Efficient Transport System Research Articles

STVANet: A spatio-temporal visual attention framework with large kernel attention mechanism for citywide traffic dynamics prediction

Enhancing the efficiency and safety of the Intelligent Transportation System requires effective modeling and prediction of citywide traffic dynamics. Most studies employ convolutional neural networks (CNNs) with a 3D convolutional structure or spatio-temporal models with self-attention mechanisms to capture the spatio-temporal information of traffic distribution. Although 3D CNNs excel at capturing local contextual information, they are computationally complex due to the large number of parameters and cannot capture long-range dependence. By contrast, although self-attention mechanisms originally designed to address challenges in natural language processing can capture long-range dependence, their application to 2D image structures requires breaking down the inherent 2D context into a 1D sequence, increasing the computational complexity and neglecting the adaptability between local contextual information and channels. Accordingly, we propose a spatio-temporal visual attention neural network (STVANet), a novel spatio-temporal visual attention 2D CNN, which integrates a unique visual attention module with a large kernel attention (LKA) mechanism, a squeeze-and-excitation (SE) mechanism and a feedforward component to capture long-range dependence and channel information in urban traffic data while preserving the 2D image structure. LKA-based spatio-temporal attention networks extract spatial and temporal features from weekly, daily, and recent hourly periods, and aggregate them with weighted consideration of external features to make predictions. Evaluation of real-world datasets demonstrates STVANet’s superiority over baseline models, showcasing its potential in citywide traffic prediction.

Power management for fuel-cell electric vehicle using Hybrid SHO-CSGNN approach

The increasing connectivity of vehicles through intelligent transportation systems presents a significant research challenge: how to simultaneously optimize power management operations and vehicle dynamics to enhance energy efficiency. This paper proposes a novel approach to address this challenge by integrating the Sea-Horse Optimization Algorithm (SHO) and Contrastive Self-Supervised Graph Neural Network (CSGNN), referred to as the SHO-CSGNN technique. The primary objectives of this approach are to reduce computation time and improve energy efficiency in electric vehicles (EVs) powered by fuel cells. The SHO is employed to optimize the power consumption of the EV by dynamically adjusting power management operations based on real-time data and vehicle dynamics. On the other hand, the CSGNN is utilized to predict the vehicle's range performance by analyzing various factors such as road conditions, traffic patterns, and driving behavior. By combining these two approaches, the SHO-CSGNN technique can effectively optimize power management operations while considering the dynamic nature of the driving environment. The proposed technique is implemented and evaluated on the MATLAB platform, where it is compared with existing methods, including the Salp Swarm Algorithm (SSA), Firefly Algorithm (FFA), and Genetic Algorithm (GA). Experimental results demonstrate that the SHO-CSGNN technique outperforms all current methods, achieving a computation time reduction of 3.3 seconds compared to existing techniques. This study contributes to the advancement of power management strategies for EVs, highlighting the effectiveness of the SHO-CSGNN technique in enhancing energy efficiency and computational efficiency in real-world applications. The findings of this research have implications for the development of more efficient and sustainable transportation systems, paving the way for the widespread adoption of electric vehicles.

