Simulating Road Networks for Medium-Size Cities: Aswan City Case Study ^†

Abstract

This research simulates Aswan City’s urban transportation dynamics utilizing the Multi-Agent Transport Simulation (MATSim) framework. As a fast-expanding urban center, Aswan has many transportation difficulties that require extensive modeling toward sustainable mobility solutions. MATSim, recognized for its agent-based methodology, offers a detailed portrayal and analysis of individual travel behaviors and their interactions within the metropolitan transportation system. This study compiled and combined many databases, including demographic data, road infrastructure, public transit plans, and travel demand trends. These data are altered to produce a realistic digital clone of Aswan’s transportation system. Simulated scenarios analyze the consequences of several actions, such as increased public transit scheduling, traffic flow management, and the adoption of alternative transport modes, on minimizing congestion and boosting accessibility. Pilot findings show that MATSim effectively captures the distinct features of Aswan’s transportation network and offers practical insights for decision-makers. The results identified some opportunities to improve mobility and promote sustainable urban growth in developing cities. This study emphasized the importance of agent-based simulations in designing future transportation systems and urban infrastructure.

1. Introduction

Transportation networks are vital in impacting the economic, social, and environmental fabric of metropolitan regions. In medium-sized cities like Aswan, Egypt, fast urbanization and expanding transportation demand generate different difficulties, such as traffic congestion, limited public transit infrastructure, and the requirement for sustainable mobility solutions. Addressing these difficulties needs powerful tools capable of studying complicated urban mobility patterns and evaluating the consequences of different changes in traffic flow and public transit networks.

Multi-Agent Transport Simulation (MATSim) version 15 offers a robust, open-source framework for simulating urban transportation systems in detail. By using agent-based modeling to represent individual travel behavior, MATSim provides insights into how people interact with transportation networks and how these interactions evolve in response to policy or infrastructure changes. The software’s flexibility in simulating multiple transportation modes, including private vehicles, public transit, cycling, and walking, makes it particularly well-suited for cities such as Aswan, where a variety of mobility requirements must be met.

Aswan, a historic city in southern Egypt, has unique transportation issues owing to its terrain, number of residents, and mixed-use highways. Public transportation, although accessible, is undeveloped in comparison to bigger cities, increasing dependence on private vehicles and peak hour congestion. Furthermore, population increase and seasonal tourists result in variable transportation patterns, necessitating adaptive planning solutions.

Decision-makers need to apply effective traffic management strategies to reduce congestion in an attempt to create effective and sustainable transportation systems. They are highly motivated to manage transport networks with effective strategies. However, before implementing any traffic management strategy, it must be thoroughly tested. Simulation software programs facilitate simulating processes at the large-scale network level, offering shorter timeframes and lower costs compared to other methods, while also providing more accurate traffic volume predictions and greater decision-making flexibility. The objective of the study is to analyze the transportation network of Aswan City and simulate it by preparing the required files used in the MATSim software version 15 for different modes (car and public transport system). Consequently, the Aswan transport network can be evaluated before implementing any management strategy.

This research leverages MATSim to model Aswan’s transportation system, addressing its unique challenges. By synthesizing local demographic, road network, and population datasets, this study assesses prevailing traffic dynamics. The simulation outputs give actionable insights, allowing policymakers to explore multiple ways for developing sustainable, efficient, and inclusive transportation solutions. Section 2 reviews the relevant literature. Section 3 delineates the approach for developing the MATSim model, comprising data collection and network design. Furthermore, Section 4 presents important insights and policy suggestions, proving MATSim’s usefulness in aiding data-driven decision-making for medium-sized communities struggling with parallel transport difficulties. Section 5 provides the main conclusions. This study expands the burgeoning realm of agent-based modeling applications in urban transport planning within developing economies.

2. Literature Review

Transportation modeling is an essential component of urban planning, particularly in quickly developing towns like Aswan, where restrictions impede economic and social advancement. Agent-based simulation frameworks, particularly MATSim, have emerged as effective methods for capturing the complexities of transportation networks via granular agent behavior modeling. This section reviews the current literature on transportation simulation technologies, with an emphasis on MATSim’s role in urban network modeling and its practical applications.

2.1. Transportation Simulation Tools

Traffic simulation models are critical tools for evaluating, predicting, and improving urban traffic dynamics. These models are classed as microscopic, mesoscopic, and macroscopic, with each giving varying degrees of granularity and computing complexity. The selection of a model hinges on the study’s objectives, ranging from granular behavioral analysis to city-scale policy formulation or strategic infrastructure planning.

