Modeling of Applying Road Pricing to Airport Highway Using VISUM Software in Jordan

Abstract

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method.

1. Introduction

Traffic congestion is a common issue in urban areas worldwide, leading to substantial economic, environmental, and social costs. In Amman, Jordan, this problem is particularly severe along the Airport Highway, a critical route connecting Queen Alia International Airport to the Amman city and serving as a vital link to the southern regions of the Jordan, including major tourist destinations like Petra and Aqaba, as well as the kingdom’s only container port.

The growing volumes of traffic on this highway, driven by both local and international travel, highlight the urgent need for effective congestion management strategies.

The significance of this research lies in its potential to address the multifaceted impacts of traffic congestion in Amman. Economically, congestion leads to lost fuel, increased travel time, and decreased productivity, imposing substantial costs on individuals and businesses. Environmentally, the idling vehicles contribute to higher emissions and noise pollution, adversely affecting air quality and public health. Socially, prolonged travel times reduce the quality of life and increase stress for commuters [1,2,3,4,5,6,7,8,9,10,11].

Given the existing and planned developments along the Airport Highway, traffic is expected to intensify in the future, exacerbating these issues. Therefore, it is imperative to investigate viable solutions to mitigate congestion and enhance the overall efficiency of the transportation network.

This paper proposes road pricing as a potential solution to Amman’s traffic congestion problem. Road pricing has been successfully implemented in various cities around the world, demonstrating its effectiveness in reducing traffic volumes, improving travel times, and generating revenue for infrastructure improvements.

This study aims to (a) evaluate the public acceptance of toll roads and their impact on society and the economy in Amman, Jordan through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price at a selected road using VISUM, and (d) validate the simulated models with the optimal revenue. These insights can assist transportation planners and decision-makers in Jordan in applying toll roads to other congested routes in Amman and in investigating the potential for road pricing on major highways across Jordan.

2. Literature Review

Many studies discussed various effects of road pricing systems, including traffic, environmental, distributive, and social impacts. In terms of traffic effects, road pricing schemes have shown positive results in reducing congestion and altering travel behavior (

Table 1. Literature on road pricing from international countries, and Jordan compared to current study.

	Country (Ref.)	Outcome Measures	Interventions	Traffic Simulation Software	Statistical Software	The Type of Payment	Payment Method	Findings
1.	Flanders, Belgium (2011) [18]	Impact of road pricing on people’s inclination to adjust their current travel behavior	The implementation of a variable road pricing system, with charges of 0.07 EUR on roads at uncongested periods and 0.27 EUR at congested periods, for each kilometer travelled by car	N/A	AMOS 4.0	Based on distance	N/A	- To make a change in behavior, charges must be greater than a certain threshold and benefits must be understood.
2.	Seattle, WA, USA (2012) [19]	Reduced travel time, increased travel reliability, reduced emissions, and reduced traffic accidents	Implementation of cordon-based road pricing and toll collection	N/A	N/A	N/A	Different scenarios	- Road pricing in downtown Seattle is projected to have positive impacts on the city and region.
3.	Jordan (2013) [20]	To investigate the travel behavioral responses of affected road users to road pricing in Amma	A pilot survey questionnaire	N/A	SPSS	N/A	N/A	- Half of the respondents reporting that they would use the public transport system and carpooling instead of using their vehicles, while firms will increase the price of their goods.
4.	Denmark (2014) [21]	To investigate the effect of price and travel mode fairness and spatial equity in transit provision	A web-based questionnaire for revealed preferences data collection	structural equation modeling (SEM)	SPSS	N/A	N/A	- Higher perceived service quality is associated with greater perceived ease of payment, leading to increased frequency of transit use.
5.	Philippines (2016) [22]	To reduce traffic congestion and fuel consumption	Manual toll collection system, Electronic Toll Collection system	N/A	N/A	Based on time	Different scenarios	- The optimal collection method is Electronic Toll Collection (ETC).
6.	Spain (2017) [23]	Delay	Participants received information about and questions regarding three different road pricing schemes: a surcharge to avoid congestion at any time (express toll lanes), a time-based pricing scheme (peak versus off-peak), and a flat fee-charging system (vignette)	N/A	Binary choice models	Based on time	Different scenarios	- Support for pricing options is not linked to income, with attitudinal factors playing a more significant role in acceptability. Users’ perceptions vary significantly depending on the type of charging scheme proposed.
7.	Malaysia (2019) [24]	Delay, queue length	Real data from the position to evaluate the traffic congestion	VISSIM	N/A	Based on distance	Different scenarios	The collection toll method is the main cause of congestion; queue and delay especially for heavy vehicles.
8.	India (2021) [25]	To reduce peak hour travel, traffic congestion and environmental impacts	Revealed preference data were derived from real-life situations and were based on users’ perceptions	N/A	Multinomial Logit Model	Based on distance	Different scenarios	The optimal collection method is Electronic Toll Collection (ETC) and open road tolling.
9.	Jordan (Current Study)	To (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan through a survey questionnaire, (b) assess the impact of the toll road on reducing congestion and delays, (c) identify the optimal toll price at a selected road, and (d) validate the simulated models with the optimal revenue	Revealed preference data were obtained from actual situations and were grounded in users’ perceptions	VISUM	SPSS	Based on distance	Different scenarios	- The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025

(N/A: not available).

). Examples from Singapore [12,13], London [14,15], and Norway [16] demonstrate significant reductions in traffic volume during peak hours, leading to improved traffic flow and modal shifts towards public transit. Studies like those by Xie and Olsozewski (2011) in Singapore further highlight the positive correlation between road pricing and enhanced accessibility to public transit, emphasizing the potential for traffic management and mode shift strategies [17].

Regarding environmental effects, road pricing aims to mitigate emissions and noise pollution associated with transport activities. Studies, such as Johansson’s analysis in Sweden [26], highlight the need for road users to internalize the environmental costs of their trips. By reducing travel time and vehicle kilometers, road pricing systems contribute to lower emissions levels, with variations in charges reflecting different vehicle types and environmental considerations. The design of road pricing charges, exemplified in Singapore’s approach to electric and hybrid vehicles, influences their environmental effectiveness, underscoring the importance of tailored strategies to address specific environmental concerns [27].

Distributive effects of road pricing systems are contingent upon charge design and revenue allocation. Revenues collected from road pricing schemes can benefit public transit users and infrastructure improvements, as seen in Oslo’s case [28]. However, disparities in income levels among road users may lead to inequities, with higher-income groups more likely to afford the charges compared to lower-income individuals. The division of road users into different types based on their response to charges highlights the complex socioeconomic dynamics at play, underscoring the need for equitable and inclusive policy considerations [29].

Moreover, social benefits of road pricing systems include improvements in travel time, accident reduction, reliability, and emissions. Studies by Parry and Bento (1999) advocate for the recycling of collected revenues to enhance public transit services, emphasizing the importance of reinvestment for broader social welfare gains [30]. Danna et al. (2012) further quantify the social benefits of road pricing, considering factors such as toll collection costs, transit subsidies, and overall societal welfare impacts [19]. Their findings suggest that while toll revenues may represent cash transfers, effective road pricing policies can yield net social benefits over the long term, highlighting the potential for sustainable transportation planning and investment strategies [31].

The evolution of road pricing acceptance can be traced through various studies conducted globally. Initially met with resistance due to perceived higher costs, road pricing gradually gained traction as studies highlighted its benefits. For example, in Gothenburg, there was an indication of minimal public agreement with the fairness of road pricing, but by 2000, support increased to 38% when presented as a solution to reduce queues [29]. Similarly, opposition was found in Oslo [15,32], Spain [23], and Jordan [20].

Oslo decreased opposition from 70% to 54% after charges were implemented, demonstrating increased support post-implementation, and in Spain, the research investigated the impact of different factors on the support for these pricing options. Interestingly, the study found that attitudes towards road pricing, rather than income levels, played a significant role in influencing support for the proposed schemes. In Jordan, a study by Jdaan explored how road pricing in Amman affected travel behavior, using a pilot survey questionnaire. The findings revealed a notable shift in respondents’ preferences towards utilizing public transport and carpooling as alternatives to using their vehicles. Studies in other cities like Singapore and Trondheim also showed growing acceptance over time [15,32].

Further research, such as the study by Ubbels and Verhoef in 2004, underscored the importance of adequate technical and administrative groundwork for road pricing acceptance [31]. Studies like those by S. Jaensirisak et al. and Ubbels and Verhoef in 2005 and 2006, respectively, revealed that acceptance varied based on factors such as personal characteristics and perceived revenue use [31,33]. Melhorado et al. in 2010 emphasized the need for policymakers to understand the purpose of road pricing for its effective implementation and economic development [34].

