Shaping a New Level of Bus Service under a Novel Concept of Bus Interaction: A Meta-Review

Vu Van-Huy^1*, Masahiko Kikuchi², Hisashi Kubota¹

¹Graduate School of Science & Engineering, Saitama University, Saitama, Japan.
²Urban Development and Improvement Division, Ministry of Land, Infrastructure, Transport, and Tourism, Kasumigaseki, Chiyoda-ku, Tokyo, Japan.

DOI: 10.4236/jtts.2023.132009                

Abstract

Public transport services, particularly bus services, play an important role in a sustainable transportation system. However, despite various efforts, bus ridership has decreased. The appearance of shared and on-demand vehicle services is one of the main reasons for this issue. In addition, bus tourism services have been successfully developed to meet the exigent needs of tourists. Therefore, a new level of daily bus service is necessary to adapt to the changing demands of customers. Bus interaction (BI) plays an important role in bus services. Nevertheless, the conventional concept of BI mainly refers to users, physical interaction, and safety, but it does not address non-users, non-physical interactions, service quality, and other aspects. This study aims to elaborate on a new concept of bus services. Based on this, we developed a theoretical framework for BI. A meta-analysis was then conducted to identify the achievements and untouched aspects. The results of this study provide three main contributions. First, an unprecedented novel concept of BI is defined, including 13 types of interactions. Second, a comprehensive theoretical framework of BI is established based on the relationships between eight sustainable bus system sub-aspects and 13 BI types. Third, based on the theoretical framework and findings of the reviewed studies, a common finding comprehensive framework of BI is completed, which is accompanied by 1) key findings of the 13 BI types, 2) conclusions of traffic conditions affecting BI research, 3) BI research gaps, and 4) 16 main suggestions for future BI research.

Keywords

Bus Service, Bus Interaction, Human Demand, Sustainable Bus System, Traffic Condition

1. Introduction

Public transport services are a key factor in a sustainable transportation system [1] . Ideally, public transit ridership should increase if we aim towards sustainable cities [2] . Although bus services have been greatly improved by various measures, the number of passengers has not increased significantly [3] , it has even declined in some places [4] [5] .

The strong rise of mobility-as-a-service (MaaS) and mobility on demand can redefine public transport [6] . The appearance of on-demand vehicle and shared- vehicle services such as ride-hailing [7] , car-hailing [8] , demand-responsive transport [9] , and ride-sharing [10] with their own advantages, have attracted customers from other modes. However, Maas is also one of the reasons for the decrease in the number of bus passengers [11] and there is no evidence that Maas contributes to sustainability goals [12] .

Many traditional solutions have been proposed to improve the quality of bus services. However, bus services cannot fully satisfy the requirements of passengers, which has led to loss of bus passengers [13] . Raising bus ridership in local bus services by improving attributes such as travel cost, comfort, and flexibility is not cost effective [14] . Bus ridership has not increased, on the contrary, it has decreased with the continuous growth of the scale of the bus in recent years in China [3] . It seems that traditional improvements may not be appropriate for the emerging challenges. In a recent trend, bus services are being redirected and focused on on-demand services [1] . The advent of demand responsive bus [15] and customized bus [16] services seems to respond to the above demand. Nevertheless, the operational performance of these modes has some limitations regarding vehicle routing, the number of stops, and the length of detour times [15] , and they are less cost-efficient [17] .

Conversely, bus tourism services with their own characteristics are strongly developed and play an important role in serving the needs of tourists [18] [19] . Bus tourism is one of the most important factors in public passenger transport demand [20] . An “ideal journey experience” is also based on the concept of being able to relax, being engaged in sightseeing, and, in some instances, enjoying the company of others [18] . Therefore, switching daily buses toward tourism is a potential solution for increasing bus passengers.

Besides, according to the theory of human motivation [21] , there are five motive levels. When a need is fairly well satisfied, the next pre-potent (“higher”) need emerges and serves as the center of organization of behavior. The hierarchy of transit needs is composed of three types of attributes: functional, security, and hedonic [22] . In the first of the two levels of BRT, the travel demand of passengers is concentrated on frequency, reliability, accessibility, speed, cost, safety, and security. The highest level of hedonic state refers to the perception of passengers of accessory attributes such as vehicle cleanliness and physical condition, noise, smell, aesthetics, convenience and ease of use, user information, customer services, driver’s courtesy, and image. There are also interactions between passengers and bus attributes.

These arguments and discussions show the central nature of the needs of bus passengers and pose a new challenge for traditional bus services. Conventional service needs to experience a breakthrough and achieve a new level in terms of adaptation to the changing demand of humans and the attraction of more passengers.

This study has three objectives: 1) To propose a new concept of improvement in bus services with a focus on the human aspect. 2) To develop a comprehensive framework for bus service development based on the proposed concept. 3) Based on the developed framework, to identify what has been done and what is needed for future studies.

This study is presented in the introduction section, Section 1, which reviews the status of the bus service and its recent declination; the impacts of emerging services (on-demand based) on the bus service as factors leading to the reduction of bus passengers; the efforts to improve and direct the bus service and their limitations; the success of bus tourism in attracting passengers; and the human motivation theory in bus service, and its connection to BI. Consequently, three main objectives are proposed. Section 2 presents a research flow chart and the methodology for implementing the aforementioned objectives. Section 3 deals with BI from the viewpoint of a sustainable bus system, including the key characteristics of a sustainable bus system, the roles and existing challenges for the bus system, and the importance of BI in bus system and dealing with the aforementioned challenges. Section 4 introduces an unprecedented and elaborate novel definition of BI and describes its characteristics compared to the conventional understanding. Section 5 presents the proposed comprehensive theoretical framework of BI and the creation of this framework. Section 6 completes the common finding comprehensive framework of BI. From this framework, research gaps emerge, the key findings of 13 proposed types of BI are summarized, and a hypothesis of research results differentiated by traffic conditions is proven. In addition, 16 main suggestions for future BI research are proposed. Finally, Section 7 discusses and concludes the paper.

2. Method

To determine the three objectives, a flow chart was built to present all the steps of the methodology, which is shown in Figure 1. First, the theory of human motivation is applied to public transport satisfaction. BI is strongly connected to human motivation and plays an important role in the bus system. From previous studies, the notions of “sustainable development”, “sustainable transport”, and “sustainable bus system” are stated. A novel concept of BI is elaborated based on three main pillars (human, vehicle, and environment), considering the theory of human motivation and sustainability. This includes some types of interactions between the dimensions of the three pillars, as listed in Table 1. The contents of this concept are described in detail in Section 4.

Figure 1. Flow chart of the study.

Table 1. Types of bus interaction.

Then, a theoretical comprehensive BI framework table was developed using the cross-table method under a combination of BI types and sustainable bus system dimensions, as presented in Table 2. The importance of this content was

Table 2. Theoretical comprehensive bus interaction framework.

validated by a group of specialists and political stakeholders [23] . The theoretical content of each cell of the framework shows the relationship between each column and row. Section 5 provides more details on the theoretical framework of BI.

Subsequently, based on the above framework, BI-related studies were reviewed. The method of searching for previous studies is presented in Section 6.1. The findings of each study are summarized in Table A1. From this, the common findings of each relationship are attained up to the time of searching. These common findings are then collated with the theoretical content. They are replaced in each cell of the theoretical comprehensive framework of BI if they have any practical content. In this step, three cases are discussed in Section 6.2 in more detail. A new framework is the common finding comprehensive framework of BI, as presented in Table 3. This table also presents the relationships that are either fully studied or lacking. This implies that there are many relationships between existing research gaps.

Table 3. Common finding comprehensive bus interaction framework.

From the common finding comprehensive framework, the key findings of 13 types of BI are summarized up to the time of searching. Simultaneously, a hypothesis for the differences in research results based on traffic conditions is proposed and proven by comparing some results of the same studies between the “car-based society” (CBS) and “motorcycle-based society” (MBS). Since then, research gaps have continued to appear.

Based on these research gaps, 16 main suggestions for future BI research are proposed and many other implicit suggestions are extracted from Table 3. The final part summarizes all contributions, discusses the research limitations, and provides solutions to address them. To approach a novel concept of bus interaction, first, we need to consider bus interaction under a sustainable bus system.

