Communications in Transportation Research最新文献

This study utilises the modified Transtheoretical Model of Change (TTMC) to investigate the levels of adoption of transport-multi-function apps or Transport-SuperApp (TSA) and also examine the influence of users’ personality, socio-demographic factors, residential location, perceived built environment, and motivations of use. The analysis is based on data collected from 1,051 users in four Indonesian cities. In this study, a latent class cluster analysis (LCCA) was used which identified four distinct classes of TSA level of adoption: Over Enthusiast (OE) (42%) comprising users who extensively explore and utilise the functions of the apps; Exploring but has Disinterest (ED) (36%) comprising those who primarily use apps for popular functions such as transportation, shopping, and payment services, but lack interest in utilising other services; Shopping-oriented but has Broad Interest (SBI) (14%) comprising those who heavily use shopping services while displaying a high interest in other functions; and Minimalist and Unaware (MU) (8%) comprising those who utilises the core functions of TSA for transportation and shopping while exhibiting low awareness of other available functions within the apps. The study found that transportation and shopping services have the highest level of adoption compared to other TSAs’ functions. The findings also suggest that the behaviour surrounding TSA utilization may evolve in the future and adoption levels are influenced by hedonic and utilitarian motivations. The OE users are associated with authority/control and tend to be more disorganised. Whereas SBI users tend to be more creative and open to new experiences. Compared to other classes, OE users are mostly found in Jakarta, a megapolitan area, and primarily reside near activity centres.

Laboratory experiments are one of the important means used to investigate travel choice behavior under strategic uncertainty. Many experiment-based studies have shown that the Nash equilibrium can predict aggregated route choices, while the fluctuations, whose mechanisms are still unclear, continue to exist until the end. To understand the fluctuations, this paper proposes a route-dependent attraction-based stochastic process model, which shares exactly the same behavioral foundation introduced in Part I of the study (Qi et al., 2023), i.e., route-dependent inertia and route-dependent preference. The model predictions are carefully compared with the experimental observations obtained from the congestible parallel-route laboratory experiments containing 312 subjects and eight decision-making scenarios (Qi et al., 2023). The results show that the proposed stochastic process model can precisely reproduce the random oscillations both in terms of flow switching and route flow evolution. Subsequently, an approximated model is developed to enhance the efficiency in evaluating the equilibrium distribution, providing a practical tool to evaluate the impacts of transportation policies in both long- and short-term runs. To the best of our knowledge, this paper is the first attempt to model and explain experimental phenomena by introducing stochastic process theories, as well as a successful example of applying experimental economics methodology to improve our understanding of human travel choice behavior.

This study proposes a method for measuring travel delays caused by traffic crashes based on taxi GPS data and other open-source spatial data. Travel delays caused by traffic crashes are quantified according to the difference between the post-crash and typical travel speeds on affected road segments. Based on multiple sources of data in Hong Kong, we also develop a generalized linear model with explanatory variables including crash characteristics, temporal attributes, road network features, traffic indicators, and built environment features, to unveil the relationship between travel delays and these factors. The findings show that crash characteristics alone inadequately explain variations in delays. The model performance improves after factors about the built environment and the dynamic road conditions are incorporated. This underscores the importance of urban factors in traffic delay analysis. Furthermore, we estimate the total travel delays caused by traffic crashes in the city. It is estimated that Hong Kong has suffered from a total delay of 713,877 vehicle-hours in 2021. The associated economic loss amounts to US$11.02 million. This study contributes to methodological advances in estimating crash-induced travel delays. The explanatory model considers factors which help policy makers and planners to identify risky factors and hot spots for devising more targeted and effective strategies of shortening crash-induced traffic congestion in the future. In addition, the findings highlight the significance and magnitude of another negative externality of traffic crashes – traffic delays – in a complex urban road network.

In the era of emerging technologies, the transportation system is witnessing the introduction of innovative mobility services, such as autonomous vehicles, which possess unique service features that cannot be seen from conventional travel modes. To facilitate the understanding of the behavioral impacts and the adoption of innovative mobilities, a novel binary weibit model with an oddball alternative (BW-O) is developed for the binary choice between conventional and emerging mobilities. The BW-O model explicitly considers the unprecedented (or unique) service features of emerging travel modes while retaining the closed-form choice probability. This study empirically illustrates the application of the BW-O model in the mode choice context. The desirable properties of the BW-O model compared to the existing binary choice models are discussed both theoretically and empirically. In the binary mode choice problem with an emerging travel mode, the unique service features of the emerging mode can lead to the “oddball” effect and “superstar” effect, which play a critical role in the travel behavior and mode adoption. The BW-O model inherently captures both effects by considering a higher perception variance for the emerging mode and asymmetric choice probabilities between different modes. Thus, as revealed by the empirical results, the BW-O model outperforms the basic binary weibit model in terms of both model fit and predictive power. The developed BW-O model is not only applicable to the mode choice problem in transportation systems, but also opens a door for more general class-imbalanced binary choice contexts where an alternative has additional attractiveness and asymmetric choice probability.

Smart mobility solutions are trending in the mobility domain realized through pilot activities and commercial solutions, but there is a lack of a broad framework defining the readiness to introduce such mobility solutions in a specific area. In this research, smart mobility solutions are examined in the perspective of the Mobility-as-a-Service (MaaS) scheme that is an adequate representation of the maturity of a region regarding smart mobility solutions including technology, business, and coopetition aspects. These three aspects define the feature selection, whereas surveys are used to collect input data from local experts (LEs). For weighting the features, the analytic hierarchy process (AHP) is used with a modified interpretive structural modeling (ISM). With this modification, an expert-friendly process is developed without affecting the results. The elaborated MaaS readiness index (MRI) is applied to six regions with different types of mobility related pilot activities to demonstrate the MRI as a comparison tool between regions and between ex-ante and ex-post pilot activities. The developed interpretive structural modeling with Graph (ISM-G) methodology requites remarkably less work from the evaluators compared to the ISM, while no important difference appeared in the results. The MRI can support smart mobility related pilot evaluations, whereas the ISM-G can be used widely in decision-making.

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.