Cools et al.’s 2011 study explored the link between driver behavior and road pricing acceptability, highlighting the importance of sociocognitive factors [18]. Meanwhile, local research by Jadaan et al. in 2013 in Jordan revealed potential shifts towards carpooling and public transportation due to road pricing, impacting both individual behavior and business practices [20]. In 2014, Kaplan et al. delved into the impact of fairness and spatial equity on transit perceptions and usage, showcasing the intricate relationship between perceived service quality, ease of payment, and frequency of transit use. Together, these studies provide insights into the evolving acceptance and understanding of road pricing worldwide, emphasizing its multifaceted implications on transportation behavior and societal dynamics [21].

Several studies have created various models to examine how individuals’ attitudes, behaviors, and characteristics influence the acceptability of RP. Simulation modeling offers a comprehensive approach to assess the impact of tolls across various dimensions, including economic, environmental, social, and traffic factors. Komada and Nagatani’s (2010) study focused on traffic dynamics on toll highways, revealing how vehicular density and tollgate configurations influence traffic flow and queuing patterns. By deriving fundamental diagrams, they provided insights into traffic behavior under different conditions [35].

Tsekeris and Vos (2010) examined the relationship between public transport and road pricing in Greece, using simulations to demonstrate that well-designed policies could bolster public transport usage without substantially raising road user charges [36].

Chakirov and Erath (2012) explored the complex influencing variables, employing Multi-Agent Transport Simulation (MATSim) to model economic and sociodemographic variables influencing travel demand patterns [37]. Moreover, in 2019, Malaysia employed VISSIM to simulate traffic conditions, revealing that toll collection methods have a notable impact on congestion, queues, and delays, particularly affecting heavy vehicles [24].

Road pricing systems employ various payment methods, categorized into three main types: distance-based [24,25], time-based [19,22], and toll station-based [14]. Distance-based payment, utilized in Switzerland, Germany, and Gothenburg, relies on technologies like GPS and the GSM to track vehicle movement and calculate charges based on driven distance. In this system, vehicles equipped with GPS transponders communicate with central units via the GSM, allowing for automatic payment processing after the vehicle enters the charging zone. This method offers real-time tracking and efficient billing, enhancing user convenience and accuracy [38].

On the other hand, payment systems based on time, exemplified by London’s congestion charging scheme, offer drivers multiple payment options and time frames for payment. Failure to pay by the designated time incurs fines, encouraging compliance. Despite the flexibility provided to users, time-based systems require strict adherence to payment deadlines, posing challenges for enforcement and revenue collection. Nonetheless, London’s scheme generates substantial annual revenues, indicating its effectiveness in managing congestion and generating funds for transportation infrastructure [39].

Toll station-based payment methods, involving manual collection, bank accounts, or smart cards, streamline toll collection processes. In Malaysia, a study was conducted employing VISSIM for traffic simulation, which highlighted the notable influence of toll collection methods on congestion, queues, and delays, especially concerning heavy vehicles. However, results of a study in India found that the optimal collection method is Electronic Toll Collection (ETC) and open road tolling. Moreover, other studies [22] in other locations like Oslo, the Philippines, Singapore, and Dubai have studied optimal toll collection methods.

In Oslo, electronic payment is preferred due to its speed and convenience, with charges deducted directly from registered bank accounts via subscription transponders. Smart card systems, like those in Singapore and Dubai, offer similar benefits but require users to preload funds onto cards for automatic deduction upon passing toll gates. These systems minimize transaction times and enhance user convenience, contributing to efficient toll collection and revenue generation [15,22,32].

Previous studies have typically focused on the impact of toll roads in distinct aspects, such as social, economic, environmental, or traffic-related effects. Some have evaluated only the public acceptance of tolls, while others have looked at the most effective charging method or toll value. The novelty of this research lies in its comprehensive approach to analyzing toll roads. By designing and administering a questionnaire on road pricing, valuable insights are observed into public perception, acceptance, and potential behavioral responses to toll implementation. After collecting and analyzing the questionnaire data, the operational costs associated with the toll road, including infrastructure and administration, are calculated. This is then compared with revenue projections derived from VISUM simulations and validation models, leading to a thorough assessment of the toll road’s financial viability and sustainability.

These insights can assist transportation planners and policymakers, both in Jordan and in other regions facing similar transportation challenges. Consequently, policymakers can use these findings to develop informed strategies for toll road deployment aimed at improving traffic flow, alleviating congestion, and enhancing road safety and mobility.

Furthermore, toll roads in Amman contribute to environmental sustainability by reducing fuel consumption and vehicle emissions. By optimizing traffic flow and decreasing congestion, toll roads minimize idling time and stop-and-go traffic, which are significant contributors to fuel waste and air pollution. Efficient toll road operation also helps mitigate greenhouse gas emissions associated with transportation.

3. Methodology

The methodology used in this study follows a scientific approach to analyze various characteristics and data related to the Queen Alia International Airport Road. Firstly, geometric data detailing the design of the highway, such as the number of lanes, lane widths, and locations of interchanges, were collected from reputable sources, including the Greater Amman Municipality and the Ministry of Public Works and Housing in Jordan. These data provide a comprehensive understanding of the physical layout of the road.

Secondly, traffic data were collected to understand the volume and flow characteristics of traffic on the Airport Road. This information was sourced from the Central Traffic Department, providing insights into traffic patterns, congestion levels, and peak traffic hours. Additionally, data on fuel prices and vehicle counts were obtained from the Al-Manaseer Oil & Gas Group and the Department of Statistics in Jordan, respectively.

Furthermore, toll infrastructure cost data and survey questionnaires were utilized to assess driver attitudes and preferences towards road pricing schemes. The cost data, obtained from relevant authorities, include expenses associated with monitoring and violation cameras, booths, signage, and toll machines. Survey questionnaires were distributed to gather feedback from drivers regarding their acceptance of toll-based pricing schemes, helping to gauge public opinion and willingness to pay.

Moreover, the operational costs of toll road methods were evaluated to identify the most economical approach. By comparing the costs associated with manual toll collection versus automatic toll machines, the study aimed to determine the most cost-effective method for toll collection.

Finally, the transportation planning software VISUM was used for traffic flow modeling. This software considers various factors such as traffic demand, network structure, and route choice behavior to simulate the impact of proposed interventions on urban transport dynamics. Different scenarios based on toll prices were evaluated using VISUM, and the level of service based on highway capacity was assessed manually, allowing for a comprehensive analysis of potential interventions and their implications for traffic management and infrastructure planning. Figure 1 provides a concise overview of the methodology used in this study.

3.1. Research Location

Amman, a prominent urban center in Jordan, was home to approximately 4.642 million people as of 2021, making up about 42% of the country’s total population. The city’s dense urban environment has led to a significant increase in vehicular traffic, contributing to congestion challenges on its road network. This study focuses specifically on analyzing two sections of the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange and from the Madaba Interchange to the QAIA Interchange), a vital transportation artery in Jordan. The Airport Highway experiences consistently growing traffic volumes, primarily because it serves as a crucial link between Queen Alia International Airport and the southern regions of Jordan. Moreover, the southern region, home to popular tourist destinations like Petra and Aqaba, also serves as a major freight hub due to its possession of the sole container port in the kingdom, as shown in Figure 2. With numerous ongoing and planned developments along the Airport Highway, traffic is anticipated to continue expanding, underscoring the importance of studying this area for effective traffic management strategies.

3.2. Geometric Data

As stated by the Greater Amman Municipality (GAM), geometric data comprise crucial information regarding the design of highways, encompassing essential details about highway design such as the number of approaches, lanes, lane widths, lengths of studied sections, and locations of interchanges. These data provide a basic understanding of the layout and structure of the road.

3.3. Traffic Data Collection

Traffic data collection involves gathering detailed information about the flow of traffic, particularly focusing on the volume of traffic passing through specific points over time. This includes determining the types and numbers of vehicles crossing selected sections. The data gathered include information on traffic volume, free flow speed, and congested speed during the morning AM peak hour along the study area. These valuable metrics are sourced from the Greater Amman Municipality (GAM) and Ministry of Public Works and Housing (MPWH), specifically collected during morning rush hours, as detailed in Appendix A.

Free-flow travel speed represents the speed at which vehicles can travel under uncongested conditions, providing a baseline for comparison, and congested speed indicates the reduced speed experienced during periods of traffic congestion, highlighting the impact of traffic volume on travel times and efficiency, as shown in Appendix A.

3.4. Cost Data of Toll Technique

The cost data collection associated with toll techniques involves assessing various economic factors related to transportation infrastructure and operations. This includes evaluating the average cost of time attributed to working hours, representing the monetary value associated with time spent due to work commitments. Additionally, the cost of fuel per gallon is examined to understand the financial implications of fuel consumption in transportation activities.

