Transition Operator Research Articles

Reliable dynamic wireless charging infrastructure deployment problem for public transport services

Dynamic wireless power transfer (DWPT) technology offers the promise of eliminating the limited driving range of electric buses (EBs), providing greater convenience and safety benefits through the ability to charge EBs in motion. It may be possible to effectively circumvent the drawbacks of EBs such that it becomes mainstream in the future EB market. In this paper, we study the robust DWPT deployment problem for an EB system, aiming to determine the DWPT facility deployment, battery size, and en-route charging schedule simultaneously. We first formulate the deterministic problem as a novel mixed-integer linear programming (MILP) model. Next, we consider the energy consumption uncertainty and travel time uncertainty to develop a two-stage robust model. We further analyze the structural properties of our proposed model, and implement the column-and-constraint generation (C&CG) method to solve it optimally. Finally, a series of experiments based on a test bus network and the National University of Singapore internal bus network are carried out to verify the efficiency and effectiveness of our models and algorithms. Some managerial insights are also provided that public transit (PT) operators could use to enhance the efficiency of the EB system in the future.

Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multiline services with a shared corridor

Bus bunching is a long-standing problem in transit operation and ruining the regularity of transit service. In a typical urban transit network setting of multiple lines with a shared corridor, bus bunching becomes more frequent as there is more uncertainty inside the shared corridor. While multi-agent reinforcement learning (MARL) has been a promising scheme for learning efficient control policy in a multi-agent system, few studies have explored its applicability in multi-line transit control scenarios. In this study, we focus on a basic transit network where there are two bus lines with a shared corridor. An efficient MARL framework is proposed to learn multi-line bus holding control to avoid bus bunching. Specifically, we design observation and reward functions that incorporate multi-line information. In addition, a preference weights producer is introduced to update the objective weights towards a good trajectory evaluation during daily transit operation. In this way, we handle the multi-objective issue in multi-line control. In experimental studies, we validate the superiority of the method in real-world bus lines. Results show that the state and reward augmented with multi-line information benefit MARL in multi-line bus control. Besides, by updating preference weights towards less passenger waiting time, the regularity of transit service is further improved.

Research on a short-sleeper type ballastless-track that can adapt to large foundation deformation

Settlement and arch deformation on the ballastless-track foundation during rail transit operations pose challenges to the existing track structure, which can only accommodate up to 30 mm of foundation deformation through the replacement of fastener adjusting pads. These limitations result in prolonged remediation cycles, increased expenses, and disruptions to train runs. To address these issues, a height-adjustable short-sleeper ballastless-track structure is proposed. Using the vehicle-track coupled dynamics theory and FEM, this study investigates the geometric matching characteristics, modal behavior of the track structure and their impact on the dynamics performance of the vehicle and track system when the HSBS structure is adjusted to 200 mm. Findings demonstrate that the ballastless-track can easily adjust the height by modifying the thickness of pads or replacing the short sleepers, enabling effective adaptation to substantial track foundation deformation with an adjustment capacity exceeding 200 mm. Importantly, the vibration mode and natural frequency of the short sleeper remain unchanged before and after lifting the track structure. Under the condition of unchanged support stiffness, the stability of the track structure, as well as the safety and smooth performance of the vehicle, are maintained at the same level as before the height adjustment.

Energy consumption structure model considering urban green and low-carbon transportation

<p class="Abstract">The importance of energy conservation and emission reduction has become the consensus of the international community, and Iraq is also actively improving the urban public transportation system to control carbon emissions. This paper collects panel data of Tikrit city in Iraq in the past 3 years, constructs a random effect variable coefficient model, and studies the impact of the development of urban low-carbon transportation system on the energy consumption structure. The study finds that the government can use public transportation pricing strategies to influence consumers. In order to realize the optimization of energy consumption structure, the impact of electric vehicles on energy consumption structure will decrease with the increase of urban development. The transportation sector can increase the purchase and travel costs of traditional cars by restricting travel, purchases, and charging parking fees, which affects the number of private cars and reduces the obstacles to optimizing the energy consumption structure. The government should increase financial subsidies, improve rail transit and reasonable bus (electric) vehicle operation systems, increase investment in new energy vehicle research and development, and encourage high energy density and low power consumption technologies. development, increase residents’ demand for new energy passenger vehicles, and optimize the energy consumption structure.</p>

