Generalized Extreme Value models for count data: Application to worker telecommuting frequency choices

Count models are used for analyzing outcomes that can only take non-negative integer values with or without any pre-specified large upper limit. However, count models are typically considered to be different from random utility models such as the multinomial logit (MNL) model. In this paper, Generalized Extreme Value (GEV) models that are consistent with the Random Utility Maximization (RUM) framework and that subsume standard count models including Poisson, Geometric, Negative Binomial, Binomial, and Logarithmic models as special cases were developed. The ability of the Maximum Likelihood (ML) inference approach to retrieve the parameters of the resulting GEV count models was examined using synthetic data. The simulation results indicate that the ML estimation technique performs quite well in terms of recovering the true parameters of the proposed GEV count models. Also, the models developed were used to analyze the monthly telecommuting frequency decisions of workers. Overall, the empirical results demonstrate superior data fit and better predictive performance of the GEV models compared to standard count models.     

Is accessibility relevant in trip generation? Modelling the interaction between trip generation and accessibility taking into account spatial effects

The influence of accessibility to opportunities in trip generation continues to be debated in the specialised literature given its relevance to simulate phenomena such as induced demand. This article estimates multiple linear regression models (MLR), spatial autoregressive models (SAR), spatial autoregressive models in the error term (SEM) and spatially filtered Poisson regression models (SPO) to discover whether or not accessibility is a significant factor in trip generation using data from the urban area of Santander (Spain). The results obtained provide evidence which shows that, on an intraurban scale, more accessibility to opportunities decreases trip production in private vehicle for work purpose, whereas it increases trip production in other transport modes for non—mandatory purposes. For the correct interpretation of the estimated parameters it was important to consider the direct and indirect effects of the independent variables in the SAR production models. Finally, the validation of the models showed that the SAR and SEM models had a mean squared error slightly lower than the MLR models in predicting overall trip production. This was because the spatial models reduced the correlation of the residuals present in the MLR models. Furthermore, the SPO models performed better in validation mode than all the continuous models.     

Inclusion of latent variables in Mixed Logit models: Modelling and forecasting

Travel demand models typically use mainly objective modal attributes as explanatory variables. Nevertheless, it has been well known for many years that attitudes and perceptions also influence users’ behaviour. The use of hybrid discrete choice models constitutes a good alternative to incorporate the effect of subjective factors. We estimated hybrid models in a short-survey panel context for data among many alternatives. The paper analyses the results of applying these models to a real urban case study, and also proposes an approach to forecasting using these models. Our results show that hybrid models are clearly superior to even highly flexible traditional models that ignore the effect of subjective attitudes and perceptions.     

Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: a review of state-of-the-art

ABSTRACT

The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.     

A generic class of first order node models for dynamic macroscopic simulation of traffic flows

Node models for macroscopic simulation have attracted relatively little attention in the literature. Nevertheless, in dynamic network loading (DNL) models for congested road networks, node models are as important as the extensively studied link models. This paper provides an overview of macroscopic node models found in the literature, explaining both their contributions and shortcomings. A formulation defining a generic class of first order macroscopic node models is presented, satisfying a list of requirements necessary to produce node models with realistic, consistent results. Defining a specific node model instance of this class requires the specification of a supply constraint interaction rule and (optionally) node supply constraints. Following this theoretical discussion, specific macroscopic node model instances for unsignalized and signalized intersections are proposed. These models apply an oriented capacity proportional distribution of the available supply over the incoming links of a node. A computationally efficient algorithm to solve the node models exactly is included.     

