Search Results (501)

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m³ and 1519 kg/m³, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article

Background: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a strategic vision, a flexible organizational culture, and the ability to support decisions through artificial intelligence (AI)-based systems. Methods: This study proposes an extended conceptual model using structural equation modelling (SEM) to explore the relationships between five constructs: technological change, strategic and organizational readiness, transformation environment, AI-enabled decision configuration, and operational redesign. The model was validated based on a sample of 217 active logistics specialists, coming from sectors such as road transport, retail, 3PL logistics services, and manufacturing. The participants are involved in the digitization of processes, especially in activities related to operational decisions and sustainability. Results: The findings reveal that the analysis confirms statistically significant relationships between organizational readiness, transformation environment, AI-based decision processes, and operational redesign. Conclusions: The study highlights the importance of an integrated approach in which technology, organizational culture, and advanced decision support collectively contribute to the transition to digital and circular logistics chains. Full article

The Ministry of Land, Infrastructure, and Transport conducted a demonstration project targeting pilot areas to commercialize drone delivery services in urban areas and to present a standard model. In this study, flight data on drone delivery routes in Ulju and drone hovering in Yeosu were collected and analyzed for flight safety. Since there are no domestic or international regulations on the stability of drone flight, we were given the task of analyzing whether drone path flight should be maintained within a 10 m error range from the planned path line by the Korea Transportation Safety Authority and whether hovering works while satisfying the left and right radius errors and altitude errors within 3 m. Accordingly, the drone flight path data analyzed in Ulju met the criteria of up to 1.07%, and the hovering data analyzed in Yeosu met the criteria of less than 3% for the entire section data. Therefore, the drone flight stability evaluation analyzed in this paper is considered to have been passed. Based on the results of this study, is the data are expected to serve as a cornerstone for establishing guidelines for drone delivery flight data analysis regulations. Full article

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

This paper analyzes the factors affecting the willingness to pay of urban households concerned with efficient waste management in Bangladesh. The multistage random sampling approach selected 1400 families from seven major cities in Bangladesh. This study addresses the socioeconomic and environmental factors that influence urban households’ willingness to pay for improved waste management services in Bangladesh. This study uniquely contributes to the literature by providing a large-scale empirical analysis of 1470 households using a logit model, revealing income, education, and environmental awareness as key predictors of WTP. Detailed survey data from respondents were then analyzed using a logit model based on the contingent valuation method. Indeed, the logit model showed that six variables (education, monthly income, value of the asset, knowledge of environment, and climate change) had a statistically significant effect on the WTP of the households. The results show that 63% of respondents were willing to pay BDT 250 or more per month. The most influential factors driving this willingness to pay were income (OR = 1.35), education level (OR = 1.45), and environmental awareness (OR = 3.56). These variables all contribute positively towards WTP. The idea is that families have some socioeconomic characteristics, regardless of which they are ready to pay for a higher level of waste collection. It is recommended that government interference be affected through various approaches, as listed below: support for public–private sector undertaking and disposal, an extensive cleaning campaign, decentralized management, cutting waste transport costs, and privatization of some waste management systems. These could be used to develop solutions to better waste management systems and improve public health. Full article

Employees’ green voice behavior (GVB), as a specific category of extra-role green behavior, plays a vital role in promoting a firm’s sustainable development. However, its underlying mechanism has not been sufficiently explored. Drawing on social learning theory (SLT), this study proposes a research model that examines the indirect influence of environmentally-specific transformational leadership (ESTFL) on GVB via psychological empowerment (PE) and ecological reflexivity (ER) as well as the moderating role of person-supervisor value congruence (PSVC). To achieve the research goals, we conducted a two-wave online survey via the convenience sampling method to collect data from 530 employees and 106 direct supervisors working in the manufacturing, hospitality and service, energy production, construction, transportation, information and communication, and finance industries in China. Regression analyses and CFA based on SPSS and Mplus were employed to test and validate the research model. Our findings show that PE and ER both partially mediated the positive association between ESTFL and GVB. Moreover, PSVC moderated the mediating effects of ESTFL on GVB via PE and ER. This study advances empirical research regarding how leadership impacts GVB by revealing dual cognitive mechanisms and identifying its boundary condition. It also offers managerial implications for leaders and enterprises in China to promote employees’ GVB and improve sustainable management. Full article

Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime. Full article

Introduction: Social prescribing is an innovative approach that connects individuals to community-based activities to promote well-being. This study explores the barriers and facilitators influencing older adults’ engagement in social prescribing programmes in Portugal. Methodology: A qualitative study was carried out in October 2024, in Portugal, with 16 participants aged 65 and over. Data was collected through two focus groups, each with eight participants. Data were analysed using the COM-B model (Capability, Opportunity, Motivation) to identify key factors affecting adherence. Results: The main barriers identified were physical limitations, digital exclusion, transport inaccessibility, and the urban-centric location of services. Facilitators comprised tailored activities, digital support and education, accessible venues and transport, and personalised interventions. Ongoing feedback mechanisms and familiar community settings were essential for sustained participation. Conclusions: These findings suggest that co-designed, inclusive, and locally accessible programmes can significantly enhance the involvement and well-being of older adults. Full article

