Reduce Travel Time Research Articles

Seventy Years of Dutch Regional Unemployment from a Spatiotemporal Perspective

Abstract This paper examines serial and both local and global cross-sectional dependence of regional unemployment rates in the Netherlands from 1950 to 2021, using a common factor spatial panel data model. A structural break test applied to this model provides significant empirical evidence that after the period 1980–1984 the half-life of an unemployment shock decreased, Dutch provinces became more locally integrated, regional unemployment rates moved more in line with the national unemployment rate and regional disparities in unemployment decreased. This break is attributed to wage restraint and work sharing laid down in the Wassenaar Agreement. This break and the further development of regional unemployment rates since then are set in the context of a number of important developments, including the government’s austerity policy changes and social security reforms, changes in unemployment measurement methods, the transition of employment from agriculture and industry to the service sector and the increased spatial flexibility of the labor force due to the reduction of travel times.

Evaluation of Urban Public Transport Priority Performance

The evaluation of urban public transport priority performance is a critical aspect of assessing the effectiveness and efficiency of public transportation systems in urban areas. With the increasing challenges posed by population growth, traffic congestion, and environmental concerns, it is imperative to prioritize and enhance the performance of public transport systems to provide sustainable and reliable mobility options. Which refers to the measures and strategies implemented to give priority to public transport modes such as buses, trams, and trains over private vehicles on the road? These measures aim to improve the efficiency and reliability of public transport, reduce travel times, enhance passenger comfort, and encourage modal shift from private vehicles to public transport. Improved urban mobility: Urban areas face increasing challenges of traffic congestion and limited road capacity. Evaluating public transport priority performance helps identify effective measures to improve the flow of public transport, reduce travel times, and enhance overall urban mobility. This research can lead to the development of more efficient and reliable public transport systems, encouraging people to choose sustainable transportation options. Sustainable urban development: Urban public transport plays a crucial role in reducing the environmental impact of transportation, including greenhouse gas emissions and air pollution. Evaluating public transport priority measures allows researchers to assess their effectiveness in promoting modal shift from private vehicles to public transport, leading to a reduction in overall vehicular emissions. This research contributes to sustainable urban development by promoting environmentally friendly transportation options. Comprehensive data gathering and analysis are part of the process used in land evaluation techniques. Various biophysical parameters, such as Punctuality for both the first and last stop, transfer convenience, the gathered data is then combined with statistical approaches, modelling techniques, and geospatial analytic tools. Punctuality for both the first and last stop, transfer convenience, Public transport site 500 m coverage rate, Morning and evening peak bus average operating speed, Public transport smart card popularity rate. Punctuality for both the first and last stop, transfer convenience, Public transport site 500 m coverage rate, Morning and evening peak bus average operating speed, Public transport smart card popularity rate. Punctuality for both the first and last stop got first rank and public transport site 500m coverage rate got last rank from this we conclude that Punctuality for both the first and last stop got first rank and public transport site 500m coverage rate got last rank

ESTABLISHING CLOUD-BASED CAB TRANSPORTATION SYSTEM IN NIGERIA

The establishment of a cloud-based cab transportation system in Nigeria addresses significant challenges in the existing transportation infrastructure, characterized by traffic congestion, inadequate public transport services, and safety threats. With a rapidly growing population and urbanization, the need for a modernized transportation solution is very important. The reason for this paper is about creating a comprehensive cloud-based system that makes use of advanced technologies such as IoT, GPS, and data analytics to improve operational efficiency, improve passenger experience, and ensure safety. The proposed system aims to expand existing services from urbanized areas to cover all states in Nigeria, involving various transport modalities, including cabs, buses, and logistics services. The major goals include increasing accessibility, reducing travel time, and creating job opportunities while improvingthe environment. An implementation strategy is explained in different stages, with the need for stakeholder engagement, infrastructural development, pilot testing, and continuous improvement. This transformation not only promises to revitalize Nigeria's transportation landscape but also positions the country favorably within the global economy.

