Sampling of alternatives in Logit Mixture models

Employing a strategy of sampling of alternatives is necessary for various transportation models that have to deal with large choice-sets. In this article, we propose a method to obtain consistent, asymptotically normal and relatively efficient estimators for Logit Mixture models while sampling alternatives. Our method is an extension of previous results for Logit and MEV models. We show that the practical application of the proposed method for Logit Mixture can result in a Naïve approach, in which the kernel is replaced by the usual sampling correction for Logit. We give theoretical support for previous applications of the Naïve approach, showing not only that it yields consistent estimators, but also providing its asymptotic distribution for proper hypothesis testing. We illustrate the proposed method using Monte Carlo experimentation and real data. Results provide further evidence that the Naïve approach is suitable and practical. The article concludes by summarizing the findings of this research, assessing their potential impact, and suggesting extensions of the research in this area.     

A bibliometric analysis on trends and characters of carbon emissions from transport sector

Transport sector’s substantial contribution to global greenhouse gas emissions has made it a growing area of study and concern. In order to identify trends and characteristics of carbon emissions research in the transportation sector we conducted a Bibexcel and complex network analysis for the period 1997–2016. In addition, we identify critical themes and contributions of research articles using h-index, PageRank and cluster analysis. We report contribution of countries, authors, institutions and journals, as well as performance of citations and keywords. Co-citing situations between different countries, authors, and institutions are also analyzed using network analysis. Between 1997 and 2016 we found a rise in publications on carbon emissions in the transportation sector and increased cooperation between countries, authors, and institutions. Authors from the USA, China and United Kingdom published the most articles and articles with the highest academic influence. Tsinghua University from China is the leading institution in carbon emissions research in the transportation sector. The most widely published author and cited author is Dr. He. We conclude our analysis by analyzing keywords and trends to suggest critical topic areas of future research. The systematic approach undertaken in this study can be extended and applied to other research topics and fields.     

ARTEMIS: French-German project for railway movements command and control

The development of railway transportation, especially through the gradual implementation of high-speed European network, is inciting railway companies to design railway movements command and control systems to enhance technical performance in terms of throughput and control of disturbances while simultaneously minimizing operational costs. The new command-control systems must be modular, adaptable and evolutive structures from both functional and geographical viewpoints if they are to satisfy all operational needs. Concurrent research into performance enhancement and cost reduction prescribes a system in which the majority of the “real time” components are aboards the locomotives. This is particularly true for those components used by the train to indicate its own position on the network as well as those enabling it to converse with operational control centers. The initial task is to design a global architecture of the command-control system that satisfies modularity and availability criteria followed by the development of numerous technical components including radar self-location systems and cellular radio transmission networks. The French (SNCF) and German (DB) national railways have implemented a vast program of co-operation aimed at providing valid, proven answers to these problems so as to allow their trains to travel freely on either network.     

Is a new applied transportation research field emerging?––A review of intermodal rail–truck freight transport literature

Intermodal freight transport has developed into a significant sector of the transport industry in its own right. This development has been followed by an increase in intermodal freight transportation research. We contend that a new transportation research application field is emerging; and that, while still in a pre-paradigmatic phase, it is now time to move on to a more mature independent research field. An independent research field can be justified because intermodal transport is a complex system that has characteristics which distinguishes it from other transport systems. We have reviewed 92 publications in order to identify the characteristics of the intermodal research community and scientific knowledge base. This paper will discuss aspects of this research, assessing the status quo and seeking directions for the future. To conclude, we will propose an intermodal research agenda which can direct the intermodal research field towards a period of “normal science”.     

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.     

Direct analysis of the perceived importance of attributes of reliability of travel modes in urban travel

Reliability of travel modes was found to be the most important characteristic of transportation systems in several attitudinal investigations of individual travel behavior. This paper represents the first part of a research effort aimed at gaining a better understanding of the characteristics of reliability of transportation modes in urban travel. In this research, reliability characteristics are identified; their importance relative to each other is assessed, and an insight into possible structure of an objective reliability index is discussed. The research is based on perceived values of reliability, which were identified through a large attitudinal survey conducted in the Chicago metropolitan area.     

