Communications in Transportation Research最新文献

Urban air mobility (UAM) extends urban transportation to low-altitude airspace using electric vertical take-off and landing (eVTOL) vehicle to reduce traffic congestion. The vertical take-off and landing (VTOL) site connecting ground and air transport is the critical infrastructure of the UAM. Determining its locations is essential for the design and operation of the air route. This study focuses on the problem of the location of the VTOL site, using Shenzhen as the study area, and establishes an integer programming model with the objective of maximizing travel cost savings to identify the optimal locations of the VTOL sites. This study is different from existing ones in that it explicitly considers the three-dimensional spatial availability of VTOL sites. Geographic information system (GIS) tools are used to identify locations that satisfy two-dimensional (2D) planar availability, and an obstacle assessment model of the approach/departure and transitional surfaces of the VTOL site is built to further screen the locations. The selected potential sites are used as input to the integer programming model, ensuring that the locations identified to establish the VTOL site are optimal. The impact of the number of VTOL sites, the user's transfer time at the VTOL sites, and the eVTOL pricing on the model solution is also discussed. Although this study uses Shenzhen as a research object, the proposed methodology is generalized and applicable to any other city or region, providing recommendations and references for initial planning and related operations of the UAM in selected areas.

Mobility-as-a-Service (MaaS) has emerged as a promising concept in the mobility landscape. Researchers are increasingly interested in understanding this digital service's potential acceptance and uptake. Despite this interest, the extant literature lacks a comprehensive overview of various factors that play a role in its acceptance by target users. In this study, we aim to establish a current state of the art of MaaS user acceptance to inform practice in the effective design of related services, and to support the scientific community by offering future directions for their research. To achieve this, we performed a Systematic Literature Review (SLR) of relevant empirical studies and analyzed the factors considered significant in MaaS acceptance by target users. Our in-depth analyses of these works resulted in several factors, which we grouped into three categories: traveler and trip characteristics, service and technology characteristics, and urban environment characteristics. Certain factors, such as age, trip frequency, MaaS bundle price, and transportation facility, were found to be consistent in their influence on MaaS acceptance. However, empirical works conflict regarding the influence of a number of factors, such as income and public transport card ownership. This study serves as a comprehensive source of the factors investigated in the literature to the moment and a basis for future research to improve our understanding of factors that require further investigation.

In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed to address visual grounding in AVs. Our Context-Aware Visual Grounding (CAVG) model is an advanced system that integrates five core encoders—Text, Emotion, Image, Context, and Cross-Modal—with a multimodal decoder. This integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features, augmented by state-of-the-art Large Language Models (LLMs) including GPT-4. The architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic (RSD) layer for attention modulation. This architectural design enables the model to efficiently process and interpret a range of cross-modal inputs, yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual scenes. Empirical evaluations on the Talk2Car dataset, a real-world benchmark, demonstrate that CAVG establishes new standards in prediction accuracy and operational efficiency. Notably, the model exhibits exceptional performance even with limited training data, ranging from 50% to 75% of the full dataset. This feature highlights its effectiveness and potential for deployment in practical AV applications. Moreover, CAVG has shown remarkable robustness and adaptability in challenging scenarios, including long-text command interpretation, low-light conditions, ambiguous command contexts, inclement weather conditions, and densely populated urban environments.

Cities worldwide are geared to promote economic growth, improve accessibility, address environmental issues, and enhance the quality of life. However, the processes that lead to the design of urban roads, particularly the space distribution, reflect the inequalities existing in the fabric of our society. Motorists often have shorter travel time and more space than passengers of other modes. Furthermore, the existing transport appraisal and planning tools that frame sustainable transport policies fall short of considering the dimension of social justice. Therefore, our urban transport systems are essential areas for advancing sustainability through a transport justice-based approach to planning that can pivot the distribution of infrastructure investments over different social groups and transport modes. This study proposes such an approach by which such suitable urban transport strategies can be identified, co-created with users and appraised while considering the commuters’ needs. Specifically, the interaction between the multidimensional characteristics of sustainability and the principles of transport justice are investigated. The proposed approach is applied to London and Birmingham. The results show that a transparent and holistic approach to integrating users within transport planning is an effective way to reflect diverse needs and local circumstances and thereby ensure a just transition to sustainable urban transport policies. The results from the case studies highlight a strong rationale for the centrality of justice in any urban transport planning and policy making efforts, particularly in the allocation of road space.

