Multimodal urban mobility and multilayer transport networks

Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modelling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.Comment: 31 pages, 4 figure

Using context-sensitive criteria to evaluate local and regional transportation policy : a case study on cordon pricing

As traffic congestion grows but existing roadway capacity remains fixed or limited, downtown congestion pricing offers potential as a tool to manage the transportation system. Though the idea is not new, congestion pricing has received a resurgence of attention in the United States in recent years because it could offer both congestion relief and transportation revenue. However, in order for a modern congestion pricing proposal to be politically feasible and publicly acceptable today it must be designed to offer more, such as equitable or progressive distribution of impacts, greenhouse gas emissions, and encouragement or support for alternate modes, including new mobility services. In Seattle, serious consideration of the implementation of congestion pricing by 2021 is underway, and numerous policy questions remain open. One which many anticipate, particularly the public, is the question of where congestion pricing revenue would be spent. It is likely that at least some of the revenue will be allocated for transit, but where should service improvements be targeted, both geographically and demographically, so that mobility and access are not impaired, particularly for the already transportation-disadvantaged, and so that multimodal travel is not just possible but preferable to driving? Could a regional partnership between transit agencies like Sound Transit and King County Metro and the City of Seattle secure transportation outcomes that align with both transit agencies’ ambitious service expansion goals and Seattle’s core equity, multimodal mobility, and climate goals? This thesis seeks to answer these questions by using a mix of statistical models of transportation system level of service and individual-level mode choice. These models are used to predict how travelers across the region would change their travel behavior in response to cordon pricing in Center City Seattle under two investment scenarios. It is projected that investing in transit broadly across the region by decreasing transit service times produces transportation system outcomes that advance both local and regional strategic goals more than concentrated investment on downtown Seattle roadways and transit could advance Seattle’s goals alone. Regional transit investment would decrease congestion more in Center City Seattle by improving transit access from outside Seattle into Center City, especially among neighborhoods with the lowest housing and transportation affordability, highest automobility, and highest transportation-related greenhouse gas emissions. Therefore, the findings strongly motivate that congestion pricing revenue in Seattle be spent on regional transit service improvement and expansion. Furthermore, the findings suggest that even regional transit investments that may not be directly linked to Center City will help to produce a mix of better transportation outcomes in Center City than concentrated investment would.Civil, Architectural, and Environmental Engineerin

Commercial Short-Haul Flight or Autonomous Mobility-On-Demand: Modeling Air Passengers’ Modal Choice

Commercial short-haul flights (SF) are vital to airports and airlines because they account for one-third of hub traffic and have higher profit margins than the long-haul market. While U.S. commercial air passenger travel has increased steadily over the past decades, SF has been declining and was doing so before the unprecedented decrease in air travel caused by restrictions related to the COVID-19 global pandemic. Once autonomous mobility-on-demand (aMoD) is more viable than the human-driven car, demand for SF could be negatively impacted. Although there is published research on SF and aMoD, studies on factors influencing the choice between SF and aMoD are missing. Based on goal framing theory (GFT) variables, contextual trip attributes, COVID-19 items, and demographics, this study used a quantitative survey design to answer two research questions. The first question sought to identify factors that most influence U.S. air travelers’ modal choice for inter-regional travel. The second question aimed to identify distinct passenger clusters for SF and aMoD and evaluate the similarities and differences within these passenger segments. An online questionnaire of 69 items was developed based on extant literature and the theoretical foundation of the GFT. The survey was administered online with an air passenger sample in October 2021 via Amazon’s MTurk Results from 1,388 air passenger respondents qualified for data analyses, including exploratory factor analysis (EFA), multinomial logistic regression (MNL), two-step cluster analysis (CA), and multivariate analysis of variance (MANOVA). The findings support the GFT as a theoretical framework for modeling future mode choice and SF and aMoD clusters. The current primary transport mode was the most critical predictor for future mode choice. Self-efficacy, value of time, trust, and habit are new variables added to the GFT framework. The first two were useful in predicting future mode choice; trust and habit were not. Two-thirds (66%) of the current SF passengers intend to shift to other transport modes once aMoD is available; 31% of the current SF market share could be lost to aMoD and 20% to conventional driving. More than half of the current most-traveled air passengers intend to use aMoD as their main transport choice. The potential significant shifts in the ground- and air-mode shares revealed in this study may have crucial impacts on airlines, airports, infrastructure, future air/land-use planning, and the travel and hospitality industries

CITIES AND ACCESSIBILITY: THE POTENTIAL FOR CARBON REDUCTIONS AND THE NEED FOR NATIONAL LEADERSHIP

This article begins by outlining the elements that should be included in the framework for understanding how people interact with their built environments. Part II describes how the framework might be made operational through the use of an emerging technique called land-use transportation scenario planning. Part III assesses how well land-use transportation scenario planning fits within the dictates and limits of U.S. transportation law. The analysis ultimately reveals that it holds substantial promise as a tool that could lead to meaningful cuts in carbon emissions

Exploring motivations for multimodal commuting: A hierarchical means-end chain analysis

Despite municipal investments in multimodal mobility infrastructure, monomodal automotive travel patterns still dominate work-related mobility. As policymakers aim to reduce associated externalities like traffic congestion, noise, and air pollution, encouraging multimodality can be a promising route toward diversified, more sustainable mobility. However, studies on modal choice and modal shift have mainly focused on investigating the consumer decision-making process concerning specific monomodal travel modes and external factors but are characterized by a lack of dedicated applications in the commuting context. Therefore, insights into consumers’ motivational patterns determining intentions to engage in multimodal commuting and factors influencing their willingness to alter the modal mix remain scarce. With a qualitative means-end chain (MEC) analysis, we explore consumers’ overarching motivational structures to choose multimodal commuting behavior through laddering interviews with forty employees from two large German employers. We contribute to existing research by revealing five motivational patterns that promote consumers' decision to become multimodal commuters: autonomy, physical health, sustainability, quality of life, and interpersonal connections, which we juxtapose with previous findings. Interestingly, we find that economic interest, security, and fun are only motives of secondary importance. Consequently, we propose implications for academics, policymakers, and practitioners to foster commuters choosing more sustainable, multimodal mobility

Transport Access Manual: A Guide for Measuring Connection between People and Places

This Manual is a guide for quantifying and evaluating access for anybody interested in truly understanding how to measure the performance of transport and land use configurations. It contains enough to help transport and planning professionals achieve a more comprehensive look at their city or region than traditional transport analysis allows. It provides a point of entry for interested members of the public as well as practitioners by being organized in a logical and straightforward way

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Incomplete: Evaluating Current Complete Streets Practice and Presenting a Toolkit for Practitioners

Incomplete: Evaluating Current Complete Streets Practice and Presenting a Toolkit for Practitioner