Multimodal Transportation最新文献

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.

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.

This literature survey aims to provide a broad view of studies on work from home (WFH) and its effect on travel behaviour published from 1975 to 5 February 2024 by employing bibliometric mapping in VOSviewer. The cluster analysis was deployed to identify collaboration among authors, institutions, and journals, the most co-cited articles and journals, and study terms co-occurrence. The detailed movement of information in each study was visualised using Sankey diagrams. This literature survey was conducted to develop the relationship between WFH and transport-related parameters, including travel behaviour, trip distance, housing location and land use management and to assess WFH's direct and indirect effects on household trips. The results revealed that most studies used questionnaire surveys for data collection and regression models for analysis. The least targeted parameters were home relocation, free choice of WFH, ICT, the effect of WFH on other household member trips per day, cross-country research, trip chaining, employers' perspectives on WFH, exact working location instead of home, and preferences of essential workers. The findings can assist researchers in identifying influential authors or institutions for future collaborations and the combination of parameters and future research directions that can be explored.

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.

Refueling decisions of liner ships are facing challenges from both fuel price fluctuations and carbon emission constraints. This paper proposes a multistage stochastic programming model to tackle the refueling problem for dual-fuel ships under carbon intensity indicator (CII) rating limit and carbon tax costs. The model also takes into account various factors, including fuel consumption of main and auxiliary engines, fuel availability at ports of call, and fuel price fluctuations. The proposed model is solved using scenario size selection and moment matching methods, and a greedy heuristic algorithm is adopted to speed up the process. Managerial insights are obtained from multinomial logistic regression and sensitivity analyses. Our numerical results reveal that low sulfur fuel oil (LSFO) refueling decisions are closely linked to the difference of LSFO and liquefied natural gas (LNG) fuel prices and that LSFO becomes more attractive when the variance of LSFO fuel price or the LNG availability decreases. Besides, carbon emission costs are found to become a true consideration when carbon taxes exceed a certain threshold. These insights can help practitioners better understand the coupling influence of carbon emissions and fuel price fluctuations on the ship refueling problem.

Park-and-Ride (P&R) facility is crucial in urban transit access and delineation of market area assists in determining its service extent. It is defined with a standardized shape but may be influenced by travel time and mode. This study examines the distinction in the market area for cars and 2-wheelers at different times on weekdays and weekends. Results show a parabolic shape, influenced by travel time, mode, and day of the week in different extents. The market area of the 2-wheelers is smaller than cars, further reduced on weekends. The proposed approach aids planners and operators in accurately defining P&R market areas.

Bicycle sharing has grown rapidly since the 2000s, but there is a lack of thorough retrospective analysis performed from a long-term perspective. This paper takes London as its focus, aiming to evaluate the performance of the London cycle hire (LCH) scheme, draw general practical implications for bicycle sharing, and summarize future research directions. This paper reviews the empirical evidence that has appeared in academic literature, policy documents, and technical reports. Issues covered in this review include: (1) LCH users and demand patterns, (2) substitutability and complementarity with other travel modes, (3) public health impacts, (4) interventions that have affected the usage and demand patterns of LCH, and (5) the impacts of COVID-19. Overall, LCH has achieved its primary goals of promoting cycling and has also brought benefits to public health and urban transportation resilience, and yet some minor problems persist. Practical implications for the implementation, operation, and evaluation of bicycle sharing schemes are offered based on our collection of evidence.

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.

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.