Communications in Transportation Research最新文献

Nowadays, electric vehicles (EVs) are increasingly equipped with advanced onboard devices capable of collecting and recording real-time charging data. The analysis of such data from a large-scale EV fleet plays a crucial role in supporting decision-making processes, particularly in the deployment of charging infrastructure and the formulation of EV-focused policies. Nevertheless, the challenges of collecting these data are significant, primarily due to privacy concerns and the high costs associated with data access. In response, this study introduces an innovative methodology for generating large-scale and diverse EV charging data, mirroring real-world patterns for cost-efficient and privacy-compliant use. Specifically, this approach combines Gibbs sampling and conditional density networks and was trained and validated using a real-world dataset consisting of approximately 1.65 million charging events from 3,777 battery EVs (BEVs) in Shanghai over a year. Results illustrate that the proposed model can effectively capture the underlying distribution of the original charging data, enabling the generation of synthetic samples that closely resemble real-world charging events. The approach is readily employed for data imputation and augmentation, and it can also help simulate future charging distributions by conditional generation based on anticipated development premises.

As urbanization and high-rise living increase, frequent delivery of goods in the building to higher floors from the ground level is becoming a pressing issue. We introduce a drone-based vertical delivery system aimed at enhancing the efficiency of high-rise building logistics. The potential of the proposed system in reducing delivery time and energy consumption compared to conventional elevator-based delivery is analyzed. By assessing the requisite number of drones, their operating frequencies, and identifying scenarios in which drones can surpass conventional methods, the advantages using drone delivery systems are highlighted. The results indicate that drone delivery is not only viable but also advantageous to meet certain demand levels, offering a promising alternative to elevator-based deliveries.

Academic papers are the cornerstone of knowledge dissemination and crucial for researchers’ career development. This is particularly true for rapidly evolving research domains such as transportation, as evidenced by the surge of journals and papers in the past decade. While abundant literature offers guidance on successful publication strategies, insights into the reasons for rejection are rare. This study fills in this gap by examining why papers are rejected in the area of transportation. We present concrete evidence based on data from over 5,000 rejected transport papers. Quantitative analyses are conducted to reveal the impacts of similarity rate, duplication submission rate, and topic on desk rejections. Additionally, we shed light on the distinct focus reviewers have when serving different journals. We hope the results could equip transport researchers with a deeper comprehension of publication criteria and a better awareness of common but avoidable mistakes.

This paper presents a spatially formulated cooperative dynamic mandatory connected automated vehicle (CAV) lane-changing and car-following approach on curved highways with the assistance of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. This work proposes mandatory lane-changing control in a spatial domain to accomplish car-following and lane-changing efficiency in a systematic manner. This control technique initially creates a virtual CAV car-following lane by assigning CAVs sequential numbers based on their spatial position. On this basis, a multi-objective model predictive control (MPC) strategy in the spatial domain is designed to optimize the trajectories in a rolling horizon fashion in order to maintain the inter-vehicle spacing and speed difference while simultaneously satisfying collision avoidances, traffic regulations, and vehicle kinematics constraints. Multi-scenario numerical simulations are conducted to validate the control efficacy of our technique.

Despite significant progress in autonomous vehicles (AVs), the development of driving policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully explored. In this paper, we propose an enhanced human-in-the-loop reinforcement learning method, termed the Human as AI mentor-based deep reinforcement learning (HAIM-DRL) framework, which facilitates safe and efficient autonomous driving in mixed traffic platoon. Drawing inspiration from the human learning process, we first introduce an innovative learning paradigm that effectively injects human intelligence into AI, termed Human as AI mentor (HAIM). In this paradigm, the human expert serves as a mentor to the AI agent. While allowing the agent to sufficiently explore uncertain environments, the human expert can take control in dangerous situations and demonstrate correct actions to avoid potential accidents. On the other hand, the agent could be guided to minimize traffic flow disturbance, thereby optimizing traffic flow efficiency. In detail, HAIM-DRL leverages data collected from free exploration and partial human demonstrations as its two training sources. Remarkably, we circumvent the intricate process of manually designing reward functions; instead, we directly derive proxy state-action values from partial human demonstrations to guide the agents’ policy learning. Additionally, we employ a minimal intervention technique to reduce the human mentor’s cognitive load. Comparative results show that HAIM-DRL outperforms traditional methods in driving safety, sampling efficiency, mitigation of traffic flow disturbance, and generalizability to unseen traffic scenarios.

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.