Key variables collected for analyzing the cost of toll techniques include the following:

-

Average yearly income (JOD): This provides insights into the financial capacity of individuals in the region and their ability to afford transportation-related expenses.

-

Working days and working hours: These parameters help determine the average time spent on work-related activities, influencing travel patterns and demand for transportation services.

-

Cost of time: Calculated by dividing the average yearly income by the working hours, this metric signifies the value of time spent on work commitments.

-

Average cost of fuel and diesel (per liter): These values reflect the cost of fuel consumption, essential for understanding the economic implications of transportation activities. A number of variables were collected from different sources, as summarized in

Table 2. The required variables to calculate congestion cost (for 2012 and 2024).

Constant	Value (2012)	Current Value (2024)
Avg. yearly income (JOD) 1	3438.6	5100.84
Working days [40]	255	255
Working hours 2	2040	2040
Cost of time 3	1.27	1.66
Avg. cost of fuel (L) [41]	0.723 JOD/L	0.925 JOD/L
Avg. cost of diesel (L) [41]	0.568 JOD/L	0.72 JOD/L

¹ From the Regression Model in Appendix B. ² By subtracting all Fridays and official holidays in 2012 and 2024 = (365–96–14), and based on job law section 56 in Jordan, the daily working hours = 8 h, so working hour per year = 255 × 8 = 2040 h. ³ Avg. yearly income divided by the working hours in 2012 and 2024 (1.27 and 1.66 JOD).

.

In addition, toll techniques include various components and equipment necessary for the operation of toll booths or collection points, as shown in Figure 3.

Table 3. Details of charging method costs.

Techniques	The Cost of One (JOD) *
Monitoring Cameras	4000
Violation Cameras	40,000
Booths	9000
Signs in Advance of a Toll Point	443.75
Signs at a Toll Point	303.75
Pavement Markings at Toll Points	150
Direction Signs	443.75
Polyvinyl Chloride Cone	14
Employer	300
Toll Machine	56,000

(*) 1 JOD = 0.72 USD.

provides a summary of the costs associated with different toll collection devices, including cameras, booths, signs, pavement markings, and automated toll machines.

3.5. Congestion Cost

The cost of congestion includes two primary components: delay and fuel consumption. Delay refers to the additional time spent by vehicles due to congestion, resulting in lateness for work or other commitments. Fuel consumption increases during congestion due to prolonged engine usage, leading to higher costs and increased emissions, contributing to environmental issues like global warming.

Data collection involved studying traffic volumes and speeds during peak hours along the Airport Highway sections. The delay cost was calculated by determining the difference in travel time between congested and uncongested periods, based on the value of a working hour in Jordan. Additionally, fuel waste costs were estimated by comparing fuel consumption rates at congested and uncongested speeds. These costs were then added to obtain the total congestion cost for each roadway section.

The following general steps were used to calculate the congestion cost in this study for each urban roadway section:

• Obtain traffic volume data by road section.
• Determine the AM peak hour and PM peak hour for the years 2012 and 2024.
• Obtain congested and uncongested speeds for each section.
• Calculate vehicle delay by measuring the sectional time lost between congested and uncongested conditions.
• Determine lost fuel for each section as the difference between fuels consumed at congested and uncongested speeds.
• Determine the total congestion cost.

3.6. Survey Questionnaire

In this study, a road pricing scheme was designed for the Airport Highway to examine drivers’ attitudes towards such schemes. A survey questionnaire was conducted with travelers at various locations along the Airport Road (Madaba, Marj al-Hamam, and the South of Jordan). Participants were asked several questions to determine their preferences regarding road pricing, their acceptance, and their willingness to pay for the implementation of such a scheme.

The primary tool of this study was a stated preference questionnaire, organized into four sections, as detailed in Appendix C.

Section 1: This section includes questions about the demographic and socioeconomic characteristics of the drivers, such as gender, age, household income, and education level.

Section 2: The objective of this section was to inform and raise awareness about road pricing schemes, focusing on their potential positive and negative impacts. It contained questions about the advantages and disadvantages of a congestion pricing scheme, which was especially relevant for users with little experience with road pricing.

Section 3: This section gathered information related to respondents’ travel behavior, specifically the number of trips and the purpose of these trips. The assumption is that travelers’ acceptance of and willingness to pay for a congestion charging scheme depend on their trip characteristics.

Section 4: This section included questions about the preferred payment method and acceptable pricing. The objective was to identify the most suitable payment method and an acceptable fee or price.

3.7. Evaluation of the Operation Cost of the Toll Road

The operation cost includes the cost of two toll road methods for the service road along the main road: the manual method and the automatic toll machine method. Specifically applied to the service road alongside the main thoroughfare of the airport road, this assessment underscores the importance of exploring various charging mechanisms to determine the most cost-effective approach that optimizes revenue generation. The manual method entails a detailed estimation of expenses, encompassing the deployment of monitoring cameras, booths, pavement markings, signage, and employee salaries during peak operational hours. Considering the airport road’s three lanes, each section requires three booths, as illustrated in Figure 4. Thus, three monitoring cameras are installed in each section’s three lanes to ensure efficient monitoring. Booths play a vital role in facilitating the payment process, occupying an area of approximately 3.10 m × 3.66 m. Detailed specifications of the booths are provided in Appendix D.

Different types of toll road signs play pivotal roles in guiding drivers and providing essential information before they enter toll roads. These include signs in advance of the toll point, signs at the toll point, pavement markings at the toll point, and direction signs. Signs in advance of the toll point serve to inform drivers about approaching toll points, ensuring they are aware of upcoming toll booths. These signs are crucial in preparing drivers for toll payment and navigating through the toll road. They typically measure 2.5 m × 1.25 m, with a sign column height of 4.5 m. Signs at the toll point are positioned at toll points; these signs detail payment information and vehicle charges and have dimensions of 1.5 m × 0.75 m and a sign column height of 4 m. Pavement markings at the toll point play a crucial role in guiding drivers within toll areas. These markings, characterized by a black legend on a yellow background, establish a visual connection with the toll patch on direction signs. They cover an area of 2 m × 2.5 m and are instrumental in ensuring smooth traffic flow within toll plazas.

In addition, polyvinyl cones are used to divide the lanes when the toll points are being installed. They are placed about 100 m before toll points. A cone is placed every 3 m, so the total number of cones required for the three lanes is 30. Three employees are required for the three lanes.

Conversely, the automatic toll machine system involves calculating the costs associated with toll machines, surveillance cameras, signage, and pavement markings. A camera is important in toll road operations to show more details of the payment and to determine a fine when vehicles do not pay the charge. Three cameras in three lanes in each section are required.

3.8. Model Development (2012 and 2024)

The inclusion of data from 2012 is intended to provide a historical baseline for comparison with the current time (2024). This temporal perspective helps to analyze trends and evaluate the effectiveness of toll interventions over time. Given that Jordan has not implemented toll roads, using historical data enables us to assess how tolls could have impacted traffic congestion.

Utilizing the transportation planning software VISUM, an intervention with the urban transport network was modeled. The PTV Group developed and maintains VISUM, a tactical modeling package for representing traffic flows in networks. Origin–destination matrices for various use classes to characterize the traffic demand and a geographically precise supply model that specifies a particular road network are the fundamental parts and inputs of the traffic planning software. Additional fundamental components of VISUM include distinct assignment protocols, which are necessary to connect supply-side and demand-side traffic. Estimated flows for each vehicle category in each network link are the outcome of the assignment procedure. While VISUM can also manage public transportation, this functionality was not included in these simulations because the demand for both public and private transportation is not very interdependent. Furthermore, compared to individual motorized traffic, calculation duration and data needs for public transport assignments are significantly higher. Owing to the strong supply-side interdependencies between passenger cars and freight traffic, both demand groups were included in our simulations. The underlying ideas of transport assignment models such as VISUM are illustrated in Figure 5.

The process begins by assigning traffic to various links on the network, with impedance serving as the “cost” for vehicles using each link. This impedance is influenced by travel time, which varies depending on traffic volume. When modeling toll roads, a fixed toll charge is added to the impedance of the affected links, increasing the cost and reducing the number of vehicles choosing to use the toll road. To model the toll road, we created the required road along the route of Airport Road with link type 13 (Freeway speed 110 km/h, 3 lanes) and edited the link to add the chosen toll charge for each vehicle type. For the service road, we adjusted the link type for the current Airport Road to provide the best level of service (LOS), selecting road type 32 (Suburban dual 2, medium intersected). To incorporate the toll charge into the impedance calculation, we accessed the calculation procedures, opened the functions tab, and selected PrT, then Impedance. For each formula, we used Create and added 1*Toll PrTsys, adjusting the coefficient for Toll PrTsys to, e.g., 2 or 4, to set different tolls for goods vehicles. The toll was input in fils, and the impedance calculation was based on the value of time. The toll charge must be added for each link, making it challenging to allow users to access different sections, and all pay a fixed cost. To address this, we considered the following options:

Option 1: The toll road extends from the Foreign Ministry to the airport with no intermediate entry points. This would result in lower traffic flow since the toll road would no longer serve South Amman.

