Generalized Maximum Benefit Multiple Chinese Postman Problem

This research is focused on a generalization on the Max Benefit Chinese Postman Problem and the multiple vehicle variant of the Chinese Postman Problem. We call this generalization, the Generalized Maximum Benefit k-Chinese Postman Problem (GB k-CPP). We present a novel Mixed Integer Programming (MIP) formulation for the GB k-CPP. Four different cases of the model are discussed. The first case, performs arc-routing with profits and assumes that the origin and destination for each vehicle is the same for each cycle and is given by the user. The next case relaxes the assumption that the origin and destination for each vehicle should be the same and allows the users to select possible origins/destinations for vehicles. Case three gets the origin for each vehicle as input and produces a solution based on finding the best destination for each vehicle. The last case, that is very general, allows the optimization model to select possibly different locations for vehicle origin and destination, during each cycle. The different cases are applied to a security patrolling case conducted on the network of University of Maryland at College Park campus and the results are compared.     

Human-like autonomous car-following model with deep reinforcement learning

This study proposes a framework for human-like autonomous car-following planning based on deep reinforcement learning (deep RL). Historical driving data are fed into a simulation environment where an RL agent learns from trial and error interactions based on a reward function that signals how much the agent deviates from the empirical data. Through these interactions, an optimal policy, or car-following model that maps in a human-like way from speed, relative speed between a lead and following vehicle, and inter-vehicle spacing to acceleration of a following vehicle is finally obtained. The model can be continuously updated when more data are fed in. Two thousand car-following periods extracted from the 2015 Shanghai Naturalistic Driving Study were used to train the model and compare its performance with that of traditional and recent data-driven car-following models. As shown by this study’s results, a deep deterministic policy gradient car-following model that uses disparity between simulated and observed speed as the reward function and considers a reaction delay of 1 s, denoted as DDPGvRT, can reproduce human-like car-following behavior with higher accuracy than traditional and recent data-driven car-following models. Specifically, the DDPGvRT model has a spacing validation error of 18% and speed validation error of 5%, which are less than those of other models, including the intelligent driver model, models based on locally weighted regression, and conventional neural network-based models. Moreover, the DDPGvRT demonstrates good capability of generalization to various driving situations and can adapt to different drivers by continuously learning. This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.     

A Delphi analysis on green performance evaluation indices for ports in China

This study identified a set of applicable and practical green performance evaluation indices for Chinese ports using the Delphi technique. The research was conducted by targeting three representative Chinese ports in Eastern China: the Shanghai Port, Ningbo Port, and Qingdao Port. An effective expert panel was structured with direct stakeholders related to green performance, which was based on a sufficient consideration of the unique and the special decision-making system for Chinese ports. Twenty-one green performance indices in six dimensions (i.e. liquid pollution management, air pollution management, noise control, low carbon regulations and energy savings, marine biology preservation, and organization and management) were eventually identified through three rounds of iterations. The identified green performance indices for Chinese ports are highly consistent with the Chinese Government’s environmental policies. Compared with European ports, the green port performance indicators in China are not as comprehensive. The indicators that are strictly consistent with national macro policies and the factors that are more easily seen, heard, and felt were perceived to be more important. From an academic perspective, this study offers a new approach for system establishment and green practice criteria exploration and thus provides a significant basis for further green port studies.     

Empirical analysis of short period traffic counts and their efficiency: the case of Indian traffic

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India.     

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.     

船舶通信英语口语与听力课程标准

结合船舶驾驶员的岗位职责,依照现代职业教育理论和成果,设计了船舶通信英语口语与听力课程标准.对该课程的课程概述、培养目标、与前后课程的联系、教学内容与学时分配、学习资源、教师要求、学习场地与设施要求、考核方式与标准、学习情境设计进行了设计和描述,将船舶通信中典型工作任务转化为学习型任务.     

The generators of paratransit trips by persons with disabilities

Identifying the generators of paratransit trips by persons with disabilities is important to comprehend the current demand patterns and forecast future demand. Only a handful of studies have been conducted so far to identify the generators of paratransit trips and most focused on the home end of the trips. Given some of the inconsistencies in past studies and the scarcity of studies on the generators of trips away from home, this study attempts to identify the generators of paratransit trips beginning and ending at clients’ homes and away from home. It uses an extremely large dataset consisting of 1.91 million trips made by NJ TRANSIT’s Access Link clients, socioeconomic data from the American Community Survey, employment data from the Longitudinal Employer-Household Dynamics, and establishment data from Dun and Bradstreet. The analytical methods include an ordinary least squares model (OLS) and several spatial generalized linear mixed models (GLMM) to identify the characteristics of census block groups associated with Access Link trip generation at home and away from home, Geographic Information System (GIS) analysis to identify the types of establishments located in the immediate vicinity of drop-offs, and a multinomial logit model (MNL) to examine the relationship between the characteristics of the establishments in the vicinity of drop-offs and the characteristics of the dropped-off clients. Together, the various analyses provide useful insights about paratransit trip generators at the macro and micro levels. Some implications of the findings are discussed.     

加强教风学风建设 不断提高人才培养质量

在改革不断深入、经济社会深度转型的当下,大学生群体呈现出新的特点和个性。在新的形势下,要加强高校教风学风建设,更好的发挥高校培养人才的作用,促使人才培养质量提升。本文以辽宁省交通高等专科学校为例,着重从文化建设、教师育人能力提升、学生职业梦想等角度入手,提出了加强教风学风建设的具体举措和途径。     

极高地应力软岩隧道超前导洞应力释放及多层支护变形控制技术

为了解决极高地应力软岩隧道大变形控制难题,以兰渝铁路木寨岭隧道岭脊核心段施工为例,通过现场试验和数据分析,得到如下主要结论:1)提出了"先放后抗,抗放结合,锚固加强"的变形控制理念;2)得出了该隧道岭脊核心段"超前导洞应力释放+圆形4层支护结构+径向注浆+长锚杆+长锚索"综合变形控制方案;3)超前导洞应力释放效果明显,正洞累计变形减小幅度约为34%;4)得到了圆形多层支护结构变形规律;5)累计变形均控制在设计预留变形量内,保证了该隧道岭脊核心段大变形控制效果。     

我国商业银行信息化建设研究综述

我国商业银行信息化建设已走过近20年的路程,在取得巨大业绩的同时也存在若干不足之处．文章在总结我国商业银行信息化建设经验的基础上,对其信息化建设的现状进行了深入的分析,同时指出了我国商业银行信息化建设的总体规划方向,以期大幅度提高我国商业银行的信息化建设绩效。