SUCCESS ANALYSIS OF PUBLIC-PRIVATE PARTNERSHIP INFRASTRUCTURE PROJECT IN KAZAKHSTAN: A CASE OF THE BIG ALMATY RING ROAD (BAKAD)

Public-Private Project (PPP) projects in highway infrastructure development are gaining popularity in emerging economies, optimizing public budgets through private investments, sharing risks between the public and private sectors, and benefiting from the multiplier effect. The goal of this study is to investigate the success of the first mega concession toll road project in Central Asia, the Big Almaty Ring Road (BARR/BAKAD). The project was analyzed from three perspectives, demonstrating the BAKAD project’s overall soundness. Both qualitative and quantitative analyses were conducted based on the publicly available data extracted from various studies and official reports. The methodology included valuation questions grouped into different key performance indicators’ groups and the scoring system. The results show that the weak point in the project is the payback period for the government due to low toll incomes, while the strong point is the traffic offloading and travel time reduction. The proposed evaluation system allows in future studies both scholars and practitioners to comprehensively assess the success of PPP projects in Central Asian countries.

Performance Benchmarking of Classical Statistic, Machine Learning, and Deep Learning Time Series Models in Forecasting Measles Cases

In the globalizing world, the reduction in travel time has facilitated the spread of infectious diseases, particularly those transmitted through contact and respiratory secretions. Measles, a highly contagious disease easily transmitted via respiratory droplets, continues to be a significant public health threat. Despite being largely preventable through vaccination, the measles virus remains endemic in regions with low vaccination rates, impacting public health in countries receiving migrants from these areas. Accurate computer-aided forecasting of measles outbreaks can assist policymakers in making informed decisions to prevent the spread of the disease. This study compares the performance of different time series models, including classical statistical methods, machine learning, and deep learning techniques, in forecasting the number of measles cases. For performance evaluation, a comparative analysis was conducted on datasets from Benin, Cameroon, and Nigeria. The forecasting performance of the models—ARIMA, HW, LSTM, Greykite, Prophet, and XGBoost—was assessed using RMSE, MAPE, MAE, and MSLE evaluation metrics. The models were trained on the first 147 months of data from each dataset, with their forecasting performance evaluated over the subsequent 12 months. The study results reveal that the XGBoost model achieved the lowest MSLE in predicting measles cases for Benin (0.08) and Nigeria (0.69), while the LSTM model performed best for Cameroon with an MSLE of 0.67. Using the developed computer-aided system, the next six months of measles cases were forecasted for these countries. To our best knowledge, this study is one of the first to benchmark different time series models, using diverse datasets in forecasting measles cases. The findings suggest that artificial intelligence-based prediction systems can play a crucial role in preventing the spread of infectious diseases like measles and in developing effective health policies.

Adaptive Signal Control and Routing using Real-Time Data Processing

This article examines the implementation and impact of adaptive signal control systems enhanced by real-time data processing to solve urban traffic congestion. The article explores how these intelligent traffic management systems dynamically adjust signal timing based on actual traffic conditions rather than predetermined schedules, resulting in significant improvements in urban mobility. The article details the system architecture, incorporating strategic sensor placement, real-time analytics frameworks, and sophisticated control algorithms that enable responsive traffic management. Through analysis of multiple case studies across diverse urban environments, the article demonstrates how adaptive systems consistently outperform conventional approaches, delivering reductions in travel times of 25-40%, decreases in harmful emissions of approximately 18%, and substantial economic benefits with favorable cost-benefit ratios. The article also investigates implementation challenges and emerging technologies, including machine learning enhancements and vehicle-to-infrastructure communication, which promise to revolutionize traffic management further. By synthesizing technical article analysis with performance data from real-world deployments, this article comprehensively examines adaptive signal control's current capabilities and future potential in creating more efficient, sustainable, and livable urban environments.

