Bus Terminals Research Articles

Network analysis of socioeconomic disparities and public transport in COVID-19 spread: a case study in northeast Brazil.

The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted public transportation systems worldwide. In this study, we evaluated the rate of COVID-19 positivity and its associated factors among users of public transportation in socioeconomically disadvantaged regions of Brazil during the pre-vaccination phase of the pandemic. This ecological study, conducted in Aracaju city in Northeast Brazil, is a component of the TestAju Program. This initiative was designed to expand COVID-19 testing to asymptomatic individuals in public spaces such as squares and bus terminals. Using logistic regression, we examined the relationship between COVID-19 positivity and factors such as demographics, socioeconomic status, and travel frequency. The Fruchterman-Reingold algorithm was used to explore transmission pathways across neighborhoods with varying living conditions. Of the 1,420 public transport users tested via real time reverse transcriptase polymerase chain reaction (RT-PCR), 249 were positive, indicating a 17.5% positivity rate (95% CI: 15.7-19.6). Our findings revealed a higher positivity rate during periods of increased viral spread (OR = 4.3, 95% CI: 3.1-5.9) and in neighborhoods with poorer conditions (OR = 1.5, 95% CI: 1.1-2.1). Network analysis revealed affluent neighborhoods as significant transmission hubs of the disease. Our study highlights the vital role of urban mobility patterns in the spread of COVID-19. Neighborhoods with better living conditions that serve as hubs of activity and movement, enable gatherings and interactions among people from diverse regions, including those from areas with higher infection rates.

Co-Design Optimization and Total Cost of Ownership Analysis of an Electric Bus Depot Microgrid with Photovoltaics and Energy Storage Systems

Due to the increasing share of battery electric buses (BEBs) in cities, depots need to be adapted to the increasing load demand. The integration of renewable energy sources (RESs) into a depot can increase the self-consumption, but optimal sizing is required for a cost-efficient and reliable operation. Accordingly, this paper introduces a co-design optimization framework for a depot microgrid, equipped with photovoltaics (PVs) and an energy storage system (ESS). Three European cities are considered to evaluate the effect of different environmental conditions and electricity prices on the optimal microgrid design. Accurate models of the different subsystems are created to estimate the load demand and the power generation. Different energy management strategies (EMSs), developed to properly control the power flow within the microgrid, are compared in terms of operational costs reduction, one of which was also experimentally validated using a hardware-in-the-loop (HiL) test setup. In addition, the total cost of ownership (TCO) of the depot microgrid is analyzed, showing that an optimally designed depot microgrid can reduce the charging-related expenses for the public transport operator (PTO) by 30% compared to a scenario in which only the distribution grid supplies the BEB depot.

Common Voice Disorders among Bus Helpers in Dhaka City

Background: There are approximately 9911 buses and 8515 minibuses are running within Dhaka city. Most of them have bus helpers who most of the time uses loud voice and yelling for their job. For which they are under a great risk of having voice disorder. No study was conduct to find the rate of voice disorder in this group. This study will seek out the rate of voice disorder and the risk among this group. Materials and methods: A total of 80 bus helpers participated in the study. Their vehicle routes were within Dhaka city. It was a cross-sectional survey. Sampling was done by convenience technique. Samples were selected from different bus depots in Dhaka city. Data were collected by face to face interview using a questionnaire. Data analysis was done by descriptive statistical analysis. Statistical Package for Social Science- 17 (SPSS) and Microsoft Excel 2007 were used for data analysis. Results: The result shows that 84% of bus helpers had voice problem throughout their carrier. Current symptoms present among them are hoarseness (92%) vocal fatigue (73%), pain in throat (58%), constant throat dryness (67%) and frequent throat clearing (48%). Less frequent symptoms are breathy voice, strained voice, monotone voice, sore throat, acid taste and loss of voice. Only risk factor found is not drinking adequate water. Conclusion: The study reveals that different types of Vocal disorders are very common among the bus helpers. So, preventive measures and education are necessary to get rid of this problem. IAHS Medical Journal Vol 7(1), June 2024; 60-64

Optimizing shared charging services at sustainable bus charging hubs: A queue theory integration approach

