Transport Authorities Research Articles

Investigating the influence of eye-catching effect on mental workload in highway tunnel entrances: a comprehensive analysis of eye blink behavior

Objective The objective of this study was to investigate the relationship between eye-catching effects and mental workload at highway tunnel entrances. Specifically, the study aimed to analyze drivers’ eye blink behavior to gain a comprehensive understanding of how visual attraction at tunnel entrances affects cognitive workload. Methods 50 participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Eye-tracking technology was utilized to record and analyze the eye blink behavior of participating drivers. Various metrics, including blink frequency, blink duration, inter-blink interval, and pupil diameter after a blink, were measured and compared across different scenarios. Results The results of the study demonstrated significant differences in drivers’ eye blink behavior across the different experimental scenarios, indicating the influence of visual attraction conditions on mental workload. The presence of eye-catching stimuli (landscape-style architecture, tip slogan, and billboard scenarios) at tunnel entrances resulted in decreased blink frequency, shorter blink duration, longer inter-blink intervals, and larger pupil diameter after a blink compared to when no specific eye-catching stimuli were present (baseline condition). These findings suggest that visual attractions capture drivers’ attention, leading to increased cognitive workload and attentional demands. Conclusions The findings of this study contribute to the existing literature on driver attention and mental workload, particularly in relation to eye-catching effect in tunnel environments. The presence of eye-catching stimuli at tunnel entrances can distract drivers and increase their mental workload, potentially compromising driving performance and safety. It is crucial for transportation authorities and designers to carefully consider the design and placement of visual attractions in tunnel entrances to minimize distraction and cognitive workload. By doing so, driving safety and performance can be enhanced in tunnel entrances.

Interactions and Behaviors of Pedestrians with Autonomous Vehicles: A Synthesis

Integrating autonomous vehicles (AVs) into public roads presents profound implications for pedestrian safety and the broader acceptance of this emerging technology. This work examines the complex interactions between AVs and pedestrians, a dynamic influenced by the variability of pedestrian behaviors and the absence of traditional communication mechanisms, such as eye contact and gestures, commonly relied upon in human-driven scenarios. Given the nascent stage of AV deployment, this research addresses the challenges of evaluating AV−pedestrian interactions amid safety concerns and technological limitations. We review and synthesize global research on pedestrian behavior in the context of AV technology to track changes in pedestrians’ acceptance over time and identify the factors driving these shifts. Additionally, this review incorporates insights from transportation authorities to highlight potential safety issues and the need for innovative communication strategies that ensure safe interactions between pedestrians and AVs. By analyzing these factors, the research aims to contribute to the development of guidelines and communication protocols that enhance pedestrian safety and facilitate the integration of AVs into urban environments.

Riding the Wave: The Growing Preference for Public Transit System among Customers

Public transport is essential for urban mobility, reducing traffic congestion, and minimizing environmental impact. However, inadequate public transport conditions in Karachi have shifted interest towards private vehicles. The Green Line Bus Rapid Transit (BRT) System aims to offer a modern alternative and improve the current situation. This study aims to explore the role of technological developments, best practices, and standard operating procedures (SOPs) in improving service quality and attracting people to public transportation in Karachi. A cross-sectional study and survey were conducted in Karachi to gauge residents’ perceptions and expectations of the Green Line BRT System. Data were collected from 450 participants using a non-probability sampling technique. The findings reveal that technological advancements contribute to improved service quality and are considered significant by consumers when evaluating and selecting public transport services. Improved road quality further reinforces this connection. These insights are essential for policymakers and transport authorities to develop strategies that increase the attractiveness of public transportation and ensure sustainable urban mobility. Addressing core issues with modern technological methods can improve public transport systems and inspire further research.