THE ENVIROMENTAL AND SOCIAL IMPACT OF CARPOOLING

Abstract: Car pooling System is an automated system which reduces the misery of travelers and makes them find cars in short period of time. Car pooling is an application of finding car in which drivers who are traveling to work alone can ask for fellow passengers through our application. For those who use public-transport system to go to work daily can use this application to find drivers who are traveling to the same destination in a short path. It provides with a simple riding platform between the car owner and car user. This project enables users to access mobility assets own by others exactly when they need. I t shows a medium for available cars to pick up them on the interest of car owner with time and capacity. This project aims at creation of a Car-pooling System. This paper delves into the design and implementation of a novel carpooling project aimed at addressing the challenges of urban transportation sustainability. With burgeoning urban populations, congestion, and environmental concerns, the need for innovative solutions is paramount. Carpooling presents a viable avenue to alleviate these issues by fostering shared mobility, reducing traffic congestion, and cutting down carbon emissions. The project employs a multi-faceted approach, leveraging advancements in mobile technology, data analytics, and user incentives to optimize the carpooling experience. Key components include a user-friendly mobile application for seamless ride matching, dynamic routing algorithms to optimize trip efficiency, and incentive structures to encourage participation and retention. Through a combination of quantitative analysis and qualitative feedback, the efficacy of the carpooling project is evaluated. Metrics such as reduction in vehicle miles traveled, greenhouse gas emissions, and user satisfaction levels are assessed to gauge its impact on urban transportation sustainability. The findings of this research contribute to the growing body of knowledge on sustainable transportation solutions and provide insights for policymakers, urban planners, and transportation stakeholders seeking to promote carpooling initiatives. Ultimately, the project aims to foster a culture of shared mobility and pave the way for more efficient and sustainable urban transportation systems. IndexTerms – HTML, CSS, Javascript. Bootsrap, MySQL, Python.

Early warning and managing system for regulating high-traffic periods in subway stations

Abstract Addressing the persistent issue of passenger overflow during peak hours in subway stations, this paper introduces an innovative adaptive early warning system. The system utilizes real-time data analytics and advanced predictive algorithms to monitor passenger volumes dynamically and generate timely alerts when traffic becomes excessive. These alerts are specifically designed to provide station staff with vital information that enables them to respond promptly. As a result, the normal operation of the subway station can be ensured, along with the safety of the passengers. The effectiveness and accuracy of this proposed system are demonstrated through a series of case studies. These studies confirm the system’s capability to enhance the management of passenger flow significantly. This research has the potential to revolutionize contemporary practices in subway station management and contribute to safer, more efficient public transportation systems.

Insights into the development of small-volume long lava flows: A case study of the Coalstoun Lakes Volcanic Field, southeast Queensland, Australia

Long lava flows exceeding 50 km in length are usually produced during large-volume flood basalt eruptions (>100 to 10,000 km3) but can also occur from small to moderate-volume (<30 km3) basaltic eruptions in continental intraplate monogenetic volcanic fields. Eruptive volume, therefore, is not an a priori barrier to producing long lava flows. Key factors that promote long lava flows include efficient lava transport systems that minimise heat loss, long-lived and sustained effusion rates to maintain flow advancement, and lava flow across low topographic gradients (<1°-10°) with minimal topographic barriers.Here, we focus on an anomalously young and poorly studied basaltic monogenetic volcanic field in southeast Queensland, Australia, that formed part of the broader intraplate volcanism in eastern Australia since the Late Cretaceous. The Coalstoun Lakes Volcanic Field (CLVF) comprises three lava fields: the Barambah Basalt Flow Field, the Deep Creek Flow Field and the Hunters Hill Flow Field. Basalt from the Barambah Basalt Flow Field has been redated here by Ar40/Ar39 analysis of groundmass material, yielding a weighted mean age of 0.520 ± 0.016 Ma. The Barambah Basalt Flow Field contains most of the eruptive volume and has advanced up to 165 km from the vent. The Hunters Hill and the Deep Creek flow fields are comparatively smaller in volume and have advanced ∼30 and ∼ 20 km from the vent, respectively. Lava tubes are only known from proximal regions and do not appear to be a significant factor in promoting long run-out in the CLVF. Flow confinement and utilisation of existing drainage networks are features of both lava flow fields, and advancement down the sand-based and ephemeral Burnett River significantly promoted long run-out despite low topographic gradients.New whole-rock geochemical data on our CLVF samples indicates that all lavas are hawaiites, a common feature of other Quaternary long lava flows globally. Overall, there is some compositional variation, but a cryptic zonation is readily apparent in trace element abundances, which helps to further distinguish the flow fields as the products of separate but closely spaced eruptions. The combination of field and geochemical data indicates that the long lava flow of the Barambah Basalt Flow Field resulted from a sustained and relatively low effusion eruption, creating a pāhoehoe flow field that continuously advanced across the landscape, utilising a drainage system that guided lava flow and helped to circumvent any topographic barriers.

