INTRODUCTION
Background
Transportation is a crucial element of socio-economic interactions, with road transport being particularly significant (Santos et al., 2010) Research indicates that the advancement of road transport fosters job creation, boosts economic activity, and positively impacts local communities (Wang and Pfister, 2008; Park et al., 2015; Kanwal et al., 2019) However, the rapid growth of economies and populations in many developing countries has led to an overwhelming demand for road infrastructure (Oxford Business Group, 2018) This increasing pressure from inadequate road infrastructure poses challenges for foreign investments, which are essential for enhancing economic performance and competitiveness in these nations (Ahukannah et al., 2003; Ivanova and Masarova, 2013) Figure 1.1 illustrates the quality of road infrastructure across ASEAN countries.
Figure 1.1 Quality of Road Infrastructure in ASEAN Countries
(Source: World Bank, Global Competitiveness Report 2014, 2019)
To alleviate public sector debt and enhance public facilities, governments are engaging the private sector for both local and international financing through long-term contractual agreements.
My an m ar Viet n am C am b o d ia Ph ili p p in es L ao s In d o n es ia T h ailan d B ru n ia Ma lay si a Si n g ap o re
7=extensive and efficient among the best in the world
1=extremely underdeveloped among the worst in the world
Despite improvements, ranking remain low
The world average for public sector infrastructure development often involves private sector entities either constructing or managing these facilities or providing essential services to the community through existing infrastructure (Grimsey and Lewis, 2002).
Toll roads, representing a commercial and private approach to transportation infrastructure, have garnered significant attention recently as a means to finance new road networks in both developing and developed nations The establishment of toll roads offers numerous advantages, including improved services for road users.
2021), reducing traffic congestion (Hansen, 2001; Low and Odgers, 2012) and promoting regional development that is equitable (Siswoyo, 2020)
Many commuters opt for alternative routes to evade tolls, leading to congestion and safety concerns on roads not designed for heavy traffic This behavior causes significant toll revenue losses, highlighting the need for a thorough analysis of the factors affecting road users' route selection.
Route choice in traffic assignment is a complex task influenced by the intricacies of human behavior, the uncertainty in decision-making, and the variability in individual preferences (Zhang and Levinson, 2010) Understanding these factors helps designers better meet consumer needs and enhance their offerings (Zhao, 2016).
Statement of the Problems
The quality of the roads in Vietnam has one of the lowest rankings among the main infrastructure sectors, according to the Global Competitiveness Report from 2008-
Vietnam continues to encounter significant challenges in developing its transport infrastructure, especially its road system As of 2016, the Vietnam National Assembly's supervision committee reported that there were 55 Public-Private Partnership (PPP) toll road projects in operation, totaling nearly $6 billion in investment Despite the recent opening of Vietnam's budget, various issues and difficulties persist in the infrastructure development process.
Many toll roads continue to struggle with service improvements during their operational phase, often due to competition with existing non-toll roads and a lack of understanding of user preferences by investors Understanding these preferences is crucial for the success of toll road investments Therefore, promoting private sector participation in public infrastructure and facility services is essential for enhancing toll road effectiveness and user satisfaction.
Table 1.1 Vietnam: International Ranking of Infrastructure
Source: World Economic Forum, Global Competitiveness Report 2008-2009
Research Questions
For the above purposes, the research question with this study is what factors affect users‟ choice of toll roads?
Objectives
The specific objectives of the study are as follow:
1 To analyze users‟ choice behaviors of routes including toll roads
2 To identify major factors affecting the users‟ choice of toll roads
3 To discuss the implications from findings to toll-road policy
Disadvantages(-) Quality of overall infrastructure 97
Quality of air transport infrastructure 92 -
Overview of the Study
This study is organized and presented in five chapters
The research background, statement of the problems, and objectives of the study including research questions will be mentioned
Chapter 2 will show some existing research papers related to this thesis‟s topic The factors influencing route choice will be pointed out from those previous papers, especially on toll road studies This chapter will also identify research gaps between previous papers and will find the unique characteristics of this research
Chapter 3 will introduce case studies, field surveys, data collection, and hypothesis development Besides, the method used in this research will be shown in detail
Chapter 4: Data Analysis and Discussion
This chapter presents the socio-demographic profiles, service quality assessments, and travel characteristics of the study's participants It also discusses the estimation results related to the study's hypotheses, providing insights and recommendations for local government and private investors to consider in toll road policy development.
Chapter 5 will conclude with discussion from the summary of the findings and recommendations and further studies direction will be discussed
The procedure for studying will be in the following, h
LITERATURE REVIEW
Introduction
This chapter outlines a three-step process for literature review on route choice factors related to toll and non-toll roads Initially, a literature screening was conducted using keywords like "route choice," "toll road," and "non-toll road" across Google Scholar, Science Direct, and Research Gate, yielding 70 studies from 2000 to 2023 From these, 31 studies were selected for a more focused analysis on route choice factors specific to toll roads Finally, the identified route choice factors were categorized, emphasizing the uniqueness of this research by pinpointing research gaps through a summary of objectives, methodologies, road service quality, and comparisons with prior studies The subsequent sections will delve into each aspect in detail.
PPP Projects in Transportation
The term "PPP" lacks a universally accepted definition, as it encompasses various concepts, standards, and perspectives presented by different authors The following definitions provide a comprehensive overview of PPP, highlighting its multifaceted nature.
A long-term partnership between private sector entities and government organizations is established to deliver public facilities or services In this arrangement, the private party takes on significant risks and management responsibilities, with compensation linked to their performance (World Bank, 2017).
