Impact of service network topology on air transportation efficiency

Each stakeholder in the air transportation system has a different perspective on the performance efficiency metrics, making analysis of system-wide design options very difficult. This paper uses topological structures of service networks to examine trade-offs between efficiency metrics established around the passenger, airline, and air navigation service provider perspectives. The findings indicate that the scale-free type topologies are preferred under most of the metrics. However, with enough density, random topologies become more appealing with its high robustness feature and performance comparable to scale-free.     

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protégé web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.     

An eigenmodel for iterative line planning,timetabling and vehicle scheduling in public transportation

Planning a public transportation system is a multi-objective problem which includes among others line planning, timetabling, and vehicle scheduling. For each of these planning stages, models are known and advanced solution techniques exist. Some of the models focus on costs, others on passengers’ convenience. Setting up a transportation system is usually done by optimizing each of these stages sequentially.In this paper we argue that instead of optimizing each single step further and further it would be more beneficial to consider the whole process in an integrated way. To this end, we develop and discuss a generic, bi-objective model for integrating line planning, timetabling, and vehicle scheduling. We furthermore propose an eigenmodel which we apply for these three planning stages and show how it can be used for the design of iterative algorithms as heuristics for the integrated problem. The convergence of the resulting iterative approaches is analyzed from a theoretical point of view. Moreover, we propose an agenda for further research in this field.     

A cognitive framework to plan for the future of transportation

ABSTRACT

Automated, connected, electrified, and shared mobility will be cornerstones of the transportation future. Research to quantify the potential benefits and drawbacks of practice, and to identify barriers to adoption, is the first step in any strategic plan for their adoption. However, uncertainties, complexity, interdependence, and the multidisciplinary nature of emerging transportation technologies make it difficult to organize and identify focused research. The contribution of this work is a cognitive framework to help planners and policymakers organize broad topics, reveal challenges, discover ideas for solutions, quantify potential impacts, and identify implications to guide preparation strategies. The authors provide example cognitive frameworks for connected, automated, and electrified vehicles.     

Assessment of large transport infrastructure projects: The CBA-DK model

This paper presents a newly developed decision support model to assess transport infrastructure projects: CBA-DK. The model combines use of conventional cost–benefit analysis to produce aggregated single point estimates, with quantitative risk analysis using Monte Carlo simulation to produce interval results. The embedded uncertainties within traditional CBA such as ex-ante based investment costs and travel time savings are of particular concern. The paper investigates these two impacts in terms of the Optimism Bias principle which is used to take account of the underestimation of construction costs and the overestimation of travel time savings. The CBA-DK methodological approach has been used to apply suitable probability distribution functions on the uncertain parameters, thus resulting in feasibility risk assessment moving from point to interval results. The proposed assessment model makes use of both deterministic and stochastic based information. Decision support as illustrated in this paper aims to provide assistance in the development and ultimately the choice of action, while accounting for the uncertainties surrounding transport appraisal schemes. The modelling framework is illustrated by the use of a case study appraising airport and runway alternatives in the capital of Greenland – Nuuk. The case study has been conducted in cooperation with the Home Rule Authorities of Greenland.     

Multiple criteria and fuzzy based evaluation of logistics performance for intermodal transportation

This paper presents a framework to evaluate the logistics performance of intermodal freight transportation. Fuzzy set techniques are applied to assess the logistics performance within the decision process of freight operators. Using a fuzzy‐based approach, fuzzy‐AHP is applied to assess the criteria by different judgment procedures. Consequently, fuzzy‐MCDM is used to assess operators' perception of the logistics performance via proper assignment of numerical scores. The subjective judgments for hierarchical criteria are transformed into fuzzy degrees of score. The methodology provides an alternative approach to facilitate the importance of a set of performance criteria. It can also entail use of improved corresponding parameters to develop a better freight transport system.     

A decision support approach for condition-based maintenance of rails based on big data analysis

In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making.     

