Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models

There is growing interest in establishing a mechanism to account for scale heterogeneity across individuals (essentially the variance of a variance term or the standard deviation of utility over different choice situations), in addition to the more commonly identified taste heterogeneity in mixed logit models. A number of authors have recently proposed a model that recognizes the relationship between scale and taste heterogeneity, and investigated the behavioural implications of accounting for scale heterogeneity in contrast to a term in the utility function, itself. In this paper we present a general model that extends the mixed logit model to explicitly account for scale heterogeneity in the presence of preference heterogeneity, and compare it with models that assume only scale heterogeneity (referred to as the scale heterogeneous multinomial logit model) and only preference heterogeneity. Our empirical assessment suggests that accommodating scale heterogeneity in the absence of accounting for preference heterogeneity may be of limited empirical interest, resulting in a statistically inferior model, despite it being an improvement over the standard MNL model. Scale heterogeneity in the presence of preference heterogeneity does garner favour, with the generalized mixed logit model an improvement over the standard mixed logit model. The evidence herein suggests, however, that compared to a failure to account for preference heterogeneity that is consequential, failure to account for scale heterogeneity may not be of such great empirical consequence in respect of behavioural outputs such as direct elasticities and willingness to pay. However additional studies are required to establish the extent to which this evidence is transferable to a body of studies.     

基于多项logit模型的枢纽乘客换乘选择研究

交通枢纽是城市交通体系的重要组成部分,由若干种运输形式所连接的固定设备和移动设备组成的整体。利用多项logit模型构建了乘客换乘方式选择模型,设计SP调查问卷,根据上海虹桥机场调查数据实测数据,对模型参数进行了标定。结果显示模型具有较高的精度,对于大型交通枢纽的换乘交通组织具有指导意义,有较高的理论和实用价值。     

Estimating traffic demand risk – A multiscale analysis

This paper proposes a novel method for estimating the traffic demand risk associated with transportation. Using mathematical properties of wavelets, we develop a statistical measure of traffic demand sensitivity with respect to GDP. This measure can be adapted in a flexible way to capture risk levels relevant for different investment horizons. We demonstrate the timescale decomposition of risk with Swedish traffic demand data for 1950–2005. In general, rail transport shows a stronger co-movement with GDP than road transport. Moreover, we examine the volatility exhibited by traffic demand. Our findings suggest that rail investments are more risky than road investments. Since the findings can be used for optimal investment timing and for choice between public investment alternatives, they are deemed important for public policy in general.     

Integrated land use — transport models

This paper is a review of computer models of the interrelationships between land use and transport, particularly of the long‐term effects of changes in transport costs on cities and the consequent effects on travel demand. The nature of this relationship is examined in terms of empirical evidence, and a set of criteria against which the models can be evaluated is defined. Four major types of model are examined: regression, mathematical programming, aggregate spatial interaction and individual choice. Each type is considered in terms of operational examples and the strengths and weaknesses of each approach are identified. However, it is recognized that few of the models are capable of representing the major social and technological changes that are currently influencing urban development, and that this is where emphasis should be put if this type of model is to be useful for policy‐making in the future.     

Disaggregate demand models—Promises and prospects

For nearly two decades it has been recognised that there are serious deficiencies in the traditional aggregate modelling approach to travel demand analysis. It was hoped that many, if not all, of these deficiencies would be overcome through the development of disaggregate models. Yet nearly 10 years after some of the first major research projects, it has been suggested that they have not yet been successfully applied in any major planning study. There can be little doubt that disaggregate model techniques do offer the scope for major improvements over aggregate models, yet there would seem to be a growing groundswell of doubt about their ability to live up to the expectations which were cultivated during the early '70s. The crucial question to most of those concerned with planning and policy development is, even if disaggregate models are not the panacea many of us hoped them to be, whether they can still improve our forecasting ability, or not.     

One city,three models: comparison of land‐use/transport policy simulation models for Dortmund

The paper reports on simulation experiments conducted by the International Study Group on Land‐Use/Transport Interaction (ISGLUTI) for the metropolitan region of Dortmund in the Federal Republic of Germany. Three land‐use/transport simulation models were applied to the Dortmund region: the DORTMUND model developed at the University of Dortmund, the LILT model being used at University College London and the MEPLAN package developed by Marcial Echenique & Partners in Cambridge. The three models are briefly characterized and their ex‐post forecasts are compared with the actual development of the region. The final section of the paper compares how the three models respond to a common set of assumptions and policies from the fields of land‐use control, traffic management and transport investment. The differences in model response give insights into the validity of the theoretical foundations and internal structure of the models.     

