公路交通经济增长中的制度因素分析

在总结传统的经济增长理论(资本决定论、技术进步论、人力资本论、专业化的人力资本和特殊知识积累论)的基础上,采用新制度经济学的分析方法,对建国以来中国公路交通的制度变迁和经济绩效及其相互关系进行了实证分析,指出了制度在公路交通发展中的极端重要性;提出了"生产要素的制度性边界"这一概念,指出制度边界具有随经济、技术等因素变化而不断变化的特性,在此基础上建立了新的经济增长分析框架和模型;为打破制度的"低度均衡",扩展生产要素的制度边界,最后提出了关于加快制度创新的建议。     

收费公路计重定价理论模型

为了遏制超限运输,解决公路通行费征收中的不公平问题,必须改变目前按车型收费的计费模式,实施计重收费。建立计重收费的定价理论模型是推动这种收费方式健康发展的关键。在提出计重收费定义和类型的基础上,根据公路的准公共产品特性,分析了各种价格结构管制定价模式的特点,提出公路收费宜采用非线性的"两部定价"理论模型。结合收费公路费用分摊的具体方法,给出了收费公路计重定价理论模型,并对相关参数进行了标定。该模型已在江苏省和青海省计重收费标准制定中使用。     

Performance Evaluation of Hybrid-Drive Buses and Potential Fuel Savings in Brazilian Urban Transit

Transporting more than 55 million passengers per day, buses are the main transit mode in Brazil. Most of these vehicles use diesel oil and this situation causes dependence on oil, extensive greenhouse gas emissions and increasing air pollution in urban areas. In order to improve this situation the options for Brazilian cities include the use of alternative fuels and new propulsion technologies, such as hybrid vehicles. This article proposes a procedure for evaluating the performance of a recently developed Brazilian hybrid-drive technology. A simple procedure is presented to compare hybrid-drive buses with conventional diesel buses in urban operation focusing on fuel economy and the potential for reducing diesel oil consumption through the use of hybrid-drive buses. Field tests carried out by the authors indicate that fuel consumption improvement through the use of hybrid-drive buses would certainly exceed 20%, resulting in lower fuel costs and reduced carbon dioxide (CO₂) emissions.     

治理公路超载超限运输的长效机制和对策研究

为建立治理超载超限运输的长效机制,首先建立了运输企业收入的计量模型,分析了企业利润与运输价格、运输成本、超载罚款等变量之间的互动关系及企业获得最大利润的条件。在此基础上,针对中国道路货运市场结构的特点,从理性经济人的角度,对运输企业在“惩罚博弈”下可能的行为选择进行了研究;其次结合美国治理超载超限运输的经验,分析了不同执法强度和执法密度组合的执法策略及其适用条件、执法效果等,对中国治理超载超限运输提出了解决方案和具体的政策建议,对中国治理超载超限运输具有指导意义。     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.     

Optimal concession contracts for landlord port authorities with different pursuing goals

This paper investigates how landlord port authorities should offer concession contracts to their terminal operators under two different goals, by building a two-stage game for each goal. If maximizing the weighted sum of fee revenues and throughout benefits is port authorities’ goal, then the optimal concession contract can be any of the two-part tariff, the unit-fee, and the fixed-fee contracts. Accordingly, our special cases include previous works assuming that port authorities maximize either fee revenues or throughput benefits. By contrast, if maximizing the social welfare is the goal, then we find that subsidizing terminal operators, instead of charging them, is port authorities’ best choice. This result is not yet discovered in the literature.     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

基于需求弹性的城市公交票价优化方法及实证分析

城市公交票价是公交系统健康发展的重要因素，票价制定需兼顾公交企业的市场性和公益性双重属性，在保证服务质量的同时，考虑运营成本和最大限度吸引公交需求，实现经济效益和社会效益的双赢.在本文中，根据用户均衡理论，构建了城市多方式交通出行的需求分析模型，采用灵敏度分析方法给出了城市公交价格-需求弹性的计算方法.在此基础上，充分考虑了各种方式的交通特征、公交需求弹性、公交运营成本及政府限价等因素，提出了城市公交票价优化的数学规划模型，并给出了求解算法.最后，以国内某城市居民出行调查及公交运营数据为基础，分析了不同条件下公交客运需求的变化规律及城市公交票价的优化策略.     

绿色公路技术LCA评价体系及效益分析

针对各类绿色公路技术难以横向比较的问题,文章从技术可行性、经济效益、节能减排效益等方面,提出建立绿色公路技术LCA评价体系,并以G312苏州西段工程为例进行综合效益分析,其综合效益最为显著,具备规模化、资源化、投资适宜的绿色公路技术;技术瓶颈问题的突破和创新,是推进绿色公路发展的根本动力;将绿色理念融入公路建设的全生命周期的前提,是因地制宜的前期规划布局研究,也是合理选择适宜绿色公路技术的基础。