Multimodal Transportation最新文献

This study employs a multi-regional input-output (MRIO) model to measure these emissions and examines the growth and transfer patterns of embodied carbon in the transportation sector across different regions of China. Then we utilize Structure Decomposition Analysis (SDA) to identify the driving factors behind the observed emissions patterns. The findings of the study indicate that the transfer of inter-provincial transportation embodied carbon emissions follows a general trend of shifting from economically developed areas to underdeveloped areas along the industrial chain. The study also highlights that the per capita demand structure plays a crucial role in driving the increase in transportation embodied carbon emissions. On the other hand, the study identifies the direct carbon emission coefficient as a key factor that inhibits the rise in transportation embodied carbon emissions.

The present study proposes a new methodology that combines quantitative and qualitative data for the generation of representative personas for commuters. The profiles can be used to better understand their travel behaviour and mode choices. The research is based on the example of the region of Agder in Norway and aims to overcome the persona development shortcomings identified by previous researchers. Data from a regional travel behaviour survey (N= 1 849) is analysed using latent class cluster analysis (LCCA), and enriched with qualitative input from 32 interviews, and information provided by an expert panel. This results in a set of 20 representative persona profiles for the case study region. The proposed methodology is easily replicable in other urban networks and has the potential to provide insight into the mobility behaviour and needs of specific groups of people in order to adapt the transport services and encourage climate-friendly behaviour.

Growing population, rapid urbanization, and increasing travel demand emphasize the need for reliable public transportation systems and sustainable transportation planning. A reliable bus service fosters a more significant, satisfied, and committed base of users. This research focuses on examining the association between on-time performance (OTP) and road network, demographic, socioeconomic, and land use characteristics to identify relevant external factors for proactive and reliable public transportation system planning. The analysis was conducted at a bus stop level. Bus arrival/departure data from the Charlotte Area Transit System (CATS) was obtained. The road network, demographic, socioeconomic, and land use characteristics were captured within 0.25-mile and 0.50-mile buffers. Pearson correlation analysis was conducted to understand the association between OTP and road network, demographic, socioeconomic, and land use characteristics by day of the week and time of the day. The results show that OTP is associated with external factors such as the number of signalized or cul-de-sac/dead-end intersections, number of lanes, network density, population, income (median and total), and land use types related to residential and commercial/employment purposes within the bus stop vicinity. The findings provide vital insights for transit agencies to enhance scheduling, service and maximize benefits, effectively utilize available resources, plan, and provide equitable services to all riders.

Considering the high ratio of group travellers to individual travellers among air-rail integrated service (ARIS) passengers, the airport arrival behaviors of both individual and group ARIS passengers are thoroughly analyzed and compared with the multinomial logit model and mixed multinomial logit model by taking the group as the decision unit. Compared with individual ARIS passengers, the results show that: first, the airport arrival behaviors of group ARIS passengers should be analyzed separately according to the goodness of fit; second, group ARIS passengers attach more importance to both group sociodemographics and ARIS trip attributes; and third, groups with close member relations and a high ratio of males prefer alternatives with the shortest total ground access time. Finally, group ARIS passengers consistently prefer ARIS trip attributes after conducting group decision-making.

Transit accessibility, the conditions and distance under which people have access to transit services, is one of the key indicators to assess the performance of cities' transit systems. The more people can access the transit system, the better its performance in terms of social equity (e.g., more equal access to jobs, education, and other opportunities). To inform policymakers and support decision-making, it is crucial to measure potential transit accessibility changes of transport investments. Due to the paucity of available data, however, calculating and monitoring transit accessibility is a difficult task. Anchored in SDG 11 for more ‘Sustainable Cities and Communities’, the UN has thus proposed a simplified, globally applicable indicator for the performance of cities’ transit systems (SDG 11.2.1) that measures the share of the population living in a walking distance of 500 m to the transit system. Building on this definition and leveraging open data sources, we analyze potential transit accessibility gains under Mobility-as-a-Service (MaaS) in Metro Manila, Philippines. We show that the integration of paratransit (i.e., jeepneys) into the transit network could almost triple access to transit from 23.9 % to 65.0 %. The integration of micro-mobility (i.e., e-scooter and bicycles) as a feeder mode could further increase this share significantly (to 97.9 % and 99.9 %, respectively). We outline and discuss evidence-based policy recommendations to exploit this potential and foster a sustainable development under MaaS. Finally, we conclude with a research agenda for micro-mobility and MaaS in developing countries, a topic which has been widely overlooked in the scientific literature so far.

We propose to solve a real-time traffic variation of the On-Demand Bus Routing Problem (ODBRP) introduced by Melis and Sörensen (2022). The ODBRP belongs to the category of the Dial-A-Ride Problems (DARP), and features departure and arrival bus station selection. This problem is specifically aimed at planning a fleet of on-demand buses in an urban environment. However, cities are frequently plagued by traffic congestion, which may cause delays and missed time windows for passengers.

To deal with this situation, we introduce, study, and solve a variant of the ODBRP in which the travel times are both time-dependent (i.e., the travel time between two nodes depends on the departure time) and updated dynamically.

In our approach, congested roads that might cause passenger delays are modeled by frequently updating the travel speed on the road segments that constitute them. The resulting problem is solved by using a K-shortest-path procedure to determine alternative paths between bus stations, as well as a Variable Neighborhood Descent (VND) procedure to repair violated time windows.

Our experimental results show the overall efficacy of this real-time control under divergent degrees of flexibility (congestion and the number of buses available). Specifically, the average tardiness, maximum tardiness, and the number of late passengers are significantly reduced under a wide range of congestion scenarios, from slight to severe. In addition, this efficacy holds for various ratios of requests to the number of vehicles.

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.