Kebijakan Trans Metro Deli sebagai Upaya Meningkatkan Layanan Transportasi Umum di Kota Medan

The Trans Metro Deli policy in Medan City is an effort to improve public transportation services. First, this policy provides an integrated and efficient mass transportation system through a special bus network with routes that cross strategic points in the city of Medan. Second, the adoption of information technology in the operational management of Trans Metro Deli allows users to obtain the latest information regarding bus schedules, routes and conditions in real-time, increasing reliability and user comfort. Apart from that, implementing affordable fares is also an important point in this policy, expanding transportation accessibility for people from various economic layers. Third, the Trans Metro Deli policy also prioritizes safety and security aspects in the use of public transportation by establishing operational standards and strict supervision of the bus fleet and personnel. This has a positive impact on public perception and trust in the service. Furthermore, efforts to improve infrastructure including the development of special bus stops and lanes are also an important part of this policy, ensuring accessibility and efficiency in daily operations. In conclusion, the Trans Metro Deli policy in Medan City is a significant step in improving public transportation services by integrating various aspects such as operational efficiency, use of technology, safety, comfort and accessibility. In this way, it is hoped that it can have a broad positive impact on the mobility and quality of life of city residents.

Scaling laws for optimized power-law fluid flow in self-similar tree-like branching networks

The power-law fluid flow in tree-like self-similar branching networks is prevalent throughout the natural world and also finds numerous applications in technology such as oil recovery and microfluidic devices. We investigate analysis of optimal power-law fluid flow conditions and the optimal structures within tree-like branching networks, focusing on maximizing flow conductance under the constraint of the network tube’s volume and the surface area. The study considered fully developed laminar power-law fluid flow regimes without considering any losses in the network system. A key observation was the sensitivity of the dimensionless effective flow conductance to the network’s geometrical parameters. We found that the maximum flow conductance occurs when a dimensionless radius ratio β∗ satisfies the equation β∗=N−1/3 and β∗=N−(n+1)/(3n+2) under constrained tube-volume and surface-area, respectively. Here, N represents the bifurcation number of branches splitting at each junction, and n is the fluid power-law index. We further find that this optimal condition occurs when pressure drops are equipartition across each branching level. We validated our results with various experimental results and theories under limiting conditions. Further, Hess–Murray’s law is justified and extended for the shear-thinning and shear-thickening fluid flows for an arbitrary number of branches N. Further, in this study, we also derive the relationships between the geometrical and flow characteristics of the parent and daughter tubes as well as the generalized scaling laws at the optimal conditions for the other essential parameters such as tube-wall stresses, length ratios, mean velocities, tube-volume, and surface-area of the tube distributing within the networks. We find that the fluid power-law index n does not influence the constrained tube-volume scaling at the optimal conditions; however, the scaling laws vary with n under the constrained tube’s surface area. These findings offer valuable design principles for developing efficient transport and flow systems.

Investigating the Key Factors Affecting Public Transport Ridership in Developing Countries through Structural Equation Modeling

Every country requires efficient public transportation to reduce the reliance on motor vehicles, decrease harmful emissions and noise pollution, and, most importantly, provide accessible transportation for urban populations with limited resources. Yet, several challenges hinder the efficiency of the public transportation system, affecting factors like daily ridership, infrastructure, revenue, and operations. Therefore, this research investigated barriers and their impact on the daily ridership of rail transit services, especially in densely populated areas characterized by high automobile usage, inadequate pedestrian facilities, and limited connectivity to other transportation modes. To accomplish this goal, a self-administered questionnaire was developed, and a survey was conducted to collect responses from commuters at various public transport hubs. Structural equation modeling (SEM) was used to analyze data gathered from a sample of 1000 participants. The findings from the SEM model indicated a positive correlation between Instrumental Attractiveness (IA) and Facility Design and Operation (FDO) with daily travel. Conversely, Service and Information (SI) along with Environmental Comfort (EC) exhibited a negative correlation with daily travel. Private sector employees without personal vehicles and a monthly income below fifty thousand exhibited a strong preference for public transport. Individuals with higher incomes who owned vehicles showed lower preferences towards public transport usage. The main contributors to low ridership in public transport included poor connectivity with metro and feeder buses, lack of availability of discount tickets, insufficient seating at stations, and inadequate route map information. Gasoline prices have a notable impact on daily ridership too, as higher fuel prices tend to attract more travelers to use public transport. The ridership data demonstrate a positive trend in response to increased gasoline prices. The findings from this study are useful for transportation engineers, practitioners, planners, and policymakers.