Option 2: Three separate toll roads are created for each of the three entry/exit points for journeys to the airport (Foreign Ministry, Marj al-Hamam Bridge, and Madaba Bridge). Congestion and speeds would need to be checked manually.

Option 3: Different tolls are applied to each section, so only vehicles travelling from the Foreign Ministry to the airport pay the full amount. Vehicles are charged for all the sections they use, requiring the definition of three link types.

Option 4: The toll is applied only to vehicles using the section from Madaba Bridge to the airport, restricting access to the toll road for those not using this section.

After testing all options, we found that due to the low levels of vehicles assigned to the toll road in 2012 and 2024, Option 1 was the most suitable. Subsequently, we tested Option 4 using two sections of the toll road. The toll was charged on the first section (from the Foreign Ministry to Madaba Bridge), and only vehicles using this section were allowed to use the second section (from Madaba Bridge to the airport). This configuration allowed the toll road to be accessed from Madaba Bridge for trips to or from Amman only.

The traffic infrastructure that is currently in place is represented in a simplified manner by the network model. It is made up of nodes with links between them. The regional location and connections of the nodes and links in the network model are first determined by the network structure, which has to be mapped, as detailed in Figure 6a,b. Furthermore, in VISUM, every network element can be given a set of unique properties.

Equation (1) calculates the length of the stretch, the speed at which a vehicle may travel freely, the maximum capacity, and the proportion of the road that is congested for linkages. Figure 6c displays an example of a VISUM network.

% Traffic Congestion = Volume/(Capacity) × 100%

(1)

The number of trips (from/to traffic zone) for each origin–destination (O-D) pair is represented by fixed, typically symmetric matrices in VISUM, which is used to analyze travel demand. As a result, distinct transport demand segments (such as those for cars and trucks) can be distinguished. The alternate routes for the travel demand of an origin–destination pair are determined by the network’s structure as well as the physical attributes of each individual route section. Usually, when modeling the simultaneous route choice of all road users, a mono-criteria method is used. This indicates that the various factors considered while selecting a route—such as travel durations, tolls, and trip times—are combined into a single value known as “generalized costs”.

For every demand segment, average values of time and distance (VoT, VoD) must be stated in addition to link-specific conditions like a toll. This makes it possible to see a road user’s route selection as a unique cost-minimization issue. When using the same links to make trips for distinct O-D relations, the routes chosen are reliant on each other. As a result, capacity-limited assignment techniques typically operate iteratively, beginning with an initial demand allocation on possible network paths that is frequently arbitrary. But in VISUM, a change in the overall costs—for example, because of a toll or newly constructed road—only affects route choice behavior and has no effect on demand levels. We exogenously modeled these effects on demand levels since the premise of a stable demand is impractical for greater changes in generalized costs.

The traffic models were tested across seven different toll scenarios (from Scenario 1 to Scenario 7) as shown in

Table 4. The different scenarios of different values of the price at the service road in the AM peak hour.

Number of Scenarios	Toll for Car	Toll for Goods Vehicle
1	0.2	0.4
2	0.2	0.4
3	0.2	0.8
4	0.25	0.5
5	0.3	0.6
6	0.4	1.6
7	0.5	2

, each with varying toll rates for cars and goods vehicles. In these simulations, a fixed toll charge was added to the road’s “impedance”, which represents the cost of using that road, factoring in travel time and toll fees. As the toll increases, the cost rises, and fewer vehicles are likely to choose the toll road, leading to a redistribution of traffic to other routes.

For the service road along Airport Road, we adjusted the road type to improve its level of service (LOS), ensuring it could efficiently handle more vehicles. The tolls were entered in fils (the currency subunit), and the overall impedance (cost of the road) was calculated based on the value of time for different types of vehicles.

Each section of the toll road had a unique toll, making it more challenging to ensure that all users paid a consistent charge while accessing different sections. To find the best tolling strategy, we aimed to balance two main objectives: generating the highest revenue and reducing travel time compared to the base scenario (which had no tolls). The optimal scenario would be the one that achieved both goals simultaneously—high revenue and lower travel times.

4. Results and Discussion

4.1. Congestion Cost

The traffic volume data provided by the Ministry of Public Works and Housing (MPWH) allowed for the identification of a morning (AM) peak period: 8:00 to 9:00. Engineering drawings from the MPWH provided the estimated lengths of sections, and speed data (congested and uncongested) were also obtained from MPWH designs. Delays were calculated for each section by measuring the time difference between congestion and uncongested conditions. This information was then used to calculate the congestion cost caused by delays. The results are detailed in

Table 5. (a) HCM control delay for vehicles (seconds), (b) delay time on the Airport Highway during the AM peak hour, and (c) delay cost, fuel consumption cost, and congestion cost during the AM peak hours for 2012 and 2024.

(a) HCM Control Delay for Vehicles (Seconds)			LOS		General Description
0–10			A		Unrestricted flow
>10–15			B		Consistent flow (minor delays)
>15–25			C		Consistent flow (tolerable delays)
>25–35			D		Approaching unsteady flow
>35–50			E		Unsteady flow
>50			F		Forced flow
(b) Delay Time on the Airport Highway during AM Peak Hour
Section	Length of the section (Km)	Travel time based on congestion speed (min)	Travel time based on uncongested speed (min)	Delay per vehicle (min)	LOS
Sec # 1	16	12	9.6	2.4	F
Sec # 2	11	8.25	6.6	1.65	F
(c) Delay Cost, Fuel Consumption Cost and Congestion Cost during AM Peak Hours
Year		Delay cost (JOD)	Fuel consumption cost (JOD)	Congestion cost (JOD)
2012		107,141.5	6,300,127.5	6,407,269
2024		1,182,960	5,911,486.6	7,094,446.6

.

The following results are based on MPWH data:

Average traffic volume = 1392 + 2330 + 2153 + 1216 = 3546 vehicles/hour;

Total delay in 2012 = 2.4 + 1.65 = 4.05 min/vehicle;

Yearly delay per vehicle = 255 × 4.05 = 17.21 h;

Total yearly delay for AM peak volume = 17.21 × 4902 = 84,363.4 h/veh;

Total cost = total delay × cost of time (hours);

Total cost = 84,363.4 × 1.21 (JOD);

Total congestion cost due to delay time for 2012 = 107,141.5 JOD.

Similar calculations were conducted for the 2024 AM peak hours, resulting in congestion costs of 1,182,960 JOD.

Air resistance is a major component that affects fuel usage. A car’s energy expenditure to overcome air resistance might reach 40%. Since air resistance increases exponentially with speed, the increase in air resistance during a 65 mph to 70 mph acceleration is greater than the increase during a 55 mph to 60 mph acceleration. More energy is needed to overcome greater air resistance, which increases fuel consumption. For instance, accelerating from 65 mph to 70 mph requires significantly more energy than accelerating from 55 mph to 60 mph because air resistance rises exponentially with speed.

In our study, we analyzed fuel consumption across different speeds to illustrate this relationship. As shown in Figure 7 and Figure 8, we found that vehicles consume more fuel at both low and high speeds compared to an optimal speed. Specifically, at lower, congested speeds, vehicles are often forced to operate inefficiently due to frequent stops and slow-moving traffic, leading to higher fuel consumption. For example, vehicles travelling at an optimal speed of around 55 kph (kilometers per hour) tend to have better fuel efficiency because this speed strikes a balance between minimizing air resistance and maintaining steady, smooth driving conditions.

Conversely, when vehicles travel at lower speeds, such as those seen in congested traffic, they consume more fuel per kilometer. This is because constant acceleration and deceleration, idling, and lower engine efficiency contribute to higher fuel use. Our figures illustrate that vehicles travelling at congested speeds (such as 90 km/h in our study) have higher fuel consumption rates than those travelling at an optimal, steady speed of 55 kph. In our studied street, the values of congested and uncongested speeds are 90 and 100 km/h, resulting in fuel consumption costs presented respectively, then total congestion cost, as shown in Table 5 for AM peak hours.

At 90 km/h (congested speed), fuel economy = 30 mpg (mile/gallon).