Sustainability performance assessment of Tunisian public transport companies: AHP and ANP approaches

This paper suggests a 29-indicator based transport sustainability governance index to shed light on the strengths and weaknesses of eight regional transit operators in Tunisia using the absolute measurement approaches of analytical hierarchy process (AHP) and analytical network process (ANP). Despite the difference in the overall sustainability scores, four companies have obtained the same ranking orders under both methods. The rest of the firms were ranked differently. Although both models generated interesting insights, ANP outperformed AHP in terms of priority distribution and significance of findings due to its capability of incorporating and quantifying criteria interdependencies. This research is the first in the literature that combines the three sustainability pillars with the governance principles using the direct rating approach of AHP and ANP to evaluate public transport firms.

Understanding post-COVID-19 hierarchy of public transit needs: Exploring relationship between service attributes, satisfaction, and loyalty

Introduction and objectiveCOVID-19 transforms travel behavior and undermines public transit (PT) operations even though the disease is under control. PT recovery partly requires instruments to prioritize service quality and satisfaction improvement based on travelers' transit needs. However, there is limited understanding of psychological needs for post-COVID-19 transit use and how the needs affect satisfaction and loyalty. Thus, this study explored the influences of post-COVID-19 psychological needs for bus service attributes on satisfaction and loyalty and thereby built a bus need hierarchy for riders in different use stages to distinguish critical service attributes from basic and excitement ones by applying Maslow's need hierarchy theory. MethodsA face-to-face questionnaire survey with systematic sampling collected data from 204 bus riders in Kaohsiung, which is a transit-developing city of Taiwan and whose PT ridership and service levels had drastically reduced because of the pandemic. Factor analysis was used to construct bus service attributes, and structural equation modeling was applied to analyze the influence of bus service attributes on overall satisfaction with and loyalty to bus services. ResultsResults indicated that (1) riders included epidemic prevention needs in transit safety assessment and (2) pursued the needs for safety, reliability, and comfort to increase overall satisfaction and thereby form loyalty; and (3) safety and reliability were respectively critical needs of riders in the beginning and intermediate use stages, whereas comfort persisted in critical needs throughout all use stages. ConclusionsThe findings implied the necessity of satisfying the needs for safety with epidemic prevention for initiating post-COVID-19 transit use and service quality improvement priorities for different PT use and system contexts. The implications can also be applied to future public health emergency management for PT administrators.

Optimization of Bridging Bus Timetable and Vehicle Scheduling under URT Disruption

After the occurrence of a disruptive event in an urban rail transit (URT) network, passengers’ travel is affected, and a large number of passengers are stranded in the stations. These stranded passengers have to be evacuated urgently. With its flexible deployment, a bus bridging service has become an effective solution to evacuate stranded passengers. In order to avoid large passenger flow at stations, in addition to evacuating the static passenger flow stranded at the disrupted stations, the bridging buses need to focus on the dynamic passenger flow arriving at the turnover stations along the short-turning trains. In this paper, we propose a mathematical model to optimize the timetable and vehicle scheduling of bridging buses considering the adjustment of rail transit operations. The model aims to minimize passenger waiting time, the number of lost passengers, and the amount of bridging buses used. The model makes buses operate flexibly on different routes, taking the bus capacity and number of buses into account. By creating an improved ε-constraint method, the Pareto front of the problem is solved using the model with a commercial solver. Finally, the accuracy and validity of the model is verified by applying an example based on China’s Hangzhou rail transit line 4. The results show that the bridging bus timetable and scheduling plan generated by the model effectively solved the problem of large passenger flow at turnover stations. In addition, compared with bridging buses with an even headway timetable, the results demonstrate that bridging buses with an uneven headway timetable, which considers coordination with rail transit, could reduce the passenger waiting time and the number of lost passengers. The model has better performance with an uneven headway timetable in the face of large passenger demand. The computational experiment shows that the total passenger waiting time and the number of bridging buses was reduced when the bus capacity was increased. However, the effect on passenger waiting time and the number of lost passengers was limited when the number of buses available at bus depots was increased.

