Multimodal Transportation最新文献

Pedestrians are the most vulnerable road users and are over-represented in casualty statistics, particularly in low- and middle-income countries like Bangladesh. To ensure the safety of pedestrians, it is necessary to identify the factors underlying pedestrian behavior while crossing. Hence, this study aims to predict the pedestrian decision regarding crosswalks using supervised machine learning techniques namely, Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boost (XGBoost). A questionnaire survey was conducted in twelve important locations of Dhaka, Bangladesh using 8 attributes related to crosswalk behavior. Analysis suggests RF model is the most effective in terms of prediction performances, specifically having a 96.00% F1 score and 95.83% MCC value. It has been found that unsuitability of crosswalk location, absence of guard rails on median, and inadequate lightning at night near crosswalks are the most important features for preferring to use crosswalks. The findings of the study will help policymakers and transport planners to plan accordingly in order to develop safe crosswalks.

Studies indicate that the amount of active travel among children is decreasing worldwide. The circumstances around travel modes and routes of children and young adolescents are less known if compared to adults’ walking or cycling. There is a growing interest in mobility patterns of children to school, especially. Active School Travel (AST) is decreasing in developed nations. This study explored which structural elements in the built environment of home and school locations influence AST of Dutch students. We used multinomial logistic regressions to study the effects of built environments on walking and cycling by students in the Netherlands. Findings about the design of environments around homes and schools suggest that variations in personal characteristics, temperature, destination proximity, traffic safety conditions, and urban form can influence AST. Safety conditions seem to play a spurious role in the relationship between built environments and AST, reflecting counterintuitive results if compared to adult travel research. Findings were mixed regarding directionality and significance levels between transport modes and variables, which highlights the importance of considering target groups, modes and locations of students and schools when planning for effective interventions. The findings can be valuable for both academia and policymakers, supporting strategies that could be adopted by municipalities and encouraged by parents or schools.

Terminal towing and barging operations play a critical role in cargo transfer within ports, especially in complex waterways. Scheduling of barges and tugs is influenced by various factors, such as speed, and the operation planning must consider the constraints of channel traffic. Additionally, the transfer system involves constructing and operating berths, transshipment equipment, barges, tugboats, and other resources. Effective scheduling and resource planning are crucial for achieving sustainability. To illustrate this point, we analyze the estuary barging process in a real-world case. We utilize the Anylogic platform to simulate the entire process of ore terminal loading, river transportation, and sea transshipment. Furthermore, we employ simulation-based optimization methods to enhance the system’s performance. Our case study has yielded valuable management insights, including: (1) Key variables related to shipping speeds and resource allocation are identified and optimized; (2) A specific tug dispatch rule has been verified to outperform other options in terms of efficiency and emissions; (3) The potential benefits of constructing and enabling an additional terminal have been identified. Our exploration provides quantitative support for decision-making related to vessel scheduling and resource planning in engineering projects for ore terminals.

In recent decades, transit-oriented development strategies have been widely applied in different countries around the world, including China. There is a mutually promoting relationship between urban land use (activities) and traffic (accessibility) in TODs. However, some TODs cannot achieve this process, which is beneficial to their development. This research evaluated the relationship between accessibility and activities in TODs by applying the SBM model and the super-efficiency SBM model. Hefei City was the case study. The results can identify the TODs whose activities and accessibility cannot promote the development of each other and should be prioritized in future planning. There are two types of priority TODs from the analysis: “Undersupplied TODs” and “Underdeveloped TODs.” In addition, this method can sort TODs according to the efficiency with which it achieves the above relationship. Identifying priority TODs would be helpful for planners to find weak TODs in the TOD network quickly.

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.