Integrating solar photovoltaic (PV) systems into bus charging infrastructure offers a promising solution to mitigate carbon emissions and reduce grid loads. However, a mismatch between the output of solar PV energy and the electric bus (EB) charging demand can arise due to the instability and intermittency of solar PV energy. To address this issue, this study introduces a novel shared charging business mode that allocates charging facilities to private electric vehicles (PEVs), leveraging idle infrastructure to maximize solar PV utilization. We develop an optimization framework to jointly determine the EB charging schedules and the allocation of charging facilities between EBs and PEVs within an EB network integrated with solar PV systems. Additionally, a loss queue model with time-dependent arrival rates is proposed to model the stochastic nature of PEV charging demand. A case study is conducted on a bus network in Beijing to validate our proposed operational strategy. The findings demonstrate that integrating shared charging in bus depots equipped with solar PV systems improves solar PV utilization from 79.30 % to 92.91 %. This integration reduces CO2 emissions and overall daily system costs by 7.93 % and 14.82 %, respectively, without negatively impacting the grid load during peak periods.

New approaches and performance analysis of on-demand delivery systems using buses

With the rapid growth of on-demand delivery, traditional third-party delivery services using electric bicycles face increasing challenges, including increased labor costs and safety issues. To address these issues, we propose two modes for on-demand delivery systems using buses. One is the “bus terminal + professional crowd shippers” (B&P) mode, which involves consolidating parcels from various origins at the bus transfer hub. From there, they are grouped and delivered by buses. The other is the “bus stop + crowdsourced passenger” (B&C) mode, which transports parcels directly from the origin to the destination by passengers without the need for consolidation and sorting at bus transfer hubs. Both modes fully utilize the surplus capacity of buses and employ resources from retail stores and third-party shippers. We evaluated these modes using key performance indicators (KPIs) such as labor costs, cost increases from en-route destination changes, and average delivery time per kilometer. This analysis was based on 18,349 delivery cases in downtown Ningbo, China. Through this evaluation, we identified the strengths, weaknesses, opportunities, and threats associated with each mode. Our findings reveal that both the B&P and B&C modes effectively reduce delivery costs and external impacts while maintaining a user experience and efficiency level comparable to traditional third-party delivery services for mid-to-long-distance (3–10 km) deliveries. However, the use of public transportation may limit the flexibility of the delivery mode. Based on these insights, we provide strategic recommendations for the further development and implementation of B&P and B&C modes.

An integrated model of electric bus energy consumption and optimised depot charging

The electrification of buses, and the depots servicing them, is a high priority for sustainability but is hindered by challenges at the intersection of the mobility and electricity systems, such as trade-offs of charging infrastructure and scheduling with the stress placed on the grid. This paper presents a model for studying and co-optimise these variables. The model has two standout features. Firstly, that it integrates a data-driven model for predicting the energy consumption of electric buses based on route details with a bus depot charging optimisation algorithm that minimises the loading on the grid. Secondly, it is compatible with the publicly available route and schedule data published by bus operators and is available open-source for extension. The results foreground the impact of route topology, temperature, and traffic congestion on energy consumption, and the interdependencies of these with timetabling, charging infrastructure and battery designs in driving grid loading.

Study on Power System of EV Bus Depot Charging System and Prospect for Smart Charging Implementation

Study on Power System of EV Bus Depot Charging System and Prospect for Smart Charging Implementation

Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing

Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility.

Correlation Analysis between Walking Permeability and Walking Effort (Case Study Terminals in Bandung City)

Abstract Bandung was chosen as a pilot area for Indonesian Mass Transit (MASTRAN) project to increase urban mobility and accessibility. With this MASTRAN project, public transportation nodes should be accessible by all modes, one of which is walking. However, in urban areas in Indonesia, walking activities are still not an option. According to some studies, this is caused by the lack of walking infrastructure and environment feasibility. Therefore, the quality of pedestrian infrastructure and environment must be improved to make these public transportation nodes easy for pedestrians to reach. In this case, walking permeability and walking effort assessment are chosen as tools to assess the pedestrian walkability. By doing these assessments, it is found that Kiaracondong Station has the largest average WPDI and WPTI values with values of 1.48 and 1.90 respectively. In addition, it is found that The South Bandung Station has the biggest walking effort index which value is 1.66. By analyzing both assessments, it can be concluded that the increase of WPDI tends to increase WPTI. Besides, it also can be concluded that stations have higher time increments than bus terminals due to an increase in the distance required for pedestrian access to the main terminal entrance.