Between 1990 and 2009, a total of 4,354 public-private partnership (PPP) infrastructure projects reached financial closure, as reported by the Private Participation in Infrastructure Projects Database These projects are categorized into four key sectors: energy, telecommunications, transport, and water Notably, the transport sector accounts for approximately 27.5% of the total, with 1,201 PPP projects Figure 2.1 illustrates the distribution of PPP projects across these sectors.
The transport sector comprises four key sub-sectors: road, seaport, airport, and railway Data indicates that road projects dominate public-private partnership (PPP) investments in the transport industry, representing 606 out of all PPP transport initiatives.
Figure 2.1 Number of PPPs Project in
Figure 2.2 Number of Transport Sub-
Overview of Route Choice on Toll Roads
Route choice analysis predicts the likelihood of selecting one route from multiple options (Lai and Sha, 2019) This method is essential for forecasting traveler behavior in hypothetical scenarios, anticipating future traffic patterns on transportation networks, and understanding how individuals respond to and adapt to various information sources (Dhulipala and Katti, 2015).
Understanding travelers' route choice behavior is crucial for various fields, including transportation forecasting, traffic management, road infrastructure design, and the development of road navigation technologies Knowledge of route choice can also inform policy analysis, such as the effects of congestion pricing Route choice models are fundamental for analyzing traveler behavior and are integral to traffic assignment methods, which rely on the principles of utility theory According to random utility theory, these models provide insights into how travelers make decisions regarding their routes.
Number of projects in each sector
Number of transport sub-sector projects
Airport Railroad Seaport Road h each individual tries to maximize their utility or they prefer the route which is having maximum utility among the set of routes (Dhulipala and Katti, 2015)
Route choice modeling is a complex aspect of traffic assignment, influenced by the intricacies of human behavior, limited traveler knowledge about the network, uncertainties in perceptions of route characteristics, and the lack of precise information on traveler preferences Identifying the factors that affect drivers' route choices is crucial for enhancing network analysis and improving transportation planning Understanding these determinants can significantly contribute to more effective transportation strategies.
Understanding route choice behavior is essential for the effective operation of toll roads, as highlighted by Sun et al (2013) Insights into these factors are crucial for designing and implementing successful toll road projects, according to Politis et al (2020).
Route Choice Rule
Commuters have the flexibility to select various distinct routes within the road network to reach their destination from a specific origin Consequently, individuals traveling between the same origin-destination pair often opt for different paths, highlighting the diversity in route choices among commuters.
Commuters select their routes based on personal preferences and available options, which helps them justify their choices If this were not the case, congestion would likely occur along the most direct or time-efficient routes According to Nakayama et al (2001), one approach to route selection is the "No Switching Criterion," which influences how commuters make their decisions.
In this scenario, the commuter consistently opts for the same route, adhering to a no-switching rule that only changes when new guidelines prompt a different path Over time, the commuter develops this no-switching criterion based on their experiences, while also considering the option of random switching when necessary.
Commuters often select their routes randomly, particularly those unfamiliar with the transportation network This spontaneous decision-making process involves minimal consideration and reflects a lack of familiarity with the available options In contrast, experienced commuters tend to make more informed choices based on their prior knowledge and familiarity with the routes.
Experienced commuters make informed decisions by adopting specific rules, selecting optimal routes, and evaluating predicted costs and recent travel times Their familiarity with the network enables them to choose routes that minimize both estimated costs and travel times Additionally, their approach to decision-making considers their attitude towards travel risk, particularly in relation to the uncertainties of the route and the day This process exemplifies the concept of Rational Choice in commuting behavior.
This procedure significantly reduces average travel time by enabling commuters to make informed decisions based on comprehensive data from previous journeys When choosing routes, commuters often rely on their experiences, considering only a portion of their total travel time Ultimately, their rational estimation of travel time is derived from the cumulative duration of all past trips.
Figure 2.3 Flow Diagram of Choice Rule
Factors Influence on Route Choice
Research has identified multiple factors influencing drivers' route choices While studies by Liu et al (2004), Ben-Elia et al (2008), Raveau et al (2011), and Yu-qin et al (2013) emphasize the significant impact of travel time on route decisions, Dalumpines and Scott (2017) and Jan et al (2000) argue that other elements, such as the number of lanes and traffic signals, also play a crucial role Thus, travel time should not be the sole consideration in route selection.
According to research conducted by Morikawa and Miwa (2006), drivers' choices are associated with the distances between their points of origin and destinations Li et al
Research indicates a connection between drivers' familiarity with origin-destination pairs and their preferences for specific route characteristics As drivers become more accustomed to the transport network, the benefits of route recommendations diminish, according to Adler (2001) To explore the influence of knowledge, learning, and habitual behavior on route selection, Bogers et al (2005) and Ben-Elia et al (2008) conducted simulation studies.
Previous research on route choice has primarily focused on how visible elements impact drivers' decisions Key factors influencing these judgments include observable route characteristics like the number of turns and travel distance, as well as driver demographics such as age and gender Additionally, the specific attributes of the origin-destination pair, including familiarity and distance, along with trip-specific factors like the purpose of the journey, play significant roles in shaping route preferences.
Latent factors such as attitudes, beliefs, and lifestyle choices significantly influence the decision-making process of drivers In addition to observable factors, variables like directness, reduced traffic, environmental effects, safety concerns, and driving habits also play a role in shaping these choices Research indicates that charging for better highways can divert traffic to toll-free secondary roads of poorer quality, negatively impacting road safety A study in Spain revealed that routes adjacent to toll highways experience significantly more accidents than those near free highways Furthermore, an Australian survey found that drivers are willing to pay an average of $0.92 per trip to prevent a death in urban areas, compared to $3.99 in non-urban areas.