At 100 km/h (uncongested speed), fuel economy = 28 mpg (mile/gallon).

Fuel wasted/vehicle/km = 30 − 28 = 2 mile/gallon = 0.28 L/km.

Annual fuel wasted cost by PC = 0.28 × 27 × 3186 × 0.72 × 255 = 4,422,219 JOD.

Annual fuel wasted cost by HV = 0.28 × 27 × 1715.7 × 0.568 × 255 = 1,877,908.5 JOD.

Total congestion cost due to wasted fuel for 2012 = 6,300,127.5

Similar calculations were conducted for 2024 the AM peak hour, resulting in congestion costs of 5,911,486.6.

Given the increase in total congestion cost from 6,407,269 JOD in 2012 to 7,094,446.6 JOD in 2024, it is essential to suggest solutions to mitigate this rising expense. Implementing road pricing strategies, such as toll roads, can be an effective approach to reducing congestion. This study investigates road pricing as a potential solution for congestion. Road pricing is normally used for systems with the main objective of reducing congestion by allocating the traffic to other less congested alternative routes and hours. Road pricing could be used to improve traffic, especially regarding the environmental impact; for example, to reduce emissions and noise, it can also be implemented with the main aim of generating revenue that could be invested in expanding the road network or improving public transport services.

4.2. Survey Questionnaire

The questionnaire was filled out through in-person, direct interviews. Decision-makers and other road users, including drivers, passengers, students, etc., participated in the sample. The goal of the field study was to gather various drivers and trip characteristics; hence, it was carried out on weekdays (working days) during various working hours. In order to give the respondents all the time they needed to answer the questions, the majority of the interviews were conducted in offices of the Greater Amman Municipality, the Ministry of Works and Housing, the Land Transport Regulatory Commission, universities like Al-Isra’a, MEU, Petra, and Zaytouna, and roadside gas stations. To ensure that participants understood every aspect of congestion pricing, thorough explanations were given both during the interview and when completing the questionnaire. In order to avoid any doubt about the responses given, the interviewers thoroughly explained each question to the respondents.

After 650 questionnaires were completed, the responses were carefully examined, and 26 of the completed surveys were excluded from the analysis because they contained errors or inconsistent information. As a result, 624 questionnaires made up the final sample.

4.2.1. The Demographic and Socioeconomic Characteristics

This section includes questions related to the demographic and socioeconomic characteristics of the drivers, including gender, age, household income, and level of education obtained, as shown in

Table 6. Summary of socioeconomic and demographic characteristics.

Gender		Age		Employment		Education		Monthly Household Income
Male	335	18–24	213	Employed	195	Uneducated	11	<250	61
		25–34	232			School	52	250–500	183
		35–44	97	Self-Employed	156	Diploma	66	500–750	177
Female	289	45–54	44	Unemployed	37	Bachelor	360	750–1000	116
		55–64	19	Retired	28	Master	98	1000–1500	49
		>65	19	Student	208	PhD	37	>1500	38

.

Table 6 shows that 53.7% of the sample were males, while 46.3% were females. The highest percentage of age groups was 37.2%, for the 25–34-year-olds, and the lowest percentage was for the groups aged between 45–64 years and more than 65 years, with about 19.0%.

Table 6 reveals that the category with the highest percentage within the sample was students, with 33.3%, and the percentage for employees reached 31.3%, while the lowest percentage was for the retired (4.5%). The highest percentage for education, 57.7% of the sample, was the Bachelor’s degree, while the lowest percentage was for the uneducated, with only 1.8%.

Table 6 also shows that the highest percentage of interviewees came from households of low-income brackets (250–500), with 29.3%, and those of the 500–750 bracket reached 28.4%. The lowest percentage of respondents came from the highest income category; the percentage earning more than 1500 JOD was a mere 6.1%.

4.2.2. Trip Characteristics

This section involved information related to elements of respondents’ travel behavior with respect to their trip characteristics, in particular, the number of travel trips and the purposes of their trips. The necessity of obtaining such information is based on the assumption that traveler acceptance and willingness to pay for a congestion charging scheme is dependent on trip characteristics.

Driver trip characteristics were also analyzed, and the results are presented in

Table 7. Trip characteristics.

Number of Trips/Week		Trip Purpose
Never	6.80%	Work	38.50%
<2	19.80%	Travel	24.20%
2–4	39.90%	Studying	35.40%
>4	16.50%	Social relations	1.9%
Every day	16.90%

. The majority of drivers driving on Airport Road make work-related trips (38.5%), and (35.4%) were on study trips to the many universities on Airport Road, either in Amman or in the South of Jordan. The majority of trips were frequent trips, 2–4 times a week (39.9%), because most workers and students come from the south to work in Amman or come from Amman to work in southern destinations like Aqaba.

Table 8. Responses to question about occurrence of traffic congestion.

Congestion Occurrence	Frequency	Percentage
Rarely	201	32.20%
Sometimes	374	59.90%
Always	49	7.90%
Total	624	100

shows that the answer to the occurrence of congestion with the highest percentage of responses was “sometimes”, which reached 59.9% (N = 374), while the response “rarely” reached 32.2%; while the lowest percentage was for “always” (7.9%). The last questions related to the road pricing charging method.

4.2.3. The Advantages and Disadvantages of a Congestion Pricing Scheme

Statistical analysis by SPSS of the data extracted from the second section of the questionnaire elicited driver preferences in relation to the measure of road pricing. What needs to be considered together with the identified trends is that the survey participants have not experienced the implementation and effects of such a measure. Increased travel time was considered to be the most important effect of traffic congestion by the participants, followed by environmental pollution and deterioration of psychological calm.

The advantages and disadvantages of a congestion scheme, as perceived by the road users, provide an indication of user acceptability factors.

Table 9. Advantages of road pricing.

Advantage	Low Effect	Moderate Effect	High Effect
Improving highway quality	18.30%	48.40%	33.30%
Reducing environmental pollution	18.40%	40.40%	41.20%
Increase in the use of transit	18.10%	43.90%	38%
Reduction in congestion	15.70%	49.40%	34.90%

and

Table 10. Means and standard deviations of responses.

	Statement	Mean	Standard Deviation
1	Applying road pricing improves highway quality	2.15	0.703
2	Applying road pricing reduces environmental pollution	2.23	0.738
3	Applying road pricing increases the use of transit	2.20	0.723
4	Applying road pricing reduces traffic congestion	2.19	0.686

illustrate drivers’ perceptions of the advantages and disadvantages of a congestion pricing scheme.

As shown in Table 9, users perceived the improvement in environmental conditions as the most important advantage of a road pricing scheme (41.2% chose it as an important advantage), while the increase in the use of transit came second, with 38% selecting it as an important advantage.

The overall means and standard deviations of the responses are displayed in Table 10. The results indicate that the mean of Statement 2 (Applying road pricing reduces environmental pollution) was the highest (2.23), with a standard deviation = 0.738, followed by the overall mean of Statement 3 (Applying road pricing increases the use of transit), which was 2.20, with a standard deviation = 0.723, while Statement 1 (Applying road pricing improves highway quality) had the lowest impact, with a mean = 2.15 and a standard deviation of 0.703.

A one-sample t-test was conducted to evaluate whether their mean was significant. The results are in

Table 11. One-way t-test of advantages.

Mean	Standard Deviation	T	df	Sig
2.19	0.462	10.390	623	000

. The sample mean (2.19) was significant: t (623) = 10.390.

Most respondents (37%) explained that road pricing was not fair because people with low incomes are not able to afford to pay for every trip; this makes sense since most monthly household incomes of respondents were between 250 and 500 JOD. Another disadvantage highlighted was the loss of privacy, with a percentage of 30.1%, as shown in

Table 12. Disadvantages of road pricing.

Disadvantage	Low Effect	Moderate Effect	High Effect
Not fair	22.30%	40.70%	37%
Loss of privacy	23.70%	46.20%	30.10%

.

A one-sample t-test was conducted of the respondent sample about the disadvantages of congestion pricing to evaluate whether their mean was significant. The results are in

Table 13. One-way t-test of Disadvantagesdisadvantages.

Mean	Standard Deviation	T	df	Sig
2.105	0.593	4.451	623	000

. The sample mean’s (2.10) standard deviation (0.593) was significant: t(623) = 4.451.

4.2.4. The Details of Payment

This section included two questions about the best method to pay and the value of the price. The objective of this section is to find the most suitable payment method and an acceptable fee or price. About 54% of respondents chose the “Travelled Distance”. The most agreed-upon response on the suitable value of the toll was (0.25 JOD), with a percentage of 34.8%, as explained in

Table 14. Road pricing characteristics.