Analyzing the flexibility potential of bus fleet operators in Germany

The transition to smart energy systems is a crucial component for ensuring sustainability and reducing carbon emissions. Electrification is a key factor in achieving these goals, with the transport sector being an integral part of the equation. The integration of the transport sector with the electricity sector will facilitate a reduction in carbon emissions. This paper assesses the potential of electric bus depots to function as smart energy infrastructures. Analyzing the energetic system flexibility of the electrified public transport system is at the core. Previous studies emphasize the importance of identifying and managing the optimal operation strategies of electrified transport to achieve system flexibility. This work concentrates on Germany as a reference market for balancing and electricity markets at the center of the EU. The flexibility potential of a bus fleet with 80 electric buses is analyzed under optimal participation in the short-term electricity and balancing market. The bus fleet operator acts as a storage systems aggregator, which combines mobile and stationary storages to enhance energy flexibility. The study measures the potential contribution for the stability of the electricity grid in Germany. The additional battery degradation that arises with the provision of balancing services is part of the economic equation. The analysis is based on historical data from 2020, 2021, and 2022 and investigates hypothetically lower and higher demand for balancing energy in the load-frequency control area of Germany and Denmark. The paper concludes by demonstrating the feasibility of the electrified bus depot as an integral component of smart energy systems. These findings contribute to a better understanding of the electrification of transport, sector integration, and the role of infrastructures in achieving smart energy systems and showcases the attractiveness of this business model.

Stress and Sleep Deprivation Experienced by Bus Drivers of Government Bus Depots in an Urban Area of Chengalpattu District in South India.

Background Driving a bus is far more stressful than other professions. Bus drivers also take more sick leave than other workers because of the physical and mental demands of their employment. Bus drivers are especially vulnerable because of their intense work environment, poor sleep, and poor food pattern. The aim of the study was to assess stress and sleep deprivation of the government bus drivers in Chengalpattu District in Tamil Nadu and to identify the sociodemographic factors influencing them. Methodology An analytical cross-sectional study was conducted among 429 government bus drivers working under Tamil Nadu State Transport Corporation (TNSTC )in selected government bus depots in the urban area of Chengalpattu District in Tamil Nadu, India. The principal investigator collected the data by using a semi-structured questionnaire which comprises six parts, i.e., sociodemographic variables, job-related factors, comorbidity, habits, stress assessed by using the Perceived Stress Scale (PSS-10), and sleep deprivation assessed by using the Epworth Sleepiness Scale (ESS). Results In the present study, the mean age of participants was 47.683 years. Hindus comprise 95.3% of the sample, and 96.7% are married. Approximately 47.6% of the drivers have achieved a higher secondary education. The average time spent working as a heavy vehicle driver was 20.4 years. About 37.1% (159 individuals) have hypertension, making it the most prevalent comorbidity. In this study, 47.3% (203 participants) reported very poor sleep, 35.7% (153 participants) reported average sleep, and 17.0% (73 participants) reported good sleep. Inferential statistics revealed that those drivers who were degree holderstook less than three breaks in duty time, slept over six hours at night, had the habit of smoking and drinking alcohol, and took more than 60 minutes to fall asleep experienced very poor sleep according to the ESS. In this study, 57.1% (245 participants) reported moderate stress levels, 24.2% (104 participants) showed high levels of perceived stress, and 18.6% (80 participants) reported low stress levels. Inferential statistics revealed that those drivers who were Muslims, degree holders, those with primary education, smokers, alcohol consumers, drivers with very poor sleep, and those who took more than six days of casual leave in the past six months experienced high stress according to the PSS-10. Conclusion Implementing effective health management strategies and minimizing work-related stress will lead to a decrease in work-related disorders among drivers. TNSTC should ensure enough lodging facilities for drivers at depots, bus terminals, and outstations. They require a minimum of eight hours of sleep every day to maintain optimal physical well-being.

Dynamic and efficient resource allocation for 5G end‐to‐end network slicing: A multi‐agent deep reinforcement learning approach

SummaryThe rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively.