Intermodal exchange stations in the city of Madrid

The City of Madrid is putting into operation Intermodal Exchange Stations (IESs) to make connections between urban and suburban transportation modes easier for users of public transportation. The purpose of this article is to evaluate the actual effects that the implementation of IESs in the City of Madrid has on the affected stakeholders: users, public transportation operators, infrastructure managers, the government, the abutters and other citizens. We develop a methodology intended to help assess the welfare gains and losses for each stakeholder. Then we apply this methodology to the case study of the Avenida de América IES in the city of Madrid. We found that it is indeed possible to arrive at win?Cwin solutions for the funding of urban transportation infrastructure, as long as the cost-benefit ratio of the project is high enough. Commuters save travel time. Bus companies diminish their costs of operation. The abutters gain in quality of life. The private operator of the infrastructure makes a fair profit. And the government is able to promote these infrastructure facilities without spending more of its scarce budgetary resources.     

Comparing the impact of future airline network change on emissions in India and the United States

In this paper we use simulation to analyze how flight routing network structure may change in different world regions, and how this might impact future traffic growth and emissions. We compare models of the domestic Indian and US air transportation systems, representing developing and mature air transportation systems respectively. We explicitly model passenger and airline decision-making, capturing passenger demand effects and airline operational responses, including airline network change. The models are applied to simulate air transportation system growth for networks of 49 airports in each country from 2005 to 2050. In India, the percentage of connecting passengers simulated decreases significantly (from over 40% in 2005 to under 10% in 2050), indicating that a shift in network structure towards increased point-to-point routing can be expected. In contrast, very little network change is simulated for the US airport set modeled. The simulated impact of network change on system CO₂ emissions is very small, although in the case of India it could enable a large increase in demand, and therefore a significant reduction in emissions per passenger (by nearly 25%). NO_x emissions at major hub airports are also estimated, and could initially reduce relative to a case in which network change is not simulated (by nearly 25% in the case of Mumbai in 2025). This effect, however, is significantly reduced by 2050 because of frequency competition effects. We conclude that network effects are important when estimating CO₂ emissions per passenger and local air quality effects at hub airports in developing air transportation systems.     

Recent applications of big data analytics in railway transportation systems: A survey

Big data analytics (BDA) has increasingly attracted a strong attention of analysts, researchers and practitioners in railway transportation and engineering. This urges the necessity for a review of recent research development in this field. This survey aims to provide a comprehensive review of the recent applications of big data in the context of railway engineering and transportation by a novel taxonomy framework, proposed by Mayring (2003). The survey covers three areas of railway transportation where BDA has been applied, namely operations, maintenance and safety. Also, the level of big data analytics, types of big data models and a variety of big data techniques have been reviewed and summarized. The results of this study identify the existing research gaps and thereby directions of future research in BDA in railway transportation systems.     

Communicating transportation carbon dioxide emissions information: Does gender impact behavioral response?

General concern and knowledge on climate change have been increasingly studied over the past decades. Gender differences have been found for general environmental concern and knowledge, but mixed findings exist with respect to climate change. In transportation, research has examined potential relations between environmental attitudes and transportation behavior, with mixed findings as well. Recently, the use of carbon dioxide (CO₂) emissions information to influence choice has been tested with women being found more willing to pay to reduce their personal impacts, suggesting that women are either more willing to change or that their response to information on climate change is stronger. However, those studies used CO₂ mass and studies that examined understanding of CO₂ information as a mass have found that people struggle to understand it. If concern and knowledge about climate change differ amongst individuals, then, according to theories such as the Transtheoretical Model, the type of information used to motivate choices is likely important. Using a unique data set (n = 236) it is possible to take a first look at how gender might affect concern, knowledge, and action in terms of transportation and climate change. Further, it is also possible to examine behavioral responses to transportation climate change information. Finally, an empirical analysis is conducted of the effect of how the information is presented might differ by gender. Thus, this work aims to investigate whether gender differences might contribute to the explanation of individual behavioral responses (from concern to action) in a transportation climate change context.     