3. Bus Interaction under the Viewpoint of a Sustainable Bus System

Sustainability is a characteristic of a process that can be maintained at a certain level. “Sustainable development” is defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [24] . A “sustainable transport” is a transport system that meets society’s economic, social, and environmental needs [25] . Strengthening the public transport can enhance the productivity and efficiency of transport supply, improve traffic safety, reduce environmental pollution, and increase economic efficiency [26] . From the results of previous related studies, a “sustainable bus system” was established and validated. It covers many aspects of the society, environment, economy, and institutions, as shown in Table 2 [23] .

However, previous bus-related studies have been spontaneous, scattered, and mainly focused on accident prevention and speed improvement, but not on enjoyment. They are limited and do not support the quality of experience of passengers, bus drivers, other vehicles, and other road users. Therefore, these studies may lose the comprehensiveness of a sustainable bus system. In addition, previous bus-related works also focused excessively on the CBS, but not on the MBS. Strategies that help achieve sustainable transportation are not integrated within a holistic framework in developing countries [27] .

Additionally, the change in travel habits and vehicle use after the Covid-19 pandemic [28] , the increasing severity of natural disasters [29] , the information technology revolution (machine learning, big data) [30] , and new means of communications are challenges in the current transportation field [31] . These emerging issues significantly influence bus-related research methods, processes, and results.

Further, BI leads to the necessity of reorganization of the bus system (Kim and Dickey, 2006). In addition, interaction with passengers is one of the top two priorities for improving the quality of bus service [32] . In another study, coordinating bus dynamics with the surrounding traffic helped maximize bus service regularity [33] .

In the past, the conventional concept of BI mainly referred to users, safety, and physical interaction [13] [34] . In recent years, there have been a few BI studies on non-users [35] , quality of service, and non-physical interactions [36] . With such an important role in the bus system, to deal with the above-stated challenges of the bus system, BI needs to cover not only users but also non-users, not only physical interactions, but also non-physical interactions. It also needs to consider not only safety but also other aspects (service quality, costs, emissions, and noise). Therefore, an elaborate novel concept of BI is necessary to have a more comprehensive view of it and to provide a solid foundation to achieve a new level of bus service.

4. A Novel Concept of Bus Interaction

To move towards a sustainable bus system and meet needs of sustainable transport, within the sustainable development goals (SDGs), a new concept of BI should cover bus-related objects as much as possible. Therefore, BI is defined in this paper as all the interplays back and forth between factors inside the bus (bus interior), and the interactions between these factors (bus interior) and factors outside the bus (bus exterior) during bus operation, as shown in Figure 2. These interactions rely on three main pillars: people, vehicles, and the environment. People include bus drivers, ticket collectors, bus passengers, and other road users; vehicles include buses and other vehicles (cars, motorcycles, bicycles, etc.); and the environment consists of elements of the physical environment (infrastructure, trees, houses, buildings, light posts, traffic density, homogeneity, etc.). Table 1 lists the 13 types of detailed BIs and their characteristics.

Conventional BI includes simple physical interactions such as bus-other vehicles, bus-passengers, and bus-environment [37] as shown by the pink solid

Figure 2. Interactions in the novel concept of bus interaction.

lines in Figure 2. The former concept has limitations; it is not possible to cover all the elements that may be related to the bus components when it is in operation. The new definition of BI covers 13 different types of both interior and exterior interactions as shown by both the pink solid line and blue dashed line interactions (new interactions in new definition of BI). The significant advances of the new definition compared to the former are as follows: 1) it considers human factors with non-physical interactions, and 2) it includes not only passengers but also other road users (potential passengers). The novel definition uncovers a comprehensive view of BI and its role in the bus system. This novel definition is in line with the orientation of the SDGs, which aim at a people-centered development. This is essential for achieving a sustainable bus system.

Based on this new concept, we need to understand what has been done in previous studies and what has not been done. Therefore, a comprehensive meta-analysis study from the viewpoint of sustainability is vital for drawing a general picture of bus interactions. Surprisingly, this type of research has not been conducted yet. The next section presents such a meta-analysis to consider the above contents.

5. Theoretical Comprehensive Framework of Bus Interaction

As mentioned above, a comprehensive meta-analysis from a sustainability perspective is necessary. A cross-table method was used to establish a theoretical framework.

Table 2 presents the comprehensive theoretical BI framework. This holistic framework was developed by combining the 13 proposed types of BI in rows and eight sub-aspects of the sustainable bus system viewpoint in columns. The 13 types of BI are discussed above. The eight dimensions of the sustainable bus system are safety and security, accessibility, quality of service, costs, revenue and vehicle efficiency, emission and noise, planning and management, and public policy. The safety and security dimension includes traffic accidents and personal security. Accessibility comprises bus stops and buses for disabled people and females. Quality of service includes bus frequency, user satisfaction, number of transfers, punctuality, reliability, comfort, and travel shops. Costs comprise fuel costs, operating costs, and accident costs. The revenue and vehicle efficiency dimension includes fare revenues, average speed, number of passengers, and vehicle occupancy. The dimension of emission and noise includes air (CO, particulate matter (PM) pollutants) and noise pollution. Planning and management comprises sub-aspects such as number of stops, location of bus stops, coverage rate, bus lanes, condition of bus stops, transfer hubs, and information provision. Public policy comprises public investment and training, public subsidies, restrictive policies, and promotional policies.

The contents of the cells in Table 2 are based on the theoretical possible relationship between each column and row. They aim to show the relationship and effects between each of the types of BI and each of the eight sub-aspects of the sustainable bus system and vice versa. These contents are stated based on the authors’ experience and the results of previous studies. These contents are then elaborated for each cell.

6. Findings of Previous Studies under the Proposed Framework

6.1. Selection of Reviewed Papers

Based on the theoretical comprehensive framework of BI and its characteristics, 109 BI-related studies were searched globally.

Documents and academic studies related to this research were found in the following order. The 13 types of BI were proposed in this study for the first time, while the eight aspects of the sustainable bus system have been mentioned in previous studies. Therefore,

For ease of searching, related articles were searched by groups of the eight sub-aspects of the sustainable bus system. We outlined the appropriate groups of keywords for the search. These keywords include BI, bus passengers, bus DB, safety, security, accessibility, quality of service, costs, revenues, vehicle efficiency, emissions, noise, planning, management, and public policy. We then searched electronically in the copyright databases of Science Direct and Saitama University Library and the following online search websites: Google Scholar, Tandfonline.com, AgeLine, ResearchGate, and Web of Knowledge. The search period of papers was from the past to September 2021.

Two main criteria were applied for selecting articles: 1) To ensure the accuracy of results and information, only peer-reviewed papers from government projects, academic institutions, and academic universities could be selected. 2) The selected studies could be suitable not only for the considered context but also for as many contexts as possible (e.g., by region and time) to enable a comprehensive comparison and evaluation. Articles with similar results in different contexts were prioritized for searching and comparison. If two or more papers have the same content, only one paper and its finding is chosen as a representative. After selecting the expected papers, topic-related findings of the articles were extracted and classified according to eight hypothetical trends corresponding to the eight sub-aspects mentioned above. Table A1 presents the findings.

6.2. Summary of Reviewed Studies

Based on the BI-based findings of selected papers included in Table A1 of the Appendix, a common finding comprehensive BI framework was built by summarization, as presented in Table 3. As previously mentioned, three cases were determined. These cases were identified based on the comparison of the theoretical and studied practical contents in each relationship and the comparison between results under different traffic conditions (if any).

In the first case, if the results of previous studies are complete in comparison with the theoretical content under both traffic conditions (if any) in each cell of the framework, the full contents will be filled in the corresponding cell. This type of cell will be classified as a full research type.

In case 2, if the results of previous studies are not complete in comparison with the theoretical content or there is a lack of research under either of the abovementioned traffic conditions (if any) in each cell of the framework, the results will still be filled in the corresponding cell, but this type of cell will be categorized as a lack of research type. In other words, there are unresolved and missing aspects of this type of research. The background is marked with a purple color. Consequently, suggestions for future research emerge from this type of cell.

In case 3, if there is no study related to the theoretical content in any cell, this type of cell will be classified as a lack of research type. Its background is highlighted in orange color. As a result, this type of research will be novel and original, and future research on BI needs to focus on them.

From these results, the key findings of the 13 types of BI are summarized as follows.

1) Bus-bus driver interaction: DB is a major cause of road accidents. A good DB ensures comfort, reduces fuel consumption, increases average speed, and reduces noise and emissions.

2) Bus-passenger interaction: The bus components are entities that can cause injuries and determine the injury severity. A good set of components is a key factor that facilitates the access of passengers when boarding the bus, makes them feel comfortable when using the bus, and helps to increase passenger numbers, although it can also increase fuel consumption, noise, and emissions.