Do travel cost models value transportation properly?

This paper examines whether travel cost models value transportation properly. It uses contingent behavior and contingent pricing analyses to explore the valuation of transport costs within the context of recreation demand. The contingent behavior analysis poses hypothetical increases in travel costs––travel distance and access fees––and examines the demand responses. In contrast, the ‘contingent pricing’ analysis asks respondents to state the increase in travel costs that is consistent with certain reductions in recreation demand, in this case, reductions that eliminate demand. By comparing distance-related responses to fee-related responses, the two analyses estimate factors for testing and improving the valuation of transport costs. To achieve these ends, the two analyses also explore the valuation of time costs.     

Vehicle identification sensor models for origin–destination estimation

The traditional approach to origin–destination (OD) estimation based on data surveys is highly expensive. Therefore, researchers have attempted to develop reasonable low-cost approaches to estimating the OD vector, such as OD estimation based on traffic sensor data. In this estimation approach, the location problem for the sensors is critical. One type of sensor that can be used for this purpose, on which this paper focuses, is vehicle identification sensors. The information collected by these sensors that can be employed for OD estimation is discussed in this paper. We use data gathered by vehicle identification sensors that include an ID for each vehicle and the time at which the sensor detected it. Based on these data, the subset of sensors that detected a given vehicle and the order in which they detected it are available. In this paper, four location models are proposed, all of which consider the order of the sensors. The first model always yields the minimum number of sensors to ensure the uniqueness of path flows. The second model yields the maximum number of uniquely observed paths given a budget constraint on the sensors. The third model always yields the minimum number of sensors to ensure the uniqueness of OD flows. Finally, the fourth model yields the maximum number of uniquely observed OD flows given a budget constraint on the sensors. For several numerical examples, these four models were solved using the GAMS software. These numerical examples include several medium-sized examples, including an example of a real-world large-scale transportation network in Mashhad.     

Estimating metro passengers’ path choices by combining self-reported revealed preference and smart card data

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers’ transfer information and factors affecting passengers’ travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors; however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers’ path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an Expectation-Maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.     

Detailed assessment of global transport-energy models’ structures and projections

This paper focuses on comparing the frameworks and projections from four global transportation models with considerable technology details. We analyze and compare the modeling frameworks, underlying data, assumptions, intermediate parameters, and projections to identify the sources of divergence or consistency, as well as key knowledge gaps. We find that there are significant differences in the base-year data and key parameters for future projections, especially for developing countries. These include passenger and freight activity, mode shares, vehicle ownership rates, and energy consumption by mode, particularly for shipping, aviation and trucking. This may be due in part to a lack of previous efforts to do such consistency-checking and “bench-marking.” We find that the four models differ in terms of the relative roles of various mitigation strategies to achieve a 2 °C/450 ppm target: the economics-based integrated assessment models favor the use of low carbon fuels as the primary mitigation option followed by efficiency improvements, whereas transport-only and expert-based models favor efficiency improvements of vehicles followed by mode shifts. We offer recommendations for future modeling improvements focusing on (1) reducing data gaps; (2) translating the findings from this study into relevant policy implications such as gaps of current policy goals, additional policy targets needed, regional vs. global reductions; (3) modeling strata of demographic groups to improve understanding of vehicle ownership levels, travel behavior, and urban vs. rural considerations; and (4) conducting coordinated efforts in aligning historical data, and comparing input assumptions and results of policy analysis and modeling insights.     

Comparison of inter‐city bi‐modal split models

Using data collected from rail and air passengers on two inter‐city routes in the U.K., seven different model formulations were set up and tested in order to ascertain the most appropriate model format. As a result of the work carried out, it is concluded that a simple Entropy‐type model based on the theoretical work of A. G. Wilson and utilising a linear generalised cost function is the most suitable. Other useful parameters emerging from the work are perceived values of travel time, and a weighting factor for night travel.     

A gradient boosting logit model to investigate driver’s stop-or-run behavior at signalized intersections using high-resolution traffic data

Driver’s stop-or-run behavior at signalized intersection has become a major concern for the intersection safety. While many studies were undertaken to model and predict drivers’ stop-or-run (SoR) behaviors including Yellow-Light-Running (YLR) and Red-Light-Running (RLR) using traditional statistical regression models, a critical problem for these models is that the relative influences of predictor variables on driver’s SoR behavior could not be evaluated. To address this challenge, this research proposes a new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors. Particularly, this research will first identify a series of related influential factors including signal timing information, surrounding traffic information, and surrounding drivers’ behaviors using thousands drivers’ decision events including YLR, RLR, and first-to-stop (FSTP) extracted from high-resolution loop detector data from three intersections. Then the research applies the proposed data mining approach to search for the optimal prediction model for each intersection. Furthermore, a comparison was conducted to compare the proposed new method with the traditional statistical regression model. The results show that the gradient boosting logit model has superior performance in terms of prediction accuracy. In contrast to other machine learning methods which usually apply ‘black-box’ procedures, the gradient boosting logit model can identify and rank the relative importance of influential factors on driver’s stop-or-run behavior prediction. This study brings great potential for future practical applications since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.     