Logo recognition of vehicles based on deep convolutional generative adversarial networks

Vehicle logo recognition plays a critical role in enhancing the efficiency of intelligent transportation systems by enabling accurate vehicle identification and tracking. Despite advancements in image recognition technologies, accurately detecting and classifying vehicle logos in diverse and dynamically changing environments remains a significant challenge. This research introduces an innovative approach utilizing a Deep Convolutional Generative Adversarial Network (DCGAN) framework, tailored specifically for the complex task of vehicle logo recognition. Unlike traditional methods, which heavily rely on manual feature extraction and pre-defined image processing techniques, our method employs a novel DCGAN architecture. This architecture automatically learns the distinctive features of vehicle logos directly from data, enabling more robust and accurate recognition across various conditions. Furthermore, we propose a refined training strategy for both the generator and discriminator components of our DCGAN, optimized through extensive experimentation, to enhance the model’s ability to generate high-fidelity vehicle logo images for improved training efficacy. The technical core of our approach lies in the strategic integration of transfer learning techniques. These techniques significantly boost classification accuracy by leveraging pre-learned features from vast image datasets, thereby addressing the challenge of limited labeled data in the vehicle logo domain. Our experimental results demonstrate a substantial improvement in logo detection and classification accuracy, achieving an Intersection over Union (IoU) ratio of 42.67 % and a classification accuracy of 99.78 %, which markedly surpasses the performance of existing methods. This research not only advances the field of vehicle logo recognition but also contributes to the broader domain of measurement science and technology, offering a technically sound and logically coherent solution to a complex problem.

Challenges in organising blood donation camps at remote military base and its impact on blood component quality: A unique helicopter-based whole blood transport experience

BackgroundThere is a huge gap between safe blood supply and clinical demand in India and voluntary blood donation camps (BDSs) are vital to address this gap. The study evaluates the challenges faced in organizing remote setting voluntary BDCs and assess the impact of helicopter-flight on the quality of the whole blood units (WBU) and blood components (BC) prepared. MethodsThis is an observational study in which two voluntary BDCs were organised in remote military-based setting in 2021. Pre-camp activities, camp organisation, community engagement, and transportation logistics were evaluated. All WBU collected were exposed to helicopter-flight for transportation to the main blood centre with cold-chain maintenance. Impact of helicopter-flight on WBU and BC prepared was evaluated by performing extensive quality control (QC) testing. ResultsA total of 123 WBU were collected in both camps with transportation time of 160 and 150 min for camp-1 and −2 respectively. 123 PRBC, 22 BC-PC, 75 FFP and 48 CRYO units were prepared in-total within recommended time-limits. No haemolysis was detected in WBU, and all BC met QC criteria as per National guidelines. ConclusionsProper pre-camp planning, prior screening of donors, clear collection process policy, feasibility of efficient transport system, regular communication, and maintenance of cold-chain are crucial factors in determining the success of remote BDCs and quality of BC. Our study provides practical recommendations for policymakers, military healthcare providers, transfusion medicine specialists and public health professionals to enhance the effectiveness and sustainability of voluntary blood donation programs in remote settings.

