A geographic information system framework for transportation data sharing

This paper develops a framework and principles for sharing transportation data. The framework is intended to clarify roles among participants, data producers, data integrators, and data users. The principles are intended to provide guidance for the participants. Both the framework and the principles are based on an enterprise geographic information systems-transportation (GIS-T) data model that defines relations among transportation data elements. The data model guards against ambiguities and provides a basis for the development of the framework and principles for sharing transportation data.There are two central principles. First is the uncoupling of graphics, topology, position, and characteristics. Second is the establishment of a schema for transportation features and their identifiers. An underlying principle is the need for a common data model that holds transportation features, not their graphical representations, as the objects of interest. Attributes of transportation features are represented as linear and point events. These are located along the feature using linear referencing. Sharing of transportation data involves exchange of relevant transportation features and events, not links and nodes of application-specific databases. Strategies for sharing transportation features follow from this approach. The key strategy is to identify features in the database to facilitate a transactional update system, one that does not require rebuilding the entire database anew. This feature-oriented enterprise GIS-T database becomes the basis for building separate application-specific network databases.     

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.     

数字运政大数据管理平台功能架构浅析

针对云南省道路运输行业管理系统较多、系统独立运行、融合复用不够等问题,本文对运政管理过程中的道路运输行业信息资源整合及数字化安全监管应用进行了浅析,提出以统一门户整合各类业务应用系统,以业务中台融合管理应用,以数据中台打造基于大数据融合支撑应用的数字运政平台功能架构研究,有效支撑道路运输的数字化建设。     

Data attribution and network representation issues in GIS and transportation

Geographic Information System (GIS) is an “intelligent” technology which integrates attribute data and spatial features and manages the relationship between them. GIS is widely used in many activities, but its application in transportation is less common. The extension of GIS into transportation (GIS‐T) offers the potential to integrate transportation data into GIS. The integration of transportation data in GIS faces a number of barriers that are discussed in the paper. These barriers can be broadly categorized into data attribution and network representation issues. This paper analyzes these issues and reviews the potential for overcoming these constraints with current GIS technology. A fully developed GIS‐T has to meet many diverse needs including transportation inventory, modeling and operational problems. If GIS is to succeed as a transportation technology, it must be capable of integrating different levels of network representation and data attribution and have the ability to link with other transportation technologies.     

Toward a data structure for computer-aided design of transportation networks

Although there are a great variety of transportation network design applications, there are important similarities across all transportation networks. These similarities permit the construction of a single, relatively simple, data structure that satisfies the needs of both graphics and algorithms. This paper describes how this data structure was implemented as the basis of the General Network Editor— a program for graphical preparation of network data.     

The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

Forecasting telecommuting

Transportation planners increasingly recognize telecommuting as an important trend. But while they often advocate telecommuting as a transportation demand management strategy, transportation planners have made little progress toward incorporating telecommuting into transportation forecasts, at least partly because of the limited data available. In this paper we explore four alternative methodologies for forecasting telecommuting and discuss the kinds of data that must be collected before these methodologies can be applied. The first approach is trend extrapolation, using curves of technological substitution. Sufficient data are currently available to produce forecasts, albeit highly uncertain forecasts, using this approach. However, even with better data this approach does not address underlying factors and trends that will affect the future of telecommuting. As a result, we explore three additional approaches that should produce more reliable forecasts but which require new data and knowledge about telecommuting: analyzing the characteristics of telecommuters in contrast to nontelecommuters, analyzing factors affecting the individual choice to telecommute, and incorporating telecommuting into traditional transportation forecasting models.     

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.     

Big data in public transportation: a review of sources and methods

ABSTRACT

The collection of big data, as an alternative to traditional resource-intensive manual data collection approaches, has become significantly more feasible over the past decade. The availability of such data, coupled with more sophisticated predictive statistical techniques, has contributed to an increase in attention towards the application of these data, particularly for transportation analysis. Within the transportation literature, there is a growing emphasis on developing sources of commonly collected public transportation data into more powerful analytical tools. A commonly held belief is that application of big data to transportation problems will yield new insights previously unattainable through traditional transportation data sets. However, there exist many ambiguities related to what constitutes big data, the ethical implications of big data collection and application, and how to best utilize the emerging data sets. The existing literature exploring big data provides no clear and consistent definition. While the collection of big data has grown and its application in both research and practice continues to expand, there is a significant disparity between methods of analysis applied to such data. This paper summarizes the recent literature on sources of big data and commonly applied methods used in its application to public transportation problems. We assess predominant big data sources, most frequently studied topics, and methodologies employed. The literature suggests smart card and automated data are the two big data sources most frequently used by researchers to conduct public transit analyses. The studies reviewed indicate that big data has largely been used to understand transit users’ travel behavior and to assess public transit service quality. The techniques reported in the literature largely mirror those used with smaller data sets. The application of more advanced statistical methods, commonly associated with big data, has been limited to a small number of studies. In order to fully capture the value of big data, new approaches to analysis will be necessary.     

The effect of socio-economic factors on the value of time in commuting to work

It is important to specify accurately the dollar value assigned to time savings, since up to eighty percent of the benefits estimated to accrue from improvements in transportation systems are associated with savings in travel time. In this paper the economic theory of consumer choice is utilized to structure a model that is used to estimate how Value of Time (VOT) measures vary with community-related variables.Parameters for this theoretical model are empirically estimated using data from transportation surveys conducted in Ithaca and Syracuse, New York and Amherst, Massachusetts. The results confirm the validity of the theoretical model and suggest that leisure time, travel cost, and household income level, as well as community population, are important determinants of the marginal value of time. These models are particularly suited for transferring data results obtained in one community to another, thereby saving survey costs, since the resulting VOT estimates are based solely on underlying socio-economic variables and community characteristics that are known for most localities. The methodology is also useful for estimating different VDT's for particular population subgroups, like the elderly, which may be the focus of a particular transportation project.     