NETWORK EXECUTION ASSESSMENT OF IPV4 & IPV6 ARRANGED PASSAGE AND V6 TO V4 CHANGE INSTRUMENTS ON WINDOWS SERVER WORKING FRAMEWORKS

The creation of IPv6 was sparked by the possibility of running out of IPv4 addresses. The new rendition of the Web Convention offers considerably more organization and host addresses, anyway progress from current to the new adaptation has been strikingly sluggish. There are various purposes behind the drowsy change: The precursors are enormousness and complexity. As a result, a number of transitional mechanisms have been developed for the interim. There are advantages and disadvantages associated with each mechanism. Performance has been empirically evaluated for two such mechanisms, the configured tunnel mechanism and the 6t04 transition mechanism, in this paper. The two components are carried out on two unique Windows Server working frameworks and execution related measurements like throughput, postponement, jitter and computer processor utilization of the change end hubs are estimated. The test-bed results indicate that the TCP, UDP throughput and jitter values of the two mechanisms are comparable, but that the transition mechanism and operating system choice significantly affect the delay and CPU reading.

Transferable supervised learning model for public transport service load estimation

AbstractDetailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.

Promoting service quality with incentive contracts in rural bus integrated passenger-freight service

A rural bus integrating passenger and freight transport is a new effective public transit mode to realise village interconnection and solve the first–last mile rural logistics service. Mixed services reduce logistics costs and generate additional income for transit operators. However, the mutual interpenetration of logistics and passenger services in a single bus trip may lead to a decline in the service quality of both passengers and goods. With this in mind, we designed a government subsidy incentive contract and a logistics alliance payment incentive contract respectively from the perspective of participants based on the principal-agent theory. In addition, a bi-level programming model consisting of two principals (government and logistics alliance) and one agent (transit operator) was proposed to incentivise bus operators and improve passenger and freight service quality. It scientifically realises the coordination of interests between the principals and agents. A Chinese case study was conducted to examine the proposed models and methods, and a passenger-based incentive subsidy programme from the government was proposed to replace the mileage-based one. With both incentive policies (government and logistics alliance), passenger travel time can be reduced by 4.2 min, and logistics transportation time can be shortened by 30 min. In addition, the total annual government budget can be reduced by 17.2% (¥450,000, $64,000) and annual fleet mileage savings by approximately 9.4% (62,000 km) compared with the mileage-based incentive programme in the case study. A sensitivity analysis is conducted to explore the impact on the collaborative system. This innovative concept, combined with incentive schemes, is a good reference for public administration to avoid the dilemma of having passenger and freight transport in low-demand areas.

Decentralized Power Flow Control Strategy Using Transition Operations of DC-Bus Voltage for Detection of Uncertain DC Microgrid Operations

To enhance the reliability and flexibility of DC microgrids (DCMGs), this paper presents a decentralized power flow control strategy (PFCS) by using the transition operation modes. The transition operation modes are introduced as an effective communication method among power units, eliminating the use of additional digital communication links (DCLs) for the purpose of ensuring the power balance as well as the voltage regulation even under uncertain conditions. During the transition operation modes, the power unit which transmits the information shifts the DC-link voltage level, and the power unit which receives the information continuously monitors the DC-link voltage with predetermined time. When uncertain conditions occur in a particular power unit, this power unit triggers the transition operation modes to send this information to all power units in the DCMG system. The proposed PFCS can maintain the DC-link voltage at the nominal value for steady-state conditions both in the grid-connected mode and islanded mode. Moreover, the proposed PFCS significantly enhances the overall reliability of the decentralized DCMG system by effectively addressing several uncertainties stemmed from electricity price fluctuations, grid availability, battery state-of-charge (SOC) levels, and wind power variations. The scalability of the DCMG system is also demonstrated by incorporating an electric vehicle (EV) unit as an additional energy storage system (ESS). The EV unit seamlessly cooperates with the existing battery unit, functioning as additional ESS to regulate the DC-link voltage when the battery SOC level is low. Simulation and experimentation results under various conditions demonstrate the effectiveness of the proposed PFCS.

