Multimodal Transportation最新文献

Blocked roads can jeopardize emergency response activities in the aftermath of disasters, such as earthquakes. In many cases, the distribution of relief supplies to affected communities, the evacuation of victims, and Search and Rescue activities cannot be resumed due to the lack of connectivity of the road network. The multi-vehicle prize collecting arc routing for connectivity problem (KPC-ARCP) aims to determine the routes of synchronized road clearing teams during the immediate response of a disaster, to reconnect the network so that the utility of the reconnection is maximized. This paper proposes an Ant Colony Optimization (ACO) algorithm to solve the KPC-ARCP and compares its performance to results of earlier studies which apply GRASP and Matheuristic methods to solve the problem. Performance comparisons consider the computation time and accuracy of the solution, and are implemented on academic and real cases in Istanbul. The runs on academic and real-world instances indicate that ACO has a reasonable performance compared to the existing methods. However, the high complexity of its parameter tuning suggests that GRASP is likely more suitable for KPC-ARCP.

This study proposes utility-based accessibility measures and a weibit-based analysis method for analyzing multi-modal transportation network vulnerability. To account for the multi-dimensional travel choice behavior in multi-modal transportation networks, an advanced weibit-based combined modal split and traffic assignment model is adopted to derive the accessibility measures based on the weibit-expected travel disutility from both route choice and mode choice dimensions. The accessibility at the modal split level is derived from the nested weibit model, while that at the route choice level is obtained based on the path-size weibit model. The proposed weibit-based vulnerability measures are inherently suitable for assessing the relative degradation in network performance and can measure the vulnerability of networks with heterogeneous scales without overestimating the importance of modes or routes sharing similar features. Using a simplified multi-modal transportation network of Hong Kong, numerical experiments are conducted to demonstrate the properties of the proposed multi-modal vulnerability analysis. The results indicate that the proposed analysis can effectively address similarity and heterogeneity issues in the analysis of choice behavior and network vulnerability, which are often ignored by the traditional logit-based measures.

The Highway Capacity Manual (HCM) recommends the use of passenger car equivalent (PCE) to account for the influence of trucks on the operational performance of roads. Researchers have derived PCE based on capacity, headway, queue discharge, and delay in the past. However, it is not clear if the influence of trucks on the operational performance of roads would be the same in terms of travel time. The focus of this research is to assess the influence of trucks on the transportation system performance from a travel time perspective. The travel time data for Mecklenburg County (urban area) and Iredell County (rural area) were gathered for the year 2017 and processed to generate sixty-nine datasets accounting for the area type, temporal variation, reference speed, and traffic condition. Ordinary Least Square (OLS) regression models were then developed to examine the relationship between the travel time of trucks (dependent variable) and the travel time of passenger cars (explanatory variable). The coefficients indicate that the average travel time (ATT) of trucks is greater than the ATT of passenger cars irrespective of the dataset used for analysis, except in two cases of Iredell County. The ATT of trucks could be up to 1.18 times the ATT of passenger cars in some cases. The area type, day-of-the-week (DOW), time-of-the-day (TOD), reference speed of the link, and traffic condition at the time of data collection all seem to influence the ATT of trucks at different scales compared to the ATT of passenger cars.

The COVID-19 pandemic is posing significant challenges to public transport operators by drastically reducing demand while also requiring them to implement measures that minimize risks to the health of the passengers. While the collective scientific understanding of the SARS-CoV-2 virus and COVID-19 pandemic are rapidly increasing, currently there is a lack of understanding of how the COVID-19 relates to public transport operations. This article presents a comprehensive survey of the current research on COVID-19 transmission mechanisms and how they relate to public transport. We critically assess literature through a lens of disaster management and survey the main transmission mechanisms, forecasting, risks, mitigation, and prevention mechanisms. Social distancing and control on passenger density are found to be the most effective mechanisms. Computing and digital technology can support risk control. Based on our survey, we draw guidelines for public transport operators and highlight open research challenges to establish a research roadmap for the path forward.

Cities across the world are expanding as a result of urbanization, resulting in growing demand for goods and services, mainly through online sales. This has resulted in the need for an urban consolidation center (UCC) to minimize travel and optimize deliveries, particularly in student accommodation settings. In this paper, we examine how an optimal consolidation center can improve urban freight transport. We conducted a multi-method and in-depth case study of student halls of residence within a UK higher education institution. In addition, we analyzed a rich secondary dataset including location, parcel, and vehicle data. We developed and tested three scenarios to present an optimal urban consolidation scenarios model using a Vehicle Routing Problem (VRP) solver; indicating the transportation costs, distance travelled, and CO₂ emitted. The operationalization of the optimal UCC as well as the limitations on direct and indirect cost, accessibility, and collaboration are discussed. Our findings show the impact of having an ideal pickup point that would give students the ability to collect their parcels effortlessly and make the return of goods easier for students. As such, we extend extant studies on the implications for urban consolidation center theory and practice.