Multimodal Transportation最新文献

In recent years, new concepts such as synchromodality have emerged to help carriers better leverage existing capacities and assets to achieve environmental and socio-economic sustainability. Synchromodality is a vast concept. It involves the intelligent utilization of various transport modes. Its main objective is to enhance the freedom and flexibility to switch between transport modes at transport network nodes. The emergence of synchromodality can be facilitated by optimization and simulation models associated with a sharing web service for decision-making. This article studies the concept of synchromodality in the scientific literature and highlights approaches using simulation and optimization techniques. The major challenge of this study lies in the effective implementation of synchromodality concept in practice, while respecting the instructions and constraints set by freight transport stakeholders from a more generic point of view. For that, we present an implementation of the modal shift on the Seine Axis Corridor. A simulation-optimization framework is proposed to generate reliable transport solutions based on the user preferences and environmental considerations. Finally, we resort to sensitivity analyses to assess the impact of variation of service times.

This paper investigates a widely discussed class of charging station location problems for the en-route charging need of electric vehicles traveling in intercity highway networks. Due to the necessity for multiple charges along an intercity long-haul trip, this type of charging station location problems implies such an individual behavior that electric vehicle drivers make self-optimal route-and-charge decisions while ensuring the driving range of their vehicles to sustain trips without running out of charge. The main contribution of this paper is on analytically and computationally comparing the modeling and solution methods for the charging station location problems within uncongested and congested networks. Two distinct modeling frameworks are presented and analyzed: A metanetwork-based two-stage model for uncongested networks and a network-based bi-level model for congested networks. Both models are tackled by the classic branch-and-bound algorithm, which, however, resorts to different problem decomposition schemes, subregion bounding strategies, and network flow evaluation methods. Specifically, for uncongested networks, a two-phase procedure first employs a bi-criterion label-correcting algorithm for constructing a metanetwork and then implements the branch-and-bound algorithm on the metanetwork embedding a single-criterion label-setting algorithm for deriving network flows; on the other hand, for congested networks, the branch-and-bound algorithm is directly applied on the original network encapsulating a convex combinations method for deriving network flows. Finally, the two network scenarios and their modeling and solution methods are quantitatively evaluated with two real-world highway networks, in terms of implementation complexity, solution efficiency, and routing behavior.

The main aim of this study was to propose a comprehensive risk indicator to identify the potential driving risk caused by the changing environment at tunnel entrance. Driving decisions are affected by many external factors, especially at the entrance of tunnels. However, driving indicators are mostly considering vehicle movement status currently. In this study, a safe potential field model including obstacle potential field, vehicle potential field and lighting potential field is constructed to evaluate influence of roads, drivers, vehicles, and change lighting conditions on driving risk. Furthermore, considering the driving risk distribution and its temporal change rate, a comprehensive driving risk indicator (CDRI) was established to evaluate the magnitude of driving risk. Finally, the comparison between CDRI and the other two classic risk indicators indicates that the CDRI proposed in this paper has a better performance in the safety assessment at tunnel entrance. It is expected that the finding in this study could be valuable in developing control and measures for in-tunnel driving risk declining.

The industry of freight transport is recognized as one of the most important sectors for sustainable economic development, both on a regional and global scale. Although significant research has been produced for modeling demand for freight cargo, the incorporation of multimodality, connectivity, and proximity still needs to be further advanced supported by recent methodological advances. Concentrating on the close relationship of freight activity with the national economy, transport infrastructure, and the social context, a multi-dimensional approach should be considered for capturing and interpreting the dynamics of freight demand and services. Taking into account the spatial and temporal integration of regional characteristics into a coherent model may accurately reveal latent perspectives of freight demand that other approaches are not designed to capture. In the current paper, a robust model able to incorporate the multiple dimensions of freight demand at a regional scale, into one Spatio-temporal model form is developed and proposed for future spatio-temporal analyses. To achieve this, an extended form of the Spatial Autoregressive (SAR) model has been developed, estimated as the Linear Mixed Effect (LME) model, and named the Spatio-Temporal Linear Mixed Effect (STLME) model. The implementation has been applied to the European region for 5 years, providing valuable evidence on the factors that mostly affect freight demand. The results of this paper provide significant information on the spatial and temporal dynamics of the phenomenon.