An Integrated Optimization Framework for Connected and Automated Vehicles and Traffic Signals in Urban Networks

This paper proposes an integrated sustainable optimization framework consisting of three modules to optimize vehicle routes, traffic signals, and CAV trajectories at the network level. The route guidance module determines optimal routes to maximize network throughput. The signal optimization module dynamically adjusts signal timings to improve network efficiency, and the trajectory planning module optimizes vehicle accelerations to enhance ride comfort and reduce travel time. All modules exchange their outputs immediately at each iteration. The optimization framework is solved using the Dijkstra algorithm, dynamic programming, and linear programming, with linearization and decomposition techniques employed to improve computational efficiency. Specifically, the network-level signal optimization and trajectory planning modules are decomposed into intersection-level and lane-level subproblems, respectively. The performance and scalability of the proposed optimization framework are demonstrated by experimental simulation, considering different traffic densities and road network sizes. The results show that the proposed framework is effective in improving traffic efficiency and sustainability, including ride comfort and travel delay, in urban road networks. A comparative analysis further evaluates the effectiveness of each module and reveals the significant impact of signal optimization on improving traffic efficiency, highlighting its importance in urban road networks.

Project Formulation and Appraisal for Construction of Veterinary Care in Madawarayapuram, Coimbatore

The construction of a state-of-the-art veterinary hospital in Madawarayapuram addresses the community's need for specialized animal care. With rising pet ownership and livestock farming, the hospital will enhance animal welfare by offering comprehensive veterinary services. Its strategic location ensures easy access, reducing travel time for timely medical attention. This facility will positively impact the local economy by creating jobs and stimulating related businesses, such as pet supply stores and feed suppliers. Designed for functionality and aesthetics, the hospital will provide a welcoming environment for animals and their owners. By incorporating modern medical technology, it will set a new standard for veterinary care, establishing Madawarayapuram as a hub for animal health and welfare.

Optimizing the train timetable considering cross-line operation in the urban rail transit network

ABSTRACT Cross-line operation stands out as one of the emerging new metro operations. This paper investigates the cooperative optimization of cross-line train stopping/overtaking schemes, and train timetables on the metro network under cross-line operation. To describe passengers’ traveling process, a travel path alternative set generation method based on rational passengers is proposed, which proactively distinguishes the travel paths of passengers. For a given passenger travel path, the paper adopts a passenger pre-classification method, dividing passengers into four groups, to separately calculate their travel time. The model’s optimization objective is to minimize the travel time of passengers across the entire network. To enhance the efficiency of the model solution, a two-stage algorithm with a search strategy is introduced. To demonstrate the correctness of the model and the effectiveness of the algorithm, the model is applied to a network comprising 4 lines and 39 stations. Comparing with split-line operation, the results demonstrate that cross-line operations can achieve an approximate 11.32% reduction in network-wide passenger travel time and a substantial 67.09% reduction in network-wide transfer flow. Finally, this paper conducts a set of sensitivity analyses to summarize the characteristics of cross-line train operation and provides recommended combinations for passengers choosing the train service.

HYBRID LEARNING IN GRADUATE SCHOOL: EXAMINING PRODUCTIVITY AND STUDENT SATISFACTION IN THE POST-PANDEMIC ERA

The COVID-19 pandemic has led to the widespread adoption of hybrid learning models in higher education. As graduate programs continue to use this model, understanding its impact on student productivity and satisfaction remains crucial. This study examines the relationship between hybrid learning, student productivity, and satisfaction among graduate students at Eastern Samar State University (ESSU). Using a descriptive correlational research design, data were gathered through a digital survey using Google Forms. A snowball sampling technique was used to reach graduate students from various academic campuses. The survey measured hybrid learning engagement, student satisfaction, and productivity. The results indicated that 78% of respondents reported moderate to high satisfaction with hybrid learning, primarily due to its flexibility and reduced travel time. Regarding productivity, 65% of students noted stable or improved performance, with significant correlations found between hybrid learning and student satisfaction (r = 0.62, p < 0.01), and moderate correlation with productivity (r = 0.49, p < 0.05). Challenges, such as internet instability and engagement issues, were highlighted by 58% and 46% of students, respectively. These findings suggest that hybrid learning enhances student satisfaction but requires improvements in digital infrastructure and instructional design. Recommendations include providing better digital resources, faculty training, and academic support to optimize the hybrid learning experience. This study contributes to understanding hybrid learning's role in graduate education and offers insights for enhancing its effectiveness in the post-pandemic era. KEYWORDS: Hybrid Learning, Student Productivity, Student Satisfaction

Telemedicine in Tanzania: asystematic literature review.