3) Bus-other vehicles: The more different types of vehicles there are in the traffic flow, the higher the chance of a collision between buses and other vehicles, the higher the costs, the lower the speed, and the higher the total fuel consumption and emissions.

4) Bus-other users: Bus movement causes negative pressure and increases the accident risk and pollution costs toward others.

5) Bus-environment: Infrastructure quality and road design are two major factors affecting passenger safety, comfort, material costs, bus speed, and air and noise pollution.

6) Bus drive-passengers: Conversations between bus drivers and passengers have both positive and negative effects on safety.

7) Bus driver-other vehicles: Poor perception of bus drivers regarding other vehicles is one of the main causes of the increased risk of traffic collisions. Horn use increases the noise level, and bus drivers experience more fatigue in heterogeneous traffic flows.

8) Bus driver-other road users: Poor perception of bus drivers regarding other road users is one of the main causes of the increased risk of traffic accidents. Slow drivers, pedestrians, and drivers swerving in front of the bus make bus drivers feel angry.

9) Bus driver-environment: The traffic infrastructure system is the main factor that affects the attention, observation, reaction, and behavior of the bus driver.

10) Passengers-passengers: Buses with an excessive number of passengers negatively affect passenger satisfaction, comfort, and accessibility. This makes passengers feel unsafe, causes noise and air pollution, and temporarily worsens their emotional state.

11) Passengers-other vehicles: No previous research.

12) Passengers-other users: No previous research.

13) Passengers-environment: No previous research.

These key findings partially represent the common trends of the 13 types of BI. The relationships in many cells in Table 3 have not been fully studied, indicating that there are still many missing aspects and research gaps. Therefore, these key findings are only temporary and will be improved and consolidated in the future once the results of further BI studies become available.

6.3. Differences in Bus Interaction Findings

Regarding BI, previous studies have often ignored one of the vital factors that influence the research results, the context factor. In this paper, a CBS area can be understood as an area where cars are the main vehicles in the traffic flow. In addition, there are other vehicles such as buses, motorcycles, and bicycles. An MBS area can be understood as an area where the primary vehicles in the traffic flow are motorcycles. Besides, there are other vehicles such as buses, cars, and bicycles. The difference between CBSs and MBSs is not just the type of vehicles, but it also relates to infrastructure conditions, travel culture, policy, and especially the number of vehicles in the heterogeneous traffic flows (buses, cars, motorcycles) in Asia. The infrastructure for the MBS is low-tech and non-well- equipped compared with that for the CBS. The MBS traffic flow is much more complex than that in CBS areas. CBS cities with car traffic flows are easier to control than MBS cities. Thus, BI in CBSs is more technologically-based, whereas it is human-based in MBSs.

According to the above summary of findings, it is clear that the number of studies in CBSs is much higher than that in MBSs. In addition, in some cases, the results of studies on CBSs differ from those on MBSs. Therefore, the hypothesis of differences in research results by traffic conditions (CBS and MBS) was proposed. The proof of this hypothesis helps us recognize the importance of traffic conditions in BI studies, have a holistic viewpoint of BI in traffic conditions, and avoid misunderstanding, especially in complicated traffic flows such as those in MBSs. Table 4 presents a comparison of the same BI studies between CBSs and MBSs. Because the number of studies on MBSs is limited compared with that on CBSs, only five types of BI were considered in this comparison.

Table 4 lists the relative differences in the results when the same studies were performed under different traffic conditions, such as those in CBSs and MBSs.

Table 4. Comparison of findings between CBS and MBS.

In other words, future research on BI should consider the aspects of CBS and MBS under different traffic conditions. In addition, the lack of BI studies on MBSs leaves a large research gap, and this comparison of findings could be complemented and expanded to other types of BI in the future.

6.4. Suggestions for Future Studies

Based on the results in Table 3 and a comparison between Table 2 and Table 3, the research gaps in each relationship are extracted. Sixteen outstanding and potential suggestions for future BI research are proposed as follows:

A) Research directions that have never been adopted

1) In-depth research on neuro-transportation in both the CBS and MBS.

2) Interaction between bus and bus stop design and perception of drivers when approaching bus stops to enhance accessibility.

3) Passenger perception of other vehicles, other road users, and the environment for enhancing comfort and satisfaction.

4) Passengers’ perception of the conditions of other vehicles and other road users encourages the use of the bus.

5) Provision of bus journey information to passengers by infrastructure planning.

6) Bus driver-other vehicles interaction affects the average speed of the bus.

7) Investment in bus components, subsidies to attract more passengers.

B) Research directions that have been partly addressed

1) Perception of buses by other road users and their interaction in terms of safety.

2) Bus driver-passenger interaction in the MBS in terms of safety and satisfaction of passengers.

3) Interplay bus-other vehicles, which causes sudden accelerations and decelerations of the bus, and its effect on comfort and satisfaction, especially in MBSs.

4) Effect of road conditions on the comfort and satisfaction of bus drivers and passenger in MBSs.

5) Studies on costs such as operating costs, vehicle costs, general costs, and external costs in MBSs.

6) Effect of bus-road surface interaction and heterogeneous traffic flow on bus average speed.

7) Influence of driving patterns, passenger loads, and road surface quality on fuel consumption and noise/air pollution of buses and other vehicles in MBSs.

8) Studies on bus stop arrangement, number of bus stops, bus lane location, and exclusive bus lanes affecting bus-other road user interactions in MBSs.

9) Effect of eco-training and enforcement programs on the number of private vehicles (cars and motorcycles) in MBSs.

7. Conclusions

This meta-analysis presents a summary of BI findings for a sustainable bus system. This study makes three major contributions to the literature.

The first contribution of this work is that it is one of the first studies to elaborate on the concept of BI under sustainability, as presented in Section 4. The novel concept of BI and its 13 types open a new comprehensive viewpoint on BI in bus journeys. This new concept extends and covers more objects than those in the usual understanding of BI. This is important because BI focuses on customers under this novel concept. It not only considers the user, physical interaction, and safety aspects, but also the non-user, non-physical interaction, and quality of service dimensions. They also indicate the nature of the deep-rooted demand for passengers. This is the fundamental foundation for proposing effective solutions to adapt to ever-increasing human needs.

The second result is the development of a comprehensive theoretical framework of BI, as presented in Table 2. This highlights the relationship between thirteen new types of BI and sustainable development. These types of BIs are considered in detail under the sub-aspects of a sustainable bus system. This unified holistic framework clarifies and demarcates the boundaries between the BI types. This has an important implication in helping researchers, planners, and policymakers to have a systematic view of the study of BI. Accordingly, they can accurately implement the necessary policies to improve and enhance the service quality of the bus, depending on their desired purposes. Finally, this framework lays the foundation for a new level of bus service and steers future studies on BI toward a sustainable bus system.

The third achievement of this study is the completion of the common finding comprehensive framework of BI, as presented in Table 3. According to the theoretical relationships, several aspects have been investigated in previous studies. However, these previous studies have been scattered, and these aspects have not been fully studied. The results of this review play a vital role in identifying what has been done, what has not been done, and what is needed for future studies to achieve a sustainable bus system. In addition, in a comparison between this framework and the theoretical framework considering traffic conditions (CBS and MBS), many missing and untouched aspects emerged. The common finding comprehensive framework also plays an important role in summarizing the lack of research on BI at the time of searching. From this, the key findings of 13 types of BI in previous studies at the time of searching were also extracted and summarized. Based on these missing aspects, researchers, planners, and policymakers can perform further research on BI toward a sustainable bus system.

In addition, the hypothesis that the difference between CBSs and MBSs leads to different research results was demonstrated based on the results of previous studies, as presented in Table 4. This result has created many other research gaps. It is recommended that we consider the traffic conditions when conducting BI research in the future. The final contribution of this study consists of 16 suggestions for future BI studies, as discussed in Section 6.4. These suggestions help avoid the currently scattered and unbalanced studies between CBSs and MBSs. The results of these potential studies, especially those that have never been conducted, will contribute to achieving a new level of bus services and a sustainable bus system development.

One of the outstanding suggestions is to study the passengers’ perception of other vehicles, other road users, and the environment. According to Table 3, there is no research on the bus passenger-external environment interaction. This type of interaction plays a vital role in the success of bus tourism services. Under the BI viewpoint of the bus service, bus tourism services have focused on the interplay between the tourists in the bus and the attributes of the environment external to the bus, such as image of the region, local beauty spots, countryside [18] [19] , scenic villages, and sightseeing [38] . This characteristic makes bus tourists comfortable and relaxed in their bus journey. Nevertheless, it seems to be novel and untouched for daily scheduled bus services thus far.