Vehicle detection algorithm for foggy based on improved AOD-Net

To strengthen the safety monitoring of foggy road traffic and maintain the safety of vehicle driving on foggy roads, image dehazing algorithms are used to improve the clarity of road images detected in foggy environments, thereby improving the detection ability and monitoring efficiency of intelligent transportation systems for vehicle targets. Due to the low accuracy of vehicle detection and serious problem of missed detections in haze environments, this paper proposes an improved All-in-One Dehazing Network (AOD-Net) algorithm for detecting foggy vehicles, which adds batch normalization (BN) layers after each layer of convolution in AOD-Net, accelerating the convergence of the model and controlling overfitting. To enhance image detail information, an effective pyramid-shaped PSA attention module is embedded to extract richer feature information, enrich model representation, and improve the loss function to a multi-scale structural similarity (MS-SSIM) + L1 mixed loss function, thereby improving the quality, brightness, and contrast of dehazing images. Compared with current image dehazing algorithms, the dehazing quality of our algorithm is superior to other dehazing algorithms, such as dark channel prior (DCP), Dehaze-Net, and Fusion Feature Attention Network (FFA-Net). Compared with AOD-Net, the improved algorithm has increased the peak signal-to-noise ratio by 3.23 dB. At the same time, after the improved AOD-Net image dehazing processing, YOLOv7 object detection was performed and experimentally validated on a real foggy dataset. The results showed that compared with the previous method, it had better recognition performance in foggy detection and recognition, and higher detection accuracy for vehicles.

Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neom Smart City

Smart cities like Neom require efficient and reliable transportation systems to support their vision of sustainable and interconnected urban environments. High-speed trains (HSTs) play a crucial role in connecting different areas of the city and facilitating seamless mobility. However, to ensure uninterrupted communication along the rail lines, advanced communication systems are essential to expand the coverage range of each base station (BS) while reducing the handover frequency. This paper presents the dual transceiver free space optical (FSO) communication system as a solution to achieve these objectives in the operational environment of HSTs in Neom city. Our channel model incorporates log-normal (LN) and gamma–gamma (GG) distributions to represent channel impairments and atmospheric turbulence in the city. Furthermore, we integrated the siding loop model, providing valuable insights into the system in real-world scenarios. To assess the system’s performance, we formulated the received signal-to-noise ratio (SNR) of the network under assumed fading conditions. Additionally, we analyzed the system’s bit error rate (BER) analytically and through Monte Carlo simulation. A comparative analysis with reconfigurable intelligent surfaces (RIS) and relay-assisted FSO communications shows the superior coverage area and efficiency of the dual transceiver model. A significant reduction of up to 76% and 99% in the number of required BSs compared to RIS and relay, respectively, is observed. This reduction leads to fewer handovers and lower capital expenditure (CAPEX) costs.

Driving Economic Growth through Transportation Infrastructure: An In-Depth Spatial Econometric Analysis

This research investigates the crucial role of transportation infrastructure in influencing economic activity, thus employing advanced econometric methods including Moran’s I index, LM, Hausman, and LR tests to ensure analytical accuracy and select the appropriate spatial model. Our findings reveal that freight volumes across road, waterway, and civil aviation significantly enhance economic activity by bolstering domestic trade, industrial production, and supply chains. Conversely, the impact of passenger turnover is comparatively minor, although it still contributes to labor mobility and urban accessibility. This study highlights the need for strategic investment in transportation infrastructure and efficient public transport systems to foster economic growth and sustainable development. We recommend that policymakers focus on optimizing transportation networks and integrating intelligent transport technologies to boost economic competitiveness and societal well-being. This analysis not only sheds light on the direct economic impacts of transportation but also underscores the broader social implications, thus advocating for a holistic approach to transportation planning and policymaking.

Machine Learning Models for Real-Time Traffic Prediction: A Case Study in Urban Traffic Management

This study introduces and evaluates the Long-term Traffic Prediction Network (LTPN), a specialized machine learning framework designed for real-time traffic prediction in urban environments. Utilizing a unique combination of convolutional and recurrent neural network layers, the LTPN model consistently outperforms established predictive models across various metrics. It demonstrates significantly lower error rates in both short and long-term traffic forecasts, highlighting its superior accuracy and reliability. The effectiveness of the LTPN model is underscored by its robust performance under diverse traffic conditions, making it a promising tool for enhancing the efficiency and responsiveness of intelligent transportation systems (ITS). This paper details the model's architecture, training processes, and a comprehensive comparison of its predictive capabilities against traditional models, providing clear evidence of its advantages in real-world applications.