The rise of the sharing economy in recent years led to changes in transportation service delivery, including the introduction of micromobility services. Case studies are needed to better understand determinants of micromobility mode choice and its impacts on transportation operations. This study used data from a micromobility pilot program in Birmingham, Alabama to analyze spatiotemporal demand variations and explore correlations between micromobility ridership and demographic characteristics and land use patterns. Using space-time pattern mining techniques, temporal and spatial variations in micromobility usage were confirmed, with peak usage observed on Fridays, Saturdays and Sundays, during afternoon and evening hours, and during warmer months. Spatial analysis employed Kernel Density techniques and revealed concentrated micromobility trip origins in high-density areas such as Railroad Park, downtown, the University of Alabama at Birmingham (UAB) campus, and the Five Points South neighborhood. Correlations between Birmingham micromobility ridership and demographic characteristics and land use patterns were studied using clustering approaches and a multilevel negative binomial model. The model identified significant positive associations between micromobility ridership and the younger population (18–34 years of age), with a negative association in the 45–54 age group, signaling a decline in usage among older individuals. Regarding land uses, the model results showed significant positive correlations with the presence of park areas and commercial, residential, and industrial land uses, and the university campus. Furthermore, a positive correlation was observed with the National Walkability Index and parking facilities, whereas increased distance from the city center was associated with reduced micromobility ridership. The study offers valuable insights that can assist decision and policymakers in Birmingham as well as other medium-sized cities, in planning, and implementing micromobility programs that serve the local needs.

Decision on travel choices in dynamic multimodal transportation networks is non-trivial. In this paper, we tackle this problem by proposing a new joint travel mode and departure time choice (JTMDTC) model based on deep reinforcement learning (DRL). The objective of the model is to maximize individuals travel utilities across multiple days, which is accomplished by establishing a problem-specific Markov decision process to characterize the multi-day JTMDTC, and developing a customized Deep Q-Network as the resolution scheme. To render the approach applicable to many individuals with travel decision-making requests, a clustering method is integrated with DRL to obtain representative individuals for model training, thus resulting in an elegant and computationally efficient approach. Extensive numerical experiments based on multimodal microscopic traffic simulation are conducted in a real-world network of Suzhou, China to demonstrate the effectiveness of the proposed approach. The results indicate that the proposed approach is able to make (near-)optimal JTMDTC for different individuals in complex traffic environments, that it consistently yields higher travel utilities compared with other alternatives, and that it is robust to different model parameter changes.

Ships are traditionally powered by fossil fuels such as heavy fuel oil (HFO) and marine diesel oil (MDO), where the emissions, such as particulates, hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NO_X) and carbon dioxide (CO₂), negatively affect the environment and human health. The International Maritime Organization (IMO) encourages shipping companies to use liquefied natural gas (LNG), which is a green fuel source to power shipping activities and is easy to store, to replace traditional marine fuels. There are three common methods of LNG bunkering: ship-to-ship, truck-to-ship, and port-to-ship. The objective of this study is to determine the optimal bunkering method at a port using an integer linear programming (ILP) model considering three kinds of costs: fixed, variable, and extra. To find the optimal bunkering method, the three methods and their related constraints are modeled into the ILP model. The results indicate that ship-to-ship is the optimal bunkering method for LNG under the scenario of the port considered. Numerical experiments are conducted to validate model performance and generate managerial insights.

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved.

The concept of connected autonomous shuttles is gaining recognition for its potential to revolutionize traditional public transport by offering a safer and more consistent travel experience. Despite technological advancements facilitating their integration into current traffic systems, research in this area is still exploratory, with public acceptance and trust in autonomous technology posing significant challenges. This paper contributes to the field by validating the benefits of deploying connected autonomous shuttles and examining their impact on ridership via an immersive experience. We introduce a VR-enabled co-simulation strategy to analyze the effects of replacing a traditional bus service line with connected shuttles in Melbourne, Australia. Our case study results reveal that while autonomous shuttles can reduce travel time because of optimized vehicle motion, their traffic efficiency is affected by fleet size, with the optimal fleet size identified as four in the study area. Furthermore, we observed a mismatch between participants’ stated intentions and actual boarding behavior, indicating that the attractive appearance of this novel mobility mode may not necessarily enhance ridership. Our work offers an alternative approach to simulation studies in futuristic public transportation and complement existing literature in the field.