Multiple Transportation Modes Research Articles

Assessment of Sustainable Mobility Patterns of University Students: Case of Cameroon

The transition to sustainable mobility is a recognized socio-economic and environmental challenge, particularly among young adults. In addressing the gap in the literature on young adults’ travel behaviors, especially in Cameroon, this paper investigates the transport mode choices, influencing factors, and barriers to sustainable mobility of students at the National Advanced School of Public Works, Yaoundé (NASPW). Data were collected through online questionnaires with 360 valid responses. Findings revealed that most students used multiple modes of transport for commuting, with moto-taxis being the most common. Accessibility, vehicle speed, and flexibility appeared as the most important reasons for the preferred transport modes, while driver’s license possession, safety perceptions, speed, and proximity were significant predictors for mode choice. Demographic factors were found to influence transport preferences, with distinct clusters prioritizing different aspects. Barriers to public transport were primarily long waiting times and congestion, while active mobility was hindered by distance, infrastructure, and weather. The usage of public transportation was encouraged by its affordability and reduced travel time, whilst active options were preferred due to cost savings and health benefits. To promote sustainable mobility for campus travel, it is crucial to encourage active modes, develop mass transport systems, and raise awareness through symposia and conferences among students and staff.

Optimal multimodal multi-echelon vaccine distribution network design for low and medium-income countries with manufacturing infrastructure during healthcare emergencies

In the event of routine immunizations for containing disease outbreaks, the Expanded Programme on Immunization (EPI) led Low and Medium-Income Countries (LMICs) follow a four-tier distribution network. Such a network is suitable for LMICs importing vaccines rather than manufacturing and may not be suitable for healthcare emergencies. Recent shift from routine immunizations to pandemic-orientation, calls for large-scale multimodal distribution models. This paper proposes a cold chain network for LMICs with manufacturing hubs during health emergencies, integrating vaccine manufacturers and incorporating multiple transportation modes. The problem is formulated as a Mixed Integer Quadratically Constrained Programming (MIQCP) problem, to minimize ‘Cumulative Transportation Time’ (CTT) with the minimal number of trips. To solve large-scale problems the paper also proposes a novel algorithm that divides certain parts of the network and retains the edges that are likely to give better solutions. The proposed model extends the existing body of the literature by introducing features such as vehicle choice between locations, generalized hierarchical structure, network decomposition, and appropriateness with LMIC networks. We conduct numerical experiments to validate the proposed algorithm using data from India. The results show that the proposed model saves cumulative transportation time along the optimally generated network when compared with the actual vaccine supply network in India during healthcare emergencies. The proposed algorithm is also faster than the formulation giving comparable results while solved in a commercial solver.

Two-echelon multi-commodity multimodal vehicle routing problem considering user heterogeneity in city logistics

The considerable increase in parcel deliveries has negatively impacted the accessibility and livability of cities. One solution strategy is to decouple short-distance from long-distance shipping so that last-mile transport can be performed with low-footprint vehicles. Such solutions are referred to in the literature as multi-echelon distribution systems. This study introduced a new variant of the two-echelon vehicle routing problem that considers multiple alternative transport modes as well as multiple commodities over a multi-period time horizon, where customers can obtain their commodities from any store. We referred to this problem as the two-echelon multi-commodity multimodal vehicle routing problem (MCM-2E-VRP). The objective of service providers is to minimize total generalized costs while satisfying customer requirements. We formulated this as a mathematical model based on a space–time network and introduced a random utility discrete choice model to capture variations in performance and preferences. We developed an adaptive large-neighborhood search (ALNS) algorithm to provide solutions for newly generated MCM-2E-VRP instances based on the Beijing Yizhuang transportation network. Extensive numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. A sensitivity analysis revealed some policy-relevant findings regarding the effects of store distribution and vehicle capacity.

Learning spatio-temporal dynamics on mobility networks for adaptation to open-world events

As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal dynamics modeling on mobility networks is a challenging task particularly considering scenarios where open-world events drive mobility behavior deviated from the routines. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes on mobility networks, nor adaptive to unprecedented volatility brought by potential open-world events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of open-world events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. What is more, experiments show generalization ability of EAST-Net to perform zero-shot inference over different open-world events that have not been seen.