Predicting Engine Emissions Using Eco-Friendly Fuels for Sustainable Transportation

In recent years, increasing concerns about vehicle emissions' environmental and public health impacts have led to the desire to use eco-friendly fuels as alternatives to traditional fossil fuels. Biofuels, hydrogen, and electric power offer lower greenhouse gas emissions and improved air quality, resulting in their development and adoption globally. Predicting vehicle emissions using these fuels is crucial for assessing their environmental benefits. This study proposes using artificial neural networks (ANN), a machine learning technique, to accurately predict vehicle emissions associated with eco-friendly fuels across different compositions and engine speeds. The ANN model has a strong correlation between predicted and observed emissions values, indicating the effectiveness of its model. The research underscores the importance of adopting innovative approaches to address environmental challenges and promote sustainable transportation solutions. This study contributes to reducing the adverse effects of vehicle emissions on air quality and public health by assisting policymakers, car manufacturers, and city planners in making effective decisions. It promotes environmental sustainability by providing valuable insights into vehicle emissions prediction and guiding the development of eco-friendly fuels for a more efficient transportation system.

Railway Train Collision Avoidance on Same track and Animal Detection Using AI - IoT

The increasing demand for safer and more efficient railway transportation systems has prompted the exploration of advanced technologies to mitigate collision risks and address emerging challenges such as animal incursions on railway tracks. This paper proposes a comprehensive solution leveraging Artificial Intelligence (AI) and Internet of Things (IoT) technologies for real-time detection and avoidance of collisions between trains operating on the same track and encounters with animals. These sensors capture various environmental and operational data such as train positions, velocities, and track conditions. The AI algorithms analyze this data to identify potential collision risks and animal intrusions. Additionally, real-time communication between trains and the centralized control system enables timely intervention and rerouting decisions to ensure safe operations. The system incorporates advanced image recognition algorithms to detect and classify animals near railway tracks. Utilizing high-resolution cameras and IoT-connected devices, the system identifies animals in the vicinity and alerts train operators or initiates automated braking mechanisms to prevent accidents caused by animal incursions. Key features of the proposed system include scalability to accommodate varying railway infrastructures, adaptability to diverse environmental conditions, and interoperability with existing railway signaling and control systems. Moreover, the integration of AI and IoT technologies enhances the system's predictive capabilities, enabling proactive risk mitigation and improving overall.

IoT-Driven solutions for VANET trustworthiness: Examining misconduct and position security challenges

Vehicular ad hoc networks (VANETs) improve the traffic management and road safety by exchanging real-time data between automobiles and roadside infrastructure. Reliable Intelligent Transport Systems (ITS) solutions tend to boost VANET data security, integrity and dependability. These systems communicate VANET data between infrastructure and automobiles using sensors, Internet of Things (IoT) devices and communication technologies. IoT-driven includes a Real-Time Traffic Observation optimizes VANET route design. Traffic management and safety improve with ITS-connected cars and infrastructure. Remote communication technologies enhance traffic management and decision-making by sending a real-time data between cars and infrastructure. Smart parking systems use real-time parking availability data from IoT sensors and devices to maximize space use and reduce congestion. Security and misbehavior are dependable problems in 40 IoT-driven VANET studies. Miscommunication and unstandardized are the main security issues which pose hazards. This review shows VANETs that requires authentication, encryption and anomaly detection to prevent attacks and IoT for transportation sector. This extensive review covers VANET reliability and IoT-driven real-time traffic monitoring, ITS, remote communication and intelligent parking systems. This review analyzes the metrics to assess the many detection techniques which includes, misconduct-Aware On-Demand Collaborative Intrusion Detection System (MACIDS) and Collision-Induced Dissociation Spectroscopy (CIDS), (i) Accuracy (ii) False Positive Rate, (iii) precision, (iv) recall (v) False Negative Rate and (vi) F1-Score in VANET security in Attack Detection Rates (ADR), misbehavior detection, evaluation metrics and detection algorithms were studied using CIC-IDS data. Due to misconduct and security issues, a strong IoT-driven solutions are required to improve VANET trustworthiness and offers a secure and reliable vehicular communication networks for safer and more efficient transportation systems.

