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\"This textbook of quantitative methods in transportation engineering comes with problems and a solutions manual for adopting course instructors. Basic mathematics and calculus are prerequisites\"-- Provided by publisher.

Understanding how congestion at one location can cause ripples throughout large-scale transportation network is vital for transportation researchers and practitioners to pinpoint traffic bottlenecks for congestion mitigation. Traditional studies rely on either mathematical equations or simulation techniques to model traffic congestion dynamics. However, most of the approaches have limitations, largely due to unrealistic assumptions and cumbersome parameter calibration process. With the development of Intelligent Transportation Systems (ITS) and Internet of Things (IoT), transportation data become more and more ubiquitous. This triggers a series of data-driven research to investigate transportation phenomena. Among them, deep learning theory is considered one of the most promising techniques to tackle tremendous high-dimensional data. This study attempts to extend deep learning theory into large-scale transportation network analysis. A deep Restricted Boltzmann Machine and Recurrent Neural Network architecture is utilized to model and predict traffic congestion evolution based on Global Positioning System (GPS) data from taxi. A numerical study in Ningbo, China is conducted to validate the effectiveness and efficiency of the proposed method. Results show that the prediction accuracy can achieve as high as 88% within less than 6 minutes when the model is implemented in a Graphic Processing Unit (GPU)-based parallel computing environment. The predicted congestion evolution patterns can be visualized temporally and spatially through a map-based platform to identify the vulnerable links for proactive congestion mitigation.

Users’ perceptions are identified as key elements to understand bicycle use, whose election cannot be explained with usual mobility variables and socio-economic characteristics. A hybrid model is proposed to model the intention of bicycle use; it combines a structural equations model that captures intentions and a choice model. The framework is applied to a case of a university campus in Madrid that is studying a new internal bike system. Results show that four latent variables (convenience, pro-bike, physical determinants and external restrictions) help explaining intention to use bike, representing a number of factors that are linked to individual perceptions.

This study examines the residential self-selection issue from conceptual and methodological aspects, with an empirical application. The study was motivated by the challenge of interpreting the results of an endogenous switching model and the lack of convenient/proper equations for calculating treatment effects when the dependent variable is log-transformed. We classify expected returns from living in a certain kind of area (urban vs. non-urban) into location-related versus transportation-related, and economic versus lifestyle, aspects. From that, we note that the outcome variable of interest to this study among many others—vehicle-miles driven (VMD)—may not correspond to the returns an individual is seeking to optimize when choosing where to live. Previous studies tend to expect negative error correlations between selection and outcome equations, on the presumption that urbanite people would desire to drive less (so, unobserved traits increasing the utility of an urban neighborhood should also tend to decrease VMD). However, given the variety of possible expected returns, we argue that error correlations (and thus the directions of selection corrections) could be either positive or negative. As an empirical application, we apply the endogenous switching model to modeling VMD, where the treatment is living in an urban area (vs. living in a less urban area). In addition, the paper provides conceptual and graphical illustrations to improve the understanding of treatment effects and their mathematical mechanisms.

Supplying public transport systems with high levels of service quality is fundamental for retaining users and attracting new ones. Policies that improve transit service quality will ultimately lead to more sustainable travel patterns. Measuring overall service quality implies measuring the quality of several specific attributes and is prevalently evaluated through the perceptions of users, using satisfaction rates. In this study, we demonstrate that there is a further element that can influence users’ perceptions, the so-called critical incidents (CI), defined as encounters that are particularly satisfying or dissatisfying. The concept is not restricted to ratings of the predefined product or service attributes, because customers who experience CI remember them well and can usually describe the experience. We implement a framework that includes CI and is innovative for several reasons. Firstly, we introduce attribute-specific (e.g. reliability, safety, comfort) CI to explain attribute-specific satisfaction levels, and then we model these with latent constructs allowing for measurement error in recalling the CI. We also demonstrate that using an Ordinal-Probit approach leads to more accurate results than its numerical counterpart, the latter possibly presenting biased results. Finally, we present a full Structural Equation Multiple Cause Multiple Indicator (SEM-MIMIC) model, which corrects for heterogeneity in the perceptions of users regarding satisfaction with the various service attributes, with the overall service, and with loyalty. For these purposes, we analyse an extensive database (96,763 interviewed passengers) derived from Customer Satisfaction Surveys in the railway services offered in the hinterland of Milan. Our main contribution to the literature is that we show that the occurrence of a CI has a substantial negative impact on passenger satisfaction for all service attributes. As it is a policy-related variable, it can be managed directly by the public transport (PT) administrators. To better plan and improve PT services, avoiding CI in specific items should be the strategy to follow. On the other hand, reliability, and added-value services are the primary service attributes that have a positive effect on satisfaction with the overall service and, in turn, on loyalty. Our model can be useful for PT administrators as it sheds light on how to improve the service according to users’ preferences, and by considering the differences among user categories.