基于节点重要度与交通区位的公路运输枢纽布局研究

从区域社会经济、公路运量及交通区位三方面出发,基于节点重要度与交通区位综合分析,建立了运输枢纽节点重要度与交通区位相结合的评价指标体系。由于各指标在比较时具有一定模糊性,因此文章运用模糊层次分析法确定了各指标的权重,通过引入重要度综合指数,对区域内各运输枢纽节点的重要度综合指数进行了分析。通过对山东省区域公路运输枢纽布局重要度综合指数的实例分析,验证了该方法的合理性和可行性,为公路运输枢纽总体布局研究提供了主要理论依据。     

Integrated analysis of GHGs and public health damage mitigation for developing urban road transportation strategies

This study attempts to present an urban road transportation strategy focusing on the mitigation of both GHGs emission and public health damage, taking Xiamen City as a case study. We developed a Public Health and GHGs Emission model to estimate the impacts of direct energy-consumption-related GHGs emissions and public health damage in Xiamen’s road transportation strategies from 2008 to 2025, considering the environmental benefits and economic costs. Two scenarios were designed to describe future transportation strategies for Xiamen City, and mitigation potentials for both GHGs emission and public health costs were estimated from 2008 to 2025 under a series of options. The results show that enacting controls on private vehicles would be most effective to GHGs mitigation, while enacting controls on government and rental vehicles would contribute the most to NO₂ and PM2.5 reductions. Compared with the Business as Usual scenario, the Integrated scenario would achieve about a 68% energy consumption reduction and save 0.23 billion yuan (95% CI: 0.16, 0.32) in health costs in 2025. It is clear that integrated and advisable strategies need to mitigate the adverse impacts of urban road vehicles on GHGs emissions and public health and economic costs, particularly in regions of rapid urbanization.     

出租车管理系统与交通公共信息平台数据交换方式研究

文章以上海大众交通(集团)有限公司提供的出租车GPS数据为基础,通过对源数据进行过滤和处理,满足了交通公共信息平台数据应用要求,实现了交通管理和建设所需要数据的提取,提高了交通资源的利用率。     

Mitigating environmental impacts in advance: Evidence of cost and time savings for transportation projects

The traditional model for mitigating a transportation project’s environmental impacts typically operates project-by-project and delivers the mitigation just-in-time. In contrast, the newer practice of advance mitigation comprehensively assesses and mitigates impacts from one or multiple transportation projects before or during project planning, sometimes long before project construction begins. The practice has gained adherents for its potential to improve ecological outcomes, by better aligning mitigation and conservation goals. Advance mitigation also stands to reduce mitigation costs, an important secondary benefit for transportation agencies with constrained resources. Evidence of cost savings, however, has been piecemeal and anecdotal. This paper advances knowledge of advance mitigation’s financial impacts in two ways. First, it critically assesses the evidence about cost savings realized through advance mitigation, both through avoided up-front costs and reduced project delay. Second, it directly estimates the project time savings that might accrue with advance mitigation of state highway projects in California. Overall, the balance of evidence is encouraging for transportation agencies that would introduce the practice, and general agreement exists on its financial benefits. Considering project delays related only to the environmental process, we estimate advance mitigation could reduce delivery times by 1.3–5.0 months per project. Still, we also identify factors limiting comprehensive analysis. Transportation agencies adopting advance mitigation practices into their operations could use a pilot approach that includes rigorous environmental and mitigation cost accounting; such pilots would build needed empirical evidence of advance mitigation’s financial and ecological outcomes.     

Local environmental impact assessment as decision support for the introduction of electromobility in urban public transport systems

The urban transport sector offers a noteworthy potential for the reduction of national greenhouse gas emissions as well as local pollutant emissions such as nitrogen oxides and particulate matter if electric drive systems are increasingly used. Owing to the fact that electric busses are still in the development phase, higher investment costs have evolved for public transport providers. Hence, decision making about where to introduce electric bus lines is mainly characterized by economic as well as technological considerations. The integration of local or regional ecological aspects is often neglected. An interdisciplinary approach was applied to the bus network of an urban public transport provider. By combining spatial-analytical techniques and statistical methods, the local environmental relief potential of electric busses has been evaluated. The results show that due to their specific line characteristics and the frequency of service, two bus lines out of 28 are particularly suitable for the introduction of electromobility in Dresden, Germany. The presented scientific work contributes to the extension of environmental assessments and decision making tools by including the spatial dimension of environmental impacts. It increases the practical relevance, especially for management decisions of political and entrepreneurial stakeholders by providing a sensible decision basis for local or regional infrastructure projects.     