This review explores the challenges and opportunities of implementing telemedicine in rural Tanzania. The study examines how telemedicine can address healthcare gaps in underserved areas and identifies key barriers that hinder its full adoption. It also provides insights into strategies for improving healthcare delivery through telemedicine in rural settings. A systematic literature review methodology was employed following the PRISMA guidelines. The study collected and analysed relevant academic and grey literature from PubMed, Google Scholar and Scopus databases. The search focused on telemedicine in rural Tanzania, with a thematic analysis used to categorise the challenges and opportunities identified in the literature. The review found that the major challenges to telemedicine implementation in rural Tanzania include inadequate telecommunications infrastructure, limited digital literacy among healthcare providers, insufficient financial investment and weak regulatory frameworks. Despite these barriers, telemedicine presents significant opportunities to improve healthcare access, particularly by connecting rural patients with specialists, reducing travel times and integrating telemedicine with national health insurance systems. Innovations such as drone-based medical deliveries and SMS-based health interventions also show promise. This paper comprehensively overviews telemedicine's potential to transform healthcare delivery in rural Tanzania. It synthesises existing literature, highlights critical gaps in telemedicine adoption and proposes actionable strategies for overcoming these barriers. The study contributes valuable insights for policymakers, healthcare providers and stakeholders interested in leveraging telemedicine to enhance healthcare outcomes in underserved regions.

Enhancing Smart City Healthcare with Hybrid Swarm Optimization: A Comparison of MFO-PSO and ACO Approaches

Optimal healthcare transportation is a key challenge of smart cities, particularly in cases of emergencies, when traffic congestion and inefficient routing cause delays. In this work, a new hybrid swarm intelligence algorithm based on Moth Flame Optimization (MFO), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) for optimizing real-time multi-source multi-destination (MSMD) traffic routing in healthcare services is presented. The hybrid approach dynamically adjusts vehicle paths based on traffic conditions to reduce travel time, improve traffic flow, and minimize fuel consumption. Simulation experiments based on the NetLogo platform show that the hybrid strategy saves travel time by 14%, maximizes throughput by 6.4%, and conserves energy by 7.5% compared to individual optimization approaches. This study demonstrates the potential of hybrid swarm optimization to optimize healthcare logistics and emergency response systems in smart cities. Future research will focus on further improving the optimization by exploring machine learning-based predictive routing.

Designing a Probable Engine for Future Supersonic Transport Aircraft

Supersonic transport (SST) is poised to revolutionize the aviation industry once again, offering the potential for significantly reduced travel times across transcontinental and transoceanic routes. This paper delves into the key engine design elements for next-generation SST aircraft, focusing on critical areas such as fuel efficiency, environmental sustainability, and noise reduction. With a particular emphasis on low-bypass turbofan engines, variable cycle technology, and afterburner integration, this paper provides a comprehensive analysis of the technological advancements necessary to address the challenges that previously plagued supersonic transport, such as the high fuel consumption and environmental impact of earlier designs like the Concorde [1]. The paper also explores the role of alternative fuels—namely, sustainable aviation fuel (SAF) and liquid hydrogen—and their implications for the future of high-speed aviation. Through computational modeling and materials analysis, the paper proposes a conceptual engine model that balances performance, sustainability, and regulatory compliance. Areas for future research, particularly in noise abatement and thermodynamic efficiency, are also outlined.