Meta-Analysis of Road Traffic Accidents in Bangladesh: A Health and Safety Perspective

Road traffic accidents represent a complex challenge with far-reaching implications for public health and safety in Bangladesh. This meta-analysis undertakes a comprehensive exploration, positioning the investigation within the expansive context of health and safety. Drawing upon data repositories from both authoritative channels, exemplified by the Bangladesh Road Transport Authority (BRTA), and independent platforms such as the Bangladesh Jatri Kalyan Samity, this study endeavors to provide an intricate understanding of the multifaceted health implications stemming from road accidents. By adopting a nuanced approach that amalgamates official and independent datasets, the study seeks to transcend traditional analyses and offer a holistic perspective on the impact of road accidents on public health. A focal point of this meta-analysis is the meticulous scrutiny of monthly trends, recognizing that temporal variations can significantly influence the health outcomes of road accidents. The aim is to discern patterns, identify peaks and troughs, and elucidate the interplay between seasonal, temporal, and health-related factors. This temporal analysis is pivotal for formulating targeted interventions and policies that align with dynamic patterns and effectively address health challenges associated with road accidents. Addressing the challenge of disparities in reporting between official and independent sources, this meta-analysis accentuates the critical need for standardized data collection methodologies. By acknowledging and reconciling variations in reported statistics, the study aspires to enhance the accuracy of health impact assessments, ensuring a more precise understanding of the consequences of road traffic accidents.

Service failure is bound to happen: Unraveling the impact of dissatisfaction, complaint behavior and re-travel intention in travel and tourism industry

Service failure is bound to happen, but dissatisfaction has always been undermined by scholars previously. The present study investigates dissatisfaction factors in travel decision-making leading towards re-travel intention with the help of the expectancy disconfirmation theory (EDT) model. Moreover, complaint behavior mediates the relationship between dissatisfaction and re-travel intention concerning the factors involved in it. For the findings, 434 real-time responses were collected through an offline survey following the lifetime value (LTV) approach. Kaiser-Meyer-Olkin (KMO) and Bartlett's tests were conducted to test the sphericity of the variables one by one. Furthermore, structural equation modeling (SEM) was implied to test the measurement and structural models. All the hypotheses supported and accepted the proposed research questions. The findings reveal that the dissatisfaction factors, with their interaction terms (transaction-based and experience-based), help study the variable dissatisfaction and its multifaceted concept in travel and tourism literature. Moreover, complaint behavior is identified as a key mediating factor in the relationship between dissatisfaction and re-travel intention, suggesting that commuters' response to dissatisfaction, including complaints, shapes their future re-travel intentions. The theoretical implications of the study are substantial, as it advances our understanding of commuter behavior in the context of public transport travel and tourism. Practically, the findings offer actionable insights for public transport authorities and service providers. Understanding the specific dissatisfaction factors influencing re-travel intention allows for targeted interventions to improve service quality and customer experience. Recognizing the importance of effective complaint management systems can help organizations better address customer grievances and mitigate the negative effects of dissatisfaction on re-travel intention.

Speed Management Blueprint: Conception of an IoT-Based Electric Vehicle Speed Limiter Monitoring System for Kigali City Vehicles

The Electric Vehicle Speed Limiter Monitoring System is an Internet of Things (IoT)-based solution to prevent traffic accidents in Kigali, Rwanda. The system utilizes advanced technology to monitor vehicle speed, identity, and position, including GPS, RFID cards, RFID 522 modules, and IR sensors. While RFID cards facilitate easy vehicle identification, RFID 522 modules identify speed infractions, IR sensors guarantee precise speed measurement, and GPS tracking offers real-time monitoring capabilities. Campaigns to raise public awareness will assist the system, which will be smoothly incorporated into Kigali’s road network and examined for patterns and trends. Ensuring seamless implementation and enforcement will require collaboration with municipal authorities, law enforcement agencies, and transportation authorities. Anticipated results include a significant decrease in traffic accidents, increased traffic safety precautions, and improved traffic management techniques. Advanced technology like IR sensors, RFID cards, RFID 522 modules, and GPS are used by Kigali, Rwanda’s IoT-based Electric Vehicle Speed Limiter Monitoring System, to track vehicle position, identification, and speed. It conducts public awareness campaigns, analyses speed-related accidents, and integrates them with Kigali’s road network. Smooth implementation is ensured by cooperation with local law enforcement and authorities, which lowers accident rates and improves traffic management techniques. The system aims to improve road safety and foster a safer atmosphere for all Kigali drivers.