This study opens up a new perspective for future BI studies. However, it only includes interactions within the bus journey of passengers (when passengers are on the bus and approach to the bus at the bus stop). It does not cover the interactions at the bus stop or the process of customers going to the bus stop. In addition, the results of previous studies were only evaluated for the time of search. Due to the lack of studies on MBSs, these results have not been completed. They will be updated from that time to the present.

To address these limitations, first, future studies of BI should expand to cover all objects in the bus system, not only on-bus related but also bus stop-related objects and interaction with objects while customers go to the bus stop. In addition, these studies should take into account bus passengers not only at their origin or destination, but also during their travel journey. Buses could play a combined role as means of transportation and sightseeing. Passengers would not only be bus users, but also tourists, and they will enjoy life along the street. Second, the results of upcoming studies on BI need to be updated systematically, considering both CBS and MBS traffic conditions, and focus on the missing aspects to complete the above presented common finding comprehensive framework of BI. Therefore, to ensure the sustainable development of the bus system in both CBSs and MBSs, in-depth studies on BI are essential, especially in the post-COVID-19 situation and under unforeseen disaster circumstances.

Appendix

Table A1. Findings of selected papers.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References

[1]	Zhang, J., Wang, D.Z.W. and Meng, M. (2020) Optimization of Bus Stop Spacing for On-Demand Public Bus Service. Transportation Letters, 12, 329-339. https://doi.org/10.1080/19427867.2019.1590677
[2]	Bhui, S. and Pandit, D. (2021) Investigating Factors Responsible for Reduction in Bus Ridership Due to Fixed Route Paratransit Modes.
[3]	Yao, D., Xu, L. and Li, J. (2020) Does Technical Efficiency Play a Mediating Role between Bus Facility Scale and Ridership Attraction? Evidence from Bus Practices in China. Transportation Research Part A: Policy and Practice, 132, 77-96. https://doi.org/10.1016/j.tra.2019.11.002
[4]	Guedes, P.C., Borenstein, D., Samara Visentini, M., de Araújo, O.C.B. and Kummer Neto, A.F. (2019) Vehicle Scheduling Problem with Loss in Bus Ridership. Computers and Operations Research, 111, 230-242. https://doi.org/10.1016/j.cor.2019.07.002
[5]	Berrebi, S.J., Joshi, S. and Watkins, K.E. (2021) On Bus Ridership and Frequency. Transportation Research Part A: Policy and Practice, 148, 140-154. https://doi.org/10.1016/j.tra.2021.03.005
[6]	Lajas, R. and Macário, R. (2020) Public Policy Framework Supporting “Mobility-as-a-Service” Implementation. Research in Transportation Economics, 83, Article ID: 100905. https://doi.org/10.1016/j.retrec.2020.100905
[7]	Hoang-Tung, N., Linh, H.T., Van Cuong, H., Binh Le, P., Takeda, S. and Kato, H. (2022) Ride-Hailing Service Adoption and Local Context in Motorcycle-Based Societies: Case Study in Hanoi, Vietnam. Sustainability (Switzerland), 14, 1-17. https://doi.org/10.3390/su14020728
[8]	Peng, B., Han, S., Han, X. and Zhang, H. (2021) Laboratory and Field Evaluation of Noise Characteristics of Porous Asphalt Pavement. International Journal of Pavement Engineering, 23, 1-14.
[9]	Papanikolaou, A. and Basbas, S. (2021) Analytical Models for Comparing Demand Responsive Transport with Bus Services in Low Demand Interurban Areas. Transportation Letters, 13, 255-262. https://doi.org/10.1080/19427867.2020.1716474
[10]	Jacob, J. and Roet-Green, R. (2021) Ride Solo or Pool: Designing Price-Service Menus for a Ride-Sharing Platform. European Journal of Operational Research, 295, 1008-1024. https://doi.org/10.1016/j.ejor.2021.03.058
[11]	Butler, L., Yigitcanlar, T. and Paz, A. (2021) Barriers and Risks of Mobility-as-a-Service (MaaS) Adoption in Cities: A Systematic Review of the Literature. Cities, 109, Article ID: 103036. https://doi.org/10.1016/j.cities.2020.103036
[12]	Hensher, D.A., Mulley, C. and Nelson, J.D. (2021) Mobility as a Service (MaaS)—Going Somewhere or Nowhere? Transport Policy, 111, 153-156. https://doi.org/10.1016/j.tranpol.2021.07.021
[13]	Shao, M., Xie, C., Li, T. and Sun, L. (2021) Influence of In-Vehicle Crowding on Passenger Travel Time Value: Insights from Bus Transit in Shanghai, China. International Journal of Transportation Science and Technology, 11, 665-677.
[14]	Tran, Y., Yamamoto, T., Sato, H., Miwa, T. and Morikawa, T. (2020) Attitude toward Physical Activity as a Determinant of Bus Use Intention: A Case Study in Asuke, Japan. IATSS Research, 44, 293-299. https://doi.org/10.1016/j.iatssr.2020.03.002
[15]	Wang, L., Zeng, L., Ma, W. and Guo, Y. (2021) Integrating Passenger Incentives to Optimize Routing for Demand-Responsive Customized Bus Systems. IEEE Access, 9, 21507-21521. https://doi.org/10.1109/ACCESS.2021.3055855
[16]	Huang, D., Gu, Y., Wang, S., Liu, Z. and Zhang, W. (2020) A Two-Phase Optimization Model for the Demand-Responsive Customized Bus Network Design. Transportation Research Part C: Emerging Technologies, 111, 1-21. https://doi.org/10.1016/j.trc.2019.12.004
[17]	Li, X. and Quadrifoglio, L. (2010) Feeder Transit Services: Choosing between Fixed and Demand Responsive Policy. Transportation Research Part C: Emerging Technologies, 18, 770-780. https://doi.org/10.1016/j.trc.2019.12.004
[18]	Capriello, A. (2014) Bus Transport Service Provision and Tourism Policies: Lessons from Piedmont, Italy. Tourism Planning and Development, 11, 210-227. https://doi.org/10.1080/21568316.2013.865664
[19]	Domènech, A., Miravet, D. and Gutiérrez, A. (2020) Mining Bus Travel Card Data for Analysing Mobilities in Tourist Regions. Journal of Maps, 16, 40-49. https://doi.org/10.1080/17445647.2019.1709578
[20]	Kos, G., Ivandić, N. and Vidović, K. (2020) Tourism as a Factor of Demand in Public Road Passenger Transportation in the Republic of Croatia. Tehnički Glasnik, 14, 76-87. https://doi.org/10.31803/tg-20191210120738
[21]	Stoyanov, S. (2017) A Theory of Human Motivation. Macat Library, London, 1-87.
[22]	Allen, J., Muñoz, J.C. and Ortúzar, J.D. (2019) Understanding Public Transport Satisfaction: Using Maslow’s Hierarchy of (Transit) Needs. Transport Policy, 81, 75-94. https://doi.org/10.1016/j.tranpol.2019.06.005
[23]	Ribeiro, P., Fonseca, F. and Santos, P. (2020) Sustainability Assessment of a Bus System in a Mid-Sized Municipality. Journal of Environmental Planning and Management, 63, 236-256. https://doi.org/10.1080/09640568.2019.1577224
[24]	Mosaberpanah, M.A. and Khales, S.D. (2013) The Role of Transportation in Sustainable Development. ICSDEC 2012: Developing the Frontier of Sustainable Design, Engineering, and Construction, Proceedings of the 2012 International Conference on Sustainable Design and Construction, Fort Worth, Texas, 7-9 November 2012, 441-448. https://doi.org/10.1061/9780784412688.053