Connected and automated vehicles (CAVs) are expected to reshape traffic flow dynamics and present new challenges and opportunities for traffic flow modeling. While numerous studies have proposed optimal modeling and control strategies for CAVs with various objectives (e.g., traffic efficiency and safety), there are uncertainties about the flow dynamics of CAVs in real-world traffic. The uncertainties are especially amplified for mixed traffic flows, consisting of CAVs and human-driven vehicles, where the implications can be significant from the continuum-modeling perspective, which aims to capture macroscopic traffic flow dynamics based on hyperbolic systems of partial differential equations. This paper aims to highlight and discuss some essential problems in continuum modeling of real-world freeway traffic flows in the era of CAVs. We first provide a select review of some existing continuum models for conventional human-driven traffic as well as the recent attempts for incorporating CAVs into the continuum-modeling framework. Wherever applicable, we provide new insights about the properties of existing models and revisit their implications for traffic flows of CAVs using recent empirical observations with CAVs and the previous discussions and debates in the literature. The paper then discusses some major problems inherent to continuum modeling of real-world (mixed) CAV traffic flows modeling by distinguishing between two major research directions: (a) modeling for explaining purposes, where making reproducible inferences about the physical aspects of macroscopic properties is of the primary interest, and (b) modeling for practical purposes, in which the focus is on the reliable predictions for operation and control. The paper proposes some potential solutions in each research direction and recommends some future research topics.

This study introduces the same-day delivery time-guarantee (SDDTG) problem for supporting online retail. In the SDDTG, orders are placed online and are processed and delivered from a depot to customer locations in the same day. The problem seeks optimal delivery time guarantees to offer customers as they consider making purchases to increase purchase decision likelihood while accounting for delivery-related, supply-side costs that can affect profits. Time guarantees are decision variables rather than parameters (as is typical) and are designed around anticipated customer satisfaction levels and purchase likelihoods. Delivery deadlines are not merely given to customers once they make their purchases, but rather the attractiveness of the offered delivery guarantees affects whether they make their purchases, i.e., whether demand is realized. The problem is conceptualized as a multi-stage, stochastic, mixed-integer program in which uncertainties associated with customer reaction to delivery time guarantee offers and their arrival over time are captured. Given a shrinking horizon over a fixed planning horizon, the multi-stage program is approximated by a series of two-stage programs. A parallelized progressive hedging solution methodology is proposed and insights from its application on a case study. The problem recognizes tradeoffs between meeting promised delivery times and capturing the market.

The node–place model has been widely used to classify and evaluate transit stations, which sheds light on individuals’ travel behaviors and supports urban planning through effectively integrating land use and transportation development. This study adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city. Moreover, the unique metric drawn from detailed visit history of the infected, i.e., the COVID-19 footprints, is proposed and exploited. This study then empirically uses the adapted model to examine the station-level factors affecting the local COVID-19 footprints. The model accounts for traditional measures of the node and place as well as actual human mobility patterns associated with the node and place. It finds that stations with high node, place, and human mobility indices normally have more COVID-19 footprints in proximity. A multivariate regression is fitted to see whether and to what degree different indices and indicators can predict the COVID-19 footprints. The results indicate that many of the place, node, and human mobility indicators significantly impact the concentration of COVID-19 footprints. These are useful for policy-makers to predict and monitor hotspots for COVID-19 and other pandemics’ transmission.

Efficiently solving the user equilibrium traffic assignment problem with elastic demand (UE-TAPED) for transportation networks is a critical problem for transportation studies. Most existing UE-TAPED algorithms are designed using a sequential computing scheme, which cannot take advantage of advanced parallel computing power. Therefore, this study focuses on model decomposition and parallelization, proposing an origin-based formulation for UE-TAPED and proving an equivalent reformulation of the original problem. Furthermore, the alternative direction method of multipliers (ADMM) is employed to decompose the original problem into independent link-based subproblems, which can solve large-scale problems with small storage space. In addition, to enhance the efficiency of our algorithm, the parallel computing technology with optimal parallel computing schedule is implemented to solve the link-based subproblems. Numerical experiments are performed to validate the computation efficiency of the proposed parallel algorithm.