Development of a Variable Multimodal Balanced Floating Catchment Area Approach for Spatial Accessibility Assessment

Rapid urbanization and expansion, stemming from demographic growth and migration from rural areas to urban centers, have heavily strained cities in recent years. These circumstances have created an ever‐growing need for equipment and essential services. On the other hand, previous research has shown that accessibility measurement is a powerful technique for assessing urban compactness. This assessment arises from the willingness of urban planners to develop transport services and land use across various cities globally. This paper addresses the computational problem of spatial accessibility, focusing on the influence of private cars versus public transport. We introduced a metric that enhances the Balanced Floating Catchment Area (BFCA) index. Our metric not only considers multiple transportation modes in the calculation of spatial accessibility but also takes into account variable catchment sizes. We applied our metric in a case study examining spatial accessibility to public hospitals in Casablanca. The results provide a geographic breakdown of each transportation mode, and the accessibility of different scenarios has been compared.

Understanding information needs for seamless intermodal transportation: Evidence from Germany

Cities worldwide are seeking to enhance their sustainable mobility by reducing individual motorized transportation. While intermodal mobility – combining multiple transportation modes in one journey – is a key solution, individuals encounter challenges initiating intermodal journeys owing to the proliferation of mobility services. Providing accurate information at the right time is crucial amidst this complexity. While research has examined information needs for each mobility mode independently, the relationships between modes, phases, and information needs have barely been empirically investigated. Through a sequential mixed-method approach involving a literature review and a survey of >500 participants, this study identifies and validates the concept of phase- and mode chain-sensitive information needs. The findings provide initial insights, emphasizing phase relationships, mode chain relationships, and the interplays between phases and mode chains — a holistic understanding. This research can guide the design of more effective traveler information systems, aiding the shift toward sustainable urban mobility.

Multimodal transportation routing optimization based on multi-objective Q-learning under time uncertainty

Multimodal transportation is a modern way of cargo transportation. With the increasing demand for cargo transportation, higher requirements are being placed on multimodal transportation multi-objective routing optimization. In multimodal transportation multi-objective routing optimization, in response to the limitations of classical algorithms in solving large-scale problems with multiple nodes and modes of transport, the limitations of directed transportation networks in the application, and the uncertainty of transport time, this paper proposes an optimization framework based on multi-objective weighted sum Q-learning, combined with the proposed undirected multiple-node network, and characterizes the uncertainty of time with a positively skewed distribution. The undirected multiple-node transportation network can better simulate cargo transportation and characterize transfer information, facilitate the modification of origin and destination, and avoid suboptimal solutions due to the manual setting of wrong route directions. The network is combined with weighted sum Q-learning to solve multimodal transportation multi-objective routing optimization problems faster and better. When modeling the uncertainty of transport time, a positively skewed distribution is used. The three objectives of transport cost, carbon emission cost, and transport time were studied and compared with PSO, GA, AFO, NSGA-II, and MOPSO. The experimental results show that compared with PSO, GA, and AFO using a directed transportation network, the proposed method has a significant improvement in optimization results and running time, and the running time is shortened by 26 times. The proposed method can better solve the boundary of the Pareto front and dominate the partial solutions of NSGA-II and MOPSO. The effect of time uncertainty on the performance of the algorithm is more significant in transport orders with high time weight. With the increase in uncertainty, the reliability of the route decreases. The effectiveness of the proposed method is verified.

Last decade of green logistics with multiple modes of transportation: a literature review

Last decade of green logistics with multiple modes of transportation: a literature review

Forced labor and Transportation: Multi-disciplinary perspectives

Transportation plays a key role in combating human trafficking. This paper discusses data from the 2021 National Outreach Survey for Transportation (NOST), from the first ever United States Department of Transportation Combating Human Trafficking in Transportation Award winning project. Survey data collected from transportation personnel, victim service providers, and trafficking survivors reinforces the documented perceptions, limitations, and challenges in understanding and combating forced labor discussed among frontline workers. Among 173 respondents working in various state DOT construction units, only 26 % feel slightly or very confident (19 % and 7 %, respectively) about identifying labor trafficking in their work environment. Participating transportation workers confirm specific training topics that may help to identify forced labor proximate to the work environment or within the community, transportation-related venues, and events. Respondents from multiple transport modes also provide valuable suggestions on how personnel and industry leaders can help combat human trafficking in supply chains. For the primarily U.S.-based transportation respondents, the most suggested strategy for encouraging companies to use products free from forced labor in their supply chains was high penalties (1,229 respondents), followed by loss of tax benefits (850 respondents) (total n = 1,766).