Node modeling for congested urban road networks

First-order network flow models are coupled systems of differential equations which describe the build-up and dissipation of congestion along network road segments, known as link models. Models describing flows across network junctions, referred to as node models, play the role of the coupling between the link models and are responsible for capturing the propagation of traffic dynamics through the network. Node models are typically stated as optimization problems, so that the coupling between the link dynamics is not known explicitly. This renders network flow models analytically intractable. This paper examines the properties of node models for urban networks. Solutions to node models that are free of traffic holding, referred to as holding-free solutions, are formally defined and it is shown that flow maximization is only a sufficient condition for holding-free solutions. A simple greedy algorithm is shown to produce holding-free solutions while also respecting the invariance principle. Staging movements through nodes in a manner that prevents conflicting flows from proceeding through the nodes simultaneously is shown to simplify the node models considerably and promote unique solutions. The staging also models intersection capacities in a more realistic way by preventing unrealistically large flows when there is ample supply in the downstream and preventing artificial blocking when some of the downstream supplies are restricted.     

输气管道泄漏率计算与扩散模拟方法述评

分析了研究输气管道泄漏率计算与扩散模拟对管道泄漏事故后果评价和事故处理的重要意义,综述了国内外输气管道泄漏率的稳态、瞬态计算模型,指出其适用范围;总结了输气管道泄漏扩散模拟的各种方法及其优缺点,建议建立较简单的气体扩散模型,应用三维计算模型及湍流统计、模式理论模拟输气管道泄漏情形.     

LUTI operational models review based on the proposition of an a priori ALUTI conceptual model

ABSTRACT

This paper focuses on understanding to what extent the components of LUTI models and their mutual interactions are conceptually represented by eight operational LUTI models. This is important for the understanding of LUTI models’ mechanisms, firstly because it may reduce communication barriers between planning communities, secondly because it may help us understand the models’ applicability, and thirdly it may highlight the models’ shortcomings and point for future research. We present a discussion about what subsystems should be considered for LUTI modelling, from which we derived an “a priori” conceptual ALUTI model (incorporating Activities, besides Land Use and Transport). By comparing the rationale behind each model with this conceptual model, we establish the basis for our review, focussing on whether these models incorporate the ALUTI components, its inner workings and the relationships between these components. Results indicate three main limitations of the reviewed models. First, models not always adequately include all the components of the a priori ALUTI model. Second, the ALUTI subsystems’ internal functions are not explicitly modelled in several of the models reviewed, making it difficult to evaluate how planning decisions affect the subsystem. Third, only few models recognise all mutual interactions, especially in respect to the Activity subsystem.     

Estimation of missing traffic counts using factor,genetic, neural,and regression techniques

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be less accurate. ARIMA models only use the historical data. In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed. It was found that genetically designed regression models based on data from before and after the failure had the most accurate results. Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns. Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases.     

A historical review of transport and land-use models in Japan

In Japan, the standard transport models for forecasting traffic demand were mostly established by the 1970s, although many land-use models had been already developed since the middle of 1960s. In this paper, the transport models are reviewed under the main headings of aggregate forecasting procedures and both a history of the land-use models and its concepts are illustrated here, focusing on the allocation procedures of activities in modeling in Japan. The paper includes the following sections: (1) transport models, (2) land-use models, (3) concepts used in modeling land-use, and (4) a view of modeling in the future.     

A class of RUM choice models that includes the model in which the utility has logistic distributed errors

A class of random utility maximization (RUM) models is introduced. For these RUM models the utility errors are the sum of two independent random variables, where one of them follows a Gumbel distribution. For this class of RUM models an integral representation of the choice probability generating function has been derived which is substantially different from the usual integral representation arising from the RUM theory. Four types of models belonging to the class are presented. Thanks to the new integral representation, a closed-form expression for the choice probability generating function for these four models may be easily obtained. The resulting choice probabilities are fairly manageable and this fact makes the proposed models an interesting alternative to the logit model. The proposed models have been applied to two samples of interurban trips in Japan and some of them yield a better fit than the logit model. Finally, the concavity of the log-likelihood of the proposed models with respect to the utility coefficients is also analyzed.     