The 2007–2008 French National Travel Survey (FNTS) included questions about the trip experience for a random subsample of the respondents’ daily travel, offering a rare opportunity to examine a national profile of attitudes toward travel. This study analyzes the self-reported (mental and/or physical) fatigue associated with the selected trip, and its (un)pleasantness. Only 8 % of trips were tiring, and fewer than 4 % were unpleasant, indicating that travel is by no means universally distasteful. We present a bivariate probit model of the mental and physical fatigue associated with the trip, and binary logit models of whether the trip was pleasant (yes/no) or unpleasant (yes/no). For the most part, socioeconomic variables and indicators of trip length, distance, purpose, and mode have logical relationships to fatigue and pleasantness. However, 11 variables out of 31 common to both sets of models have impacts on fatigue that are opposite to those on un/pleasantness, pointing to conditions under which a trip can be fatiguing but pleasant, or conversely. Accordingly, a key contribution of the research is to demonstrate the value of jointly considering both constructs in order to more comprehensively capture the overall attitudes toward the travelling activity. It is also of interest that activities conducted during the trip appear in both sets of models. In particular, the results suggest that although listening to the radio/music decreases the tendency to rate the trip as mentally fatiguing, it tends to be seen as ameliorating the disutility of a tedious trip more than increasing the pleasantness of the trip. Among the policy-relevant findings, we note the especially negative attitudes towards multimodal trips and trips mainly involving driving cars.

In recent years, airline practitioners and academics have started to explore new ways to model airline passenger demand using discrete choice methods. This book provides an introduction to discrete choice models and uses extensive examples to illustrate how these models have been used in the airline industry. These examples span network planning, revenue management, and pricing applications. Numerous examples of fundamental logit modeling concepts are covered in the text, including probability calculations, value of time calculations, elasticity calculations, nested and non-nested likelihood ratio tests, etc. The core chapters of the book are written at a level appropriate for airline practitioners and graduate students with operations research or travel demand modeling backgrounds. Given the majority of discrete choice modeling advancements in transportation evolved from urban travel demand studies, the introduction first orients readers from different backgrounds by highlighting major distinctions between aviation and urban travel demand studies. This is followed by an in-depth treatment of two of the most common discrete choice models, namely the multinomial and nested logit models. More advanced discrete choice models are covered, including mixed logit models and generalized extreme value models that belong to the generalized nested logit class and/or the network generalized extreme value class. An emphasis is placed on highlighting open research questions associated with these models that will be of particular interest to operations research students. Practical modeling issues related to data and estimation software are also addressed, and an extensive modeling exercise focused on the interpretation and application of statistical tests used to guide the selection of a preferred model specification is included; the modeling exercise uses itinerary choice data from a major airline. The text concludes with a discussion of on-going customer modeling research i

Studying the control properties of complex networks provides insight into how designers and engineers can influence these systems to achieve a desired behavior. Topology of a network has been shown to strongly correlate with certain control properties; here we uncover the fundamental structures that explain the basis of this correlation. We develop the control profile, a statistic that quantifies the different proportions of control-inducing structures present in a network. We find that standard random network models do not reproduce the kinds of control profiles that are observed in real-world networks. The profiles of real networks form three well-defined clusters that provide insight into the high-level organization and function of complex systems.

Ridership prediction at station level plays a critical role in subway transportation planning. Among various existing ridership prediction methods, direct demand model has been recognized as an effective approach. However, direct demand models including geographically weighted regression (GWR) have rarely been studied for local model selection in ridership prediction. In practice, acquiring insights into subway ridership under multiple influencing factors from a local perspective is important for passenger flow management and transportation planning operations adapting to local conditions. In this study, we propose an adapted geographically weighted LASSO (Ada-GWL) framework for modelling subway ridership, which involves regression-coefficient shrinkage and local model selection. It takes subway network layout into account and adopts network-based distance metric instead of Euclidean-based distance metric, making it so-called adapted to the context of subway networks. The real-world case of Shenzhen Metro is used to elaborate our proposed model. The results show that the proposed Ada-GWL model performs the best compared with the global model (ordinary least square, GWR, GWR calibrated with network-based distance metric and geographically weighted LASSO (GWL) in terms of estimation error and goodness-of-fit. Through understanding the variation of each coefficient across space (elasticities) and variables selection of each station, it provides more realistic conclusions based on local analysis. Besides, through clustering analysis of the stations according to the regression coefficients, clusters’ functional characteristics are found to be in compliance with the policy of functional land use in Shenzhen, indicating the high interpretability of Ada-GWL model from the spatial angle. In other words, the regression coefficients of different stations can provide us the local prospective to understand the influence of factors on stations’ ridership.

The development of new routes and stations, as well as changes in land use, can have significant impacts on public transit ridership. Thus, transport departments and governments should seek to determine the level and spatio-temporal dependency of these impacts with the aim of adjusting services or improving planning. However, existing studies primarily focus on predicting ridership, and pay relatively little attention to analyzing the determinants of ridership from temporal and spatial perspectives. Consequently, no comprehensive cognition of the spatio-temporal relationship between station ridership and the built environment can be obtained from previous models, which makes them unable to facilitate the optimization of transportation demands and services. To rectify this problem, we have employed a Bayesian negative binomial regression model to identify the significant impact factors associated with entry/exit ridership at different periods of the day. Based on this model, we formulated geographically weighted models to analyze the spatial dependency of these impacts over different periods. The spatio-temporal relationship between station ridership and the built environment was analyzed using data from Beijing. The results reveal that the temporal impacts of most ridership determinants are related to the passenger trip patterns. Furthermore, the spatial impacts correspond with the determinants’ spatial distribution, and the results give some implications on urban and transportation planning. This analysis gives a common analytical framework analyzing impacts of urban characteristics on ridership, and extending researches on how we capture the impacts of urban and other factors on ridership from a comprehensive perspective.