Corrosion-induced fragility of existing prestressed concrete girder bridges under traffic loads

Italian and European transportation networks include a significant number of existing bridges, built since the early ‘60s, characterised by simply supported prestressed concrete (PC) girders with post-tensioned steel tendons. Corrosion of tendons, which may lead to significant loss of structural capacity, cannot be detected by simple visual inspections and requires advanced and expensive testing by bridge owners. Therefore, procedures aimed at risk-informed prioritisation of advanced inspections and possibly retrofit are needed. The paper presents a study on the fragility of existing PC girder bridges considering traffic loads, accounting for corrosion-induced effects. An automated framework is proposed, aiming at the efficient probabilistic structural assessment of the investigated bridge typology accounting for different critical corrosion scenarios and the influence of knowledge-based uncertainty related to geometric and mechanical properties. To simulate corrosion effects, prestressing steel tendon geometric and mechanical characteristics are modified through a specific algorithm able to estimate variations in flexural and shear bearing capacity of critical cross-sections. To simulate the traffic loads, a simplified analysis is performed by using code-based traffic load models. In the paper, the framework is tested with reference to a dataset of case-study superstructures. The obtained fragility curves are deeply discussed, highlighting the effects of the number of girders and span length on the corrosion-induced increase in fragility. Although the proposed methodology presents some simplifications, it could improve the current practices of risk prioritisation, by supporting transportation authorities in ensuring the safety of the existing bridge stock.

An Evaluation of Human Resource Practices on Employee Retention in State Corporations in Kenya

Organisations can achieve a competitive advantage by enhancing and expanding human resource knowledge, skills, motivation, and morale. The study aimed to determine the effect of performance management techniques on staff retention at the National Transport and Safety Authority-Kenya. The study focused on 2100 employees working in 15 of Kenya's National Transport and Safety Authority branches. A descriptive research design was used in the study, with a sample size of 336 respondents selected using a stratified random sampling technique. Findings showed that Staff retention is positively correlated with performance management. There was a strong positive correlation (association) (r = 0.725) between performance management and employee retention. The ANOVA findings determined the regression model's significance, from which an f-significant value of P<0.001 was derived, indicating that the model's likelihood (probability) of producing an incorrect prediction is less than 0.001. Management of NTSA-Kenya should appropriately handle aspects of employee relations management, such as HR initiatives, leadership styles, and shared values. They should also provide their employees with rewards in the form of cash bonuses, payments based on performance and pension schemes. Previous research on human resource practices and employee retention has mostly focused on the private sector, leaving a study gap on the effect of these policies on staff retention in Kenyan state corporations such as NTSA. The study, therefore, fills the gap in Knowledge of the performance management practice affecting staff retention.

Traffic intensity and vegetation management affect flower‐visiting insects and their response to resources in road verges