[25]	Cossio, M.L.T., et al. (2011) Impact Assessment Accompanying Document to the White Paper Roadmap to a Single European Transport Area—Towards a Competitive and Resource Efficient Transport System. European Commission, 358, 81-87.
[26]	Boltze, M. and Tuan, V.A. (2016) Approaches to Achieve Sustainability in Traffic Management. Procedia Engineering, 142, 205-212. https://doi.org/10.1016/j.proeng.2016.02.033
[27]	Abu-Eisheh, S., Kuckshinrichs, W. and Dwaikat, A. (2020) Strategic Planning for Sustainable Transportation in Developing Countries: The Role of Vehicles. Transportation Research Procedia, 48, 3019-3036. https://doi.org/10.1016/j.trpro.2020.08.184
[28]	Basu, R. and Ferreira, J. (2021) Sustainable Mobility in Auto-Dominated Metro Boston: Challenges and Opportunities Post-COVID-19 Transport Policy, 103, 197-210. https://doi.org/10.1016/j.tranpol.2021.01.006
[29]	Lu, H., Chen, M. and Kuang, W. (2020) The Impacts of Abnormal Weather and Natural Disasters on Transport and Strategies for Enhancing Ability for Disaster Prevention and Mitigation. Transport Policy, 98, 2-9. https://doi.org/10.1016/j.tranpol.2019.10.006
[30]	Ye, L., Pan, S.L., Wang, J., Wu, J. and Dong, X. (2021) Big Data Analytics for Sustainable Cities: An Information Triangulation Study of Hazardous Materials Transportation. Journal of Business Research, 128, 381-390. https://doi.org/10.1016/j.jbusres.2021.01.057
[31]	Salazar-Cabrera, R., Pachón de la Cruz, á. and Madrid Molina, J.M. (2020) Sustainable Transit Vehicle Tracking Service, Using Intelligent Transportation System Services and Emerging Communication Technologies: A Review. Journal of Traffic and Transportation Engineering (English Edition), 7, 729-747. https://doi.org/10.1016/j.jtte.2020.07.003
[32]	Hu, K.C. (2010) Evaluating City Bus Service Based on Zone of Tolerance of Expectation and Normalized Importance. Transport Reviews, 30, 195-217. https://doi.org/10.1080/01441640902884780
[33]	Chow, A.H.F. and Li, S. (2019) Modelling and Managing Bus Service Regularity with Influence of Prevailing Traffic. Transportmetrica B, 7, 82-106. https://doi.org/10.1080/21680566.2017.1353450
[34]	Wu, K.F. and Lin, P.J. (2021) Using On-Board Video Data for Safety Analysis—An Analysis of Right Hook Crashes Involving Large Buses at Signalized Intersections. IATSS Research, 45, 79-86. https://doi.org/10.1016/j.iatssr.2020.06.005
[35]	Mahmoud, M. and Hine, J. (2013) Using AHP to Measure the Perception Gap between Current and Potential Users of Bus Services. Transportation Planning and Technology, 36, 4-23. https://doi.org/10.1080/03081060.2012.745316
[36]	Hoang-Tung, N. and Hoang, T.L. (2020) Driving Behavior in Mixed Traffic Flow: A Novel Model for Assessing Bus Movement Considering the Interaction with Motorcyclists. IATSS Research, 44, 125-131. https://doi.org/10.1016/j.iatssr.2019.10.003
[37]	Vismara, L., Chew, L.Y. and Saw, V.L. (2021) Optimal Assignment of Buses to Bus Stops in a Loop by Reinforcement Learning. Physica A: Statistical Mechanics and Its Applications, 583, Article ID: 126268. https://doi.org/10.1016/j.physa.2021.126268
[38]	Lumsdon, L.M. (2006) Factors Affecting the Design of Tourism Bus Services. Annals of Tourism Research, 33, 748-766. https://doi.org/10.1016/j.annals.2006.03.019
[39]	Singh, H. and Kathuria, A. (2021) Analyzing Driver Behavior under Naturalistic Driving Conditions: A Review. Accident Analysis and Prevention, 150, Article ID: 105908. https://doi.org/10.1016/j.aap.2020.105908
[40]	Singh, M.K. and Rao, K.R. (2020) Cellular Automata Models for Signalised and Unsignalised Intersections with Special Attention to Mixed Traffic Flow: A Review. IET Intelligent Transport Systems, 14, 1507-1516. https://doi.org/10.1049/iet-its.2020.0062
[41]	Karekla, X., Gkiotsalitis, K. and Tyler, N. (2020) The Impact of a Passenger-Safety-Driven Acceleration Limit on the Operation of a Bus Service. Accident Analysis and Prevention, 148, Article ID: 105790. https://doi.org/10.1016/j.aap.2020.105790
[42]	Mallia, L., Lazuras, L., Violani, C. and Lucidi, F. (2015) Crash Risk and Aberrant Driving Behaviors among Bus Drivers: The Role of Personality and Attitudes towards Traffic Safety. Accident Analysis and Prevention, 79, 145-151. https://doi.org/10.1016/j.aap.2015.03.034
[43]	Sümer, N. (2003) Personality and Behavioral Predictors of Traffic Accidents: Testing a Contextual Mediated Model. Accident Analysis and Prevention, 35, 949-964. https://doi.org/10.1016/S0001-4575(02)00103-3
[44]	Gadbois, E.A. and Dugan, E. (2015) The Big Five Personality Factors as Predictors of Driving Status in Older Adults. Journal of Aging and Health, 27, 54-74. https://doi.org/10.1177/0898264314535806
[45]	Klauer, S.G., et al. (2006) The Impact of Driver Inattention on Near Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data. https://doi.org/10.1037/e729262011-001
[46]	Hickman, J.S. and Hanowski, R.J. (2012) An Assessment of Commercial Motor Vehicle Driver Distraction Using Naturalistic Driving Data. Traffic Injury Prevention, 13, 612-619. https://doi.org/10.1080/15389588.2012.683841
[47]	Sam, E.F., Daniels, S., Brijs, K., Brijs, T. and Wets, G. (2018) Modelling Public Bus/Minibus Transport Accident Severity in Ghana. Accident Analysis and Prevention, 119, 114-121. https://doi.org/10.1016/j.aap.2018.07.008
[48]	Tse, J.L.M., Flin, R. and Mearns, K. (2006) Bus Driver Well-Being Review: 50 Years of Research. Transportation Research Part F: Traffic Psychology and Behaviour, 9, 89-114. https://doi.org/10.1016/j.trf.2005.10.002
[49]	Prato, C.G. and Kaplan, S. (2014) Bus Accident Severity and Passenger Injury: Evidence from Denmark. European Transport Research Review, 6, 17-30. https://doi.org/10.1007/s12544-013-0107-z
[50]	Yuan, Y., Yang, M., Wu, J., Rasouli, S. and Lei, D. (2019) Assessing Bus Transit Service from the Perspective of Elderly Passengers in Harbin, China. International Journal of Sustainable Transportation, 13, 761-776. https://doi.org/10.1080/15568318.2018.1512691
[51]	Khoo, H.L. and Ahmed, M. (2018) Modeling of Passengers’ Safety Perception for Buses on Mountainous Roads. Accident Analysis and Prevention, 113, 106-116. https://doi.org/10.1016/j.aap.2018.01.025
[52]	Salonen, A.O. (2018) Passenger’s Subjective Traffic Safety, In-Vehicle Security and Emergency Management in the Driverless Shuttle Bus in Finland. Transport Policy, 61, 106-110. https://doi.org/10.1016/j.tranpol.2017.10.011
[53]	Zhou, H., Yuan, C., Dong, N., Wong, S.C. and Xu, P. (2020) Severity of Passenger Injuries on Public Buses: A Comparative Analysis of Collision Injuries and Non-Collision Injuries. Journal of Safety Research, 74, 55-69. https://doi.org/10.1016/j.jsr.2020.04.003
[54]	Edwards, M., Edwards, A., Appleby, J. and Beaumont, D. (2019) Banging Heads Onboard Buses: Assessment Scheme to Improve Injury Mitigation for Bus Passengers. Traffic Injury Prevention, 20, S71-S77. https://doi.org/10.1080/15389588.2018.1563293
[55]	Wretstrand, A. (2007) Comfort and Safety as Perceived by Wheelchair-Seated Bus Passengers. Transportation Planning and Technology, 30, 205-224. https://doi.org/10.1080/03081060701390890
[56]	Zunjic, A., Sremcevic, V., Sijacki, V.Z. and Sijacki, A. (2012) Research of Injuries of Passengers in City Buses as a Consequence of Non-Collision Effects. Work, 41, 4943-4950. https://doi.org/10.3233/WOR-2012-0790-4943
