Experimental Economics and choice in transportation: Incentives and context

This paper reviews the preconditions for successful applications of Experimental Economics methods to research on transportation problems, as new transportation and research technologies emerge. We argue that the application of properly designed incentives, the hallmark of Experimental Economics, provides a high degree of experimental control, leading to internal validity and incentive compatibility. Both of these are essential for ensuring that findings generalize to contexts outside the immediate application. New technologies, such as virtual reality simulators, can generate external validity for the experiments by providing realistic contexts. GPS and other tracking technologies, as well as smart phones, smart cards and connected vehicle technologies can allow detailed observations on actions and real-time interactions with drivers in field experiments. Proper application of these new technologies in research requires an understanding of how to maintain a high level of internal validity and incentive compatibility as external validity is increased. In this review of past applications of Experimental Economics to transportation we focus on their success in achieving external and internal validity.     

New approaches to transportation forecasting models

In 1992, the Federal Highway Administration awarded small research contracts to four teams of transportation researchers to design alternative approaches for improving the urban travel demand forecasting process. The purpose of these contracts was to enable each research team to explain how transportation planning models could and should be improved to meet the new forecasting requirements brought on by recent legislation, to address the impacts of new transportation technology, and to exploit the travel behavior theories and methodologies that have developed over the past two decades.This paper presents a summary and synthesis of the ideas which emerged from the four research reports. Its purpose is to identify common themes suggested by several of the research teams, to point out what appear to be critical elements missing from some approaches, and to combine the best aspects of the four approaches into a research plan for improving the current generation of travel demand models.Abbreviations CAAA Clean Air Act Amendments - FHWA Federal Highway Administration - GIS Geographic Information System - IIA Independence of Irrelevant Alternatives - IT Information Technology - IVHS Intelligent Vehicle Highway System - SUE Stochastic User Equilibrium - TCM Transportation Control Measures - UTPS Urban Transportation Planning System - VMT Vehicle Miles of Travel The paper was prepared as a report for the Federal Highway Administration.     

Resilience in transportation systems: a systematic review and future directions

ABSTRACT

The Belt and Road (B&R) initiative was introduced by the Chinese government to promote the worldwide economic development and multilateral cooperation between China and the associated countries. As a crucial part of global supply chains, transportation plays a key role to ensure the implementation of the B&R. Safety is one of the issues with great importance in transportation research. However, its foci have been expanded from traditional risk through security to resilience and sustainability. Resilience has attracted considerable interests from both researchers and practitioners across different research domains in recent years. Various studies have been conducted on transportation resilience from different perspectives. Consequently, different definitions have been developed to define and describe resilience. This paper presents a systematic review on transportation resilience with emphasis on its definitions, characteristics, and research methods applied in different transportation systems/contexts. It aims to figure out what transportation resilience is and what kind of essential characters it usually has. More importantly, research challenges are analysed and a future research agenda on the resilience of transportation systems is proposed. This paper will provide comprehensive insights into understanding the transportation resilience, as well as establish new horizons for relevant research topics within the context of the B&R.     

Logistic modeling of the equilibrium speed-density relationship

The fundamental diagram, as the graphical representation of the relationships among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering. Seventy-five years after the seminal Greenshields model, a variety of models have been proposed to mathematically represent the speed-density relationship which underlies the fundamental diagram. Observed in these models was a clear path toward two competing goals: mathematical elegance and empirical accuracy. As the latest development of such a pursuit, this paper presents a family of speed-density models with varying numbers of parameters. All of these models perform satisfactorily and have physically meaningful parameters. In addition, speed variation with traffic density is accounted for; this enables statistical approaches to traffic flow analysis. The results of this paper not only improve our understanding of traffic flow but also provide a sound basis for transportation engineering studies.     

Measuring and enhancing the transferability of hidden Markov models for dynamic travel behavioral analysis

As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM.

     

A review of input–output models on multisectoral modelling of transportation–economic linkages

Understanding the role of transportation in urban and regional economy is a persistent analytical topic within the transportation research community. Multi-sectoral input–output (IO) modelling, as a standard economic analysis tool, has great advantages in reflecting industrial interdependencies in an economy. The simplicity of IO and the well-known concept of multiplier effect also make it broadly used in both academia and practice. This paper provides an introduction of IO models and reviews the past IO studies from 2000 onward on modelling transportation–economic linkages. The following types of models are included: single-region, multi-region and random utility-based multi-region IO models, with central methodological features described. An evaluation of modelling issues brought to light by reviewing the literature is then presented. For future research, more critical attention should be directed towards IO’s modelling assumptions, spatial linkages and the static representation of the economy. In addition, there are needs for more attention in the following areas: sectoral aggregation, specification of household sector and the integration with transportation forecasting models. The paper concludes with brief recommendations on future IO applications.     