Distribution-free travel time reliability assessment with probability inequalities

An assumption that pervades the current transportation system reliability assessment literature is that probability distributions of the sources of uncertainty are known explicitly. However, this distribution may be unavailable (inaccurate) in reality as we may have no (insufficient) data to calibrate the distribution. In this paper we relax this assumption and present a new method to assess travel time reliability that is distribution-free in the sense that the methodology only requires that the first N moments (where N is a user-specified positive integer) of the travel time to be known and that the travel times reside in a set of bounded and known intervals. Because of our modeling approach, all sources of uncertainty are automatically accounted for, as long as they are statistically independent. Instead of deriving exact probabilities on travel times exceeding certain thresholds via computationally intensive methods, we develop semi-analytical probability inequalities to quickly (i.e. within a fraction of a second) obtain upper bounds on the desired probability. Numerical experiments suggest that the inclusion of higher order moments can potentially significantly improve the bounds. The case study also demonstrates that the derived bounds are nontrivial for a large range of travel time values.     

A new role for planners: Working with community residents in formulating alternative plans for street patterns—Before decision making

Citizen participation at the neighborhood level will become effective only if reliable procedures can be developed for involving residents in the formulation of alternative plans before official decisions are made. Therefore, a research project in transportation planning was conducted in the Pico-Union neighborhood of Los Angeles, California, in order to design and test methods for producing reliable information that could be used by the neighborhood as well as by city transportation planners.The transportation problem of most concern to the residents of Pico-Union was the patterns and uses of their local streets, rather than the usual trip-to-work problem. Two independent methods were designed for determining what street patterns and uses were most preferred by the resident: a home-interview method and a photo-comparison method. The second enabled residents to compare photographic simulations of many possible re-designs of one of their local streets, and to make individual and group decisions about them. Results of the two methods generally agreed. However, one part of the research revealed that significant differences in decisions occur when simple changes are made in the orientation and sequence of the same two photographic simulations.Methods can be developed for obtaining information useful in urban planning at the neighborhood level by involving residents in an iterative learning and decision process. Broadly representative interdisciplinary teams are needed for further research to improve the reliability of such methods and for putting them into practice.     

The recoverable robust facility location problem

This work deals with a facility location problem in which location and allocation (transportation) policy is defined in two stages such that a first-stage solution should be robust against the possible realizations (scenarios) of the input data that can only be revealed in a second stage. This solution should be robust enough so that it can be recovered promptly and at low cost in the second stage. In contrast to some related modeling approaches from the literature, this new recoverable robust model is more general in terms of the considered data uncertainty; it can address situations in which uncertainty may be present in any of the following four categories: provider-side uncertainty, receiver-side uncertainty, uncertainty in-between, and uncertainty with respect to the cost parameters.For this novel problem, a sophisticated branch-and-cut framework based on Benders decomposition is designed and complemented by several non-trivial enhancements, including scenario sorting, dual lifting, branching priorities, matheuristics and zero-half cuts. Two large sets of instances that incorporate spatial and demographic information of countries such as Germany and US (transportation) and Bangladesh and the Philippines (disaster management) are introduced. They are used to analyze in detail the characteristics of the proposed model and the obtained solutions as well as the effectiveness, behavior and limitations of the designed algorithm.     

Enhancing stated preference surveys in transportation research: the contribution of geographic information systems

In this paper we argue that visualization, data management and computational capabilities of geographic information systems (GIS) can assist transportation stated preference research in capturing the contextual complexity of many transportation decision environments by providing respondents with maps and other spatial and non-spatial information in graphical form that enhance respondents' understanding of decision scenarios. We explore the multiple inherent contributions of GIS to transportation stated preference data collection and propose a framework for a GIS-based stated preference survey instrument. We also present the design concepts of two survey prototypes and their GIS implementation for a sample travel mode choice problem.     

Intelligent freight-transportation systems: Assessment and the contribution of operations research

While it is certainly too early to make a definitive assessment of the effectiveness of Intelligent Transportation Systems (ITS), it is not to take stock of what has been achieved and to think about what could be achieved in the near future. In our opinion, ITS developments have been up to now largely hardware-driven and have led to the introduction of many sophisticated technologies in the transportation arena, while the development of the software component of ITS, models and decision-support systems in particular, is lagging behind. To reach the full potential of ITS, one must thus address the challenge of making the most intelligent usage possible of the hardware that is being deployed and the huge wealth of data it provides. We believe that transportation planning and management disciplines, operations research in particular, have a key role to play with respect to this challenge. The paper focuses on Freight ITS: Commercial Vehicle Operations and Advanced Fleet Management Systems, City Logistics, and electronic business. The paper reviews main issues, technological challenges, and achievements, and illustrates how the introduction of better operations research-based decision-support software could very significantly improve the ultimate performance of Freight ITS.     