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

Road traffic anomaly detection is vital for reducing the number of accidents and ensuring a more efficient and safer transportation system. In highways, where traffic volume and speed limits are high, anomaly detection is not only essential but also considerably more challenging, given the multitude of fast-moving vehicles, often observed from extended distances and diverse angles, occluded by other objects, and subjected to variations in illumination and adverse weather conditions. This complexity has meant that human error often limits anomaly detection, making the role of computer vision systems integral to its success. In light of these challenges, this paper introduces MEDAVET - a sophisticated computer vision system engineered with an innovative mechanism that leverages spatial and temporal structures for high-precision traffic anomaly detection on highways. MEDAVET is assessed in its object tracking and anomaly detection efficacy using the UA-DETRAC and Track 4 benchmarks and has its performance compared with that of an array of state-of-the-art systems. The results have shown that, when MEDAVET’s ability to delimit relevant areas of the highway, through a bipartite graph and the Convex Hull algorithm, is paired with its QuadTree-based spatial and temporal approaches for detecting occluded and stationary vehicles, it emerges as superior in precision, compared to its counterparts, and with a competitive computational efficiency.

Insights into Passenger Transport Choices: A Mumbai Case Study

Understanding and studying passenger preferences in passenger transportation systems is important to optimise transportation infrastructure and increase passenger satisfaction. This paper presents a comprehensive case study conducted in Mumbai, India, which aims to understand the diverse preferences of commuters using different modes of transport within the city. Adopting a mixed-methods approach, data were accumulated by means of surveys, interviews, and observational studies conducted across different demographic sectors and geographical locations of Mumbai. The study analysed factors influencing commuters' mode choice, including cost, comfort, convenience, travel time, safety, and environmental concerns. The findings revealed significant differences in the preferences of commuters, impacted by factors like socioeconomic status, occupation, travel distance, and access to infrastructure. While public transportation emerged as the dominant mode for daily commuting, preferences were nuanced, some demographics prefer private modes like cars and motorcycles. Additionally, the study identifies areas of improvement in the existing transport infrastructure and suggests measures to enhance the reliability, frequency, safety, and connectivity of public transport services. The knowledge gleaned from this research provides valuable guidance for policymakers, urban planners, and transportation authorities in Mumbai, offering opportunities to tailor transportation solutions that align with commuters' preferences, thereby promoting sustainable and efficient passenger transport systems in the city.

Sustainability of Nigeria’s Rail Transport System: An Economic Perspective

The availability of an efficient transport system is germane in achieving trade promotion, social integration, business connectivity and economic development because transport plays relevant role in the growth of nation’s market. Some scholars have given undeniable linkages to the role of rail transport in economic development while there are deferring summations by some scholars hence this study explored the economic sustainability of Nigeria’s rail transport system to contribute to the body of knowledge debating the economic sustainability of rail transport globally. Previous researches have also been done in Nigeria and internationally on the subject area using different approaches but this research used concurrent triangulation design to validate the formulated hypothesis that rail transport system has not significantly affected local economic development. This was validated using simple linear regression and key informant interview (KII). The theoretical lens for the study is the Rostow’s stages of economic growth. The result, [R2= .12, F (1, 172) = 24.31, p &lt; .01] showed that rail transport has contributed significantly to the local economic development and the KII generally reinforce the empirical findings hence hypothesis is rejected. This study recommended the permitting of more vendors providing services at the terminal as this will lead to vibrant local economic development whilst improving the standard of living of residents and the adoption of a holistic economic sustainability policy be embraced by the managers of rail transport system in the country.