According to Van Dijk et al (2015), implementing road pricing can enhance network reliability by reducing traffic demand and prompting route adjustments In scenarios with low tolls, route changes are minimal, primarily attracting low-income commuters (Van and Krygsman, 2015) To effectively improve network dependability, toll rates should be set appropriately (Li and Bovy, 2006) When determining toll rates, it is essential to consider the financial interests of the government, investors, and road users, ensuring that the rates offer benefits to users while being environmentally sustainable Additionally, the funding for the construction, operation, and maintenance of toll roads is typically sourced from these toll revenues (Yusuf et al., 2014).
Drivers prioritize reducing trip distance and avoiding obstacles like traffic, stop signs, and traffic lights when selecting their routes (Papinski et al., 2009) Research indicates that factors such as the number of traffic lights and travel speed significantly influence route choices (Tawfik, Rakha, and Miller, 2010; Ramaekers et al., 2013; Palat, Delhomme, and Saint Pierre, 2014).
Herwangi et al (2015) emphasize the significance of mode selection in route choice, particularly for low-income Indonesians who predominantly travel by motorbike, often avoiding toll roads due to restrictions Consequently, this demographic is less likely to utilize toll roads, despite research by Anas et al (2017) and Ardiyono et al indicating that toll road usage can enhance economic performance in the regions they serve.
2018), it is unclear how the construction of a toll road affects the travel behaviour of people with various socioeconomic backgrounds and trip characteristics in developing country contexts
Route choice scenarios are influenced by various factors, including the availability of travel information, how passengers process it, their prior knowledge, perspectives on uncertainty, and behavioral patterns While numerous studies address these issues, a comprehensive analysis of these factors working together is lacking Bogers and Hoogendoorn (2005) emphasize the importance of travel information in route selection, highlighting its role in shaping travel behavior through Advanced Traveller Information Systems (ATIS) such as radio, television, the internet, variable message signs, and navigation apps Recent reviews by Chorus, Molin, van Wee (2006a, 2006b), and van Essen et al (2016) provide valuable insights into the ongoing research on the impact of travel information on travel decisions.
Table 2.1 presents a categorization of characteristics influencing route choice behaviors, highlighting that traffic conditions like congestion and travel time are the most extensively studied factors Traditional route choice models typically consider road pricing and the purpose of road use as key elements This research seeks to delve deeper into route choice behavior by examining the impact of latent factors on route selection among various alternatives Additionally, it compares the effects of toll and non-toll roads across different demographic groups and trip parameters, aiming to enhance the understanding of route choice beyond conventional factors.
Table 2.1 Categorization of Factors Affecting Route Choice h
Relationship between Road Services Quality and Route Choice
Road services are essential facilities that cater to users who pay fees for their use, with private investors acting as service providers (Agarwal, 2008) To maintain long-term customer satisfaction, service providers must effectively meet user needs (Walker and Baker, 2000) The quality of road infrastructure significantly impacts economic activity, highlighting its importance to the economy Measuring and evaluating road performance is crucial for maintaining high service standards (Zuna et al., 2015) Additionally, customer expectations and perceptions play a vital role in assessing service quality (Eboli and Mazzulla, 2008).
Service quality in transportation has been defined by various authors, emphasizing key factors such as comfort and safety (Kadiyali, 2003; Horsu and Yeboah, 2015) Research by Suanmali et al (2015) highlights that improved road surface conditions significantly enhance convenience by reducing travel times Agarwal (2008) identifies accessibility, comfort, convenience, and safety as critical components of road service quality Furthermore, toll road users prioritize the reliability of the toll road and the operator's ability to respond to emergencies (Zuna and Rahadian, 2014) The Toll Road Minimum Service Standard (MSS Toll Road) encompasses essential elements such as toll road conditions, average speed, accessibility, mobility, safety, rescue units, relief services, environmental factors, and rest areas (Pratama and Sabar, 2016).
Understanding consumer behavioral intentions is essential for organizational success, particularly in the toll road sector Evaluating service quality significantly influences client choices and satisfaction levels (Cronin et al., 2000) Identifying the relationship between service quality attributes and customer satisfaction can enhance customer involvement in the service provision process, known as co-creation Customers play a vital role in value creation, helping service providers establish high standards of service quality (Gronroos, 2011).
The route choice for road services is influenced by various relative factors, including accessibility, amenities, safety, quality and comfort, connection levels, and serviceability Understanding these elements is essential for enhancing logistics, which can contribute to economic security in Vietnam and Myanmar, offering a model that could be replicated globally.
Summary of Previous Route Choice Studies
Objective, method, road‟s service quality, and comparison with other roads are summarized
Table 2.2 Summary of Previous Route Choice Studies
Studies Objective Method Service Quality
To investigate how real-time information affects how drivers choose their routes
To explore the influences on the drivers' route selection
To examine how people choose the routes
To analyze route choice models in relation to earlier research and making suggestions for future study
To examine the variables that affect the choice of route
Rank-Order Logit Models, Multinomial Logit Models, Linear
Studies Objective Method Service Quality
Bogers (2005) To show the characteristics, preferences and perceptions in commuters‟ route choice
Andani (2021) To improve how to analyze route choice behavior
To study the decision- making process and the factors that affect truck routing
Traditional route choice models primarily focus on the service-level characteristics of alternative routes, lacking research on how these factors influence user decisions This study introduces a novel analysis of route choice in toll networks, incorporating variables such as users' travel behaviors, sociodemographic information, and evaluations of road service quality.