USERS’ SATISFACTION WITH INTERCITY BUS TERMINAL QUALITY IN LAGOS STATE, NIGERIA

Globally, efficient intercity public transport relies on well-designed and high-quality terminal facilities and services to enhance accessibility, mobility, and safety for passengers and freight. Despite governmental efforts, bus terminals in Nigerian urban areas are becoming hotspots for unregulated activities and traffic chaos. In response, this study investigates users’ satisfaction with Lagos State's intercity bus terminal quality, analysing socio-economic profiles, satisfaction levels with quality of terminal facilities (QoTF) and services (QoTS), and challenges impacting terminal operations. Using a cross-sectional survey, 200 questionnaires were distributed to terminal users, employing a multistage sampling technique. Descriptive (weighted mean analysis) and inferential (multiple linear regression [MLR] analysis) statistics were employed for data analysis. Results indicate that the majority of respondents are male, aged below 40, and permanent terminal users. Satisfaction is noted with 13 out of 24 parameters of QoTF, with top-ranking parameters including parking space, restaurant facilities, and waste disposal facilities. However, dissatisfaction is expressed with 8 out of 14 parameters of QoTS. Least-satisfied QoTS parameters include staff attitudes, passenger safety, and bus service reliability. Top-rated challenges affecting terminal operations are security concerns, sanitation facility deficiencies and insufficient passenger amenities. MLR analysis indicates a significant influence of terminal facilities on overall service satisfaction (F= 2.899, p= 0.000 <0.05). In conclusion, enhancing both terminal facilities and services is crucial for efficient operations in Lagos State's bus terminals. Recommendations include improving security measures, sanitary infrastructure and staff training to address user concerns and enhance satisfaction.

The value of waiting spaces: Tourists’ willingness to pay for high-quality bus terminals

Transport quality has long been recognized as an important factor in influencing travelers’ behavior, and transport terminal quality undoubtedly plays no small part. Indeed, transit promotion policies explicitly based on qualitative factors and high-standard architecture are increasingly being adopted in designing new bus terminals and stops. High architectural standards can be found in several bus terminals worldwide. Nevertheless, the literature in transport sector has yet to explore the impact of bus terminal “hedonic quality” on users’ behavior or use their willingness to pay (WTP) in cost–benefit analyses or other transport policy applications. Hedonic value is here intended as the aggregate of all elements related to travelers’ pleasure in spending time in a terminal where architectural beauty and the passenger services offered are arguably the most visible and representative attributes. Within this context, we propose a quantitative analysis of the perceived hedonic value of bus nodes in terms of users’ WTP (pure preference) for a high-quality bus terminal. To this end a discrete choice experiment based on a visual immersive experience was performed, and data were collected from 324 residents of Milan (Italy) traveling for tourism. Different binomial Mixed Logit models with panel data and random coefficients were estimated for the purpose. Monetary valuation of pure preference for using a high-quality bus terminal was estimated with a mean of about 25 % of the average trip cost observed for the case study considered. This pure preference means that the Italian tourist is willing to spend up to €4.35/trip more for a high-quality bus waiting space or travel up to 28.2 min/trip more, instead of using a traditional bus terminal for the same trip. Category-specific analyses show that females > 30 years old have a greater pure preference for a high-quality bus terminal (up to + 220 %) against males and young users. The employed also have a higher (+42 %) pure preference for the beauty of a bus terminal against others. The results of this paper should be compared with those from other case studies as they have a potential impact for transportation planning applications, underling the importance of also incorporating “hedonic quality” as explicit design variables for new/revamped transportation hub. At the same time, new challenges are posed for modeling user behavior and determining quality-related indicators and measures.

Smart Commute Application for Public Transport Management

An urban city's ever-growing population and everyday commuters have created a serious issue, making it difficult for the governing bodies to handle public transit. This project attempts to present a concise overview of the main issues. The latest technical developments, such as IOT, GPS, smartphones, etc., must b e incorporated.This study proposes a bus tracking system that uses software technologies such as flutterflow and firebase database management system to monitor and control bus movements. Here, we suggest a system that integrates the newest technology into already-in-use bus networks so that passengers may learn about the buses' current location as well as their arrival and departure timings via display boards at bus terminals or through apps on smartphones. Keywords— Public transportation, governing body, daily commuters, management, technological advancements, IOT, GPS, Smartphone, App’s, flutterflow, firebase, bus terminal.

Public transport reliability in growing cities: the case of Asokore Mampong, Ghana

ABSTRACT The reliability of public transport systems is often reported in transportation literature as an essential factor in assessing the quality of urban transport services. Studies have largely examined the effects of transport reliability from the perspective of both customers and operators. Policies and interventions have been largely based on the experiences in developed countries, with a dearth of studies within the global south, particularly Africa, with rapidly urbanizing cities and where the transport sector is largely informal with several reliability challenges. This paper thus examines the reliability of public transport services in Ghanaian cities, using Asokore Mampong Municipality as a case study. Using a sample of 250 passengers interviewed at various bus terminals in the Municipality, the paper establishes five factors – the number of stops to pick up and drop off passengers, overaged and rickety vehicles, congestion on the roads, frequent breakdown of vehicles, and the number of vehicles available as affecting the reliability of public transportation in the study area. The paper establishes that public transportation in the study area is unreliable regarding waiting and boarding times of operating vehicles and recommends an effective regulation of public transport operators by the Asokore Mampong Municipal Assembly to improve reliability.

