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IntroductionNon-recurrent congestion caused by traffic incident is difficult to predict but should be dealt with in a timely and effective manner to reduce its influence on road capacity reduction and enormous travel time loss. Influence factor analysis and reasonable prediction of traffic incident duration are important in traffic incident management to predict incident impacts and aid in the implementation of appropriate traffic operation strategies. The objective of this study is to conduct a thorough review and discusses the research evolution, mainly including the different phases of incident duration, data resources, and the various methods that are applied in the traffic incident duration influence factor analysis and duration time prediction.MethodsIn order to achieve the goal of this study, we presented a systematic review of traffic incident duration time estimation and prediction methods developed based on various data resource, methodologies etc.Resultsbased on the previous studies, we analyse (i) Data resources and characteristics: different traffic incident time phases, data set size, incident types, duration time distribution, available data resources, significant influence factors and unobserved heterogeneity and randomness, (ii) traffic incident duration analysis methods, mainly including hazard-based duration model and regression and statistical tests, (iii) traffic incident duration prediction methods and evaluation of prediction accuracy.ConclusionsAfter a comprehensive review of literature, this study identifies and analyses future challenges and what can be achieved in the future to estimate and predict the traffic incident duration time.

The on-going globalisation and the increasing demand for flexibility in modern businesses have made transport, together with business logistics, a major functional domain. Transport growth is essentially for economic growth but is not without negative impacts. External effects such as pollution, congestion, accidents and damage to infrastructure generate considerable social costs that impose a heavy burden on society. This title addresses the need to develop new freight transport models and scientific tools to provide sound solutions that consider the wide range of internal and external impacts. The international contributions push forward frontiers in freight transport modelling and analysis.

Urban mobility significantly contributes to global carbon dioxide emissions. Given the rapid expansion and growth in urban areas, cities thus require innovative policies to ensure efficient and sustainable mobility. Urban typologies can serve as a vehicle for understanding dynamics of cities, which exhibit high variability in form, economic output, mobility behavior, among others. Yet, typologies relevant for sustainable urban mobility analyses are few, outdated and not large enough in scope. In this paper, we present a new typologization spanning 331 cities in 124 countries. Our sample represents 40% of the global urban population and contains the most recent data from 2008 to date. Using a factor analytic and agglomerative clustering approach, we identify 9 urban factors and 12 typologies. We discuss the implications of this new framework for researchers and planners and investigate the relationships between mobility and environmental sustainability indicators. Notably, we show an immediate application of the urban typologies to better understanding travel behavior and also describe their usage for detailed large-scale simulation in representative prototype cities for insights into sustainable future mobility policy pathways. Our data and results are publicly available for further exploration and will serve as a foundation for future analyses toward desirable urban and environmental outcomes.

Endogeneity is a model misspecification that precludes the consistent estimation of the model parameters. The control-function method is the most suitable tool to address endogeneity for several discrete choice models that are relevant in transportation research. However, the estimators obtained with the control-function method are consistent only up to a scale. In this paper, we first depict the determinants of this change of scale by adapting an existing result for omitted orthogonal attributes in logit models. Then, we study the problem of forecasting under these circumstances. We show that a procedure proposed in previous literature may lead to significant biases, and we suggest novel alternatives to be used with synthetic populations. We use Monte Carlo experimentation and real data on residential location choice to illustrate these results. The paper finishes by summarizing the findings of this investigation and suggesting future lines of research in this area.

The rapid growth in online shopping and associated parcel deliveries prompts investigation of the factors that contribute to parcel delivery demand. In this study, we evaluated the influence of locational and household characteristics on e-commerce home delivery demand. While past research has largely focused on the impacts of the adoption of online shopping using individual/household survey data, we made use of data from an e-commerce carrier. A linear regression model was estimated considering factors such as degree of urbanization, transit and shopping accessibility, and household attributes. The results both confirm and contradict prior research findings, highlighting the potential for a non-negligible influence of the local context on demand for parcel deliveries.

Although the basic structure of logit-mixture models is well understood, important identification and normalization issues often get overlooked. This paper addresses issues related to the identification of parameters in logit-mixture models containing normally distributed error components associated with alternatives or nests of alternatives (normal error component logit mixture, or NECLM, models). NECLM models include special cases such as unrestricted, fixed covariance matrices; alternative-specific variances; nesting and cross-nesting structures; and some applications to panel data. A general framework is presented for determining which parameters are identified as well as what normalization to impose when specifying NECLM models. It is generally necessary to specify and estimate NECLM models at the levels, or structural, form. This precludes working with utility differences, which would otherwise greatly simplify the identification and normalization process. Our results show that identification is not always intuitive; for example, normalization issues present in logit-mixture models are not present in analogous probit models. To identify and properly normalize the NECLM, we introduce the 'equality condition', an addition to the standard order and rank conditions. The identifying conditions are worked through for a number of special cases, and our findings are demonstrated with empirical examples using both synthetic and real data.