Measuring energy and environmental efficiency of transportation systems in China based on a parallel DEA approach

Because of China’s rapid economic development, its transportation system has become one of China’s high-energy-consumption and high-pollution-emission sectors. However, little research has been done which pays close attention to China’s transportation system, especially in terms of energy and environmental efficiency evaluation. In this paper, data envelopment analysis (DEA) is applied to measure the energy and environment performance of transportation systems in China with the goal of sustainable development. This paper treats transportation as a parallel system consisting of subsystems for passenger transportation and freight transportation, and extends a parallel DEA approach to evaluate the efficiency of each subsystem. An efficiency decomposition procedure is proposed to obtain the highest achievable subsystem efficiency. Our empirical study on 30 of mainland China’s provincial-level regions shows that most of them have a low efficiency in their transportation system and the two parallel subsystems. There are large efficiency differences between the passenger and freight transportation subsystems. In addition, unbalanced development has occurred in the three large areas of China, with the east having the highest efficiency, followed by central China and then west. Therefore, more measures should be taken to balance and coordinate the development between the three large areas and between the two subsystems within them. Our analysis approach gives data for determining effective measures.     

Determining optimal frequency and vehicle capacity for public transit routes: A generalized newsvendor model

The level of service on public transit routes is very much affected by the frequency and vehicle capacity. The combined values of these variables contribute to the costs associated with route operations as well as the costs associated with passenger comfort, such as waiting and overcrowding. The new approach to the problem that we introduce combines both passenger and operator costs within a generalized newsvendor model. From the passenger perspective, waiting and overcrowding costs are used; from the operator’s perspective, the costs are related to vehicle size, empty seats, and lost sales. Maximal passenger average waiting time as well as maximal vehicle capacity are considered as constraints that are imposed by the regulator to assure a minimal public transit service level or in order to comply with other regulatory considerations. The advantages of the newsvendor model are that (a) costs are treated as shortages (overcrowding) and surpluses (empty seats); (b) the model presents simultaneous optimal results for both frequency and vehicle size; (c) an efficient and fast algorithm is developed; and (d) the model assumes stochastic demand, and is not restricted to a specific distribution. We demonstrate the usefulness of the model through a case study and sensitivity analysis.     

Agent-based simulation framework for mixed traffic of cars,pedestrians and trams

In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.     

Bridging the gap between multi-objective optimization and spatial planning: a new post-processing methodology capturing the optimum allocation of land uses against established transportation infrastructure

The optimal allocation of multiple land uses constitutes a complex multi-objective optimization problem with unknown feasible objective space and optimal planning alternatives. Despite the effectiveness of evolutionary algorithms to capture the underlying Pareto set of optimum maps, land use planners are bound to pursue the best possible spatial allocation of each use within an enormous population of non-dominated solutions. This article presents a novel post-processing methodology enhancing the comparative evaluation of alternative planning approaches without making any assumptions about the (relative) importance of each objective function. The proposed consolidated post-processing module is applied in a land use planning paradigm, revealing: (a) the existence of substantial planning guidelines whose validity is not affected by the relative significance of each criterion and (b) the variable planning component emerging from the (varying) relative importance of objective functions. Such planning feedback could not be extracted by the exhaustive review of non-dominated maps.     

A stochastic fleet size and mix model for maintenance operations at offshore wind farms

A fleet of vessels and helicopters is needed to support maintenance operations at offshore wind farms. The cost of this fleet constitutes a major part of the total maintenance costs, hence keeping an optimal or near-optimal fleet is essential to reduce the cost of energy. In this paper we study the vessel fleet size and mix problem that arises for the maintenance operations at offshore wind farms, and propose a stochastic three-stage programming model. The stochastic model considers uncertainty in vessel spot rates, weather conditions, electricity prices and failures to the system. The model is tested on realistic-sized problem instances, and the results show that it is valuable to consider uncertainty and that the proposed model can be used to solve instances of a realistic size.     