Transportation modeling: Lessons from the past and tasks for the future

In spite of the recent progress made in household activity analysis and travel budget studies, urban transportation modeling still remains a “not-too-well developed” research field. There are conflicting theories, analysis units are not uniform, terms are not precisely defined, basic studies of sub-systems involved are not yet completed, and many models lack behavioral background as well as basic attributes such as simplicity, sensitivity, compatibility, transferability and forecasting ability. Gaps in methodology may be partially responsible for this situation. There is an urgent need for simple, yet not primitive, easily applicable urban transportation models which can respond to the technical needs of planners and engineers. Lessons from the past, as well as experiences from other disciplines, suggest that future research should concentrate on: (1) new, “unconventional” approaches based on systematic, basic studies of all sub-systems involved; (2) proper definition and stratification of an analysis unit; (3) revision and unification of definitions, classifications, etc., in order to improve the behavioral background of the models; (4) dynamic rather than static approaches, able to describe feedbacks between transportation and land-use as well as between transportation demand and supply; (5) interrelations between subsequent sub-models, particularly between car availability, trip generation and modal split; (6) developing models which are not only sensitive to transportation policies but also to other local policies (e.g. land use, city development, social, etc.).     

Complex Network Topology of Transportation Systems

ABSTRACT

As a strategic factor for a country to survive in the global competition, transportation systems have attracted extensive attention from different disciplines for a long time. Since the introduction of complex network theory in the last decade, however, studies on transport systems have witnessed dramatic progress. Most roads, streets, and rails are organized as a network pattern, while link flows, travel time, or geographical distance are regarded as weights. In this article, the authors will present the current state of topological research on transportation systems under a complex network framework, as well as the efforts and challenges that have been made in the last decade. First, different kinds of transportation systems should be generalized as networks in different ways, which will be explained in the first part of this paper. We follow this by summarizing network measures that describe topological characteristics of transportation networks. Then we discuss the empirical observations from the last decade on real transportation systems at a variety of spatial scales. This paper concludes with some important challenges and open research frontiers in this field.     

Transportation agencies experiences with decision support systems for airport ground access planning

This article reports on a field investigation into the ways that transportation agencies use quantitative and qualitative information for making strategic decisions regarding airport ground access. The study analyzes the value of this information for planning airport ground access improvements at seven major international airport sites.The major finding of the research is that quantitative modeling for strategic decision support is very difficult, costly and time consuming. Modelers are confident that the models are accurate and reliable but executives generally lack confidence in the results. Transportation officials believe that the information supplied is flawed by a number of defects that minimize its value for strategic decision makers. The information defects described in this article provide an analysis of the structural difficulty of using quantitative modeling for transportation problems of strategic importance. To date, qualitative information is not frequently used, but some transportation agencies are considering its application to designing transportation services. Although this study is limited to airport ground access, the authors feel that this evidence, in conjunction with the evidence from other studies in the transportation area, dictates a need for wariness in the development of decision support systems for transportation planners. Developers of decision support systems for transportation planners must be aware of modeling costs and defects and consider how to improve the timeliness, relevance and credibility of information quantitative models provide transportation executives. Fundamentally it is important to recognize that decision makers tend, either singularly or in concert with other individuals or groups, to be the champions of a long-term vision for the community. When modeling produces inconsistent or wide ranging results that contradict their position, decision makers may not only discard modeling activities, but lose confidence in the models altogether. As a consequence, transportation planners are faced with the challenge of how to improve quantitative modeling. The most reliable and effective means for improvement is incorporation of qualitative techniques which provide greater understanding of customer perceptions and human behavior.     

A hybrid deep learning based traffic flow prediction method and its understanding

Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.     