This study conducts an empirical analysis to compare route choice decision-making between toll and non-toll roads using a consistent modeling approach A primary objective is to establish a uniform framework for evaluating and contrasting traveler preferences across both road types, ultimately offering valuable insights for general transportation planning.
METHODOLOGY
Introduction
This qualitative study aims to identify the factors influencing route choice on toll roads The chapter includes the selection and introduction of case studies, the process of surveying and data collection, and a conceptual framework derived from field survey results and literature review Additionally, it outlines the development of hypotheses based on this framework and evidence from previous studies, while also detailing the research methodology employed.
Field Survey
The survey is performed to find current problems of toll roads in Vietnam
Case studies in Hanoi, Vietnam, reveal that toll roads under the Public-Private Partnership (PPP) system have been in operation since 2011 By 2016, Vietnam had 55 toll road projects in operation, with a total investment nearing US$ 6 billion, according to the Supervision Committee of the National Assembly of Vietnam (2017).
The Phap Van - Cau Gie (PV-CG) expressway in Hanoi, Vietnam, exemplifies a toll road that addresses the two-route dilemma This expressway provides a quicker travel option between Phap Van and Cau Gie, surpassing the existing government-managed NH.1A national highway However, the connection between southern and central provinces to Hanoi's city center leads to significant traffic congestion, particularly during peak hours and weekends.
Pair-1: Route 1.1: Toll road = Phap Van- Cau Gie h
Route 1.2: Non-toll road = National Highway 1A
The CT.04 connects Chan Cau Thanh Tri and Hung Yen, serving as a crucial access road to Hanoi This segment of the toll road, which also includes non-toll road NH 39, faces challenges due to the dual-route issue with the existing government-managed national highway It is vital for commuters traveling from Hanoi and supports numerous factories in Hung Yen, Hai Duong, and Hai Phong, making it an essential route for accessing Hai Phong.
Pair-2: Route 2.1: Toll road = CT.04 + Non-toll= NH 39
Route 2.2: Non-toll road = PR 379, NH 39
The case studies Pair-1 and Pair-2 analyze users' route choice intentions between toll roads and non-toll roads Pair-1 includes both toll and non-toll sections running in parallel, while Pair-2 focuses on a partial toll road alongside a non-toll road These comparisons aim to understand how users make decisions when faced with similar road options.
Route 2.1~ Toll road+Common section Route 1.1~ Toll road h
Figure 3.2 Pair-2 (Chan Cau Thanh Tri- Hung Yen)
TR NTR TR + NTR NTR
The survey results from the case studies are as follows,
In Pair-1, all sections of toll and non-toll are parallel with no shared sections, while in Pair-2, a portion of the sections is non-parallel due to the presence of a common/shared section.
The non-toll road is currently under construction, with only a section of the toll road expected to be fully completed in the near future in Pair-2.
• The above common non-toll road section on which road construction is taking place, potentially leading to congestion and confusion in Pair-2 h
• Road alignment of Pair-1 is straight and easy to drive, but that of Pair-2 is winding and dangerous
• Road surface of toll road is smoother than that of non-toll road
• Road safety facilities such as road signages, medians/ traffic separator, street lights and emergency lanes are better installed on toll road compared with non- toll road
• Few rest stop stations are available along toll road in Pair-2
• More households and shops are along the non-toll road
• According to information from local people, residents who live near non-toll road do not use toll road
• Allow all types of vehicles to be used in non-toll road, however, motorbikes are banned in toll road
According to the above results, it can be concluded that Amenity, Safety and Quality and Comfort are controlled factors from the field survey.
Data Collection
The purpose of data collection is to understand local people‟s route choice behaviour Detailed information of data collection is shown in Table 3.1
Schedule • June – August, 2021 (9:00AM-5:00PM)
Method Face-to-face interviews (15-20 min/person)
Respondents Total 502 individuals years old or older (304 individuals at Pair-1 and 184 individuals at Pair-2) h
Figure 3.3 Data Collection from Local People
Hypothesis Development
The conceptual diagram is created through a comprehensive literature review and field survey, encompassing a total of 36 variables categorized into three distinct groups: socio-demographic factors, service quality evaluation, and users' travel characteristics.
Conceptual diagram that affects users' route choice intention is shown in Fig3.4
Part 3: Purpose to use road
Part 4: Important of the route in daily life
Part 5: Attitude toward road use fee Part 6: Route-finding applications h
Five working hypotheses are set up based on the conceptual model as follow;
Two working hypotheses relating the service quality
Three working hypotheses relating users‟ travel characteristics
Working Hypotheses of Service Quality
Toll roads provide enhanced services, including quicker routes, smoother traffic, improved safety, and increased comfort (Zuna et al., 2016) Research by Ivanauskiene and Volungenaite (2014) indicates that customer satisfaction and loyalty are closely tied to service quality, with satisfaction influencing the behavioral intention to choose a service This study aims to explore the connection between service quality and route choice intention, leading to the formulation of hypotheses H1 and H2.
H1 Users evaluating highly road’s Safety prefer Toll Road (TR) than those evaluating poorly, but not in non-TR h
H2 Users evaluating highly road’s Quality & Comfort prefer TR than those evaluating poorly, but not in non-TR
Working Hypotheses of Users’ Travel Characteristics
A study by Gomez et al (2017) reveals that road users in Spain, a developed nation, express a willingness to pay for road use fees to access the superior services and facilities offered by toll roads, which include time savings and reduced congestion This finding is relevant for understanding similar attitudes in less affluent countries.