Enhancing Electric Bus Charging Scheduling: An Energy-Integrated Dynamic Bus Replacement Strategy with Time-of-Use Pricing

Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which may lead to inflexible charging plans, high scheduling costs, and low utilization of electricity energy. To address these challenges, this paper proposes a dynamic bus replacement strategy. When the power of an in-service EB is insufficient, a standby EB stationed at nearby charging stations is dispatched in advance to replace this in-service EB at a designated bus stop. Passengers then transfer to the standby bus to complete their journey. The replaced bus proceeds to the charging station and transitions into a “standby bus” status after recharging. A mixed-integer nonlinear programming (MINLP) model is established to determine the dispatching plan for both standby and in-service EBs while also designing optimal charging schemes (i.e., the charging time, location, and the amount of charged power) for electric bus systems. Additionally, this study also incorporates the strategy of time-of-use electricity prices to mitigate the adverse impact on the power grid. The proposed model is linearized to the mixed-integer linear programming (MILP) model and efficiently solved by commercial solvers (e.g., GUROBI). The case study demonstrates that EBs with different energy levels can be dynamically assigned to different bus lines using bus replacement strategies, resulting in reduced electricity costs for EB systems without compromising on scheduling efficiency.

Electric bus fleets during urban flash floods: A mixed bus fleet strategy

Bus electrification is widely cited as a key technique to reduce carbon emissions and mitigate climate change. In some Chinese cities, the electrification rate of buses has even reached 100%. However, compared with diesel buses (DBs), electric buses (EBs) cause scheduling problems during urban flash floods due to their limited wading performance. This study proposes a mixed EB-DB fleet strategy to mitigate bus fleet problems during urban flash floods. The difference in wading height thresholds between EBs and DBs is considered in the fleet schedule. Utilizing a two-fleet strategy, the requisite number of DBs for bus depots is calculated. The mixed bus assignment problem is then solved by linear programming to establish the optimal dispatch scheme for the individual bus route. This study emphasizes the advantages of mixed bus fleets in emergency response and contributes to improving urban transportation resilience.

Ride-Hailing Preferences for First- and Last-Mile Connectivity at Intercity Transit Hubs

This study aims to fill a research gap by focusing on ride-hailing services (RHSs) as first- and last-mile (FLM) modes linking intercity hubs, which have been explored less than metro hubs. Involving 418 RHS users in the Yogyakarta conurbation, Indonesia, this study applies confirmatory factor analysis to identify the motivations for using RHSs as FLM modes and employs a seemingly unrelated regression model to analyse factors influencing RHS usage and the relationship between first-mile and last-mile use, which remains underexplored. The model’s results reveal that the utilization of RHSs for the first mile is mostly seen among younger, educated, and wealthy persons. However, these variables did not impact last-mile-mode use. The model’s results also show that in FLM contexts, vehicle ownership did not substantially impact RHS use. In addition, RHSs are less often used for trips to intercity bus terminals compared to airports and railway stations. This study also highlights the significance of user preferences, such as comfort and safety, seamless transaction and service, and cost and time efficiency, in influencing the use of RHSs for FLM modes. Increasing RHS accessibility at transit hubs, expanding e-payment options, simplifying payments, and prioritizing fairness are suggested strategies to improve urban sustainability through RHSs.

Utilizing electric bus depots for public Charging: Operation strategies and benefit analysis

The uneven distribution of public charging infrastructure poses significant challenges for private electric vehicles (EVs). Many Chinese city bus depots have abundant chargers for electric buses (EBs), often unused during daytime operations. This study proposes an innovative mechanism to utilize bus depot chargers for private EVs, prioritizing the charging demands of EBs. Uncertainty in EB and EV charging demand, EV arrival time are considered. The objective is to maximize bus company revenue through a two-stage stochastic model. Practical applicability is demonstrated through a real-world bus depot case in Shanghai with 127 EBs and 376 private EVs. The results show that allocating 58% of the charging power and 30% of the chargers to the public from 10:00 to 24:00 meets 97.54% of the private EVs’ charging demand. The bus operator faced a charging cost of 10,747 CNY, while now it expects a profit of 1733 CNY with the project running.