A State-of-the-Art Review of Car-Following Models with Particular Considerations of Heavy Vehicles

Abstract

Car-following (CF) models are fundamental in the replication of traffic flow and thus they have received considerable attention. This attention needs to be reflected upon at particular points in time. CF models are in a continuous state of improvement due to their significant role in traffic micro-simulations, intelligent transportation systems and safety engineering models. This paper presents a review of existing CF models. It classifies them into classic and artificial intelligence models. It discusses the capability of the models and potential limitations that need to be considered in their improvement. This paper also reviews the studies investigating the impacts of heavy vehicles in traffic stream and on CF behaviour. The findings of the study provide promising directions for future research and suggest revisiting the existing models to accommodate different behaviours of drivers in heterogeneous traffic, in particular, heavy vehicles in traffic.     

Point queue models: A unified approach

In transportation and other types of facilities, various queues arise when the demands of service are higher than the supplies, and many point and fluid queue models have been proposed to study such queueing systems. However, there has been no unified approach to deriving such models, analyzing their relationships and properties, and extending them for networks. In this paper, we derive point queue models as limits of two link-based queueing model: the link transmission model and a link queue model. With two definitions for demand and supply of a point queue, we present four point queue models, four approximate models, and their discrete versions. We discuss the properties of these models, including equivalence, well-definedness, smoothness, and queue spillback, both analytically and with numerical examples. We then analytically solve Vickrey’s point queue model and stationary states in various models. We demonstrate that all existing point and fluid queue models in the literature are special cases of those derived from the link-based queueing models. Such a unified approach leads to systematic methods for studying the queueing process at a point facility and will also be helpful for studies on stochastic queues as well as networks of queues.     

Modelling Externalities using Dynamic Traffic Assignment Models: A Review

Recently, there has been a growing interest in externalities in our society, mainly in the context of climate and air quality, which are of importance when policy decisions are made. For the assessment of externalities in transport, often the output of static traffic assignment models is used in combination with so-called effect models. Due to the rapidly increasing possibilities of using dynamic traffic assignment (DTA) models for large-scale transportation networks and the application of traffic measures, already several models have been developed to assess the externalities using DTA models more precisely. Different research projects have shown that there is a proven relation between the traffic dynamics and externalities, such as emissions of pollutants and traffic safety. This means that the assessment of external effects can be improved by using temporal information about flow, speed and density, which is the output of DTA models. In this paper, the modelling of traffic safety, emissions and noise in conjunction with DTA models is reviewed based on an extensive literature survey. This review shows that there are still gaps in knowledge in assessing traffic safety, much research is available concerning emissions, and although little research has been conducted concerning the assessment of noise using DTA models, the methods available can be used to assess the effects. Most research so far has focused on the use of microscopic models, while mesoscopic or macroscopic models may have a high potential for improving the assessment of these effects for larger networks.     

Statistical models for the estimation of bridge replacement costs

Making accurate estimates of bridge replacement costs is essential to assess present and future bridge funding needs. A series of analyses of variance was performed on bridge replacement costs to evaluate the effects of bridge attributes. Replacement cost prediction models were then developed by regression techniques. Bridge attributes which can be easily understood by bridge inspectors and engineers were used as predictor variables. Nonlinear and log-linear models were evaluated for developing cost prediction models. A residual analysis of these models showed that log-linear models were preferred to nonlinear models. Costs of bridges that had been replaced between 1980 and 1985 by the Indiana Department of Transportation (INDOT) were used as a data base. Replacement costs were converted to 1985 price using construction price indices. The final cost prediction models were validated using the costs of selected bridges which were replaced between January and June 1986, by the INDOT. Bridge replacement costs estimated by these models showed a fairly good correlation with the actual contract costs. To estimate current or future costs at a place other than in Indiana, one need to multiply appropriate cost indices.     