Vulnerability assessment in urban metro systems based on an improved cloud model and a Bayesian network

To effectively evaluate the vulnerability of urban rail transit operations, a hybrid method that combines an improved cloud model and Bayesian network is proposed. This research consists of four parts: evaluation system development, model establishment, result aggregation, and analysis. The developed improved cloud-Bayesian network model is composed of 13 root factors and 4 s-level factors. The Wuhan Metro is adopted as a case study to provide instructions and verify the proposed method. The results indicate the following: (1) Line 2 is the most vulnerable line in the case; (2) The equipment-related factor (D2) is the most significant second-level variable in the vulnerability management of urban rail transit operation; (3) The employee professional level (X2), equipment anti-interference ability (X6), and urban rail transit line density (X11) display high correlations with the corresponding second-level factors; and (4) Peak duration rate (X3), platform passenger density (X4), urban rail transit line density (X11) and disaster seriousness level (X12), especially X4, are considered the key factors when urban rail transit reaches a high vulnerability level. Accordingly, corresponding countermeasures are proposed. The results show that the research conclusion is consistent with the actual situation, and the proposed method can provide a reference for other similar circuits.

Auto-Oriented Communities in Developing Countries: Bus Rapid Transit Implementation Prospects

Public transportation services often face challenges in middle- and low-income nations from both public acceptance and economic constraints. Jordan is classified as a middle-income country with a population of over 10 million, 4 million of whom live in Amman, the capital. The development and operation of a bus rapid transit (BRT) system in Amman city was recently proposed. The BRT project is anticipated to offer a solution to the city’s escalating congestion problem. This study’s objective is to conduct a “before” analysis to identify the variables that affect the willingness of people to use the Amman BRT system. The socioeconomic characteristics and travel habits of individuals were used to model the willingness to use BRT. An online survey was distributed to Amman residents and 238 valid responses were returned. Two popular techniques were utilized: binary logistic regression and Bayesian networks. Ten models were developed: one binary logistic model and nine Bayesian network models. The results of these models were compared based on accuracy, sensitivity, specificity, area under the Receiver Operating Characteristic (ROC) curve, complexity, and number of selected variables. It was found that Bayesian networks were more effective in modeling willingness to use BRT. Willingness to use BRT was shown to be higher among households without cars, youths, females, and university students, and if there were fewer transfers along the route. It became clear that introducing a new public transportation system is well appreciated, particularly in areas with low income, insufficient existing public transportation services, and where driving a car is the norm.

Feasibility of using phase-shifting transformers to increase the throughput of interconnected power transmission systems

This paper discusses measures aimed at improving the efficiency of power systems by increasing the capacity of power transmission lines. To that end, a FACTS technology based on a phase-shifting transformer was used. The feasibility of using phase-shifting transformers to increase the throughput capacity of interconnected power transmission systems was investigated by determining the maximum allowable cross-section flows of the United Energy System of the Urals – the United Energy System of Siberia. The studied cross-section included 500 kV transmission lines and the extended 220 kV Nizhnevartovskaya GRES – Tomskaya transit. Calculations were performed for normal and various post-emergency schemes using the RastrWin3 software package. The regulation of phas e-shifting transformer branches and the direction of power flow in the section were taken into account. For the considered cross -section, the use of a phase-shifting transformer was shown to provide for the 220 kV transit operation in a closed mode. This i mproved the reliability of power supply in the region and allowed the maximum allowable overflow to be increased by 35 – 71%. In addition, similar calculations were carried out for the option of strengthening the 220 kV transit through the construction of a parallel 500 kV line. The effect of increasing the capacity of this option was established to reach 20 –35%. The decisive factor limiting the maximum permissible cross-section flows during 220 kV transit short circuit in the normal and reinforced versions was found to be the current overload of the head sections. Recommendations on the preferable use of an open transit mode are formulated. In conclusion, the efficiency of power systems can be improved by increasing the transmission capacity of power lines through the use of phase-shifting transformers. A segment of the Unified National Power Grid of Russia, where such devices are technologically expedient, was identified.