Road Performance Analysis in Maliana District, Bobonaro Regency, Timor Leste

Transportation is an important part that cannot be separated from human life, as it plays a crucial role in various aspects, including socio-cultural, economic, engineering, political, defense, and legal sectors. Roads are one of the primary means of transportation that significantly impact the development and progress of a region by facilitating the movement of goods, services, and people. The purpose of this study is to determine the performance of the Maliana - Bobonaro road transportation section, which serves as a vital route connecting key areas and supporting local economic growth. This study utilizes the Calculation Capacity Road method based on the PKJI 2023 (Indonesian Road Capacity Guidelines) to assess road performance and travel time. The results of the study indicate that the Maliana – Bobonaro road section demonstrates good performance, with a capacity (C) of 2735.85 smp/hour, a degree of saturation of 0.235 and 0.195, and a level of service categorized as A and B. These results suggest that the road operates efficiently with minimal congestion, ensuring smooth traffic flow and reducing travel time. However, continuous monitoring and periodic evaluations are essential to maintain this performance level, especially with potential future increases in traffic volume due to regional development. This study provides valuable insights for policymakers and transportation planners in improving road infrastructure and ensuring sustainable transportation systems.

Road Performance Analysis in Maliana District, Bobonaro Regency, Timor Leste

Transportation is an important part that cannot be separated from human life, as it plays a crucial role in various aspects, including socio-cultural, economic, engineering, political, defense, and legal sectors. Roads are one of the primary means of transportation that significantly impact the development and progress of a region by facilitating the movement of goods, services, and people. The purpose of this study is to determine the performance of the Maliana - Bobonaro road transportation section, which serves as a vital route connecting key areas and supporting local economic growth. This study utilizes the Calculation Capacity Road method based on the PKJI 2023 (Indonesian Road Capacity Guidelines) to assess road performance and travel time. The results of the study indicate that the Maliana – Bobonaro road section demonstrates good performance, with a capacity (C) of 2735.85 smp/hour, a degree of saturation of 0.235 and 0.195, and a level of service categorized as A and B. These results suggest that the road operates efficiently with minimal congestion, ensuring smooth traffic flow and reducing travel time. However, continuous monitoring and periodic evaluations are essential to maintain this performance level, especially with potential future increases in traffic volume due to regional development. This study provides valuable insights for policymakers and transportation planners in improving road infrastructure and ensuring sustainable transportation systems.

PRO-MTL : Parameterized Route Optimization using Multi-Task Learning

In the current ridesharing scenario, finding a compatible passenger is highly challenging and largely dependent on chance. Existing algorithms prioritize the shortest route without considering future requests or traffic conditions, which reduces the likelihood of matching with another compatible passenger. This uncertainty leads to increased congestion along shortest routes and fewer ridesharing trips overall. This paper proposes a route recommendation strategy that goes beyond the shortest route, aiming to address these issues. The proposed strategy results in higher demand, reduced congestion, broader coverage of points of interests, and an increased probability of finding compatible passengers during a trip. To achieve this, we introduce a time-series forecasting method leveraging a multi-task long short-term memory model to predict demand and traffic patterns in city-zone neighborhoods. These predictions are then used to recommend optimized routes. To evaluate our approach, we tested it on three datasets containing trip and traffic details from New York City, Los Angeles, and Shenzhen. Our model demonstrated 96% accuracy and a 2% RMSE loss in predicting the expected number of passengers. Furthermore, during route recommendations, we observed a 23% increase in passenger count for 97% of trips and a reduction in travel time for the shortest path in 60% of trips. In light of the above experimentation, we believe that while our approach recommends a longer route than the shortest one (for 40% cases), it helps taxi drivers to find compatible passengers on most trips which increases the profit of ridesharing services, and reduces the waiting time for passengers.

Interaction of travel time and transfer frequency in trip chains affected by contagion impedance during COVID-19 in Seoul