An empirical comparison of disaggregate category and regression trip generation analysis techniques

Category and regression household trip generation analysis techniques were compared and contrasted. The comparative research was facilitated through a discussion that revealed the interchangeability of two methods of calibrating a category model. While the cell mean method is simple to implement, it does not readily yield statistical indexes for comparison with regression models. The general linear model analysis of variance (GLANOVA) readily provides statistical indexes for the comparison of category and regression trip generation models, and it produces identical empirical results to the simpler cell mean approach of calibrating a category model.The empirical comparison supports the widespread use of category models for trip generation analysis in transportation planning studies. It was found that regression and category models yielded equivalent results for typical planning applications at the district level of aggregation. In addition, both techniques estimated overall trip rate with equal accuracy in the calibration phase, and the two approaches were indistinguishable with respect to sample size sensitivity. However, households with extremely large trip rates were underestimated to a greater degree by category models than regression models. This tendency, in turn, resulted in larger calibration coefficients of determination for regression models. Since the cell mean method of calibrating a model is simpler and easier to understand than a regression model representation, category models can be recommended over regression models for planning studies.     

State-of-the art of freight forecast modeling: lessons learned and the road ahead

In this paper we review freight forecasting models and current advances and needs with respect to data and model development. We then present a case study to suggest which models should be developed for the State of California in the US. We suggest several alternatives including an aggregate commodity flow model, a disaggregate regional logistics model and a hybrid regional logistics model with a truck touring model. We point out however, that the data requirements for the latter model would be extensive. In addition, the development of hybrid models, for example progress in the integration of regional logistics models with urban truck touring models, will introduce new problems such as reconciling the outputs of multiple models for consistency.     

An integrated, dynamic approach to travel demand forecasting

This paper presents a unified approach for improving travel demand models through the application and extension of supernetwork models of multi-dimensional travel choices. Proposed quite some time ago, supernetwork models solved to stochastic user equilibrium can provide a simultaneous solution to trip generation, distribution, mode choice, and assignment that is consistent with disaggregate models and predicts their aggregate effects. The extension to incorporate the time dimension through the use of dynamic equilibrium assignment methods is proposed as an enhancement that is necessary in order to produce realistic models. A variety of theoretical and practical problems are identified whose solution underlies implementation of this approach. Recommended future research includes improved algorithms for stochastic and dynamic equilibrium assignment, new methods for calibrating assignment models, and the use of Geographic Information Systems (GIS) technology for data and model management.     

Vehicular pollution prediction modelling: a review of highway dispersion models

Air quality modelling plays an important role in formulating air pollution control and management strategies by providing guidelines for better and more efficient air quality planning. Several line source models, mostly Gaussian‐based, have been suggested to predict pollutant concentrations near highways/roads. These models, despite several assumptions and limitations, are used throughout the world, including in India, to carry out air pollution prediction analysis due to vehicular traffic near roads/highways. These models are being continuously upgraded and modified based on field experiments, and numerical and physical modelling results. An effort has been made in the present paper to review briefly the philosophy and basic features of most of the commonly used highway dispersion models. The paper also discusses various theories and techniques that led to the development and modification of these models along with the statistical analysis tools to evaluate the performance of these models. An attempt has also been made to summarize briefly the various line source models currently used in India and to highlight the difficulties being faced while using them in an Indian context.     

A family of macroscopic node models

The family of macroscopic node models which comply to a set of basic requirements is presented and analysed. Such models are required in macro-, mesoscopic traffic flow models, including dynamic network loading models for dynamic traffic assignment. Based on the behaviour of drivers approaching and passing through intersections, the model family is presented. The headway and the turn delay of vehicles are key variables. Having demand and supply as input creates a natural connection to macroscopic link models. Properties like the invariance principle and the conservation of turning fractions are satisfied. The inherent non-uniqueness is analysed by providing the complete set of feasible solutions. The node models proposed by Tampère et al. (2011), Flötteröd and Rohde (2011) and Gibb (2011) are members of the family. Furthermore, two new models are added to the family. Solution methods for all family members are presented, as well as a qualitative and quantitative comparison. Finally, an outlook for the future development of empirically verified models is given.