[57]	Kendall, I.G., Hassan, T. and Bodiwala, G.G. (1994) A Review of Injuries Sustained by Bus Passengers. Journal of Accident & Emergency Medicine, 11, 57. https://doi.org/10.1136/emj.11.1.57-a
[58]	Samerei, S.A., Aghabayk, K., Mohammadi, A. and Shiwakoti, N. (2021) Data Mining Approach to Model Bus Crash Severity in Australia. Journal of Safety Research, 76, 73-82. https://doi.org/10.1016/j.jsr.2020.12.004
[59]	Budhkar, A.K. and Maurya, A.K. (2017) Characteristics of Lateral Vehicular Interactions in Heterogeneous Traffic with Weak Lane Discipline. Journal of Modern Transportation, 25, 74-89. https://doi.org/10.1007/s40534-017-0130-1
[60]	Hu, X., Liu, T., Hao, X., Su, Z. and Yang, Z. (2021) Research on the Influence of Bus Bay on Traffic Flow in Adjacent Lane: Simulations in the Framework of Kerner’s Three-Phase Traffic Theory. Physica A: Statistical Mechanics and Its Applications, 563, Article ID: 125495. https://doi.org/10.1016/j.physa.2020.125495
[61]	Wegman, F. and Goldenbeld, C. (2006) Speed Management: Enforcement and New Technologies. 31.
[62]	Li, D., Zhao, Y., Bai, Q., Zhou, B. and Ling, H. (2017) Analyzing Injury Severity of Bus Passengers with Different Movements. Traffic Injury Prevention, 18, 528-532. https://doi.org/10.1080/15389588.2016.1262950
[63]	Olapoju, O.M. (2016) Culture of Distracted Driving among Intra-City Commercial Bus Drivers in Ile-Ife, South-Western Nigeria. Transportation Research Part F: Traffic Psychology and Behaviour, 42, 425-432. https://doi.org/10.1016/j.trf.2016.07.006
[64]	Michael, A.R., John. D.L. and Kristie. Y. (2008) Driver Distraction.
[65]	Chen, H.K., Chou, H.W., Su, J.W. and Wen, F.H. (2019) Structural Interrelationships of Safety Climate, Stress, Inattention and Aberrant Driving Behavior for Bus Drivers in Taiwan. Transportation Research Part A: Policy and Practice, 130, 118-133. https://doi.org/10.1016/j.tra.2019.09.007
[66]	La, Q.N., Van Duong, D., Lee, A.H. and Meuleners, L.B. (2017) Factors Underlying Bus-Related Crashes in Hanoi, Vietnam. Transportation Research Part F: Traffic Psychology and Behaviour, 46, 426-437. https://doi.org/10.1016/j.trf.2016.06.023
[67]	Molino, J.A., Wachtel, J., Farbry, J.E., Hermosillo, M.B. and Granda, T.M. (2009) The Effects of Commercial Electronic Variable Message Signs (CEVMS) on Driver Attention and Distraction (Rep. No. FHWA-HRT-09-018). Federal Highway Administration.
[68]	Stradling, S., Carreno, M., Rye, T. and Noble, A. (2007) Passenger Perceptions and the Ideal Urban Bus Journey Experience. Transport Policy, 14, 283-292. https://doi.org/10.1016/j.tranpol.2007.02.003
[69]	Hadiuzzman, M., Das, T., Hasnat, M.M., Hossain, S. and Rafee Musabbir, S. (2017) Structural Equation Modeling of User Satisfaction of Bus Transit Service Quality Based on Stated Preferences and Latent Variables. Transportation Planning and Technology, 40, 257-277. https://doi.org/10.1080/03081060.2017.1283155
[70]	Nguyen-Phuoc, D.Q., Phuong Tran, A.T., Van Nguyen, T., Le, P.T. and Su, D.N. (2021) Investigating the Complexity of Perceived Service Quality and Perceived Safety and Security in Building Loyalty among Bus Passengers in Vietnam—A PLS-SEM Approach. Transport Policy, 101, 162-173. https://doi.org/10.1016/j.tranpol.2020.12.010
[71]	Jain, S., Aggarwal, P., Kumar, P., Singhal, S. and Sharma, P. (2014) Identifying Public Preferences Using Multi-Criteria Decision Making for Assessing the Shift of Urban Commuters from Private to Public Transport: A Case Study of Delhi. Transportation Research Part F: Traffic Psychology and Behaviour, 24, 60-70. https://doi.org/10.1016/j.trf.2014.03.007
[72]	Petzäll, J. (1993) Ambulant Disabled Persons Using Buses: Experiments with Entrances and Seats. Applied Ergonomics, 24, 313-326. https://doi.org/10.1016/0003-6870(93)90070-P
[73]	Tirachini, A. (2013) Bus Dwell Time: The Effect of Different Fare Collection Systems, bus Floor Level and Age of Passengers. Transportmetrica A: Transport Science, 9, 28-49. https://doi.org/10.1080/18128602.2010.520277
[74]	Silvano, A.P. and Ohlin, M. (2019) Non-Collision Incidents on Buses Due to Acceleration and Braking Manoeuvres Leading to Falling Events among Standing Passengers. Journal of Transport and Health, 14, Article ID: 100560. https://doi.org/10.1016/j.jth.2019.04.006
[75]	Aceves-González, C., May, A. and Cook, S. (2016) An Observational Comparison of the Older and Younger Bus Passenger Experience in a Developing World City. Ergonomics, 59, 840-850. https://doi.org/10.1080/00140139.2015.1091513
[76]	Tirachini, A., Hensher, D.A. and Rose, J.M. (2014) Multimodal Pricing and Optimal Design of Urban Public Transport: The Interplay between Traffic Congestion and Bus Crowding. Transportation Research Part B: Methodological, 61, 33-54. https://doi.org/10.1016/j.trb.2014.01.003
[77]	Nguyen, T., NguyenDinh, N., Lechner, B. and Wong, Y.D. (2019) Insight into the Lateral Ride Discomfort Thresholds of Young-Adult Bus Passengers at Multiple Postures: Case of Singapore. Case Studies on Transport Policy, 7, 617-627. https://doi.org/10.1016/j.cstp.2019.07.002
[78]	Corazza, M.V., Guida, U., Musso, A. and Tozzi, M. (2016) A European Vision for More Environmentally Friendly Buses. Transportation Research Part D: Transport and Environment, 45, 48-63. https://doi.org/10.1016/j.trd.2015.04.001
[79]	Morton, C., Caulfield, B. and Anable, J. (2016) Customer Perceptions of Quality of Service in Public Transport: Evidence for Bus Transit in Scotland. Case Studies on Transport Policy, 4, 199-207. https://doi.org/10.1016/j.cstp.2016.03.002
[80]	AlHadidi, T. and Rakha, H.A. (2019) Modeling Bus Passenger Boarding/Alighting Times: A Stochastic Approach. Transportation Research Interdisciplinary Perspectives, 2, Article ID: 100027. https://doi.org/10.1016/j.trip.2019.100027
[81]	Zannin, P.H.T. (2008) Occupational Noise in Urban Buses. International Journal of Industrial Ergonomics, 38, 232-237. https://doi.org/10.1016/j.ergon.2006.06.014
[82]	Silva, J., Sá, E.S., Escadas, M. and Carvalho, J. (2021) The Influence of Ambient Scent on the Passengers’ Experience, Emotions and Behavioral Intentions: An Experimental Study in a Public Bus Service. Transport Policy, 106, 88-98. https://doi.org/10.1016/j.tranpol.2021.03.022
[83]	Lee, J.H. and Jo, W.K. (2005) Exposure to Airborne Fungi and Bacteria While Commuting in Passenger Cars and Public Buses. Atmospheric Environment, 39, 7342-7350. https://doi.org/10.1016/j.atmosenv.2005.09.013
[84]	Nassiri, P., Ebrahimi, H., Monazzam, M., Rahimi, A. and Shalkouhi, P. (2014) Passenger Noise and Whole-Body Vibration Exposure—A Comparative Field Study of Commercial Buses. Journal of Low Frequency Noise Vibration and Active Control, 33, 207-220. https://doi.org/10.1260/0263-0923.33.2.207
[85]	Wu, J., Yang, M., Rasouli, S. and Xu, C. (2016) Exploring Passenger Assessments of Bus Service Quality Using Bayesian Networks. Journal of Public Transportation, 19, 36-54. https://doi.org/10.5038/2375-0901.19.3.3