H3 Users satisfied with road-use fee prefer TR than those unsatisfied
Previous research indicates that high-income car owners tend to favor toll roads over non-toll alternatives (Andani et al., 2021; Andani and Geurs, 2021) This study aims to further explore this preference within motorcycle-centric cultures, specifically in Hanoi, Vietnam.
H4 Car users prefer TR more than non-car users, but not in non-TR
Higher income earners and educated individuals tend to embrace new technologies more readily (Grahn et al., 2020) However, as drivers become more familiar with their local road networks, their interest in using road information applications declines Those who are less familiar with the roads, alongside high-income users, show a greater interest in toll road information that promises low risk and comfortable driving conditions (Qi et al., 2022).
Bivariate Binary Probit Model
This study explores the factors influencing the choice between toll and non-toll roads, employing discrete outcome models due to the binary nature of the dependent variables Bivariate probit models are utilized to address potential correlations between these choices, considering common unobserved variables that affect both decisions The analysis assumes normally distributed error components with covariance, making it an appropriate framework for this investigation.
) ~ N [( ) ( )] c and m: the association of toll and non-toll roads, respectively
𝞫: a vector of coefficients associated with determinants
𝞮: a standard normally distributed random error term ρ: cross-equation correlation coefficient of 𝞮 y: a binary outcome b: variables for binary models i: an individual
The correlation parameter 𝜌 assesses the relationship between toll road and non-toll road choices after accounting for the effects of explanatory variables in the model The estimated coefficient 𝛽 for each independent variable indicates its influence on the choice between toll and non-toll roads; a positive 𝛽 suggests an increased likelihood of selecting toll roads, while a negative 𝛽 implies a decreased likelihood.
DATA ANALYSIS AND DISCUSSION
Introduction
This chapter analyzes data collected from questionnaire surveys at Pair-1 and Pair-2 to examine the factors influencing users' route choice behavior The analysis involves selecting variables using the Variance Inflation Factor (VIF) test All analyses will be conducted using R programming The results will be discussed in the subsequent sections.
Socio-demographic Characteristic of Respondents
In the survey results illustrated in Figure 4.2.1, male respondents constitute 59.54% in Pair-1 and 64.14% in Pair-2, significantly surpassing the female respondents at 40.46% and 35.68%, respectively This indicates that males are the predominant participants in the survey across both pairs, suggesting that they possess more road transportation experience than their female counterparts.
Besides, the male-female ratio (59.54-40.46) of Pair-1 is quite close with Hanoi‟s male-female population (49.92-50.08), but this ratio (65.22-34.78) of Pair-2 has a rather large difference with Hanoi‟s population
Figure 4.2.2 illustrates the age distribution of survey participants, revealing that the predominant age group in Pair-1 is between 25 and 35 years, comprising approximately 55.59% of responses In contrast, 31.25% of respondents are over 35, while those under 25 make up 13.16% of the total.
But in Pair-2, respondents between the ages of 25 and 35 are the majority (46.20%), followed by respondents over the age of 35 (44.02%), and respondents under 25 (10%)
The survey primarily targets individuals aged 16 and older, revealing a notable difference in age group proportions compared to those in Ha Noi Significantly, over 80% of respondents from both groups are aged 25 and above.
The respondents' monthly incomes are divided into two categories: >20 million and
Figure 4.2.3 shows that 79.93% of respondents in Pair-1 and 76.09% in Pair-2 have
< millions VND, and most of them are staffs, workers and newly graduated
35 Aged h students Therefore, the appropriate toll charges should be established for them in order to ensure equitable use of the toll services and facilities
4.2.4 Ratio of Travel Cost to Total Income
The respondents' ratio of travel cost/total income are divided into two categories:
According to Fig 4.2.4, both Pair_1 and Pair_2 exhibit a travel cost to total income ratio of less than 5% For individuals earning less than 20 million VND per month, their travel expenses are likely to remain below 1 million VND monthly.
A ver age incom e in H anoi
< 10 million VND 10~20 million VND 20~30 million VND 30~50 million VND > 50 million VND
Figure 4.2.4 Ratio of Travel Cost to Total Income
A significant majority of respondents reported frequent travel, with 92.76% from Pair-1 and 90.22% from Pair-2 indicating they travel more than 20 times per month This high frequency of travel suggests that most participants possess substantial experience with road conditions.
Aspect of Route Choice Behaviour Related to Service Quality
Table 4.1 Route Choice Behaviour Related to Service Quality (%)
Vehicle control entering the road
Road signage go in and out 1 0.66 0.00 8.15 1.09
Quality of rest stop station
Convenience of rest stop station locations
Quality of road signage and road marking
Quality of traffic safety system
Connect well from Origin to Destination
Conformity in the connection between regions
Well respond to demands journey of people
(1-Congested, 2-Bad congested, 3-Average congested, 4-Good congested, 5-Free)
In Pair-1, the toll road demonstrates significantly better vehicle control and road signage effectiveness, achieving nearly 90% higher ratings compared to the non-toll road, which scored 72.7% for vehicle control and 45.07% for road signage.
Users rated vehicle control and road signage for toll roads at 59.78% and 48.37% positive feedback, which is lower than the ratings for non-toll roads, which received 60.87% and 58.16% respectively.