On urban co-modality: Non-cooperative and cooperative games among freight forwarder, carrier and transit operator

This paper models a public transit system that can serve both passengers and urban freight, i.e., urban co-modality, and investigates the system-wide impacts of co-modality on existing urban freight forwarding service, freight carrier and urban transit services. In the co-modal system, we model one transit operator that serves passengers and provides co-modal services, one freight forwarder (an intermediary) that serves freight customers and uses freight transportation services provided by a freight carrier or the transit operator, and one freight carrier that provides services to the freight forwarder. We derive the analytical conditions under which co-modal operations can improve the profits of the freight forwarder, carrier and transit operator, and the consumer surpluses of freight customers and passengers compared with the status quo without co-modality. We also analytically and numerically compare the optimal operation decisions, the three operators’ profits, and the users’ welfare under different games among the operators (non-cooperative and cooperative games). Our results show that when the three operators are in a non-cooperative relationship, the freight carrier might have a profit loss due to the decreased freight units allocated to the direct road channel; whereas, both the freight forwarder and the transit operator would have profit gains from the co-modality. The numerical studies further reveal that to ensure a Pareto-improving co-modal system, the operators have to reduce the freight/transit service fare and co-modal transportation price.

Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints

Recent advances in vehicle technology offer new opportunities for an electric, automated, modular bus (MB) unit with an adjustable capacity to be applied to transit systems, promising to tackle the resource allocation challenges of traditional buses in coping with uneven travel demand. Drawing on the concept of modular vehicles, this paper introduces a novel scheduling system in which MB units can be combined/separated from fulfilling imbalanced trip demands through capacity adjustments. We develop an optimization model for determining the optimal formation and trip sequence of MB units. In particular, given that the vehicles are electrically powered, battery range limits and charging plans are considered in the system scheduling process. A column-generation-based heuristic algorithm is designed to efficiently solve this model, with constraints related to travel demand and charging station capacity incorporated into the master problem and the trip sequence for modular units with limited energy solved by the subproblem. Taking real data from transit operations for numerical examples, the proposed model performs well in terms of both algorithmic performance and practical applications. The generated optimal MB dispatching scheme can significantly reduce the operating cost from $1534.31 to $1144.26, a decrease of approximately 25% compared to conventional electric buses. The sensitivity analysis on the MB dispatch cost and battery capacity provides some insights for both the scenario configuration and the battery selection for MB system implementation.

How the driving behaviors and customer handling of public transportation operators have been impacted by the COVID-19 pandemic in Addis Ababa, Ethiopia: the perspective of protection motivation theory?

The COVID-19 pandemic has had an impact on every aspect of human life and economic sectors such as transportation operations around the world. This paper tries to investigate how COVID-19 pandemic factors influenced the driving and customer handling behaviors of urban public transportation operators in Addis Ababa city, Ethiopia. To that end, a framework of mixed research, pre/post study design, and protection motivation theory (PMT) was used to guide the development, quantification, and analysis of the causal relationships of pandemic-related constructs on driving and customer handling behaviors. The examined driving behavior variables included harsh speeding, harsh braking, and drink-drive events concerning the time period before and during the COVID-19 pandemic outbreak. Customer handling was also operationalized in terms of the level of friendly handling and care provided to customers. As a result, primary data were collected through transport surveys on 143 randomly selected public transit operators, including city buses, midsize buses, minibuses, bajajs, and light rail transits. Furthermore, contingency indexes, likert scales, and binary logistic regression models were used to estimate and predict the most significant factors that affected driving and customer handling behavior. Findings showed that the new COVID-19 pandemic and response measure-related factors were the most significant factors impacting driving behavior during the pandemic. Driving frequencies and intentions, as well as driving decisions or choices, were significantly influenced and reduced. Due to the pandemic-related factors, harsh driving behaviors such as harsh speeding and braking became more frequent during the pandemic compared to the pre-COVID period. Additionally, the correlation between harsh driving behavior and other factors, mainly trip distance traveled, driving during risky nighttime hours, and driving requests, was significant. Furthermore, due to the impacts of COVID-19, government measures, and changes in driving behavior, customer handling and care behaviors were predominantly unfriendly. Findings revealed that the performance of PMT was relevant, and it could inform and guide studies to understand such an impact analysis. Thus, policymakers must learn from the harsh lessons of COVID-19 and make bold investments in preparedness, prevention, and response, including pandemic-sensitive strategies and customer-oriented strategies.

Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach

The analysis of passenger travel choice behaviours under train delays has become a crucial topic in research on urban rail transit operation management. In this paper, we focus on analysing travel choices of affected regular passengers under train delays by utilizing the data collected through an automatic fare collection (AFC) system along with train delay log records. Along this line, we propose a data-driven four-stage framework for studying regular passengers’ responses under delays, consisting of data profiling, regular passenger screening and travel patterns extraction, affected regular passenger identification, and affected passenger behaviour prediction modelling. Using a real-world case of the Shenzhen Metro in China, we conduct extensive experiments for method validation and feature insights analysis. The proposed framework could provide a microscopic view of passenger travel behaviours under train delays for fine prediction and exhibit a possibility for multi-source heterogeneous data mining in passenger behaviour analysis and train delay-related tasks.

Transit Safety System Evaluation and Hotspot Identification Empowered by Edge Computing Transit Event Logging System

This paper discusses the importance of near-crash events and associated metadata as valuable sources for smart transit applications, such as surrogate safety measures for transit safety research. The STAR Lab at the University of Washington, sponsored by the Federal Transit Administration, has developed an edge computing system that processes onboard videos for near-crash detection. This paper builds on previous work by addressing two research questions: first, how to leverage the near-crash detection system to synthesize rich data sources on transit vehicles, and second, how to use the smart data hub to support transit operation and safety studies. The proposed procedures for event-based transit data collection, evaluation of commercial collision avoidance warning systems (CAWS) technologies, and transit safety hotspot identification are detailed. CAWS’ performance was benchmarked on four transit buses that were operated for almost a year in Pierce County, WA, U.S. Furthermore, the meta-information of near-crash events enables hotspot analysis and the identification of several exemplar clusters that can be explained by driver behavior and roadway geometries. The results of the experiments demonstrate the system’s promising performance and its applicability to addressing various transit operation questions.

An economic analysis of a multi-modal transportation system with ride-sourcing services and multi-class users

This paper proposes a tractable bi-level model to explore the mode choice of travelers and optimal operation decisions of a public transit operator and a ride-sourcing operator under a multi-modal transportation system. In such a multi-modal system, users may travel by bus, metro, and/or ride-sourcing service. The lower-level network model is utilized to capture the equilibrium mode choices with the consideration of travelers’ heterogeneity in terms of the value of time (VOT), where deterministic user equilibrium (DUE) and stochastic user equilibrium (SUE) are formulated and compared. In the upper-level model, subject to the DUE/SUE, the two operators optimize their operation decisions in the notion that the profit is maximized or the total system cost is minimized. The existence and uniqueness/non-uniqueness of the multi-modal mode choice equilibrium are analyzed. The optimal operation decisions are also compared under different operation regimes. Our results show that SUE model provides a more conservative estimate of the ride-sourcing operator’s profit and total system travel cost compared to DUE. Besides, the total system cost can be reduced while the profit of the ride-sourcing company can be increased under appropriate operation decisions of the public transit operator and the ride-sourcing operator. Sensitivity analysis further indicates that travelers with longer total travel distance tend to use short-distance ride-sourcing services, which suggests that the ride-sourcing operator should dispatch more vehicles to suburbs located in remote areas to enhance service quality.