Value of Toll (JOD)		Road Pricing Method
0.25	34.80%	Travelled distance	54.20%
0.5	25.50%
0.75	7.70%	Travel time	28.70%
1	20.80%
1.25	7.10%	Type of vehicle	17.10%
1.5	4.10%

.

4.3. Costs of Road Pricing Scheme

The road pricing scheme encompasses two methods, each requiring a thorough cost estimation for comparison: the manual method and the automatic toll machine method. In the manual method, costs include 12,000 JOD per section for cameras, 27,000 JOD for booths, and variable costs for signs and pavement markings. The signs at the toll point, for instance, amount to 303.75 JOD each, while pavement markings cost 150 JOD per lane. Direction signs are priced at 443.75 JOD each, and PVC cones total 420 JOD. Additionally, the monthly salary for three employees per section is 900 JOD.

In contrast, the automatic toll machine method involves significant equipment costs. Each toll machine costs approximately 56,000 JOD per section, with a total of three machines required. The costs for cameras for this method amount to 12,000 JOD per section. Similar to the manual method, signs and pavement markings incur costs, with advance signs estimated at 443.75 JOD each and signs at the toll point at 303.75 JOD each. Pavement markings cost 150 JOD per lane, direction signs 443.75 JOD each, and PVC cones 420 JOD in total.

Two charging methods are explained. The costs of both methods are summarized in

Table 15. The total cost of charging methods.

Techniques	The Cost of One (JOD)	Total Number	Total Cost (Manual Method)	Total Cost (Automatic Toll Method)
Monitoring Cameras	4000	9	36,000 JOD	-
Violation Cameras	40,000	9	-	360,000 JOD
Booths	9000	9	81,000 JOD	-
Signs in Advance of a Toll Point	443.75	6	2662.5 JOD	2662.5 JOD
Signs at a Toll Point	303.75	3	911.25 JOD	911.25 JOD
Pavement markings at Toll Points	150	9	1350 JOD	1350 JOD
Direction Signs	443.75	3	1331.25 JOD	1331.25 JOD
Polyvinyl Chloride Cone	14	70	980 JOD	980 JOD
Employees	300	9	2700 JOD	2700 JOD
Toll Machines	56,000			504,000 JOD
Total Cost			126,935 JOD	873,935 JOD

Note: excluding the maintenance costs and cycle life.

.

4.4. Outputs of Model Development

The results from 2012 and 2024 are essential for evaluating and potentially implementing toll roads in Jordan. These results provide crucial insights needed to make informed decisions about introducing toll roads in the country.

4.4.1. Modeling the Toll Road (2012)

The traffic models were tested both without a toll road and with a toll road, across seven different scenarios (from Scenario 1 to Scenario 7), each with different toll rates for cars and goods vehicles. In these simulations, a fixed toll charge was applied to the road’s “impedance”, which reflects the cost of using the road, taking into account both travel time and toll fees. As the toll charges increased, the overall cost for road users rose, resulting in fewer vehicles choosing the toll road and a redistribution of traffic to alternate routes.

For the service road along Airport Road, we adjusted the road type to improve the level of service (LOS), ensuring it could handle more traffic efficiently. The tolls were set in fils (the currency subunit), and the impedance calculation was based on the value of time for different vehicle types.

Each section of the toll road had a distinct toll charge, which presented challenges in ensuring that all users paid a consistent fee when accessing different parts of the road. The goal was to find the best tolling strategy by balancing two objectives: maximizing revenue and reducing travel time, compared to the base scenario without tolls. The optimal scenario would achieve both high revenue and reduced travel delays.

After evaluating all scenarios, Scenario 2, with a toll of 0.2 JOD for cars and 0.4 JOD for goods vehicles, was identified as the best option. This scenario not only generated the highest revenue but also, when compared to Airport Road without toll pricing, showed significant improvements in traffic conditions by reducing delays—an important factor in improving the LOS on Airport Road.

The various scenarios tested during the AM peak hour in 2012 explored different toll rates for cars and goods vehicles, each resulting in different levels of revenue. The goal was to find the scenario that generated the maximum revenue while maintaining traffic flow. Among all the scenarios, Scenario 2 was identified as the best, as it provided the highest revenue, with a toll of 0.2 JOD for cars and 0.4 JOD for goods vehicles.

The following table (

Table 16. The scenarios of different values of price with different values of revenue in AM peak hour (2012).

Number of Scenario	Toll for Car	Toll for Goods Vehicle	Number of Goods Vehicles	No. of Cars—South	No. of Cars—North	No. of Taxis—South	No. of Taxis—North	Revenue (JOD)
1	0.2	0.4	0	0	680	0	20	140
2	0.2	0.4	0	0	684	0	21	141
3	0.2	0.8	0	0	0	0	0	0
4	0.25	0.5	0	0	0	0	0	0
5	0.3	0.6	0	0	0	0	0	0
6	0.4	1.6	0	0	0	0	0	0
7	0.5	2	0	0	0	0	0	0

Note: No goods vehicles are using the toll road in either direction.

) shows the details of each scenario, including toll rates, the number of vehicles in different traffic zones (southbound and northbound for cars and taxis), and the corresponding revenue generated in JOD.

• Scenario 2 stands out as the optimal choice, generating the highest revenue (141 JOD) while maintaining a toll of 0.2 JOD for cars and 0.4 JOD for goods vehicles.
• Scenarios 3 to 7 did not yield any revenue due to a lack of vehicle assignment to the toll road, likely because the increased tolls deterred traffic.
• This indicates that moderate toll rates, like in Scenario 2, strike a balance between generating revenue and maintaining traffic flow, whereas higher tolls discouraged vehicle use of the toll road entirely.

In Scenario 2, the toll pricing must be compared to the situation on Airport Road without tolls in order to assess traffic conditions such as travel time, delay, and the percentage of congestion. This comparison helps determine how the introduction of tolls impacts overall traffic flow and road efficiency.

By analyzing both cases—one with tolls (Scenario 2) and one without pricing—key metrics like travel time and congestion levels can be evaluated.

Travel time: The time it takes for vehicles to complete their journey along Airport Road, both with and without tolls, can reveal whether the tolls help reduce or increase travel times.

Delay: The amount of time vehicles are delayed due to congestion. A reduction in delay suggests that tolls are effectively managing traffic and improving flow.

Congestion: The percentage of the road that is congested. Comparing the congestion levels with and without tolls will show whether pricing reduces overcrowding on the toll road.

In the AM peak hour model for 2012, the following outputs and results were obtained.

Travel Time Analysis:

Table 17. The (a): travel time in the AM peak hour on both roads in 2012 (without pricing) and (b): Travel time on the main road and toll road in AM peak hour (2012).

(a) Travel Time in AM Peak Hour on Both Roads in 2012 (without Pricing)
Travel Time	Main Road		Service Road
	Southbound	Northbound	Southbound	Northbound
Free flow time (min)	15.33	15.33	22.20	22.20
Current time (min)	16.76	30.80	24.06	35.68
(b)Travel time on the main road and toll road in AM peak hour (2012)
Travel Time	Main Road		Toll Road
	Southbound	Northbound	Southbound	Northbound
Free flow time (min)	15.33	15.33	14.26	14.26
Current time (min)	16.76	30.80	14.26	14.53

summarizes the travel time between the Foreign Ministry and QAIA during the AM peak hour on both the main road and service road, in both directions. Notably, southbound travelers on the main road spent 16.76 min, while it took 24.06 min on the service road in congested conditions. The difference in travel time between the two roads is 7.30 min. Similarly, in the northbound direction, the difference between the two roads is 4.88 min, as detailed in Appendix E. Additionally, Figure 9 illustrates the difference between free flow time and the current service time of the toll road.

-

Speed Analysis: Figure 10 depicts the speed variations on the main road and the service road, respectively, during the AM peak hour. Speeds reached up to 107 km/h on the main road and 70 km/h on the service road.

-

Traffic Congestion Percentage: Figure 10 illustrates the percentage of traffic congestion during the AM peak hour on both the main road and the service road for 2012. Congestion levels are depicted as 179% in the northbound direction and 75% in the southbound direction on the main road. The service road experienced congestion starting at 153% and 70%, respectively, which gradually decreased along the distance.

4.4.2. Modeling the Toll Road (2024)

In the 2024 traffic models, both toll and non-toll scenarios were tested across seven different configurations (Scenario 1 to Scenario 7), each with varying toll rates for cars and goods vehicles. The simulations applied a fixed toll charge to the road’s “impedance”, which represents the cost of using the road, considering both travel time and toll fees. As the toll rates increased, the overall cost for users rose, leading to a reduction in the number of vehicles opting to use the toll road, thus redistributing traffic to alternate routes.

