Multimodal Transportation最新文献

Mobility is an indispensable part of modern human societies, but the dominance of motorized individual traffic (MIV, i.e., the private car) leads to a prohibitive waste of energy as well as other resources. Public transportation with line services, such as light rail, can pool many more passengers, thereby saving resources, but often is less convenient (longer transit times). Door-to-door shuttle services, on the other hand, are convenient but have a limited pooling efficiency due to detours scaling with shuttle occupancy. Combining line services with a fleet of shared shuttles in an integrated so-called bi-modal system may provide on-demand door-to-door service at a service level superior to current public transport with significantly less resource consumption than MIV. Here we introduce a generic model of bi-modal public transit and characterize its critical parameters of operation. We identify the conflicting objectives for optimization, i.e., user convenience and energy consumption, and evaluate the system’s performance in terms of Pareto fronts. By means of simulation and analytical theory, we find that energy consumption can be as low as 20% of MIV, at line service densities typically found in real settings. Road traffic can be reduced to less than 10% of MIV. Surprisingly, we find favorable performance not only in urban, but also in rural settings. We thereby provide a possible answer to the pressing question of designing sustainable future mobility solutions.

To give full play to the role of fatigue supervision of intelligent monitoring platforms, We consider the shortage of the traditional management model of enterprises. The management game model of enterprises and drivers is built from the benefits of drivers. Taking 79 drivers of enterprise A in Jiaozuo City as an example, the number of fatigue violations of each driver in each of the six consecutive months was counted. Combined with the system clustering method, the drivers are classified according to the trend of the number of violations. Finally, different regulatory measures were proposed for different categories of drivers according to the evolution of the regulatory game system. The model evolution simulation results show that when the cost paid by the driver for violating the law (c) is greater than the additional benefit generated by the violation (d), the driver will choose not to drive fatigued to protect his benefits. The classification results show that drivers can be divided into four categories:① class no fatigue violation records; ② class fatigue violation records show a downward trend; ③ class fatigue violation records show wavy changes, indicating repeated violations; ④ class fatigue violation records show an upward trend. The number of violations varies for different categories of drivers. The d increases as the number of violations increases. Therefore, different management measures are proposed to increase c for the 4-type drivers so that the parameters of each type of driver satisfy the range of values of c>d. Thus, the driver evolves in the direction of no-fatigue driving. It can effectively regulate fatigue driving and improve driving safety.

Predicting passenger flow within a city is crucial for intelligent transportation management systems, especially in the context of urban development, post-pandemic policy changes, and infrastructure improvements. Traditional macro models have limitations in accurately capturing the complex structure of real traffic flows, and recent advancements in machine learning offer promising approaches for improving transportation simulations. This research aims to compare the effectiveness of traditional simulation models with a selective machine learning (ML) model for traffic flow prediction in Oslo, Norway. Sensitivity and scenario analyses are conducted to examine the models’ parameters and derive the city’s characteristics. Results substantiate that the traditional Spatial Interaction model (SIM), although interpretable and requiring fewer parameters, has limitations in accurately capturing real flow structures and exhibits greater variability compared to the ML model. Statistical analyses support these findings and raise questions about the validity of the ML model’s results over the SIM. The research highlights the potential of ML models to identify trends in passenger flows and simulate traffic flows in different scenarios related to city development. Overall, the research presents a decision support system for planners and policymakers to predict traffic flow accurately and efficiently. It highlights the benefits and drawbacks of both the traditional SIM and ML models, contributing to the ongoing discussion of the role of machine learning in transportation modeling.

Despite spending billions of dollars on urban transport infrastructure, Malaysia has a poor reputation for its public transit service. Consequently, the majority of urban residents continue to rely on their cars. Hence, this paper aims to understand commuters’ intention to switch from private vehicles to public transit and spread word-of-mouth (WOM) about its services and assess the roles of satisfaction and service quality as antecedents. A cross-sectional survey of 421 rail and bus passengers was undertaken in Kuala Lumpur's urban and sub-urban areas, and the data was analyzed using SmartPLS. According to the data, empathy is the strongest predictor of satisfaction with transit services. Except for reliability, the service quality characteristics influence satisfaction and yield positive indirect effects on both switching intention and WOM. Transit operators and planners should take into account that providing a pleasant and hassle-free riding experience has a substantial impact on commuter satisfaction and long-term commitment. This study contributed to the body of knowledge by exploring the intervention role of satisfaction in the relationship between service quality and switching intention, as well as WOM.

China has taken a number of positive measures to meet the requirement of environmental protection. The switch to electricity especially in transport sector is considered as a promising way to reducing greenhouse gas (GHG) emissions and facilitating to meet China's carbon neutral target in 2060. Besides, because of the overall impact of the COVID-19 on the transport sector, the future measures imposed by the government on electric vehicles (EVs) remain in high uncertainty. Taking the characteristics of different vehicles, business models, uncertainty of government financial subsidies and environmental factors into consideration, a replacement optimization model for a taxi fleet is proposed in this study under the cap-and-trade system. We assume that the taxi company has four types of vehicles to purchase or lease and manage to maximum the pecuniary advantages and environmental benefits simultaneously. Experimental results analyze that EVs and battery-swap electric vehicles (BSVs) are highly competitive when government subsidies do not decline. During the early stage of the planning horizon, adjusting the fleet continuously and timely can help the company to realizing the maximum revenue.