Abstract Road verges can support high densities of flowers and could therefore provide new opportunities for the conservation of flower‐visiting insects. One way of optimizing road verges for vascular plant diversity is to adjust mowing regimes, but to date it is unclear how this affects flower‐visiting insects. Furthermore, for mobile organisms like wild bees and butterflies, there is a risk that the benefit of increased habitat quality in road verges is limited by the proximity to traffic, but this is poorly studied. In a crossed study design, we separated mowing time and frequency (early summer and autumn, or only late summer) from road verge habitat classification (valuable for biodiversity according to transport authority, or regular). We did so along a gradient of traffic intensity, to investigate if a mowing regime designed to enhance plant diversity can also benefit wild bees and butterflies, and if traffic limits the conservation potential of road verges. Road verges that were mown only in late summer had higher flower densities, and there was a positive relationship between flower density and wild bee abundance and species richness. Butterfly abundance and species richness only benefitted from a late summer mowing in valuable but not in regular road verges. Traffic intensity had a substantial negative impact on abundance and species richness of wild bees and butterflies. Higher traffic intensities limited the positive relationship between plant and butterfly species richness that we observed at lower traffic intensities. Increasing width of the road verges buffered negative effects of the traffic on wild bee as well as butterfly abundances, and on wild bee species richness. Synthesis and applications. Road verges can play a valuable role for the conservation of wild bees and butterflies, but there is a need to consider both traffic intensity and resource availability when implementing management strategies. To support wild bee and butterfly diversity, we recommend actions to enhance plant species richness and flower resource availability, and to focus these conservation efforts on roads with low traffic intensity, or on wide road verges.

Research on Active Safety Situation of Road Passenger Transportation Enterprises: Evaluation, Prediction, and Analysis.

The road passenger transportation enterprise is a complex system, requiring a clear understanding of their active safety situation (ASS), trends, and influencing factors. This facilitates transportation authorities to promptly receive signals and take effective measures. Through exploratory factor analysis and confirmatory factor analysis, we delved into potential factors for evaluating ASS and extracted an ASS index. To predict obtaining a higher ASS information rate, we compared multiple time series models, including GRU (gated recurrent unit), LSTM (long short-term memory), ARIMA, Prophet, Conv_LSTM, and TCN (temporal convolutional network). This paper proposed the WDA-DBN (water drop algorithm-Deep Belief Network) model and employed DEEPSHAP to identify factors with higher ASS information content. TCN and GRU performed well in the prediction. Compared to the other models, WDA-DBN exhibited the best performance in terms of MSE and MAE. Overall, deep learning models outperform econometric models in terms of information processing. The total time spent processing alarms positively influences ASS, while variables such as fatigue driving occurrences, abnormal driving occurrences, and nighttime driving alarm occurrences have a negative impact on ASS.

Safety and Mobility Performance Comparison of Two-Plus-One and Two-Lane Two-Way Roads: A Simulation Study

Two-plus-one (2+1) highways are a special configuration of two-lane two-way (TLTW) highways with a continuous center lane that is used to alternate passing lanes. The main objective of this paper is, therefore, to evaluate the suitability of the 2+1 design for Middle East conditions as a replacement for traditional TLTW roads with higher traffic volumes or as an interim solution before transforming TLTW roads into four-lane highways. In our analysis, we considered both safety and mobility performances by comparing the 2+1 and TLTW designs. The new suggested 2+1 designs were evaluated, with the first design prohibiting overtaking in the opposite direction, while the second design permitted it. Additionally, two-speed-limit strategies, uniform speed limit (USL), and differential speed limit (DSL) were also evaluated. The results showed that the 2+1 design, which prohibited overtaking in the opposite direction, was superior to TLTW in terms of mobility and safety, while the other design compromised safety compared to TLTW. The results provide valuable information to policymakers, urban planners, and transport authorities to guide evidence-based decisions on the integration of the 2+1 design as a viable solution for sustainable and efficient transportation.