Incorporating Uncertain and Incomplete Subjective Judgments into the Evaluation Procedure of Transportation Demand Management Alternatives

This paper proposes a methodology for evaluating transportation demand management (TDM) alternatives in the context of multi-criteria decision making (MCDM). The proposed approach takes into account not only quantitative criteria (i.e. transportation and environmental impacts) but also qualitative criteria (i.e. social impacts) which are intrinsically uncertain and subjective. The transportation impacts of the TDM alternatives are estimated by TRIPS¹ program, while the MOBILE5a² is employed in order to estimate environmental impacts in terms of NO_X, CO and Hydrocarbon. The social impacts of the TDM alternatives are estimated by interviewing relevant experts. Consequently, the uncertain subjective judgements were quantified by the evidential reasoning (ER) approach based on decision theory and Dempster–Shafer theory of evidence. In order to measure the weights of criteria, analytical hierarchy process (AHP) is adopted. As a last step, the CODASID³ method based on a complete concordance and discordance analysis is used to rank alternative TDM schemes. The proposed approach is demonstrated by ranking 14 TDM alternatives, which are chosen for the central business area in Bangkok, Thailand of 22-square-kilometers.     

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”.     

A bus route evaluation model based on GIS and super-efficient data envelopment analysis

When compared to large cities in developed countries, the shares of public transportation in most Chinese cities are low. Increasing the competitiveness of urban public transportation remains an urgent problem. A capable evaluation method for public transportation is required to assist the development of urban transit systems. This paper focuses on the bus system. Being devoid of standard criteria, it is difficult to determine the efficiency of a transit system or any bus line using a single evaluation index. This paper proposes a comparative analysis to evaluate bus lines so as to filter out candidates for further optimization. From the viewpoints of transit planning, operation and quality of service, this paper establishes 10 subordinate evaluation indices and then uses geographical information system tools, global positioning system data and smart card data to assist the index definition and calculation. Super-efficient data envelopment analysis (DEA) method is adopted for the proposed single factor and comprehensive evaluation models. Finally, the bus system in Shenzhen, China is used as a case study. The comparable significant results validate the capability of the proposed model.     

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.     

Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data

Dynamic origin-destination (OD) demand is central to transportation system modeling and analysis. The dynamic OD demand estimation problem (DODE) has been studied for decades, most of which solve the DODE problem on a typical day or several typical hours. There is a lack of methods that estimate high-resolution dynamic OD demand for a sequence of many consecutive days over several years (referred to as 24/7 OD in this research). Having multi-year 24/7 OD demand would allow a better understanding of characteristics of dynamic OD demands and their evolution/trends over the past few years, a critical input for modeling transportation system evolution and reliability. This paper presents a data-driven framework that estimates day-to-day dynamic OD using high-granular traffic counts and speed data collected over many years. The proposed framework statistically clusters daily traffic data into typical traffic patterns using t-Distributed Stochastic Neighbor Embedding (t-SNE) and k-means methods. A GPU-based stochastic projected gradient descent method is proposed to efficiently solve the multi-year 24/7 DODE problem. It is demonstrated that the new method efficiently estimates the 5-min dynamic OD demand for every single day from 2014 to 2016 on I-5 and SR-99 in the Sacramento region. The resultant multi-year 24/7 dynamic OD demand reveals the daily, weekly, monthly, seasonal and yearly change in travel demand in a region, implying intriguing demand characteristics over the years.     

Substantial Changes and Their Impact on Mobility: A Typology and an Overview of the Literature

During the past 40 years, mobility patterns have enduringly changed several times as a result of the occurrence of a number “substantial changes”. Examples of such substantial changes are the rapid emergence of affordable air travel, the oil crises, and profound ICT developments. To most researchers and policy-makers in transportation, it seems more than likely that the next 40 years will also witness a number of substantial changes, some of which might even have larger impacts on mobility than the ones described above. However, literature on substantial changes and their impact on mobility are difficult to access as it is spread across different research fields and suffers from ambiguous use of terminology. As a result, overview of the literature on substantial changes and their findings is missing and discussions on the impacts on future mobility of potential substantial changes are hampered. To overcome these problems, this paper (1) proposes a typology of substantial changes and (2) ties together and reviews the scholarly literature that has focused on identifying the impacts of past substantial changes on mobility patterns. In this paper, we show how the proposed typology on substantial changes can be applied to contemplate on substantial changes and on their impacts.     

油气混输管道数据库管理平台开发

采用SQL Server 2005和Visual C#.NET软件建立了油气混输管道数据库,并开发了数据库管理平台,能够实现管道数据的录入、修改、删除、查询,温度、压力的计算和修正等功能。该数据库管理平台在工程实际管道和实验环道装置的数据基础上,对已有模型进行修正,使之适用于现场,并提出了查询插值的新方法,成为实现混输管道压降预测的有效途径。同时,该数据库可应用于工程设计建设及混输管道压降计算方法的研究。     

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.