The influence of attitude towards individuals’ choice for a remotely piloted commercial flight: A latent class logit approach

The Remotely Piloted Commercial Passenger Aircraft Attitude Scale (RPCPAAS) was created to measure positive and negative attitudes towards a new and plausible form of air travel. This information was then used, in combination with a latent class logit model built on data generated from a stated choice experiment to gain insight into the choice behaviour between conventionally piloted aircraft (CPA) with a pilot on-board and remotely piloted aircraft (RPA) with a pilot on the ground. The results revealed that individuals, on-average, if presented a choice between a CPA and a RPA of equivalent attributes, would elect for the CPA option. However, there is variability in the passengers’ sensitivity to various flight attributes, and these sensitivities were influenced by individuals’ attitude towards the new technology (i.e., RPA). From an operational perspective, and assuming that one day passengers of commercial airlines are offered the choice between CPA and RPA, the strategies employed by airlines to encourage the use of the new technology need to be different, based on individuals’ attitude towards RPA.     

How can we alter our carbon footprint? Estimating GHG emissions based on travel survey information

The city of Montreal has taken recent initiatives to significantly reduce overall greenhouse (GHG) emissions from the transport sector and has made large investments in alternative transportation. In particular, the city has called upon the participation of all businesses and institutions to further these goals. In light of these recent plans, this study identifies with two objectives: first, to develop a methodology for estimating GHG emissions generated by commuters to McGill University’s downtown campus; and secondly, to better understand who, how, and when each commuter to McGill generates travel-related GHG. Mode split, travel distance, age, gender and job category were uncovered by a 2011 travel survey that we conducted across the University, from which daily individual GHG emissions are estimated. Details about these trips not only reveal who the largest polluters are and where they are coming from, but also the seasonality of their emissions. These associations are then used to narrate scenarios which present alternatives to the structure of individuals’ commutes by examining the outcomes of selected shifts in travel behavior on total GHG emissions.     

“新基建”和“铁公基”，“互为表里”还是“割席分坐”？

正铁路、公路、港口航道、机场等重大基础设施建设,简称为铁公基。新基建与铁公基的关系,应当从理论到实践进行深入研究,特别是目前正处在政策法规的酝酿阶段。铁公基和新基建共同构成国家基础设施网络(一)交通运输、信息等基础设施共同构成国家基础设施网络。党的十九大报告中提出要贯彻新发展理念,建设现     

“看门人”和“守护者”

<正>近有"博鳌亚洲论坛2014"信用评级论坛思想交锋,远有二十世纪初现代评级业自西方源起。迄今,不少的中国政府机构与民间组织已经不再满足于用西方的语言与西方对话,意图建立中国特色的本土信用评级体系。1 00多年的发展,致使现代企业评级业得到了各国的认可。事实上,企业信用评级在经济中发挥着重要的作用,它可以给投资者提示风险并有助于降低投资者的信息成本,为筹资者提供资信等级证明并降低筹资成本。同时,它也可为政府的金融监管提供依据。因此在西方,信用评级机构有着市场"看门人"和"守护者"的美称。信用关系正成为人类社会的基本经济关系经济学家吴敬琏认为:"市场经济就是信     

战略“质变”:从“传统”到“新兴”

正从1973年中国召开第一次全国环境保护会议,至今,中国的环保事业四十正惑,形势严峻依旧;有投入无产出,常常面临资金、技术等多方面压力的环保工程,步履蹒跚;好在,环保这块儿曾经的蛮荒之地,总有"人"可以拔出泥脚,"破茧"而行;从改造"毒地"到重现"绿地",从"传统的疏浚吹填"到"新兴的环保业务",天航局环保公司另辟蹊径的经营战略之下,也经历了一番柳暗花明;而今,其以资源重组和战略并购为先导、以股权投资为主要手段的投资拉动板块正在快速形成,奋战正酣……     

从“旁观者”到“参与者”

对于公车改革,汽车租赁以往更多是“旁观者”,如今变成了“参与者”,这种转变会遇到什么样的问题？我们应该如何应对,才能接好政府递过来的“接力棒”？