A fuzzy-stochastic optimization model for the intermodal fleet management problem of an international transportation company

ABSTRACT

In this paper, a fuzzy-stochastic optimization model is developed for an intermodal fleet management system of a large international transportation company. The proposed model integrates various strategic, tactical and operational level decisions simultaneously. Since real-life fleet planning problems may involve different types of uncertainty jointly such as randomness and fuzziness, a hybrid chance-constrained programming and fuzzy interactive resolution-based approach is employed. Therefore, stochastic import/export freight demand and fuzzy transit times, truck/trailer availabilities, the transport capacity of Ro-Ro vessels, bounds on block train services, etc. can also be taken into account concurrently. In addition to minimize overall transportation costs, optimization of total transit times and CO₂ emission values are also incorporated in order to provide sustainable fleet plans by maximizing customer satisfaction and environmental considerations. Computational results show that effective and efficient fleet plans can be produced by making use of the proposed optimization model.     

A zonal inference model based on observed smart-card transactions for Santiago de Chile

The collection of origin–destination data for a city is an important but often costly task. This way, there is a need to develop more efficient and inexpensive methods of collecting information about citizens’ travel patterns. In this line, this paper presents a generic methodology that allows to infer the origin and destination zones for an observed trip between two public transport stops (i.e., bus stops or metro stations) using socio-economic, land use, and network information. The proposed zonal inference model follows a disaggregated Logit approach including size variables. The model enables the estimation of a zonal origin–destination matrix for a city, if trip information passively collected by a smart-card payment system is available (in form of a stop-to-stop matrix). The methodology is applied to the Santiago de Chile’s morning peak period, with the purpose of serving as input for a public transport planning computational tool. To estimate the model, information was gathered from different sources and processed into a unified framework; data included a survey conducted at public transport stops, land use information, and a stop-to-stop trip matrix. Additionally, a zonal system with 1176 zones was constructed for the city, including the definition of its access links and associated distances. Our results shows that, ceteris paribus, zones with high numbers of housing units have higher probabilities of being the origin of a morning peak trip. Likewise, health facilities, educational, residential, commercial, and offices centres have significant attraction powers during this period. In this sense, our model manages to capture the expected effects of land use on trip generation and attraction. This study has numerous policy implications, as the information obtained can be used to predict the impacts of changes in the public transport network (such as extending routes, relocating their stops, designing new routes or changing the fare structure). Further research is needed to improve the zonal inference formulation and origin–destination matrix estimation, mainly by including better cost measures, and dealing with survey and data limitations.     

A planning tool for evaluating vehicles miles travelled and traffic safety forecasts of growth management scenarios: A case study of Baton Rouge and New Orleans

This study describes an adaptable planning tool that examines potential change in vehicle miles travelled (VMT) growth and corresponding traffic safety outcomes in two urbanized areas, Baton Rouge and New Orleans, based on built environment, economic and demographic variables. This model is employed to demonstrate one aspect of the potential benefits of growth management policy implementation aimed at curbing VMT growth, and to establish targets with which to measure the effectiveness of those policies through a forecasting approach. The primary objective of this research is to demonstrate the need to break with current trends in order to achieve future goals, and to identify specific policy targets for fuel prices, population density, and transit service within the two study regions. Models indicate based on medium growth scenarios, Baton Rouge will experience a 9 percent increase in VMTs and New Orleans will experience 10 percent growth. This translates to corresponding increases in crashes, injuries and fatalities. The paper provides forecasts for planners and engineers to consider an alternative future, based on desired goals to reduce VMTs and therefore improve safety outcomes. A constrained-forecast model shows a cap on VMTs and crash rates is achievable through policy that increases fuel prices, population density and annual transit passenger miles per capita at reasonable levels through a growth management approach.