Assessing shared auto-rickshaws adoption by intra-city commuters as part of the public transport system: The influence of negative encounters on passenger satisfaction

Emerging research has highlighted the significant role auto-rickshaws play in travel services, and yet, compared to other transport modes, they have received relatively less attention in research. No study has investigated travel behaviour and negative encounters in shared auto-rickshaws. The assumption that negative encounters deter users from utilising shared auto-rickshaws has not yet been validated. Moreover, previous studies have focused on adopting auto-rickshaws and travel behaviour, neglecting the specific dynamics of shared services within the public transport landscape. Here, we investigate the determinants influencing shared auto-rickshaw usage as a distinct mode in Ghana. We also identify negative attributes by drawing the link between negative user encounters and their impact on satisfaction, loyalty, and openness to policy propositions. The study deployed structural equation modelling, which is generally preferred in transport research. We discovered that ‘Availability’ is the most significant factor in predicting the adoption of shared auto-rickshaws. The estimated model established that negative encounters substantially impact users’ satisfaction, and users’ satisfaction was impacted by discomfort with seating arrangement, driver conduct, fleecing of users, and long travel time, among others. Older commuters reported higher levels of satisfaction than younger commuters, whereas commuters with higher levels of education tend to report lower levels of satisfaction. This research expands the limited understanding of shared auto-rickshaw usage by advancing our theoretical grasp of how negative encounters influence satisfaction and loyalty from a user-centric perspective. These insights can aid policymakers, service providers, and transport authorities in crafting legislation that prioritises user needs, enhances service quality, and creates a positive commuting experience. Furthermore, this study discusses managerial implications for transport authorities and policymakers.

Modeling driver injury severity using machine learning algorithms

This study planned to predict and analyze the driver injury severity (DIS) using 12 machine learning (ML) algorithms. Police reports of single- and two-vehicle accidents that occurred during 2011–2020 in the two cities of India (Itanagar and Imphal) were used in this study. The best-performing model to predict the DIS for Itanagar was Gradient Boosting Trees (GBT). “Causes of Accident” variable had shown maximum impact on the DIS. In the case of Imphal, it was the GBT, Extra Trees, and Random Forest models across all k-fold cross-validation for train ratios 0.70, 0.80, and 0.90, respectively. “Causes of Accident” and “Vehicle Type” had shown maximum impact on the DIS. These results reveal that the ML models can be applied in hilly areas to predict and identify the important factors that affect DIS. Transportation authorities can analyze road accident data using these models while implementing various road safety measures.

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

Public transport provision and social sustainability in Sweden

ABSTRACT The provision of public transport is important for a sustainable society and is vital for many groups. Children, older people, and people from carless households are examples of such groups: they rely on public transport and risk social exclusion without it. The aim of this study is to identify areas in Sweden where an improved public transport system might enhance social sustainability. To this end, an index of public transport needs at a sub-municipality level was calculated along with an index of public transport provision using data from public transport authorities in Sweden. The results indicate that areas with a mismatch between the provision of and estimated demand for public transport are relatively heterogenous. High gaps exist both in rural and peripheral regions of Sweden as well as the suburbs of urban regions.

Analyzing the Prevalence of and Factors Associated with Road Traffic Crashes (RTCs) among Motorcyclists in Bangladesh.

A cross-sectional survey was conducted using a structured questionnaire involving 402 motorcyclists from four major southeastern towns, comprising 350 (86.07%) males and 52 (12.93%) females. The chi-square test was applied in bivariate analysis, and binary multivariable logistic regression was performed to determine the risk factors of road traffic crashes. This study's findings revealed that the overall reported prevalence of road traffic crashes involving motorcycle drivers over one year was 68.66%. Multivariable logistic regression analysis revealed several factors that significantly impacted road traffic crashes. These factors included driving without a valid driving license, the young age (<20) of motorcyclists, driving in rainy weather, exceeding the speed limit, per-week working hours, smoking status, motorcycle ownership, the brand of motorcycle, and not wearing a helmet while driving. The study findings highlight the need for improving motorcycle safety by implementing measures such as imposing per-week work hour limits for riders, enforcing traffic regulations, and promoting helmet use among motorcycle drivers. The results of this study draw attention to the Bangladesh Road Transport Authority (BRTA) and motorcycle drivers in the country to decrease motorcycle crashes and the severity of injuries by implementing efficient guidelines and strategies for driving motorcycles. The findings of this study can assist policymakers and concerned authorities in taking the essential steps to lessen road traffic crashes among motorcyclists in Bangladesh.