In a comparison of accessibility, toll roads received a more favorable evaluation than non-toll roads in Pair-1 However, in Pair-2, the combination of toll and non-toll roads was assessed less positively than non-toll roads alone.
The toll road in Pair-1 boasts an amenity characteristic rating exceeding 50% for the quality of rest stops, their convenient locations, lighting, and road signage In contrast, non-toll roads fall below this threshold, with ratings under 50%.
The Pair-2 toll road has a lower amenity characteristic rating compared to non-toll roads, scoring over 50% less in terms of the quality and convenience of its rest stops.
Toll roads' safety characteristics has nearly 90% higher positive ratings for their safety systems and designs when compared to non-toll roads, which received positive evaluations of 27.3% and 18.09% in Pair-1
In Pair-2, the safety characteristics of toll roads show a significant advantage, with a safety system rating of 79.89% and positive evaluations in safety design at 64.67% In contrast, non-toll roads exhibit much lower safety ratings, with only 15.2% for the safety system and 35.87% for safety design evaluations.
Overall, Pair-1 and Pair-2 share similar safety characteristics with a higher rating on toll roads
In Pair-1, toll road's road surface condition, design, and comfortable feeling have over 90% positive ratings, which is much greater than those of non-toll road with below 40% positive ratings
In Pair-2, the combination of toll and non-toll roads received significantly higher positive evaluations, with 50.54% for road surface condition and 48.91% for comfort level In contrast, non-toll roads only achieved 1.63% and 2.17% positive evaluations in these categories.
In a comparison of road types, over 90% of respondents rated the quality and comfort of toll roads more favorably than non-toll roads in Pair-1 However, in Pair-2, the combined evaluation of toll and non-toll roads received a significantly lower positive rating, with less than 50% satisfaction compared to non-toll roads.
The well O-D connection, guaranteed travel time, and consistency in regional connections are the top three factors at the toll road connection level in Pair-1, exhibiting positive rates of 77.31%, 78.94%, and 79.61%, respectively, when compared to non-toll roads.
However, in Pair-2, well O-D connection at 47.83% and conformity connection between regions at 36.42% in toll+ non-toll road have lower good rates compared with non-toll road having 76.08% and 66.85%
Pair-1‟s connection characteristic of toll road is over 70% higher positive evaluation while that of Pair-2‟s toll+ non-toll road has lower positive evaluation at below 50%
In Pair-1, the non-toll road demonstrates a strong response to demand with a satisfaction rate of 71.71% However, the congestion of routes and reliability of travel time, both rated at 16.44%, are significantly lower than the over 90% ratings for toll roads.
But, in the case of Pair-2, non-toll road‟s well response to demands has 76.09% higher positive evaluation than that of toll+ non-toll road with 50% positive evaluation
Pair-1‟s serviceability characteristics of toll road is over 80% higher positive evaluation than those of Pair-2‟s toll + non-toll road having less than 80% positive evaluation.
Aspect of Route Choice Behaviour Related to Users‟ Travel Characteristics
Table 4.2 Route Choice Behaviour Related to Users‟ Travel Characteristics (%)
Role of road in daily life
Importance of road in daily life (1-Not important, 2-
Ratio of route usage to total daily trips
Frequency of doing activities along the route
Frequency Go to work/ study
Frequency Going out/ visiting relatives
(1-Driving, 2-Used to drive,3 Do not use)
(1-Driving, 2-Used to drive,3 Do not use)
(1-Driving, 2-Used to drive,3-Do not use)
Attitude toward road use fee
Satisfaction about road use fee (1-Strongly disagree, 2-
Disagree, 3-Neither agree nor disagree, 4-Agree, 5-
Agree toll/fare of the toll roads (1-Strongly disagree,
2-Disagree, 3-Neither agree nor disagree, 4-Agree, 5-
Level of influence on Apps
Level of influence of Apps when choosing routes
Uninfluential, 3-Neither uninfluential nor influential, 4-Influential, 5-
Level of influence of Apps when traveling
Non- toll road (%) uninfluential nor influential, 4-Influential, 5-
In Pair-1, the majority of toll road users always travel by private car with 34.54%, followed by 11.51% bus and 3.62% truck
In Pair-2, the majority of toll+ non-toll road users always travel by private car with 35.87%, followed by 6.52% truck and 3.26% bus
In Vietnam, the usage of toll and non-toll roads shows similar numbers across different modes of transport Notably, motorbikes are prohibited on toll roads, leading to a significant majority of non-toll road users traveling by motorbike, with 93.42% in Pair-1 compared to 76.63% in Pair-2 Additionally, truck usage in Pair-2 is double that of Pair-1, highlighting distinct differences in vehicle distribution on these road types.
4.4.2 Role of Road in Daily Life
In Pair-1, non-toll roads hold greater significance in daily life, boasting a 9.93% positive rating, surpassing that of toll roads, which have a significantly lower 16.12% rating Notably, user activity levels also reflect this disparity, with less than 5% of users utilizing toll roads, whereas over 5% of users engage in activities on non-toll roads, further solidifying the importance of non-toll roads in everyday life.
However, Pair-2 has not much different proportions with 42.93% and 35.87% in importance of toll+ non-toll road and non-toll road in daily life
While non-toll is more important than toll road in Pair-1, toll road is more important than non-toll road in Pair-2
In both pairs, >10 times in work and study purposes having 6.58% and 3.26% is the most popular within different types of purposes h
In Pair-1, a significant 58.22% of users utilize toll roads for work and study less than once, compared to 17.76% who use non-toll roads for the same purpose Conversely, 65.13% of users prefer toll roads for visiting relatives less than once, while a higher 85.53% favor non-toll roads for such visits This indicates a clear preference among users for toll roads when visiting relatives, whereas non-toll road users lean towards work and study activities.