This paper proposes a general workflow for collecting and analyzing open data from Bicycle Sharing Systems (BSSs) that was developed using data from the Valenbisi system, operated in Valencia by the French company JCDecaux; however, the stages of the proposed workflow are service-independent and can be applied broadly. Cycling has become an increasingly popular mode of transportation, leading to the emergence of BSSs in modern cities. Parallel to this, Smart City solutions have been implemented using Internet of Things (IoT) technologies, such as embedded sensors and GPS-based communication systems, which have become essential to everyday life. When public transportation services or bicycle sharing systems are used, real-time information about the services is provided to customers, including vehicle tracking based on GPS technology and the availability of bikes via sensors installed at bike rental stations. The bike stations were examined from two different perspectives: first, their daily usage, and second, the types of facilities located in their surroundings. Based on these two approaches, the overlap between the clustering results was analyzed—specifically, the similarity in how stations could be grouped and the correlation between their usage and locations. To enhance the raw data retrieved from the service provider’s official API, the stations were annotated based on OpenStreetMap and Overpass API data. Data visualization was created using Tableau from Salesforce. Based on the results, an agreement of 62% was found between the results of the two different clustering approaches. Full article

Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables. Full article

Various platforms, such as Google Maps, provide information about the services of public transport companies worldwide. Operators publish the planned (static) timetable using the General Transit Feed Specification (GTFS) format, while the GTFS Realtime (GTFS-RT) specification provides live (dynamic) information about the services. In this paper, we present our dataset that was built by retrieving and pre-processing the data sources of the open data platform of BKK Futár, hosted by the Centre for Budapest Transport Company (BKK). The paper contains a well-detailed description of our methods for retrieving and pre-processing the data among statistical features. The dataset covers a one-year period in which the data collection mechanism used for realtime data was continuously improved from collecting only live vehicle positions to covering all the available feeds and increasing the query frequency. We merged the static data with the vehicle positions to filter them, yielding a clean set of tracked trips. As a result, more than 90% of the daily planned trips could be reconstructed from the responses. We provide an interactive web-based visualization for the analysis of the GTFS schedule’s, and the GTFS-RT Vehicle Positions feed’s, geospatial features. The dataset and also our methodology can serve as input for various research studies to investigate the common characteristics of delays and disruptions or predict real departure times based on the current vehicle positions and historical data. Full article

Mobility as a Service (MaaS) is anticipated to revolutionize transport by integrating conventional public transport with on-demand and shared services. This innovation promises enhanced convenience, flexibility, and sustainability in urban mobility, drawing interest from both researchers and industry. However, those systems heavily rely on the collection and sharing of personal data among various stakeholders, introducing security and privacy risks. To understand the scale and scope of cyber security and privacy concerns and risks associated with MaaS, we conducted a systematic literature review (SLR) covering 87 relevant research papers published between 2017 and April 2025. Our review represents the most comprehensive examination focusing on cyber security and privacy issues of MaaS systems. Our findings reveal three themes discussed within the MaaS literature: (i) cyber security and privacy risks inherent to MaaS systems, alongside proposed solutions to mitigate such risks; (ii) users’ concerns about these risks and how they affect MaaS adoption; and (iii) laws and policies that govern cyber security and privacy aspects of MaaS systems and solutions. As such, our research serves to not only inform MaaS service providers and users but also advise policymakers and legislators on the potential risks involved and the regulatory measures required to address them. Full article

Microplastic pollution presents a significant and rising risk to both ecological integrity and the long-term viability of economic activities reliant on marine ecosystems. The Black Sea, a region sustaining economic sectors such as fisheries, tourism, and maritime transport, is increasingly vulnerable to this form of contamination. Mytilus galloprovincialis, a well-established bioindicator, accumulates microplastics, providing a direct measure of environmental pollution and indicating potential economic consequences deriving from degraded ecosystem services. While previous studies have documented microplastic pollution in the Black Sea, our paper specifically quantified microplastic contamination in M. galloprovincialis collected from four sites along the western Black Sea coast, each characterised by distinct levels of anthropogenic influence: Midia Port, Constanta Port, Mangalia Port, and 2 Mai. We used statistical analysis to quantify site-specific microplastic contamination in M. galloprovincialis and employed machine learning to develop models predicting accumulation patterns based on environmental variables. Our findings demonstrate the efficacy of mussels as bioindicators of marine plastic pollution and highlight the utility of machine learning in developing effective predictive tools for monitoring and managing marine litter contamination in marine environments, thereby contributing to sustainable economic practices. Full article

With rapid urbanisation and population growth, transport equity has become a critical issue, especially when considering the mobility gap among people with disability. Understanding the dynamics between the quality of public transport services and the mobility of people with disability is critical to fostering transport equity and inclusivity. This research investigated service quality barriers encountered by people with disability in the City of Tshwane while navigating the city’s public transport system. A quantitative research method was employed, using a structured questionnaire to collect primary data from people with mobility, vision, and hearing disability. The responses were analysed using descriptive statistics, exploratory factor analysis (EFA), and multiple comparison tests to uncover trends and differences among the groups. The findings reveal that people with all types of disability experience considerable service quality challenges. Long travel and waiting times are major concerns amongst people with mobility disability, which lead to heightened inconvenience. The research also found a pervasive lack of transport information, which aggravates the difficulties faced by people with disability. Lastly, the absence of announcements of stops further compounds the challenges experienced by people with a vision disability. The study emphasises the need for high quality public transport services that prioritise accessible and inclusive public transport that caters to all. Addressing service quality barriers in public transport promotes participation in socio-economic life among people with disability. This study contributes to the broader goal of transport equity and highlights the importance of inclusive transport policies and the priority areas that require consideration in a typical developing country. Full article