The development of activity-based models as a tool to analyse travel behaviour and forecast transport demand has been motivated by the growing complexity in activity patterns resulting from socio-economic changes, growing congestion, and negative externalities, as well as the need to estimate changes in travel behaviour in response to innovative policies designed to achieve sustainability. This paper reviews how the trade-off between behavioural realism and complexity, one of the main challenges facing the travel-demand modeler, is made in the best practical activity-based models. It proposes an approach that captures key behavioural aspects and policy sensitivities, while remaining practical with reasonable requirements of computational resources. The three main model elements in this trade-off—model structure, data, and application method—are analysed. Drawing on examples from a model developed for Tel Aviv and from existing US models, this paper shows that behavioural realism and policy sensitivity can be achieved with a reasonable level of model complexity.

This paper discusses choice set generation and route choice model estimation for large-scale urban networks. Evaluating the effectiveness of Advanced Traveler Information Systems (ATIS) requires accurate models of how drivers choose routes based on their awareness of the roadway network and their perceptions of travel time. Many of the route choice models presented in the literature pay little attention to empirical estimation and validation procedures. In this paper, a route choice data set collected in Boston is described and the ability of several different route generation algorithms to produce paths similar to those observed in the survey is analyzed. The paper also presents estimation results of some route choice models recently developed using the data set collected. [PUBLICATION ABSTRACT]

This paper is motivated by the increasing popularity of efficient designs for stated choice experiments. The objective in efficient designs is to create a stated choice experiment that minimizes the standard errors of the estimated parameters. In order to do so, such designs require specifying prior values for the parameters to be estimated. While there is significant literature demonstrating the efficiency improvements (and cost savings) of employing efficient designs, the bulk of the literature tests conditions where the priors used to generate the efficient design are assumed to be accurate. However, there is substantially less literature that compares how different design types perform under varying degree of error of the prior. The literature that does exist assumes small fractions are used (e.g., under 20 unique choice tasks generated), which is in contrast to computer-aided surveys that readily allow for large fractions. Further, the results in the literature are abstract in that there is no reference point (i.e., meaningful units) to provide clear insight on the magnitude of any issue. Our objective is to analyze the robustness of different designs within a typical stated choice experiment context of a trade-off between price and quality. We use as an example transportation mode choice, where the key parameter to estimate is the value of time (VOT). Within this context, we test many designs to examine how robust efficient designs are against a misspecification of the prior parameters. The simple mode choice setting allows for insightful visualizations of the designs themselves and also an interpretable reference point (VOT) for the range in which each design is robust. Not surprisingly, the D-efficient design is most efficient in the region where the true population VOT is near the prior used to generate the design: the prior is $20/h and the efficient range is $10–$30/h. However, the D-efficient design quickly becomes the most inefficient outside of this range (under $5/h and above $40/h), and the estimation significantly degrades above $50/h. The orthogonal and random designs are robust for a much larger range of VOT. The robustness of Bayesian efficient designs varies depending on the variance that the prior assumes. Implementing two-stage designs that first use a small sample to estimate priors are also not robust relative to uninformative designs. Arguably, the random design (which is the easiest to generate) performs as well as any design, and it (as well as any design) will perform even better if data cleaning is done to remove choice tasks where one alternative dominates the other.

Uncertainty pervades most aspects of life. From selecting a new technology to choosing a career, decision makers rarely know in advance the exact outcomes of their decisions. Whereas the consequences of decisions in standard decision theory are explicitly described (the decision from description (DFD) paradigm), the consequences of decisions in the recent decision from experience (DFE) paradigm are learned from experience. In DFD, decision makers typically overrespond to rare events. That is, rare events have more impact on decisions than their objective probabilities warrant (overweighting). In DFE, decision makers typically exhibit the opposite pattern, underresponding to rare events. That is, rare events may have less impact on decisions than their objective probabilities warrant (underweighting). In extreme cases, rare events are completely neglected, a pattern known as the \"Black Swan effect.\" This contrast between DFD and DFE is known as a description—experience gap. In this paper, we discuss several tentative interpretations arising from our interdisciplinary examination of this gap. First, while a source of underweighting of rare events in DFE may be sampling error, we observe that a robust description—experience gap remains when these factors are not at play. Second, the residual description—experience gap is not only about experience per se but also about the way in which information concerning the probability distribution over the outcomes is learned in DFE. Econometric error theories may reveal that different assumed error structures in DFD and DFE also contribute to the gap.