In Pair-1, 44.57% of users on toll roads engage in work and study activities less than once, compared to 48.37% on non-toll roads Additionally, 64.67% of toll road users visit relatives less than once, while 61.96% of non-toll road users do the same This indicates a preference among toll road users for work and study, whereas non-toll road users tend to prioritize visiting relatives.
So, Pair-2 characteristic is reversed with Pair-1 characteristic for purpose to use road
4.4.4 Attitude toward Road Use Fee
Road use fees are viewed positively by 48.35% of Pair-1 users than by 58.16% of Pair-
2 users While fewer users in Pair-1 with 30.27% than in Pair-2 with 46.7% have a positive attitude towards toll fares
When choosing a route and travelling, Pair-1 users had positive app options at 79.28% and 80.92%, respectively, compared to Pair-2 users at 23.92% and 33.69% respectively.
Selecting Variables
Independent variables were narrowed down in three steps to avoid the following problems:
• The model reproducibility becomes poorer
• Interpretation of estimation results becomes difficult
Step 1 The number of independent variables was reduced based on the research hypotheses
Independent variables are reduced from 110 to 36 h
Table 4.3 Final selection of 36 Variables through the First Step
The data encompasses various metrics including Generation Age, Income Frequency, Total Customer Interactions (TCI), Total Transaction Counts (TTC), and Account details (ACCT1, ACCT2) Additionally, it highlights amounts across multiple categories (AMT1 to AMT4) and satisfaction ratings (SAT1, SAT2) Quality Control metrics (QCT1 to QCT3) and client loyalty indicators (CLT1 to CLT3) are also included, alongside settings (SET1 to SET3) and performance factors (PFT1 to PFT4) The report further details engagement rates (ERT1 to ERT3), service response frequency (SRF), and attributes like Average Income Change (AIC) and Average Income Trends (AIT).
Step 2 To avoid multicollinearity, the variance inflation factor (VIF) test is performed
Multicollinearity is commonly assessed using the Variance Inflation Factor (VIF), which measures how much the variance of an estimator is inflated due to multicollinearity among independent variables in a regression model (Greene, 2003) A higher VIF indicates greater multicollinearity, leading to increased variance in the estimation process.
It is generally recommended that a variable with VIF over 5 should be avoided VIFs estimated for 36 variables as shown in Figure 4.3, and the results show that ACCT1&2,
SET2&3 and PFT3&4 may not be used simultaneously in the same model
Step 3 Many models with different combinations of 36 variables are estimated
• The best models are estimated after many trials-and-errors
G en A geG Inc om e F req T C I A C C T 1 A C C T 2 A MT 1 A MT 2 A MT 3 A MT 4 SA T 1 SA T 2 Q C T1 Q C T 2 Q C T 3 C L T 1 C L T 2 C L T 3 SE T 1 SE T 2 SE T 3 T TC T T B T T T C P FT 1 P FT 2 P FT 3 P FT 4 E R T 1 E R T 2 E R T 3 SR F A T R A IC A IT
• The best one will be chosen with the minimum Akaike Information Criterion (AIC).
Results for Pair-1 and Pair-2
Five hypotheses are tested with an estimated model in Pair-1
Dependent Variable: Intention to choose TR Non-TR
Income (1 if > 20 million VND, 0 otherwise) -0.02 0.25
Frequency (1 if > 20 times/month, 0 otherwise) 0.36 0.71 *
Travel cost/ total income (1 if >5%, 0 otherwise) 0.09 -0.72 *
Vehicles control entering (1 if Easy, 0 otherwise) -0.33 0.33
Quality of lighting (1 if Good, 0 otherwise) -0.59 0.75 **
H1 Quality of traffic safety system (1 if
Condition of road surface (1 if Good, 0 if otherwise) 0.66 -0.13
Comfortable connection bet regions (1 if Good, 0 otherwise) 0.18 -0.30 h
Dependent Variable: Intention to choose TR Non-TR
Well respond to demands (1 if Good, 0 otherwise) 0.28 0.18
Frequency Go to work/ study (1 if
Importance of road in daily life (1 if
H4 Satisfaction about road use fee
H5 Subjective level of influence of Apps
Five hypotheses are tested also with an estimated model in Pair-2
Dependent Variable: Intention to choose TR + Non-TR Non-TR
Dependent Variable: Intention to choose TR + Non-TR Non-TR
Income (1 if > 20 million VND, 0 otherwise) -0.06 0.75
Frequency (1 if > 20 times/month, 0 otherwise) 0.39 0.99 *
Travel cost/ total income (1 if >5%, 0 otherwise) 0.24 -0.26
Vehicles control entering (1 if Easy, 0 otherwise) 0.29 0.86 **
Quality of lighting (1 if Good, 0 otherwise) 0.11 -0.21
Quality of traffic safety system (1 if
Condition of road surface (1 if Good, 0 if otherwise) 0.91 ** 0.07
Comfortable connection bet regions (1 if
Well respond to demands (1 if Good, 0 otherwise) 0.44 0.95 **
Frequency Go to work/ study (1 if > 10 times, 0 otherwise) -1.17 0.28
Importance of road in daily life (1 if
H4 Satisfaction about road use fee (1 if 0.35 h
Dependent Variable: Intention to choose TR + Non-TR Non-TR
H5 Subjective level of influence of Apps (1 if Influential, 0 otherwise) -0.58 -0.63 *
Discussion Results
H1: Users evaluating highly road’s Safety prefer TR than those evaluating poorly, but not in non-TR
Road signages and markings are better installed on TR in Pair 1
Figure 4.4 Safety System/ Design in Pair 1 (Photo from Field Survey)
Road users cannot differentiate the two routes because TR+NTR route contains the non-TR section
H2: Users evaluating highly road’s Quality & Comfort prefer TR than those evaluating poorly, but not in non-TR
As the common section has poor surface condition, road users who highly evaluate TR feel more strongly the better quality of TR
Figure 4.5 Condition of Toll Road
Figure 4.6 Condition of Non-Toll Road
Surface in Pair-2 (Photo from Field Survey)
Road users‟ evaluation on road surface conditions does not affect the choice of route
H3: Users satisfied with road-use fee prefer TR than those unsatisfied
Road users who are satisfied with road-use fee have high willingness to pay for
While some road users in Turkey appreciate the existing road-use fees, their satisfaction may be influenced by factors beyond just cost, particularly due to the mixed nature of the TR+NTR route, which includes both Turkish and non-Turkish sections.