Giving birth on the way to the clinic: undocumented migrant women’s perceptions and experiences of maternal healthcare accessibility along the Thailand–Myanmar border

BackgroundMillions of women give birth annually without the support of a trained birth attendant. Generally and globally, countries provide maternal health services for their citizens but there is a coverage gap for undocumented migrant women who often can’t access the same care due to their legal status. The objective of this investigation is to explore undocumented migrants’ experiences and perceptions of maternal healthcare accessibility.MethodsWe held focus groups discussions with 64 pregnant women at 3 migrant health clinics on the Thailand–Myanmar border and asked how they learned about the clinic, their health care options, travel and past experiences with birth services. In this context undocumented women could sign up for migrant health insurance at the clinic that would allow them to be referred for tertiary care at government hospitals if needed.ResultsWomen learned about care options through a network approach often relying on information from community members and trusted care providers. For many, choice of alternate care was limited by lack of antenatal care services close to their homes, limited knowledge of other services and inability to pay fees associated with hospital care. Women travelled up to 4 h to get to the clinic by foot, bicycle, tractor, motorcycle or car, sometimes using multiple modes of transport. Journeys from the Myanmar side of the border were sometimes complicated by nighttime border crossing closures, limited transport and heavy rain.ConclusionsUndocumented migrant women in our study experienced a type of conditional or variable accessibility where time of day, transport and weather needed to align with the onset of labour to ensure that they could get to the migrant clinic on time to give birth. We anticipate that undocumented migrants in other countries may also experience conditional accessibility to birth care, especially where travel is necessary due to limited local services. Care providers may improve opportunities for undocumented pregnant women to access maternal care by disseminating information on available services through informal networks and addressing travel barriers through mobile services and other travel supports.Trial registration The research project was approved by Research Ethics Committee at the Faculty of Medicine, Chiang Mai University (FAM-2560-05204), and the Department of Community Medicine and Global Health at the University of Oslo—Norwegian Centre for Research Data (58542).

Data-driven robust optimization of dual-channel closed-loop supply chain network design considering uncertain demand and carbon cap-and-trade policy

With the popularization of Internet applications and the development of e-commerce, the number of online consumers is constantly increasing. This study aims to provide a framework for a sustainable and environmentally friendly closed-loop supply chain network with dual-channel (online channel and offline channel). The goal is to assist managers in making economically and environmentally sustainable decisions. Accordingly, a multi-period mathematical model is designed to address the challenges of the appliance industry, which faces uncertain demand. The model considers the carbon cap-and-trade policy, the regional protection policy of the retailer, and multiple transport modes in the design of the supply chain network. To handle demand uncertainty, we propose a robust model that utilizes a data-driven polyhedral uncertainty set based on principal component analysis and kernel smoothing methods and transforms the robust model into a mixed-integer linear programming problem. Additionally, we present a two-stage adaptive genetic algorithm to effectively tackle the large-scale and complex nature of the supply chain network. Extensive computations are performed to verify the algorithm's effectiveness. Finally, sensitivity analysis is conducted on key parameters to gain valuable insights. The results demonstrate that an increase in online consumers can significantly reduce stockouts, while an increase in uncertain demand leads to a substantial increase in the total cost.

Resilient and sustainable global COVID-19 vaccine supply chain design considering reverse logistics

This study proposes a multi-mode, multi-product, and multi-objective optimization model to design a resilient and sustainable global COVID-19 vaccine supply chain network under operational risks. The network includes a forward chain for vaccine supply, production, and distribution and a reverse chain for vaccine waste management. The objective functions comprise minimizing the total network costs and environmental pollution caused by the waste of vaccination centers and the transportation between facilities, as well as considering social impacts (maximizing regional development and the effect of domestic vaccine production on self-sufficiency, and minimizing the impact of vaccine side-effects). Resilience strategies are employed to deal with the disruption risks in different facilities and transportation links of the chain, including multiple sources of raw material supply, vaccine production and distribution, and waste management facilities; import; multiple transportation modes and different vehicles in each mode; inventory holding; capacity expansion; transshipment; and fortification of facilities. Financial parameters, including exchange rate and international transport insurance for importing raw materials and vaccines, are also considered in the presented model. An actual-world case study in Iran is conducted to indicate the model's applicability. Finally, by analyzing the computational results, important research implications are presented. The analyses investigate the conflict of objectives, examine the impact of vaccine side-effects on production and import, assess the efficiency of resilience strategies in the face of disruptions, demonstrate the effect of disruptions on network components, and determine suppliers' share for the supply of raw materials.