Transportation habits: Evidence from time diary data

The interdisciplinary Time Use Observatory workshops learned that transportation research and social sciences strive for the same multi-day time-diary data in order to make interferences about human habitual (travel) behavior. It also is learned that when it comes to the mathematics and analytics involved both disciplines are miles apart, though both with founded reasons to do so. In brief, transportation research relies on modeling to make predictions whereas social sciences apply statistics to their data to draw conclusions. In line with the interdisciplinary philosophy of the Time Use Observatory workshops, this contribution aims to communicate 30 years of experience in analyzing time-diary data. To do so, it demonstrates the latter by calculation transportation habits and aims to illustrate that multi-day time-diary data might have some additional benefits for computing temporal regularities. It shows that including a flexible notion of both regular tempo (or recurrence) of activities (e.g. every day) and regular timing of activities (e.g. always at 6 am) produces different results for different kind of transportation purposes. It also shows that these calculations using multi-day time-diary data result in an indicator at the individual level that can be analyzed in terms of socio-demographic and socio-economic characteristics. This work concludes that partitioning temporal regularities in regular reoccurrence and regular timing is a crucial element of (transportation) habits.     

Meeting an 80% reduction in greenhouse gas emissions from transportation by 2050: A case study in California

This paper investigates how California may reduce transportation greenhouse gas emissions 80% below 1990 levels by 2050 (i.e., 80in50). A Kaya framework that decomposes greenhouse gas emissions into the product of population, transport intensity, energy intensity, and carbon intensity is used to analyze emissions and mitigation options. Each transportation subsector, including light-duty, heavy-duty, aviation, rail, marine, agriculture, and off-road vehicles, is analyzed to identify specific mitigation options and understand its potential for reducing greenhouse gas emissions. Scenario analysis shows that, while California’s 2050 target is ambitious, it can be achieved in transport if a concerted effort is made to change travel behavior and the vehicles and fuels that provide mobility. While no individual ‘‘Silver Bullet” strategy exists that can achieve the goals, a portfolio approach that combines strategies could yield success. The 80in50 scenarios show the impacts of advanced vehicle and fuels technologies as well as the role of travel demand reduction, which can significantly reduce energy and resource requirements and the level of technology development needed to meet the target.     

An integrated sea–land transportation system model and its theory

To enhance the efficiency of intermodal transportation for large quantities of goods, the current sea–land transportation system has been reviewed and systematically analyzed, and a bottleneck is pinpointed in the linkage or goods transfer between the waterway and railway. The bottleneck impacts the efficiency of the goods through transportation combining the two modes. To eliminate the existing bottleneck and inefficiency towards intermodality, a new type of flexible double-rail track has recently been invented together with innovations both in trestle bridges and in train ferries. The outcomes of the research in progress show that the flexible double-rail track is feasible from the viewpoints of both geometry and engineering mechanics, it can be used to improve the compatibility of a trestle bridge with various types of train ferries and hence it can support the development of train ferries on a large scale. Based on the innovations, an integrated sea–land transportation system model is proposed as a systematic solution, which is expected to effectively overcome the existing bottleneck and to enhance the efficiency of the whole sea–land combined transportation. Further research on this system model and its major components is also addressed in the paper.     

Ontologies for transportation research: A survey

Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transportation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.     

A capacity related reliability for transportation networks

A capacity related reliability for transportation networks with random link capacity is introduced. It is defined as the probability that the road network can accommodate a certain level of traffic demand, and is built on the concept of network reserve capacity. Network reserve capacity is defined as the largest multiplier applied to an existing origin-destination demand matrix that can be allocated to a transportation network in a user-optimal way without violating the link capacities. Due to large variability associated with link capacities, a probabilistic approach is adopted to model the different physical and operational factors that often degrade the capacity of roadways. A Monte Carlo simulation procedure is developed to estimate the capacity related reliability measure. Numerical results are provided to demonstrate the feasibility of the approach.