Commuter ride-sharing using topology-based vehicle trajectory clustering: Methodology,application and impact evaluation

This paper illustrates a ride matching method for commuting trips based on clustering trajectories, and a modeling and simulation framework with ride-sharing behaviors to illustrate its potential impact. It proposes data mining solutions to reduce traffic demand and encourage more environment-friendly behaviors. The main contribution is a new data-driven ride-matching method, which tracks personal preferences of road choices and travel patterns to identify potential ride-sharing routes for carpool commuters. Compared with prevalent carpooling algorithms, which allow users to enter departure and destination information for on-demand trips, the proposed method focuses more on regular commuting trips. The potential effectiveness of the approach is evaluated using a traffic simulation-assignment framework with ride-sharing participation using the routes suggested by our algorithm. Two types of ride-sharing participation scenarios, with and without carpooling information, are considered. A case study with the Chicago tested is conducted to demonstrate the proposed framework’s ability to support better decision-making for carpool commuters. The results indicate that with ride-matching recommendations using shared vehicle trajectory data, carpool programs for commuters contribute to a less congested traffic state and environment-friendly travel patterns.     

Toward regional transportation governance: a case study of Greater Vancouver

This paper discusses the evolving institutional structure and governance of transportation planning, policy development and transit delivery within one major North American city-region, the Greater Vancouver area. Various methods of transportation governance are explored from complete independence to full regional integration. The move away from a direct provincial role in transportation management to a greater regional transit authority is discussed and critiqued. This revised version was published online in June 2006 with corrections to the Cover Date.     

Airport demand management: The operations research and economics perspectives and potential synergies

Airport demand management aims to mitigate air traffic congestion by limiting the imbalances between demand and capacity at busy airports through administrative measures (e.g., slot controls) or economic incentives (e.g., congestion pricing, slot auctions). This paper provides an integrated synthesis of the contributions of the fields of operations research/management science (OR/MS) and economics on the subject matter. From an operating standpoint, assessing the benefits of demand management requires estimates of airport capacity and models of airport on-time performance. From a managerial standpoint, the design of demand management mechanisms can be supported by decision-making models of flight scheduling. From an economic standpoint, the welfare impact of congestion pricing, slot controls and slot auctions depends on the market structure at the airport. This paper proposes an integrated framework that underscores the interdependencies between these operating, managerial and economic aspects to foster cross-disciplinary approaches toward more effective demand management policies at busy airports worldwide.     

Personalized real-time traffic information provision: Agent-based optimization model and solution framework

The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time congested traffic management in a congested network. This paper proposes an agent-based optimization modeling framework to provide personalized traffic information for heterogeneous travelers. Based on a space–time network, a time-dependent link flow based integer programming model is first formulated to optimize various information strategies, including elements of where and when to provide the information, to whom the information is given, and what alternative route information should be suggested. The analytical model can be solved efficiently using off-the-shelf commercial solvers for small-scale network. A Lagrangian Relaxation-based heuristic solution approach is developed for medium to large networks via the use of a mesoscopic dynamic traffic simulator.     

Multi-step prediction of experienced travel times using agent-based modeling

This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60 min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.     

A carbon footprint-based closed-loop supply chain model under uncertainty with risk analysis: A case study

The management of products’ end-of-life and the recovery of used products has gained significant importance in recent years. In this paper, we address the carbon footprint-based problem that arises in a closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed of or be remanufactured to be resold as new ones. Given this environment, an optimization model for a closed-loop supply chain (CLSC) in which carbon emission is expressed in terms of environmental constraints, i.e., carbon emission constraints, is developed. These constraints aim to limit the carbon emission per unit of product supplied with different transportation modes. Here, we design a closed-loop network where capacity limits, single-item management and uncertainty on product demands and returns are considered. First, fuzzy mathematical programming is introduced for uncertain modeling. Then, the statistical approach to the possibility to synthesize fuzzy information is utilized. Therefore, using a defined possibilistic mean and variance, we transform the proposed fuzzy mathematical model into a crisp form to facilitate efficient computation and analysis. Finally, the risk caused by violating the estimated resource constraints is analyzed so that decision makers (DMs) can trade off between the expected cost savings and the expected risk. We utilize data from a company located in Iran.     

Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data

Assessing sustainability of supply chains is a critical and increasingly complex problem. In recent years sustainability has received more attention in supply chain management (SCM) literature with triple bottom lines including social, environmental, and economic factors. Conventional data envelopment analysis (DEA) models consider decision making units (DMUs) as black boxes that consume a set of inputs to produce a set of outputs and do not take into consideration internal interactions of DMUs. Two-stage DEA models deal with such DMUs. However, existing two-stage DEA models are applicable only in technologies characterized by positive inputs/outputs. This paper aims to build and present a new two-stage DEA model considering negative input-intermediate-output data. Some numerical examples along with some theorems and properties are given to show capability of proposed method. The proposed ideas are used in a case study where 29 Iranian supply chains producing equipment of expendable medical devices are evaluated in terms of sustainability.     

加强建设单位在项目实施阶段的管理

在工程实施阶段，建设单位在项目管理中有着不可替代的核心作用。其管理职能主要包括决策、计划、组织、协调与控制等。其管理内容主要有合同管理、质量管理、投资控制、进度控制等方面。     

北京地铁4号线与伦敦地铁PPP项目案例分析

北京地铁4号线和伦敦地铁都是通过公私合营(PPP)的方式与融资能力强、具有一定经验的社会资本合作,北京地铁4号线自2009年开通以来一直运行良好,伦敦地铁PPP项目在运营不到7年就宣布破产.运用比较分析方法从融资结构、运营管理和合同履行3个方面对北京地铁4号线和伦敦地铁进行分析,发现伦敦地铁失败的原因主要是其融资结构不合理、竞争机制不完善、契约精神缺失,并针对这些问题为以后PPP项目的实践提出3点建议:1)在伙伴选择方面,引入竞争机制,项目公司市场化运营;2)优化融资结构,合理分担风险;3)强化契约精神,加强政府监管.     

中国北车集团公司投资项目管理信息系统开发

阐述中国北车集团公司投资项目管理信息系统的开发设计思想，对业务进行描述，阐述系统开发目标、系统设计结构、系统功能和系统采用的技术，并对系统使用所产生的效果进行定量分析。