[86]	Dimitrov, S., Ceder, A. (Avi), Chowdhury, S. and Monot, M. (2017) Modeling the Interaction between Buses, Passengers and Cars on a Bus Route Using a Multi-Agent System. Transportation Planning and Technology, 40, 592-610. https://doi.org/10.1080/03081060.2017.1314504
[87]	Hu, K.C. and Jen, W. (2006) Passengers’ Perceived Service Quality of City Buses in Taipei: Scale Development and Measurement. Transport Reviews, 26, 645-662. https://doi.org/10.1080/01441640600679482
[88]	Hoque, M., Halder, P.K., Fouzder, P.K. and Iqbal, Z. (2016) Ergonomic Design of a Bangladesh Bus Passenger Seat. Occupational Ergonomics, 13, 157-172. https://doi.org/10.3233/OER-170249
[89]	Maternini, G. and Cadei, M. (2014) A Comfort Scale for Standing Bus Passengers in Relation to Certain Road Characteristics. Transportation Letters, 6, 136-141. https://doi.org/10.1179/1942787514Y.0000000020
[90]	Sekulić, D. (2020) Influence of Road Roughness Wavelengths on Bus Passengers’ Oscillatory Comfort. International Journal of Acoustics and Vibrations, 25, 41-53. https://doi.org/10.20855/ijav.2020.25.11512
[91]	Nguyen, T., Lechner, B., Wong, Y.D. and Tan, J.Y. (2021) Bus Ride Index—A Refined Approach to Evaluating Road Surface Irregularities. Road Materials and Pavement Design, 22, 423-443. https://doi.org/10.1080/14680629.2019.1625806
[92]	Hu, S.R. and Chen, S.Y. (2019) Effects of Mixed Traffic and Elderly Passengers on City Bus Drivers’ Work-Related Fatigue. Transportation Research Part F: Traffic Psychology and Behaviour, 66, 485-500. https://doi.org/10.1016/j.trf.2019.09.020
[93]	Kwon, Y., Byun, J. and Park, S. (2019) Exploring the Determinants of Bus Drivers Job Satisfaction: Evidence from South Korea. Research in Transportation Business and Management, 33, Article ID: 100436. https://doi.org/10.1016/j.rtbm.2020.100436
[94]	Zhang, L., Zhou, S., Kwan, M.P., Chen, F. and Dai, Y. (2020) The Threshold Effects of Bus Micro-Environmental Exposures on Passengers’ Momentary Mood. Transportation Research Part D: Transport and Environment, 84, Article ID: 102379. https://doi.org/10.1016/j.trd.2020.102379
[95]	Shang, H.Y., Huang, H.J. and Wu, W.X. (2019) Bus Timetabling Considering Passenger Satisfaction: An Empirical Study in Beijing. Computers and Industrial Engineering, 135, 1155-1166. https://doi.org/10.1016/j.cie.2019.01.057
[96]	Sullman, M.J.M., Dorn, L. and Niemi, P. (2015) Eco-Driving Training of Professional Bus Drivers—Does It Work? Transportation Research Part C: Emerging Technologies, 58, 749-759. https://doi.org/10.1016/j.trc.2015.04.010
[97]	Carrese, S., Gemma, A. and La Spada, S. (2013) Impacts of Driving Behaviours, Slope and Vehicle Load Factor on Bus Fuel Consumption and Emissions: A Real Case Study in the City of Rome. Procedia—Social and Behavioral Sciences, 87, 211-221. https://doi.org/10.1016/j.sbspro.2013.10.605
[98]	Kivekäs, K., Lajunen, A., Vepsäläinen, J. and Tammi, K. (2018) City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis. Energies, 11, 1755. https://doi.org/10.3390/en11071755
[99]	Ivković, I.S., Kaplanoví, S.M. and Milovanoví, B.M. (2017) Influence of Road and Traffic Conditions on Fuel Consumption and Fuel Cost for Different Bus Technologies. Thermal Science, 21, 693-706. https://doi.org/10.2298/TSCI160301135I
[100]	Reisi, M., Aye, L., Rajabifard, A. and Ngo, T. (2014) Transport Sustainability Index: Melbourne Case Study. Ecological Indicators, 43, 288-296. https://doi.org/10.1016/j.ecolind.2014.03.004
[101]	McKnight, C. (2003) Impact of Congestion on Bus Operations and Costs. University Transportation Research, New York.
[102]	Litman, T. and W.chara, N. (2010) Evaluating Public Transit Benefits and Costs Best Practices Guidebook. Victoria Transp. Policy Inst., Victoria.
[103]	Daraio, C., Diana, M., Di Costa, F., Leporelli, C., Matteucci, G. and Nastasi, A. (2016) Efficiency and Effectiveness in the Urban Public Transport Sector: A Critical Review with Directions for Future Research. European Journal of Operational Research, 248, 1-20. https://doi.org/10.1016/j.ejor.2015.05.059
[104]	Sun, X. and Wu, J. (2017) Combinatorial Optimization of Bus Lane Infrastructure Layout and Bus Operation Management. Advances in Mechanical Engineering, 9, 1-11. https://doi.org/10.1177/1687814017703341
[105]	Dreyer, C.M.W. and Steyn, W.J.V.D.M. (2015) Evaluation of the Effect of Deteriorating Riding Quality on Bus-Pavement Interaction. Journal of the South African Institution of Civil Engineering, 57, 2-8. https://doi.org/10.17159/2309-8775/2015/V57N3A1
[106]	Zhu, W., Yang, X., Ge, H. and Xie, B. (2017) Cost-Efficiency of Buses Using Stochastic Frontier Analysis. Proceedings of the Institution of Civil Engineers: Transport, 170, 185-193. https://doi.org/10.1680/jtran.14.00084
[107]	af Wåhlberg, A.E. (2007) Effects of Passengers on Bus Driver Celeration Behavior and Incident Prediction. Journal of Safety Research, 38, 9-15. https://doi.org/10.1016/j.jsr.2006.10.002
[108]	De Oña, J., De Oña, R., Eboli, L. and Mazzulla, G. (2013) Perceived Service Quality in Bus Transit Service: A Structural Equation Approach. Transport Policy, 29, 219-226. https://doi.org/10.1016/j.tranpol.2013.07.001
[109]	Schelenz, T., Suescun, á., Wikström, L. and Karlsson, M.A. (2014) Application of Agent Based Simulation for Evaluating a Bus Layout Design from Passengers’ Perspective. Transportation Research Part C: Emerging Technologies, 43, 222-229. https://doi.org/10.1016/j.trc.2013.11.009
[110]	Mouratidis, K. and Cobeña Serrano, V. (2021) Autonomous Buses: Intentions to Use, Passenger Experiences, and Suggestions for Improvement. Transportation Research Part F: Traffic Psychology and Behaviour, 76, 321-335. https://doi.org/10.1016/j.trf.2020.12.007
[111]	Guzman, L.A., Arellana, J. and Camargo, J.P. (2021) A Hybrid Discrete Choice Model to Understand the Effect of Public Policy on Fare Evasion Discouragement in Bogotá’s Bus Rapid Transit. Transportation Research Part A: Policy and Practice, 151, 140-153. https://doi.org/10.1016/j.tra.2021.07.009
[112]	Hwang, H. and Lee, M. (2020) A Simple Makeover Can Increase Bus Ridership: The Story of Tayo Bus. Transport Policy, 97, 103-112. https://doi.org/10.1016/j.tranpol.2020.07.005
[113]	Lin, B. and Tan, R. (2017) Are People Willing to Pay More for New Energy Bus Fares? Energy, 130, 365-372. https://doi.org/10.1016/j.energy.2017.04.153
[114]	Hawas, Y.E., Hassan, M.N. and Abulibdeh, A. (2016) A Multi-Criteria Approach of Assessing Public Transport Accessibility at a Strategic Level. Journal of Transport Geography, 57, 19-34. https://doi.org/10.1016/j.jtrangeo.2016.09.011
[115]	Wu, K., Lu, M. and Guler, S.I. (2020) Modeling and Optimizing Bus Transit Priority along an Arterial: A Moving Bottleneck Approach. Transportation Research Part C: Emerging Technologies, 121, Article ID: 102873. https://doi.org/10.1016/j.trc.2020.102873
[116]	Anas, A., De Sarkar, S. and Timilsina, G.R. (2021) Bus Rapid Transit versus Road Expansion to Alleviate Congestion: A General Equilibrium Comparison. Economics of Transportation, 26-27, Article ID: 100220. https://doi.org/10.1016/j.ecotra.2021.100220
[117]	Huo, Y., Li, W., Zhao, J. and Zhu, S. (2018) Modelling Bus Delay at Bus Stop. Transport, 33, 12-21. https://doi.org/10.3846/16484142.2014.1003324
