分动器转矩分配比确定理论研究

提出了分动器转矩分配比确定方法。以某越野车为例,分5种工况对其进行仿真分析计算,得出了与试验相吻合的结论。     

车路协同混合交通场景要素解析与测试案例生成

面向混合交通环境下多车效率类和单车安全类场景测试需求,研究了基于混合交通场景要素解析的车路协同测试案例生成方法; 为提高测试案例的多样性和覆盖度,分析了混合交通特征要素相互作用机理,构建了混合交通场景要素层次模型,提出了场景要素重要度的一致性描述指标,并在此基础上建立了测试案例复杂度模型; 针对多车效率类场景仿真测试,提出了复杂度激励的组合测试案例生成方法,设计了场景要素强耦合组合策略; 针对单车安全类场景仿真测试,提出了基于复杂度聚类的蒙特卡洛测试案例生成方法,设计了风险场景特征参数抽样机制; 选取车路协同混合交通典型场景开展仿真试验,验证了提出的测试案例生成方法的有效性。研究结果表明,对于多车效率类混合交通高速公路匝道合流场景测试,提出的方法比传统成对测试方法的场景最大复杂度提高了11.93%,高复杂度场景占比提高了60.02%,测试案例覆盖度提高了12.08%;对于单车安全类车路协同换道预警场景测试,提出的方法比传统蒙特卡洛测试方法的危险场景数提高了195%,且其参数估计误差降低了5.95%,高风险场景数提高了119%,且其参数估计误差降低了4.78%。可见,提出的方法能够提高测试案例的多样性和覆盖度,有助于开展复杂环境和风险条件下车路协同系统功能测试,能够有效满足多车效率类和单车安全类场景测试需求。     

A hybrid Genetic Algorithm approach to minimize the total joint cost of a single-vendor multi-customer integrated scheduling problem

This paper addresses the scheduling of supply chains with interrelated factories consisting of a single vendor and multiple customers. In this research, one transporter is available to deliver jobs from vendor to customers, and the jobs can be processed by batch. The problem studied in this paper focuses on a real-case scheduling problem of a multi-location hospital supplied with a central pharmacy. The objective of this work is to minimize the total cost, while satisfying the customer’s due dates constraints. A mathematical formulation of the problem is given as a Mixed Integer Programming model. Then, a Branch-and-Bound algorithm is proposed as an exact method for solving this problem, a greedy local search is developed as a heuristic approach, and a hybrid Genetic Algorithm is presented as a meta-heuristic. Computation experiments are conducted to highlight the performance of the proposed methods.     

Adaptation planning for climate-resilient urban infrastructures

This paper analyzes the interdependency across two critical infrastructures of transportation and motor fueling supply chains, and investigates how vulnerability to climatic extremes in a fueling infrastructure hampers the resilience of a transportation system. The proposed model features both a bi-stage mathematical program and an extension to an ‘α-reliable mean-excess’ regret model. The former aspect allows decision makers to optimize the pre-disaster asset prepositioning against the maximum post-disaster system resilience. The latter aspect of the proposed model devalues the impact of ‘low-probability, high-cost’ sub-scenarios upon model results. The model reveals the reliance of post-disaster urban mobility on the interdependent critical infrastructure of motor fueling supply chains. The results also suggest how investment in the fueling infrastructure’s vulnerable elements protects urban mobility while the transportation network is stressed or under attack.     

Taxi vacancy duration: a regression analysis

Taxi vacancy duration is a major efficiency measure for taxi services. A clear understanding of the various factors and their effect on vacancy duration is necessary for the optimal operational management of taxis. Previous research has only dealt with vacancy duration by assuming probability distributions and has not investigated heterogeneity in the data caused by various factors. We develop a parametric duration model using not only new operational characteristics but also variables associated with taxi demand, such as weather, land use, demographics, socioeconomic variables, and accessibility of public transportation. The model is applied to a large-scale New York City (NYC) taxi trip dataset that covers operations for 2013. The results show that all the attributes have significant associations with vacancy duration that follows a log-normal distribution. Our study is expected to help improve the efficiency of taxi operations by decreasing the time spent in vacant states.     

Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai

ABSTRACT

Smartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3?min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.