ABSTRACT COVID-19 instilled a fear of contagion and altered the travel behaviour of public transport (PT) users. Unlike car travel, PT users are in close proximity to many individuals. Efforts to prevent the spread of contagion and ensure the safety of PT users necessitate an understanding of the factors influenced by fear that lead to behavioural changes. Building on existing research regarding the behavioural aspects of PT route choice, this paper posits that PT users adjusted their travel times and transfer frequencies to minimize exposure to contagion. Understanding these adjusted travel times and transfer frequencies is more straightforward than calculating the marginal disutilities associated with in-vehicle travel time and transfer frequency, which is the primary focus of most impedance research in transportation science. The study aims to investigate how PT users modified their travel times and transfer frequencies in response to COVID-19. Specifically, this paper develops new measures of behavioural response: Contagion Impedance (CI) and Contagion Impedance Strength (CIS). These measures indicate how travel time and transfer frequency interact in response to the spread of contagion and the intensity of that interaction. The study analyses individual trip records obtained from smartcard data of PT users in Seoul, comparing data from before and after the COVID-19 outbreak. The analysis revealed that trip chains exhibited reduced travel times and increased transfer frequencies following the significant spread of contagion. This suggests that the fear of contagion while on board was more pronounced than the fear experienced during transfers. Furthermore, the intensity of the interaction increases as the severity of contagion spread intensifies. Additional analyses segmented by age and time of day revealed distinct patterns in the interaction and its intensity. The results clearly indicate that the relationships between in-vehicle travel time and transfer time during a contagious disease differ from those in ordinary situations.

Enforcement of the Law Against Negligent Traffic Violators Resulting in Fatalities: A Case Study of Decision Number 34/Pid.B/2021/PN.Mgt

Toll roads are constructed to connect distant locations, aiming to reduce travel time and facilitate mobility and transportation. However, the extended travel distances often result in driver fatigue and drowsiness, increasing the risk of traffic accidents. Law enforcement in cases of traffic accidents resulting in fatalities is an effort to uphold legal norms as guidelines for behavior in traffic and to maintain legal order in society. This study examines two main issues: (1) how law enforcement is carried out against negligent drivers responsible for fatalities and (2) the legal considerations of judges in adjudicating cases of fatal traffic negligence. This study employs a normative juridical method with a statutory approach and qualitative normative analysis using inductive reasoning. Law enforcement against drivers causing fatal accidents involves investigations to collect evidence clarifying the crime and identifying suspects. In this case, the suspect was charged with violating Article 310 paragraph (4) and Article 312 of the Traffic and Road Transportation Law (LLAJ Law), carrying a maximum penalty of six years’ imprisonment and/or a fine of IDR 12,000,000, along with an additional penalty of up to three years’ imprisonment for further violations. The findings reveal that the suspect was legally and convincingly proven guilty of negligence in driving, which resulted in an accident causing four fatalities. In their legal considerations, the judges emphasized the ultimum remedium principle as the basis for sentencing, while also taking into account aggravating and mitigating factors in issuing a verdict of three years and two months’ imprisonment. These findings highlight the challenges in enforcing traffic laws in Indonesia, including the low level of legal awareness among drivers and the lack of an effective preventive system to reduce accidents caused by negligence.

Optimization of a semiflexible demand‐responsive feeder bus system using variable neighborhood search

Optimization of a semiflexible demand‐responsive feeder bus system using variable neighborhood search

Application of IoT and AI Based on ESP32CAM to Support Sustainable Mobility in Smart Cities

Sustainable mobility is a crucial component of smart city development, aiming to create an efficient, ecofriendly, and high quality urban environment. The rapid urbanization and increasing vehicle numbers have led to traffic congestion, high carbon emissions, and inefficient public transportation systems. To address these challenges, integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies, particularly through the ESP32CAM platform, offers innovative solutions for realtime monitoring, analysis, and management of transportation systems. This study aims to develop an intelligent mobility system that leverages ESP32CAM as the primary device for capturing visual data, including traffic density, vehicle tracking, and parking area occupancy. The collected data is processed using AI algorithms to generate insights, such as traffic pattern predictions and alternative route recommendations. The gap in existing smart mobility solutions lies in their high implementation costs and limited adaptability to developing regions. this study introduces an affordable and flexible system that optimizes transportation efficiency by up to 30%, reducing travel time and carbon emissions while enhancing public transportation management. The results indicate that implementing this system significantly contributes to sustainable urban development by improving mobility, reducing environmental impact, and accelerating the transformation towards smart cities, especially in developing countries where cost-effective solutions are essential.