A system dynamics approach to land use / transportation system performance modeling Part II: Application

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model system consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is also examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited to some extent due to data availability, the results clearly indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling     

Identifying the most critical transportation intersections using social network analysis

Traffic congestion negatively impacts our society. Most of the traditional transportation planning techniques – though effective – require rigorous amounts of data and analysis which consumes time and resources. This paper uses social network analysis (SNA) to analyze transportation networks, and consequently corroborate the effectiveness of SNA as a complementary tool for improved transportation planning. After creating the connection between the language and concepts of SNA and those of transportation systems – as well as developing a model that utilizes different SNA centrality measures within the transportation context – the authors utilize SNA to investigate traffic networks in three case studies in the state of Louisiana, analyze the results and draw conclusions. To this effect, with minimal cost and time, the model identifies the most critical intersections that should be further investigated using traditional techniques. These results are in agreement with the findings of Louisiana’s Department of Transportation and Development.     

A system dynamics approach to land use/transportation system performance modeling Part I: Methodology

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited due to data availability, the results indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling.     

What transportation modeling needs from a GIS: a conceptual framework

Both the geographic information system (GIS) and transportation modeling environments have seen continually developing analytic concepts and techniques. However, these developments have seldom resulted in the integration of GISs and transportation models. This paper explores the potential inherent in merging of these environments through a systematic investigation of the fundamental basis of integration. To do this, the traditional four step transportation modeling process is extended to include input and output steps. We then define functional components for GIS data handling — data management, manipulation, and analysis. The steps of modeling are matched against the list of GIS data handling functions within a matrix‐based framework. GIS functions that enhance a land‐use based urban transportation modeling process are then categorized. Conclusions are drawn and directions for future developments are discussed.     

Empirical analysis of transportation investment and economic development at state, county and municipality levels

Numerous studies have found positive correlation between transportation infrastructure investment and economic development. Basically these studies use a conventional production function model augmented by a public capital input, mainly highways, rail and other transportation facilities. While the range of the measured economic growth effects varies widely among studies, the positive elasticity between transportation investment and economic development is now commonly accepted. Still a major puzzling issue is that the magnitude of the measured effect seems to decline significantly as the econometric model is further refined, mainly with regard to space and time lags. That is, the use of national or state data produces elasticity results, which are much larger than when using county or municipality data. Similarly, when we introduce into the econometric model a lag between the times when the transportation investments are made and when the economic benefits transpire, the measured elasticities decline with the size of the lag. Thus, the main objective of this paper is to investigate these issues analytically and empirically and provide a plausible explanation. We do so by using alternative econometric models, applying them to a database, which is composed of longitudinal state, county and municipality observations from 1990 to 2000. The key result is that transportation investments produce strong spillover effects relative to space and time. Unless these factors are properly accounted for many reported empirical results are likely to be overly biased, with important policy implications.     

Granger-causality between transportation and GDP: A panel data approach

This paper investigates the Granger-causality relationship between income and transportation of EU-15 countries using a panel data set covering the period 1970–2008. In the study, inland freight transportation per capita in ton-km (TRP), inland passenger transportation per capita in passenger-km (PAS), and road sector gasoline fuel consumption per capita in kg of oil equivalent (GAS) are used as transportation proxies and GDP per capita is used as measure of income. Our findings indicate that the dominant type of Granger-causality is bidirectional. Instances of one-way or no Granger-causality were found to correspond with countries with the lowest income per capita ranks in 1970 and/or in 2008. Although we conclude that there is an endogenous relationship between income and transportation, this is not observed until after an economy has completed its transition in terms of economic development.     

Transportation mode detection – an in-depth review of applicability and reliability

The wide adoption of location-enabled devices, together with the acceptance of services that leverage (personal) data as payment, allows scientists to push through some of the previous barriers imposed by data insufficiency, ethics and privacy skepticism. The research problems whose study require hard-to-obtain data (e.g. transportation mode detection, service contextualisation, etc.) have now become more accessible to scientists because of the availability of data collecting outlets. One such problem is the detection of a user's transportation mode. Different fields have approached the problem of transportation mode detection with different aims: Location-Based Services (LBS) is a field that focuses on understanding the transportation mode in real-time, Transportation Science is a field that focuses on measuring the daily travel patterns of individuals or groups of individuals, and Human Geography is a field that focuses on enriching a trajectory by adding domain-specific semantics. While different fields providing solutions to the same problem could be viewed as a positive outcome, it is difficult to compare these solutions because the reported performance indicators depend on the type of approach and its aim (e.g. the real-time availability of LBS requires the performance to be computed on each classified location). The contributions of this paper are three fold. First, the paper reviews the critical aspects desired by each research field when providing solutions to the transportation mode detection problem. Second, it proposes three dimensions that separate three branches of science based on their main interest. Finally, it identifies important gaps in research and future directions, that is, proposing: widely accepted error measures meaningful for all disciplines, methods robust to new data sets and a benchmark data set for performance validation.     

交通运输能耗与排放统计监测平台及应用研究

在我国交通运输行业节能减排的背景下,本文以交通运输行业能耗特点与统计监测方法为基础,建立涵盖了公路客运、公路货运、城市公交、城市出租、水路运输、港口生产等行业的能耗碳排统计监测系统,分析了省级交通运输能耗与排放特点,明确了能耗数据统计对象与内容,并对能耗数据采集的方法进行了研究,建立了省级交通运输能耗统计监测系统,并以辽宁省交通运输为依托,对全省交通运输行业能耗统计监测系统进行了验证与应用,同时也为交通运输行业的节能减排提供了决策支撑和数据支持。