To improve traffic flow on the service road along Airport Road, we modified the road type to enhance its level of service (LOS), ensuring that it could effectively handle the increased traffic load. The toll charges were set in fils (the currency subunit), and the impedance calculations were based on the value of time for different vehicle types.

Each section of the toll road was assigned a distinct toll, making it challenging to ensure that all users were paying a consistent fee when accessing different sections of the road. The goal was to identify the best tolling strategy by balancing two main objectives: maximizing revenue and reducing travel time, as compared to the base scenario without tolls. The optimal scenario would achieve both high revenue and lower delays in traffic.

After evaluating all the scenarios, Scenario 2, which applied a toll of 0.2 JOD for cars and 0.4 JOD for goods vehicles, was found to be the most effective. This scenario not only generated the highest revenue but also, when compared to the scenario without toll pricing on Airport Road, showed notable improvements in traffic conditions. Specifically, Scenario 2 resulted in a reduction in delays, which is a key factor in improving the LOS for the road.

In the 2024 simulations, different toll rates were tested during the AM peak hour (see

Table 18. The scenarios of different values of price with different values of revenue in AM peak hour (2024).

Number of Scenario	Toll for Car	Toll for Goods Vehicle	Number of Goods Vehicles	No. of Cars—South	No. of Cars—North	No. of Taxis—South	No. of Taxis—North	Revenue (JOD)
1	0.2	0.4	0	1002	3552	29	127	942
2	0.2	0.4	0	1000	4252	29	167	1089.6
3	0.2	0.8	0	1002	3557	29	127	943
4	0.25	0.5	0	608	3958	18	111	1173.75
5	0.3	0.6	0	103	2532	3	91	818.7
6	0.4	1.6	0	0	1547	0	54	640.4
7	0.5	2	0	0	0	0	0	0

Note: No goods vehicles are using the toll road in either direction.

). The goal was to find the scenario that achieved the highest revenue while maintaining effective traffic flow. Scenario 2, with a toll rate of 0.2 JOD for cars and 0.4 JOD for goods vehicles, proved to be the most successful, generating the highest revenue while maintaining efficient traffic conditions (see Figure 11).

Scenario 2 was identified as the optimal choice, generating the highest revenue of JOD 1089.6 while applying a moderate toll of 0.2 JOD for cars and 0.4 JOD for goods vehicles. Scenarios 3 to 7 did not produce significant revenue, as higher toll rates discouraged vehicles from using the toll road entirely.

In Scenario 2, the toll pricing was compared to Airport Road without any tolls to assess key traffic metrics such as the following:

Travel Time: The time it took for vehicles to complete their journey with and without tolls.

Delay: The amount of time vehicles were delayed due to congestion.

Congestion Percentage: The proportion of the road that was congested during peak hours.

This comparison helped determine how toll pricing affected overall traffic efficiency, showing that moderate tolls could improve traffic flow by reducing delays and congestion, thus enhancing the level of service (LOS) on Airport Road.

In the AM peak hour model for 2024, the following outputs and results were obtained.

Travel Time Analysis:

Table 19. The (a): travel time in the AM peak hour on both roads in 2024 (without pricing) and (b): travel time on the main road and toll road in the AM peak hour (2024).

(a) Travel time in AM peak hour on both roads in 2024 (without pricing)
Travel Time	Main Road		Service Road
	Southbound	Northbound	Southbound	Northbound
Free flow time (min)	15.36	15.36	19.5	22.06
Current time (min)	32.86	51.7	28.25	42.18
(b) Travel time on the main road and toll road in AM peak hour (2024)
Travel Time	Main Road		Toll Road
	Southbound	Northbound	Southbound	Northbound
Free flow time (min)	15.36	15.36	14.21	14.21
Current time (min)	32.86	51.7	14.21	15.43

summarizes the travel time between the Foreign Ministry and QAIA during the AM peak hour on both the main road and service road, in both directions. Notably, southbound travelers on the main road spent 32.86 min, while it took 28.25 min on the service road in congested conditions. The difference in travel time between the two roads is 4.61 min. Similarly, in the northbound direction, the difference between the two roads is 9.52 min, as detailed in Appendix E. Additionally, Figure 12 illustrates the difference between free flow time and the current service time of the toll road.

-

Speed Analysis: Figure 13 depicts the speed variations on the main road and the service road, respectively, during the AM peak hour. Speeds reached up to 77 km/h on the main road and 60 km/h on the service road.

-

Traffic Congestion Percentage: Figure 13 illustrates the percentage of traffic congestion during the AM peak hour on both the main road and the service road for 2024. Congestion levels are depicted as 180% in the northbound direction and 74% in the southbound direction on the main road. The service road experienced congestion starting at 140% and 142%, respectively, gradually decreasing along the distance.

4.4.3. Validation of Toll Road Modeling (2025)

For 2025, the traffic models were simulated to validate the findings from the last simulations. The goal was to test the impact of tolls on traffic distribution and efficiency across various scenarios and assess whether the results align with projected traffic conditions and revenue goals.

The validation model was simulated around seven distinct toll scenarios (from Scenario 1 to Scenario 7), each featuring different toll rates for both cars and goods vehicles. These simulations aimed to measure the impact of toll pricing on traffic behavior. A fixed toll charge was added to the “impedance” of the road, which represents the cost for users to travel, factoring in travel time and the toll fee.

As toll charges increase, the impedance rises, leading to fewer vehicles choosing to use the toll road. This redistribution effect pushes more vehicles onto alternative routes. The model analyzed how different levels of tolls affected overall traffic flow and congestion across the network.

Along Airport Road, the service road’s capacity was improved to better accommodate traffic loads and enhance the level of service (LOS). The road type was adjusted to handle increased vehicle volumes efficiently, ensuring that LOS improvements directly reflected changes in travel times and vehicle distribution across the toll road network.

The tolls for the road sections were entered in fils (Jordan’s currency subunit), with impedance calculations based on the time value for various vehicle categories. However, different toll charges for different sections of the road introduced complexity, as the toll strategy needed to ensure consistent fees for road users regardless of where they entered or exited the toll network.

The results from the simulations were evaluated based on two primary objectives: maximizing revenue and minimizing travel time. Scenario 2, with a toll of 0.2 JOD for cars and 0.4 JOD for goods vehicles, was identified as the optimal scenario. This scenario struck the right balance between generating revenue and maintaining efficient traffic flow, yielding the highest revenue while improving overall traffic conditions.

In the table attached (

Table 20. (a): Travel time in the AM peak hour on both roads, (b): the scenarios of different values of price with different values of revenue in AM peak hour, and (c): travel time in the AM peak hour on both roads in 2025.

(a) Travel Time in the AM peak hour on both roads in 2025 (without pricing)
Travel Time		Main Road				Service Road
		Southbound		Northbound		Southbound		Northbound
Free flow time (min)		15.38		15.38		22.08		22.08
Current time (min)		33.83		53.43		35.81		43.30
(b) The scenarios of different values of price with different values of revenue in AM peak hour (2025)
Number of scenario	Toll for car	Toll for goods vehicles	Number of goods vehicle	Number of cars insouth	Number of cars -innorth	Number of taxis insouth	Number of taxis innorth	Revenue (JD)
1	0.2	0.4	0	1033	3659	30	131	970.6
2	0.2	0.4	0	1030	4380	30	173	1122.6
3	0.2	0.8	0	1032	3664	30	131	971.4
4	0.25	0.5	0	626	3150	19	115	977.5
5	0.3	0.6	0	106	2608	4	94	843.6
6	0.4	1.60	0	0	1594	0	56	660
7	0.5	2.00	0	0	0	0	0	0
(c) Travel Time in the AM peak hour on both roads in 2025 with pricing
Travel Time		Main Road				Toll Road
		Southbound		Northbound		Southbound		Northbound
Free flow time (min)		15.38		15.38		14.20		14.20
Current time (min)		33.83		53.43		14.20		15.51

Note: No goods vehicles are using the toll road in either direction.

), the following key outcomes for each scenario are shown, including the following: number of goods vehicles, number of cars (southbound and northbound sections), number of taxis (southbound and northbound sections), and revenue generated in JOD.

Scenario 2 resulted in significant improvements, as indicated by its revenue generation, which was JOD 1089.6, and a reduction in traffic congestion and delays compared to the base case.

For the 2025 validation model, the tolling scenario with the best performance was compared to the baseline Airport Road scenario without tolls. Key metrics such as travel time, delays, and congestion percentages were analyzed to evaluate the impact of toll pricing on traffic flow.

Travel Time: The time taken for vehicles to traverse Airport Road was analyzed for both toll and non-toll cases. Reduced travel times in the toll scenario indicated more efficient road usage.