Traffic Congestion Analysis of Asaba Road Using Volume to Capacity Ratio and Speed Performance Index

With Nnebisi Road acting as a case study, this study focuses on the investigation of traffic congestion on Asaba Road. The study aims to examine the relationship between vehicle volume and road capacity, enabling transportation authorities to assess congestion levels and make informed decisions to alleviate traffic issues. Peak traffic counts were conducted, revealing the highest traffic flow on specific road sections on different days. The Ibusa – Stadium junction saw the highest volume on Monday, reaching 3,056 vehicles, while Ibusa – Ofili-Ilukwu junction peaked at 3,128 vehicles, also on Monday, and Ibusa – Ogbeogonogo junction hit 3,151 vehicles on Tuesday. Speed performance index varied across junctions and days, generally exceeding 60%. For instance, Ibusa – Stadium Junction ranged from 69.57% to 71.82% from Monday to Friday, Ibusa – Ofili-Ilukwu junction showed values between 63.59% and 68.95%, and Ibusa – Ogbeogonogo Junction ranged from 61.57% to 70.11%. These findings suggest a better road traffic state with higher average speeds. Moreover, the volume to capacity ratio, indicates the level of congestion and delays experienced. The ratio values along the studied road sections ranged from 0.61 to 0.70. These ratios, along with the speed performance index, classify the level of service as class B, indicating moderate congestion and delays with slightly longer travel times, yet still acceptable to users.

Foresight plus: serverless spatio-temporal traffic forecasting

Building a real-time spatio-temporal forecasting system is a challenging problem with many practical applications such as traffic and road network management. Most forecasting research focuses on achieving (often marginal) improvements in evaluation metrics such as MAE/MAPE on static benchmark datasets, with less attention paid to building practical pipelines which achieve timely and accurate forecasts when the network is under heavy load. Transport authorities also need to leverage dynamic data sources such as roadworks and vehicle-level flow data, while also supporting ad-hoc inference workloads at low cost. Our cloud-based forecasting solution Foresight, developed in collaboration with Transport for the West Midlands (TfWM), is able to ingest, aggregate and process streamed traffic data, enhanced with dynamic vehicle-level flow and urban event information, to produce regularly scheduled forecasts with high accuracy. In this work, we extend Foresight with several novel enhancements, into a new system which we term Foresight Plus. New features include an efficient method for extending the forecasting scale, enabling predictions further into the future. We also augment the inference architecture with a new, fully serverless design which offers a more cost-effective solution and which seamlessly handles sporadic inference workloads over multiple forecasting scales. We observe that Graph Neural Network (GNN) forecasting models are robust to extensions of the forecasting scale, achieving consistent performance up to 48 hours ahead. This is in contrast to the 1 hour forecasting periods popularly considered in this context. Further, our serverless inference solution is shown to be more cost-effective than provisioned alternatives in corresponding use-cases. We identify the optimal memory configuration of serverless resources to achieve an attractive cost-to-performance ratio.

Enhancing Women's Commuting Experience: A Study of Ladies’ Special Trains in the Mumbai Suburban Railway System

The suburban railway network in Mumbai plays a crucial role in the daily lives of millions of people. To improve safety and comfort for female commuters, Ladies’ Special Trains have been introduced. This research paper undertakes a comparative analysis of Ladies’ Special Trains and conventional local trains, examining their historical backgrounds, developmental trajectory, and influence on the commuting experiences of women. Through the evaluation of safety, comfort, and convenience, we analyse the advantages and disadvantages of both transportation methods to inform future enhancements. The results of this study make a valuable contribution to the scholarly conversation and provide valuable insights for policymakers, transportation authorities, and civil society organizations involved in promoting inclusive and secure mobility solutions in Mumbai.

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.