H4: Car users prefer TR more than non-car users, but not in non-TR
Car users with high-income are higher willing to pay to reduce travel time using
TR than non-car users while the travel time of car users is the same as that of non-car users in non-TR
High-income car users may be willing to pay more to reduce travel time through toll roads (TR), but the difference in travel time between toll roads combined with non-toll roads (TR+NTR) and non-toll roads alone (NTR) is minimal As a result, these users lack motivation to opt for toll roads.
H5: Users whose route choice is affected by apps prefer TR more than those not
Users influenced by routing apps tend to exhibit a higher value of time (VOT) In Pair 1, the travel time for TR is notably shorter compared to non-TR, leading users with a higher VOT to favor TR for their journeys.
• As the travel time in TR+NTR is not much different from that in non-TR in Pair
2 due to the long common section, users with higher VOT may not significantly prefer TR
Figure 4.7 Toll Gate in Pair-1 (source: vietnamnet.vn)
Figure 4.8 Toll Gate in Pair-2 (source: vietnamplus.vn) h
Policy Implications
Individuals who evaluate highly TR‟s safety prefer using TR
Good safety facilities on TR will attract road users
High-quality road signages and markings should be introduced
Modernize them with new technology
Figure 4.9 VMS LED sign in Japan (source: finepixel led)
Individuals who evaluate highly TR‟s quality and comfort prefer using TR
High quality of road surface conditions on TR will attract TR users
High-quality technology and materials should be used for constructing road surface
Adequate monitoring and maintenance of road conditions should be operated
Individuals‟ satisfaction with road-use fees affects TR demand
Appropriate price should be set by balancing user satisfaction and financial viability h
Toll-road operators should regularly collect information about the users‟ satisfaction and properly adjust the price and services
Car users prefer using TR
Authorities may reallocate the toll revenue from car users to improve road services/facilities for non-car users
This may enable the improvement of general road conditions
Individuals who are affected by apps may use TR more
Toll road operators should make efforts to promote the use of app for increasing the TR users
This should highlight the individuals who do not own smartphones h
CONCLUSIONS
Discussion and Conclusion
This research investigates the factors influencing road users' route choices on Vietnam's toll roads Understanding these variables is crucial for enhancing the current toll road system and addressing two key route-related issues The study aims to shed light on the elements affecting toll road usage compared to non-toll highways, providing valuable insights that can inform future toll road projects and improve overall infrastructure planning in Vietnam.
This research utilized bivariate binary probit models to identify key variables influencing users' route choices The findings indicate that factors such as safety from service quality evaluations, satisfaction with road use fees, car usage, and the subjective impact of the app based on users' travel characteristics significantly affect Pair-1, which includes all sections of toll and non-toll roads running in parallel without any shared sections In contrast, Pair-2, which consists of only a portion of toll and non-toll roads that share a section, is influenced solely by the quality and comfort aspects derived from service quality evaluations.
Figure 5.1 Factors Affecting Route Choice on Toll Road
Recommendations for toll road policy will be discussed in the following,
• High-quality road signages and markings should be introduced with high technology
• High-quality road surface conditions should be provided to toll road
• Satisfaction of road- use fee
• Subjective level of app‟s influence
Route choice on toll roads
• Appropriate price should be set by balancing user satisfaction and financial viability
• Toll revenue from car users should be smartly used for improving the road network
• Smartphone users may be one of key factors in toll road demand.
Suggestion for Further Studies
This research is limited by the data being collected solely in Vietnam, highlighting the need for similar studies in other developing countries looking to enhance their toll road networks To improve data collection, future studies should leverage advanced technologies, such as QR codes at toll gates Additionally, it is recommended that future research compare all sections of toll and non-toll roads simultaneously, rather than only selected segments, to capture a broader range of unobserved variables.
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95 Zuna, H T., Hadiwardoyo, S P., & Rahadian, H (2015) Analyzing Service Quality of Toll Road and Its Relation with Customer Satisfaction in Indonesia using Multivariate Analysis In Proceeding 14th International Conference on QIR
96 Zuna, H T., Hadiwardoyo, S P., & Rahadian, H (2016) Developing a model of toll road service quality using an artificial neural network approach International Journal of Technology, 7(4), 562-570 h
97 Henningsen, A., & Henningsen, M A (2015) Package „mvProbit‟ R collection Package „mvProbit‟