In recent decades, increasing emphasis is being placed on sustainable freight transport. The aim of this study is to discern how detailed the calculation of CO₂e emissions in road transport is. For this, the study conducted a sample review with 53 scientific sources and 121 annual reports. Only 14 out of the 53 scientific sources incorporated emissions that occur as a by-product of energy supply. Not more than 14 of the 121 companies included emissions from transportation activities of both the upstream and downstream processes. Therefore, the study highlights the necessity for new and validated models to support decision-making processes that sustainably reduce CO₂e emissions.

This study examines three methods of collaboration and profit-sharing for less-than-truckload (LTL) carriers. The collaboration methods are evaluated under the scenario that all participating carriers share some or all of their pickup and delivery jobs with a central authority who will assign jobs to carriers and determine optimal vehicle routes to maximize profit. The novelty of this work is that it allows carriers to retain some of their jobs. Collaboration Method 1 is a two-step approach where the first step involves the central authority determining the job allocation for the shared jobs and the vehicle routes for each carrier that includes their retained and allocated jobs to maximize total profit. The second step involves dividing the total profit among the carriers using a contribution-based profit-sharing model. Collaboration Method 2 is a one-step approach where the central authority simultaneously determines the job allocation and vehicle routes, and at the same time allocates profit to the carriers with fairness constraints included in the model. Collaboration Method 3 is also a two-step approach similar to Method 1, except that in the first step, the central authority determines the job allocation and vehicle routes for only the shared jobs (not including retained jobs). Mathematical models and solution algorithms based on large neighborhood search (LNS) are proposed for all three methods. The numerical experiments are conducted using hypothetical networks with up to 30 jobs and 3 carriers. Results indicate that on an average the total profit from Method 1 is 5.3% higher than that of Method 2 and 11.88% higher than that of Method 3. The total profit from Method 2 is 6.60% higher than that of Method 3.

New mobility concepts are needed as cities become ever more congested and polluted by traffic. One factor for the growing traffic volume is the increasing delivery traffic leading to a need for sustainable concepts for urban logistics and last-mile delivery. Innovative vehicle concepts, like automated micro-vehicles, present one promising approach. However, not much is yet known about drivers and barriers for the acceptance of these vehicles from the perspective of other road users – i.e. those people sharing traffic space with automated micro-vehicles. In two consecutive animation-based experiments (n = 107, n = 543), we analysed the effect of varying behaviour and design of automated micro-vehicles on other road users’ risk perception, trust, and acceptance. Speed, braking behaviour, automation mode, number of vehicles in convoy, distance between vehicles as well as colour design significantly affect risk perceptions, trust, and partly acceptance. The results inform the development and regulation of automated micro-vehicles.

Ride pooling (RP) is a transport mode using on-demand buses to combine the trips of multiple users into one vehicle. Its required fleet size and carbon emissions are quantified by the system’s efficiency. Due to the complex interplay between street network, buses, users and dispatch algorithm, efficiency case studies are available but bottom-up predictions are not. Here we close this gap using probabilistic and geometric arguments in an analytical model framework. Its modular design allows for adaptation to specific usage scenarios and provides an over-arching view of them. In a showcase on Euclidean spaces, our model quantifies how RP outperforms private cars as user demand increases. Its predicted power-law scaling is verified using a custom simulation framework, which further reveals improved scaling on real street networks and graphs with hierarchical structures. Henceforth, our work may help to identify street networks well-suited for RP, and predict key performance indicators analytically.

Several Canadian cities observed a shift from public transit use to private cars and active transport modes during the COVID-19 pandemic. At a moment where pre-pandemic public transit users are reconsidering their travel options, studies describing their attitudes toward cycling are lacking. Because most trips in urban areas involve short- and mid-range travel, cycling is seen as a promising environmentally sustainable means of transportation. This study aims to describe how pre-pandemic public transit users in Toronto and Vancouver view cycling, including their comfort with available infrastructure, cycling frequency, and perceived barriers to adoption. Data from the Public Transit and COVID-19 Survey, a web-based panel survey of over 3500 regular transit riders in Toronto and Vancouver administered in May 2020 and April 2021 were analysed. Applying Geller's typology, 70% of participants could be classified as interested but concerned and one fifth as no way no how regarding their comfort levels toward cycling. Women were more likely to be no way no how cyclist type. Weather, lack of safe routes, and having to carry things were the main barriers to cycling in both cities. Our results give insight on who should be targeted by city initiatives aiming to promote changes toward more active modes of transportation. Further studies with a causal design are required to identify possible mitigating strategies to the main barriers to cycling.