Evaluating the Impact of COVID-19 on Multimodal Cargo Transport Performance: A Mixed-Method Study in the UAE Context

This research is a case study on the United Arab Emirates (UAE), exploring multimodal logistics, which involves transporting cargo using multiple modes of transportation, and investigating its challenges and opportunities during the COVID-19 pandemic from 2020 to 2022. Through a mixed-method approach of qualitative interviews and quantitative surveys, this study examines factors influencing multimodal cargo transport and its performance. Five senior executives from the logistics industry were interviewed to identify key variables, and a questionnaire was administered to 120 participants to assess the impact on shipping costs and utilization. This study reveals a significant relationship between geographical and geopolitical risks and increased shipping costs in certain regions, highlighting the need for secure and cost-effective multimodal solutions in these areas. However, shipping costs did not mediate the performance of intermodal transportation at transit hubs during the pandemic. The findings offer valuable insights for transit hubs to enhance the utilization of multimodal cargo transport during uncertain times, ultimately leading to improved logistics performance in similar hub countries. This study’s originality lies in its investigation of the resilience and sustainability dimensions in multimodal logistics during the pandemic, proposing mitigation strategies and enhancing strategic decision making in the logistics industry under volatile business environments. Future research is recommended to expand the model’s results by including data from other logistics corridors and hubs.

Research on the construction of passenger travel chain based on utility

Large-scale integrated passenger transportation hubs are places where multiple modes of transportation are used to switch within the city, and they are also the convergence points of traffic inside and outside the city, and it is important to ensure their smooth under different traffic conditions. Firstly, the passenger travel utility in hubs model and transportation system utility model are proposed; then the travel demands of various types of passengers are accurately identified based on data mining and user portrait technology when the passenger volume is low, and the typical user priority filtering recommendation algorithm and information dynamic update strategy are proposed with the goal of optimal passenger travel utility; then for the large passenger volume scenario, the optimal transportation system operation utility is the goal for us, as the passenger volume increases continuously, the transportation system service is close to the critical state, a bi-level optimization model is established to avoid the resonance of the transportation system and guarantee the balance of optimal utility between the passenger side and the transportation system side. After the internal test of ‘Smooth Hongqiao’ APP, the travel time of passengers in the hub reduced by 5.6%, the utility value of the travel chain increased by 16%, and the saturation of the surrounding road network reduced by 5.7% after the above methods are flexibly applied to build the passenger travel chain according to different traffic scenarios.

Multi-period empty container repositioning approach for multimodal transportation

Due to the trade imbalance and poor management, a large number of empty containers are piled up after unpacking in importing regions, while the export-oriented areas are in urgent need of empty containers for replenishment. This paper aims to determine the volume of empty containers transferred from the surplus area to the shortage area and the inventory of each node from the operational level. We subdivide container freight stations into rail stations and road stations and consider using sea, road, and railway as transportation modes to transfer empty containers. A multi-period linear programing model of multimodal empty container repositioning is established aiming at minimizing the cost of empty container repositioning. A real case study of the transportation network in Northeast China is conducted to demonstrate that using multiple modes of transportation can significantly reduce the cost of empty container repositioning compared to using a single mode of transportation.

Proximity-centred accessibility—A conceptual debate involving experts and planning practitioners

Recognised as an important dimension of quality of life, accessibility has gained increasing attention from research and planning practice in the last decades. Rising concern with global warming and the dehumanization of cities has spotlighted the proximity-centred dimension of accessibility, which for decades has been mostly undervalued. In both research and practice, we witness a rise in references to proximity-centred accessibility, however, using a variety of different terms, with somewhat nuanced meanings or underlying concerns. Proximity-centred accessibility concepts are still in flux, inhibiting the establishment of a comprehensive understanding for the development of a cohesive research field and of effective public policies.This paper provides a first contribution to the development of a conceptual framework for proximity-centred accessibility, through a co-development process involving both research and practice. For this we used a mixed methods approach incorporating bibliographic research, surveys, and a focus group, involving both experts in the research field and a sample of planning practitioners from Portugal and Germany.Our research revealed over a dozen different proximity-centred accessibility terms currently in use in the scientific research field, of which, local accessibility and neighbourhood accessibility are the most commonly used. A difference in favoured terminology was identified between research and practice as well as different national contexts, requiring further explorations. While Proximity thresholds have been defined using a variety of physical distances or travel times, our survey revealed a nearly unanimous consensus around a threshold of 1600 m (roughly 20 min walking). Mixed evidence was found with regard to activities relevant at proximity. Regardless, playgrounds, green spaces, food shopping and elementary education showed particular relevance.Building on the empirical evidence collected, we strike out a first conceptual framework for proximity-centred accessibility. Building on the general definition for accessibility, we include a maximum distance threshold of 1600 m to consider accessibility as proximity-centred. The proposed framework encourages multiple distances (up to 1600 m), transport modes, and activities (although the relevance of activities seems to be clearly dependent of distance and local context). The importance of clearly specifying the exact distances, transport mode(s) and activities for each application of the framework is stressed.