[118]	Miranda, H.F. and Rodrigues da Silva, A.Ô.N. (2012) Benchmarking Sustainable Urban Mobility: The Case of Curitiba, Brazil. Transport Policy, 21, 141-151. https://doi.org/10.1016/j.tranpol.2012.03.009
[119]	Gu, W., Gayah, V.V., Cassidy, M.J. and Saade, N. (2014) On the Impacts of Bus Stops near Signalized Intersections: Models of Car and Bus Delays. Transportation Research Part B: Methodological, 68, 123-140. https://doi.org/10.1016/j.trb.2014.06.001
[120]	Durán-Hormazábal, E. and Tirachini, A. (2016) Estimation of Travel Time Variability for Cars, Buses, Metro and Door-to-Door Public Transport Trips in Santiago, Chile. Research in Transportation Economics, 59, 26-39. https://doi.org/10.1016/j.retrec.2016.06.002
[121]	Fernandez, R. and Tyler, N. (2005) Effect of Passenger-Bus-Traffic Interactions on Bus Stop Operations. Transportation Planning and Technology, 28, 273-292. https://doi.org/10.1080/03081060500247747
[122]	Zhou, C., Dai, P., Wang, F. and Zhang, Z. (2016) Predicting the Passenger Demand on Bus Services for Mobile Users. Pervasive and Mobile Computing, 25, 48-66. https://doi.org/10.1016/j.pmcj.2015.10.003
[123]	Ahmad Shafie, N.E., Mohamed Kamar, H. and Kamsah, N. (2015) A CFD Simulation of PM1 and CO Air Contaminants in a Bus Passenger Compartment. Jurnal Teknologi, 77, 35-39. https://doi.org/10.11113/jt.v77.6863
[124]	Golinko, V., Cheberyachko, S., Deryugin, O., Tretyak, O. and Dusmatova, O. (2020) Assessment of the Risks of Occupational Diseases of the Passenger Bus Drivers. Safety and Health at Work, 11, 543-549. https://doi.org/10.1016/j.shaw.2020.07.005
[125]	Pastramas, N., Samaras, C., Mellios, G. and Ntziachristos, L. (2014) Update of the Air Emissions Inventory Guidebook—Road Transport 2014 Update. 1-53.
[126]	Chakhtoura, C. and Pojani, D. (2016) Indicator-Based Evaluation of Sustainable Transport Plans: A Framework for Paris and Other Large Cities. Transport Policy, 50, 15-28. https://doi.org/10.1016/j.tranpol.2016.05.014
[127]	Borén, S. (2020) Electric Buses’ Sustainability Effects, Noise, Energy Use, and Costs. International Journal of Sustainable Transportation, 14, 956-971. https://doi.org/10.1080/15568318.2019.1666324
[128]	Silva, L.F. and Mendes, R. (2005) Exposição combinada entre ruído e vibração e seus efeitos sobre a audição de trabalhadores. Revista de Saúde Pública, 39, 9-17. https://doi.org/10.1590/S0034-89102005000100002
[129]	Hallmark, S.L., Wang, B. and Sperry, R. (2013) Comparison of On-Road Emissions for Hybrid and Regular Transit Buses. Journal of the Air and Waste Management Association, 63, 1212-1220. https://doi.org/10.1080/10962247.2013.813874
[130]	Chen, X., Shan, X., Ye, J., Yi, F. and Wang, Y. (2017) Evaluating the Effects of Traffic Congestion and Passenger Load on Feeder Bus Fuel and Emissions Compared with Passenger Car. Transportation Research Procedia, 25, 616-626. https://doi.org/10.1016/j.trpro.2017.05.446
[131]	Ahmad Shafie, N.E., Mohamed Kamar, H. and Kamsah, N. (2015) Field Measurement of Particulate Matter inside a Bus Passenger Compartment. Jurnal Teknologi, 77, 69-73. https://doi.org/10.11113/jt.v77.6870
[132]	El-Fadel, M. and Abi-Esber, L. (2009) In-Vehicle Exposure to Carbon Monoxide Emissions from Vehicular Exhaust: A Critical Review. Critical Reviews in Environmental Science and Technology, 39, 585-621. https://doi.org/10.1080/10643380701798264
[133]	Jost, P., Hassel, D. and Sonnborn, K.S. (1995) A New Method to Determine Exhaust Emission Factors for Heavy Duty Vehicles. Science of the Total Environment, 169, 213-217. https://doi.org/10.1016/0048-9697(95)04650-P
[134]	Kalaiselvi, R. and Ramachandraiah, A. (2016) Honking Noise Corrections for Traffic Noise Prediction Models in Heterogeneous Traffic Conditions like India. Applied Acoustics, 111, 25-38. https://doi.org/10.1016/j.apacoust.2016.04.003
[135]	Okokon, E.O., Yli-Tuomi, T., Turunen, A.W., Taimisto, P., Pennanen, A., Vouitsis, I., Samaras, Z., Voogt, M., Keuken, M. and Lanki, T. (2017) Particulates and Noise Exposure during Bicycle, Bus and Car Commuting: A Study in Three European Cities. Environmental Research, 154, 181-189. https://doi.org/10.1016/j.envres.2016.12.012
[136]	M Medina, M., Giesen, R. and Muñoz, J. (2013) Model for the Optimal Location of Bus Stops and Its Application to a Public Transport Corridor in Santiago, Chile. Transportation Research Record, 2352, 84-93. https://doi.org/10.3141/2352-10
[137]	Srinivas, A., Bharadwaj, V. and Manjunath, S. (2018) Influence Area of Speed Cameras: Based on Naturalistic Driving Data.
[138]	Rothengatter, T. (1982) The Effects of Police Surveillance and Law Enforcement on Driver Behaviour. Current Psychological Research, 2, 349-358. https://doi.org/10.1007/BF02684467
[139]	Tay, R. (2009) The Effectiveness of Automated and Manned Traffic Enforcement. International Journal of Sustainable Transportation, 3, 178-186. https://doi.org/10.1080/15568310801915559
[140]	Han, Q., Zeng, L., Hu, Y., Ye, L., Tang, Y., Lei, J. and Zhang, X. (2018) Driving Behavior Modeling and Evaluation for Bus Enter and Leave Stop Process. Journal of Ambient Intelligence and Humanized Computing, 9, 1647-1658. https://doi.org/10.1007/s12652-018-0802-7
[141]	Hadas, Y. and Nahum, O.E. (2016) Urban Bus Network of Priority Lanes: A Combined Multi-Objective, Multi-Criteria and Group Decision-Making Approach. Transport Policy, 52, 186-196. https://doi.org/10.1016/j.tranpol.2016.08.006
[142]	Gibson, J., Baeza, I. and Willumsen, L. (1989) Bus-Stops, Congestion and Congested Bus-Stops. Traffic Engineering and Control, 30, 291-296, 302.
[143]	Sohail, M., Maunder, D.A.C. and Cavill, S. (2006) Effective Regulation for Sustainable Public Transport in Developing Countries. Transport Policy, 13, 177-190. https://doi.org/10.1016/j.tranpol.2005.11.004
[144]	Rolim, C., Baptista, P., Duarte, G., Farias, T. and Shiftan, Y. (2014) Quantification of the Impacts of Eco-Driving Training and Real-Time Feedback on Urban Buses Driver’s Behaviour. Transportation Research Procedia, 3, 70-79. https://doi.org/10.1016/j.trpro.2014.10.092
[145]	Pantangi, S.S., Fountas, G., Anastasopoulos, P.C., Pierowicz, J., Majka, K. and Blatt, A. (2020) Do High Visibility Enforcement Programs Affect Aggressive Driving Behavior? An Empirical Analysis Using Naturalistic Driving Study Data. Accident Analysis and Prevention, 138, Article ID: 105361. https://doi.org/10.1016/j.aap.2019.105361
[146]	Schelenz, T., Suescun, á., Karlsson, M.A. and Wikström, L. (2013) Decision Making Algorithm for Bus Passenger Simulation during the Vehicle Design Process. Transport Policy, 25, 178-185. https://doi.org/10.1016/j.tranpol.2012.11.010
[147]	Zhang, L.L., Long, R. and Chen, H. (2019) Do Car Restriction Policies Effectively Promote the Development of Public Transport? World Development, 119, 100-110. https://doi.org/10.1016/j.worlddev.2019.03.007
[148]	Batty, P., Palacin, R. and González-Gil, A. (2015) Challenges and Opportunities in Developing Urban Modal Shift. Travel Behaviour and Society, 2, 109-123. https://doi.org/10.1016/j.tbs.2014.12.001