具有较高的预测精度,且较单一预测模型具有更高的预测精度,也一致通过相关检验,验证了多元体系组合预测模型的有效性。     

柴油机起动过程缸内窜机油现象的可视化研究

在单缸机上利用内窥镜技术观察到了起动过程中缸内有窜机油的现象。采用图像分析和试验验证相结合的方法,分析了缸内窜机油现象发生的原因,研究了转速和进气压力对柴油机起动倒拖工况下缸内窜机油现象的影响,研究了起动着火运行时缸内的窜机油现象以及炭烟排放特性。研究表明:柴油机高转速起动倒拖工况下,在压缩上止点前后以及排气上止点后均有机油窜入缸内;当转速升高时,窜入缸内的机油量增加;当缸内压力升高时,窜入缸内的机油量减少;在起动着火过程中,运行初期缸内出现窜机油现象,随着发动机运行时间增加,窜机油现象消失,炭烟减少。     

基于灰色残差GM（1，1）模型的道路交通量预测的研究

道路交通体系是一个多因素、多层次、多目标的复杂系统。其中交通量信息系统具有明显的层次复杂性,结构关系的模糊性,动态变化的随机性,指标数据的不完全和不确定性。由于技术方法、人为因素、自然环境变化的影响,造成各种数据误差、短缺甚至虚假现象,系统的作用机制不明确,系统的状态、结构、边界关系难以精确描述,属于典型的灰色系统。在作量化、模型化、实体化研究时,能作为反映系统主要动态特征的数据是很少的。由于环境对系统的干扰,系统信息中原始数据序列往往呈现离乱情况,离乱数列即为灰色数列或称灰色过程,灰色理论利用那些较少的或不确切的表示系统行为特征的原始数据序列作生成变换后建立微分方程,对灰色过程建立的模型称为灰色模型（Greymodel）,简称GM模型。本文从理论上介绍了GM（1,1）模型和灰色残差GM（1,1）模型建立的一般过程,然后将其应用于交通量预测的实际例子中。预测结果表明,该方法是可行的。     

立交线形动态拖动设计的关键技术

对立交线形动态拖动设计的几个关键技术进行论述.分析了缓和曲线计算精度,提出了动态拖动模式设计模型,以及约束满足条件下的非线性方程组的数值解算法.     

基于路网容量的商圈停车需求预测研究

分析商圈停车、交通拥堵与商圈持续繁荣的关系,商圈停车需求预测对商圈的可持续发展起着重要作用。为合理预见与科学估算商圈停车需求,结合路网容量、区位关系提出基于路网容量的停车需求预测模型。该模型一方面能平衡区域停车供需,缓解静态设施供需矛盾;另一方面能保证停车需求与路网容量相匹配,对动态交通起到必要的控制作用,缓解商圈周边路网的交通拥堵。最后,通过实例分析证明该模型的可行性。