“This is where public transport falls down”: Place based perspectives of multimodal travel

No single transport mode can replace the private car in terms of its support for flexible mobility. Combinations of multiple transport modes are required. If we are to contribute to reduced car dependency it is crucial that we understand what makes such multi-modal journeys difficult. Despite large bodies of literature on mode choice and on perceptions and experiences of various travel modes, few scholars have looked specifically at journeys that combine more than one transport mode. This research fills that gap, taking a qualitative approach to explore end-user perspectives of the challenges therein. Specifically, it focusses on the barriers people in urban, peri-urban, and rural areas perceive when thinking about combining two or more transport modes in a single journey. Thematic analysis of the responses of 146 participants of a series of asynchronous online focus groups revealed an overall picture of challenges shared, with some differences in the relative importance of different barriers for those in different locations. The time incurred, the difficulties in synchronising timetables, and the criticality of each leg’s reliability are core barriers for all, while those in rural areas are more affected by basic service provision and the physical linking of different modes. Of greater significance for urban and peri-urban residents were the complexities around planning a multi-modal journey where there are multiple options available, as well as the facilities available at stations and stops. Results are discussed in terms of interventions that could help people in different residential settings use their car less.

Assessment of drivers and barriers in the adoption of Mobility as a Service (MaaS): a case study of Noida, India

ABSTRACT Mobility as a Service (MaaS) has emerged as a transformative concept in urban transportation, integrating multiple modes of transportation through digital platforms to provide seamless travel experiences. By utilizing smartphone applications, MaaS simplifies trip planning, real-time information access, and consolidated payment systems, offering convenience to users. However, MaaS research has primarily focused on developed countries with well-established transport systems, making it crucial to explore its potential and challenges in developing cities of the global south, such as Noida in India. In this study, Noida, a satellite town of NCT Delhi, was chosen as only case study to gather data on user behaviour and preferences regarding app-based mobility services. A comprehensive survey collected information on socio-economic factors, personal vehicle ownership, commuting patterns, public transport usage, and attitudes towards digital ecosystems and app-based mobility services. Principal Component Analysis and K-means Clustering techniques were applied to identify distinct user types and categories, providing insights into user preferences and expectations. The analysis of the collected data revealed user clusters and their respective characteristics. The sustainability aspects of on-demand mobility services were evaluated, comparing user perceptions with private vehicle usage. The study also examined the impact of app-based mobility services on public transport and identified barriers and constraints specific to different user clusters, contributing to a better understanding of the feasibility of implementing MaaS. The findings will provide valuable insights for policymakers and transportation authorities, enabling the development of strategies and interventions to enhance urban mobility and foster MaaS adoption. By addressing the specific needs and preferences of users, MaaS can play a significant role in improving the efficiency and sustainability of transportation in complex urban environment cities like Noida in India.

Multi-item dynamic lot sizing with multiple transportation modes and item fragmentation

This paper addresses a tactical joint inventory and transportation planning problem for multiple items with deterministic and time-varying demand, considering different transportation modes and item fragmentation. The latter corresponds to the splitting of the same item ordered quantity between several trucks or containers. On the one hand, fragmenting the items potentially reduces the number of containers used. On the other hand, loading the item lot fragments on several containers may negatively impact the handling and shipping operations. This new problem is proposed as a way to tackle such conflict. Several Mixed Integer Linear Programming models are proposed for the problem, which rely on two multi-item lot-sizing models with mode selection and two bin-packing models with item fragmentation. A relax-and-fix heuristic is also proposed. Using realistic instances, computational experiments are first conducted to identify the most efficient model in terms of computational time, to study the impact of key parameters on the computational complexity and to analyze the efficiency of the heuristic. Then, managerial insights are derived through additional computational experiments, in particular, to identify contexts requiring joint optimization of lot-sizing and bin-packing decisions, as well as the impact of item fragmentation constraints. Directions for future research are finally proposed.

New hybrid algorithm combining multiple transportation modes for an environmental protection workshop layout

New hybrid algorithm combining multiple transportation modes for an environmental protection workshop layout