Privacy-preserving MaaS fleet management

On-demand traffic fleet optimization requires operating Mobility as a Service (MaaS) companies such as Uber, Lyft to locally match the offer of available vehicles with their expected number of requests referred to as demand (as well as to take into account other constraints such as driver’s schedules and preferences). In the present article, we show that this problem can be encoded into a Constrained Integer Quadratic Program (CIQP) with block independent constraints that can then be relaxed in the form of a convex optimization program. We leverage this particular structure to yield a scalable distributed optimization algorithm corresponding to computing a gradient ascent in a dual space. This new framework does not require the drivers to share their availabilities with the operating company (as opposed to standard practice in today’s mobility as a service companies). The resulting parallel algorithm can run on a distributed smartphone based platform.     

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.     

Optimal joint distance and time toll for cordon-based congestion pricing

This paper addresses the optimal toll design problem for the cordon-based congestion pricing scheme, where both a time-toll and a nonlinear distance-toll (i.e., joint distance and time toll) are levied for each network user’s trip in a pricing cordon. The users’ route choice behaviour is assumed to follow the Logit-based stochastic user equilibrium (SUE). We first propose a link-based convex programming model for the Logit-based SUE problem with a joint distance and time toll pattern. A mathematical program with equilibrium constraints (MPEC) is developed to formulate the optimal joint distance and time toll design problem. The developed MPEC model is equivalently transformed into a semi-infinite programming (SIP) model. A global optimization method named Incremental Constraint Method (ICM) is designed for solving the SIP model. Finally, two numerical examples are used to assess the proposed methodology.     

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.     

基于模拟退火-自适应布谷鸟算法的城市公交调度优化研究

为改善城市常规公交运营效率,提出基于模拟退火-自适应布谷鸟算法的公交调度优化模型.通过结合线路实际客流数据反映的客流特征,建立考虑公交公司和乘客双方利益的公交调度优化模型;改进布谷鸟算法固定步长并加入模拟退火算法退火操作,设计模拟退火-自适应布谷鸟算法,改善寻优过程中跳出局部最优解而全局寻优的能力;以福州125路公交线...     

高速铁路枢纽站技术作业计划与动车所调车作业计划协同编制研究

分析高速铁路枢纽站技术作业计划与动车所调车作业计划的协同编制过程,提出了将两个计划一体化编制的思想。以需安排作业效益最大化为优化目标,构建基于动车组车底的高速铁路枢纽站与动车所作业计划协同编制模型。针对模型特点,提出瓶颈工序、启发式分配规则及粗粒度主从进程模式的并行禁忌搜索策略(PTS)相结合的混合优化算法,首先确定作业安排瓶颈工序,然后启动主进程和若干从进程,主进程运用启发式分配规则快速生成初始解分配给从进程,从进程运用与瓶颈工序相关的禁忌算法搜索优化解,并反馈给主进程,主进程记录全局最优解并根据交叉策略生成新的初始解,重新分配给从进程进行TS搜索。最后,用实例验证了模型和算法的有效性。     

舰载直升机应召搜索时使用特种浮标问题研究

在使用声纳浮标搜索时,水声环境对搜索效能影响很大,通过布设特种浮标可以测得当面海区水声环境,确定浮标工作深度和作用距离。分析了舰载直升机布设特种浮标的方法和布设模型,针对布设时存在的问题提出了改进对策,为舰载直升机应召搜索时使用特种浮标提供参考。     

浅析遇险渔业船舶救助现状及建议

目前,渔业船舶已经成为我国海上搜救工作中的一支重要力量,但发生海难事故的渔业船舶自身的救助成功率却偏低。文中试结合近几年渔业遇险船舶搜救的大量案例,分析影响渔业船舶救助成功率的几大主要原因,并对如何提高渔业遇险船舶救助成功率提出了若干建议。     

Can ports increase traffic while reducing inputs? Technical efficiency of Spanish Port Authorities using a directional distance function approach

The focus of this paper is on the productive efficiency of ports. We estimate a directional technology distance function using parametric techniques to analyze the production technology and technical efficiency of a set of Spanish Port Authorities observed over the period 1993–2012. Technical inefficiency is conceived as the ability of ports to simultaneously expand a given output and contract variable inputs while maintaining quasi-fixed inputs and other outputs constant. Thus, for containerized cargo we address the following question: Given the amount of quasi-fixed inputs used by the firm and given the volume of the other outputs (liquids, solids, non-containerized cargo and passenger traffic), could ports increase their containerized merchandise while simultaneously reducing their variable inputs? Similar questions are asked for solid bulk and non-containerized general cargo. Our results show evidence of technical inefficiency among the ports in our sample. In particular, if the ports operated efficiently, we find that containerized cargo could be expanded by an average of over 6.4%, with an equivalent reduction in variable inputs. Solid bulk cargo and general non-containerized cargo could be increased by 4.1% and 6.1% respectively, with corresponding reductions in variable inputs. An implication of our results is that there is ample scope for specialization on the part of ports with no increase in infrastructure costs. Given that large investments in infrastructure have been made in Spanish port over the last decade, this opens the possibility of moving towards a management model based on taking advantage of existing capacity rather than new investments.