Delays: Vehicle delays due to congestion were compared across both scenarios. A reduction in delays in Scenario 2 highlighted the effectiveness of toll pricing in mitigating traffic bottlenecks.

Congestion Percentage: The level of congestion on the toll road, measured as the percentage of vehicles causing overcrowding, was another critical factor. A decrease in congestion levels in the tolling scenario suggested that pricing effectively distributed traffic to less congested routes.

In the AM peak hour model for 2025, the following outputs and results were obtained. Detailed information can be found in Appendix E.

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Travel Time Analysis: Table 20 summarizes the travel time between the Foreign Ministry and QAIA during the AM peak hour on both the main road and service road, in both directions. Notably, southbound travelers on the main road spend 33.83 min, while it takes 35.81 min on the service road in congested conditions. The difference in travel time between the two roads is 1.98 min. As for the northbound direction, the difference between the two roads is 10.13 min. Additionally, Figure 14 illustrates the difference between free flow time and the current service time of the toll road.

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Speed Analysis: Figure 15 depicts the speed variations on the main road and the service road, respectively, during the AM peak hour. Speeds reach up to 70 km/h in the southbound direction and 60 km/h in the northbound direction on both roads. Furthermore, various model runs were conducted to maximize revenue. The toll price was set at 0.25 JOD, resulting in reduced travel time to half its value in the northbound direction. Table 20 presents the travel time values with and without the imposed toll.

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Traffic Congestion Percentage: Figure 15 illustrates the percentage of traffic congestion during the AM peak hour on both the main road and the service road for 2025. Congestion levels are depicted as 155% in the northbound direction and 155% in the southbound direction on the main road. The service road experiences congestion starting at 125% and 115%, respectively, gradually decreasing along the distance. Furthermore, multiple model runs were conducted to maximize revenue. The toll price was set at 0.2 JOD. Figure 16 shows the revenue values obtained from the different simulated models. Seven scenarios were investigated to achieve the maximum revenue, which was about 1122.6 JOD when the toll was set at 0.20 JOD for cars and 0.40 JOD for goods vehicles. Finally, Table 20 presents the difference in travel time with or without the toll. Travel time changed from 33.83 min to 14.20 min in the southbound direction and from 53.43 min to 15.51 min in the northbound direction.

The 2025 validation model confirmed that the introduction of tolls, particularly as in Scenario 2, can significantly improve traffic conditions by reducing delays and travel time while generating sustainable revenue for the transportation network. This supports the earlier findings from the 2024 models and further validates the toll road strategy as a viable solution for improving urban traffic conditions in Jordan.

5. Significance of the Study

The significance of toll roads in Jordan, particularly as outlined in this study, is primarily focused on addressing the significant congestion costs on Airport Road. This road has become a major traffic problem due to the increasing volume of vehicles and inadequate infrastructure. The study highlights the significant challenge of traffic congestion in Amman, especially along the Airport Highway, a crucial route in Jordan. This congestion is intensified by increasing traffic volumes due to Queen Alia International Airport, which serves as a main connection to southern Jordan, including major tourist sites like Petra and Aqaba, and the kingdom’s only container port. With current and planned developments along this highway, traffic is expected to worsen.

While the Savonius wind turbine (SWT) study, which explored wind turbines along Queen Alia Airport Road, offers a valuable approach to renewable energy, its impact on directly alleviating traffic congestion is less immediate [44]. On the other hand, toll roads provide a more direct and immediate solution to addressing traffic congestion. This study stands out in its approach by focusing on practical solutions to traffic congestion, such as road pricing, and evaluating their feasibility through survey questionnaires and simulation tools like VISUM, whereas other approaches, like the cellular automata model study, delve into advanced simulation technology and its effects on traffic flow [45].

In contrast to other research that examines toll road strategies from profit-maximizing perspectives [46], local and federal government conflicts over non-price measures [47], and theoretical toll allocation methods [48], this study provides a real-world context and immediate solutions to congestion through toll implementation. Moreover, while other studies introduce innovative concepts like mobility consumption theory [49] and models for social welfare maximization in urban planning [50], the current study emphasizes the specific challenges of Jordan’s Airport Road, supported by comprehensive data analysis and public feedback.

The current study analyzes the congestion costs for both historical and current years, revealing the growing expenses related to traffic and emphasizing the urgent need for a solution. It also calculates the initial operational costs of implementing a toll road system, considering the use of an alternative service road for toll pricing, making it a practical and cost-effective approach.

To evaluate the feasibility of toll roads, a comprehensive survey questionnaire was conducted, showing that the public is generally in favor of the idea. The survey data on toll pricing were then inputted into the VISUM software to simulate their impact on traffic flow and vehicle movement. This simulation helped analyze how tolls could change traffic patterns, reduce congestion, and improve overall transportation efficiency. The potential revenue from toll roads was compared with the initial costs to determine economic viability, with positive results supporting the implementation of toll roads.

By examining traffic data from past, present, and future years, the study offers a detailed understanding of traffic trends, which is crucial for future transportation planning. The detailed analysis, practical solutions, and favorable public feedback underscore the importance of toll roads as a strategic solution to manage congestion and enhance traffic flow on Jordan’s Airport Road. The study highlights that toll roads could be a feasible and beneficial improvement for Jordan’s transportation system.

6. Conclusions and Recommendations

The study highlights the significant challenge of traffic congestion in Amman, particularly along the Airport Highway, a vital route in Jordan. To evaluate the feasibility of toll roads as a solution, a comprehensive survey questionnaire was conducted, which revealed broad public support for the concept. Data from the survey on toll pricing were then inputted into VISUM software to simulate the impact on traffic flow and vehicle movement.

This simulation enabled an assessment of how tolls might influence traffic patterns, reduce congestion, and enhance overall transportation efficiency. By comparing the potential revenue from toll roads with the initial implementation costs, the study assessed their economic viability. Therefore, the study concludes the findings with the main points presented below:

• The study calculated congestion costs for the years 2012 and 2024, revealing a consistent annual increase. In 2012, congestion costs due to delay time and wasted fuel consumption were estimated at 6,407,269 JOD during the AM peak hour. By 2024, these costs had risen to 7,094,446.6 JOD. To address this issue, the study proposed implementing road pricing as a potential solution.
• In order to evaluate this proposed solution, a questionnaire was devised to investigate driver attitudes towards such schemes. The results suggest that higher acceptance is achieved when applying such schemes to reduce congestion in peak hours, especially for users on their trips to work or education. When required to identify the advantages of road pricing, 41% of respondents identified the environmental benefits. On the other hand, about 30% mentioned the disadvantage of reduced privacy. The results indicate that users preferred the charging method to be based on travelled distance and the value of the toll to be equal to 0.25 JOD. Despite the value being too low, it was agreed upon by users that it is a fair value as a starting point, because of the implications of higher fees on low-income groups.
• To determine the most economical method for applying the road pricing scheme, the initial operation costs of the toll road for two methods were calculated. The total cost for the manual method was 126,935 JOD, while the automatic toll machines incurred a cost of 873,935 JOD. The results indicate that the manual toll collection method proved to be economically viable.
• Simulation models of toll roads were used in the study for two years during the AM peak hour (2012, 2024). The results from 2024 indicate that road pricing with the optimum scenario can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. Upon simulating the 2012 data model, positive results were observed. The travel time difference between the two roads was 7.30 min in the southbound direction and 4.88 min in the northbound direction. The positive results from both 2012 and 2024 suggest that road pricing remains a viable solution.
• The toll road (across seven different scenarios at different prices) was evaluated for an optimal revenue in the current year. The results indicated the maximum revenue, which was about 1089.6 JOD when the toll was set at 0.20 JOD for cars and 0.40 JOD for goods vehicles.
• Validation models for various scenarios and pricing options for 2025 were evaluated using VISUM. The findings indicate that the optimal toll values for achieving the best revenue (1122.6 JOD) are 0.20 JOD for cars and 0.40 JOD for goods vehicles.
• The results from the validation model indicate a significant reduction in travel time, decreasing from approximately 33.83 min to 14.20 min in the southbound direction, and from 53.43 min to 15.51 min in the northbound direction. This demonstrates positive economic effects. Additionally, the reduction in travel time contributes to environmental benefits, including decreased emissions and noise pollution.
• Practical recommendations include extending the toll road solution to other congested routes within Amman, such as AlMadina Almonawarah Street and Queen Rania Street. Additionally, implementing toll roads on crucial links like Alordon Street, connecting Amman to northern cities, could alleviate congestion and reduce accidents, enhancing overall traffic flow and safety. Future research could explore the applicability of road pricing solutions to other major highways, like the Desert Highway, to further alleviate congestion and improve transportation efficiency across Jordan.