To simulate traffic conditions, analyze infrastructure, and optimize traffic flow, simulation tools are crucial for supporting research and policy development. VISSIM and Aimsun (e.g., [1]) are examples of multipurpose tools that span (micro, meso, and macro) levels. SimTraffic (e.g., [2]) and HCS+ (e.g., [3]), for example, concentrate on certain components like highways and junctions. Policy studies and mobility behavior analysis benefit greatly from the large-scale urban demand modeling capabilities of MATSim and DynaSmart-P (e.g., [4]). Commercial alternatives like VISSIM and PARAMICS (e.g., [5]) provide specialized functionality and enhanced support, while SUMO (e.g., [6]), an open-source program, permits bespoke creation. Some tools, such as TransModeler (e.g., [7]) and VISSIM, integrate with GIS and third-party software, enhancing their real-world applicability.

Table 1. Traffic simulation models by simulation tools for different applications.

Simulation Tool	Model Type	Tool Applications	Features	Example Scenario References
Aimsun	Microscopic, Mesoscopic, Macroscopic	Urban, Highway, Public Transit	Multi-layered simulation, AI-driven routing, traffic demand modeling	[1]
VISSIM	Microscopic	Urban Traffic, Public Transport	Signal control integration, high-precision driver behavior	[1]
SUMO	Microscopic, Mesoscopic	Urban Traffic, Highway Networks	Open-source, API integration, high scalability	[6]
PARAMICS	Microscopic	Urban Networks, Events	3D visualization, real-time analysis, adaptive control	[5]
CORSIM	Microscopic, Macroscopic	Roadway, Freeway, Signal Operations	Focus on signal control, ramp metering, and intersection modeling	[8]
MATSim	Agent-based, Microscopic	Urban Mobility, Traffic Demand	Activity-based modeling, large-scale, multimodal	[9]
TransModeler	Microscopic, Mesoscopic, Macroscopic	Urban, Regional Transport	GIS integration, dynamic assignment, 3D visualization	[7]
DynaSmart-P	Mesoscopic	Corridor, Regional Traffic	Real-time traffic information, user- equilibrium modeling	[4]
SimTraffic	Microscopic, Mesoscopic	Intersection, Roadway Segments	Animation-focused, integrates with traffic analysis software	[2]
HCS+ (Highway Capacity Software)	Macroscopic	Highways, Freeways	Analytical modeling based on HCM, queue length estimation	[3]
VisSim	Systems Engineering, Control Systems	Vehicle Control Systems	Visual programming for control design, behavioral modeling	[10]

categorizes these tools by their applications and highlights their use in urban intersection management, freeway control, and large-scale mobility planning. These studies offer valuable insights into tool functionality and its potential application.

2.2. MATSim Applications

Agent-based models (ABMs) are currently vital in transportation research. Unlike classic macroscopic models, ABMs mimic individual passengers as autonomous agents, enabling a thorough investigation of travel behavior, route choices, and activity patterns. Reference [11] noted that this microscopic technique increases simulation realism by capturing user behavior variation and reacting to system changes. Recent studies also indicated ABMs’ usefulness in simulating complicated urban dynamics, such as congestion patterns, multimodal networks, and behavioral reactions to initiatives.

MATSim is a popular open-source platform for agent-based transportation modeling. As highlighted by [12], its modular design enables the simulation of complex, large-scale urban systems with remarkable precision. The platform incorporates many modes of transportation—private automobiles, public transit, cycling, and pedestrian movement—to provide a complete framework for assessing urban mobility dynamics. The system’s agents repeatedly optimize their trip plans using established utility functions, ensuring that individual decision-making processes are accurately represented. Furthermore, MATSim makes it easier to evaluate a variety of policy-oriented scenarios, such as road pricing methods, infrastructure enhancements, and demand management measures, giving urban planners and policymakers useful information.

Berlin is one of the oldest and most widely investigated cities utilizing MATSim, which demonstrated the platform’s capacity to replicate multi-modal networks, including buses, trains, and private car vehicles [13]. The research focused on congestion patterns, alternative pricing techniques, and the installation of low-emission zones. Reference [14] used MATSim to evaluate the effects of toll systems and urban transportation infrastructure improvements, emphasizing its capacity to forecast modal share changes and improve public transportation timetables. In a separate setting, Reference [15] used MATSim to replicate Jakarta’s crowded and chaotic traffic conditions. Their research was the first to include informal means of transportation, such as “ojeks” (motorbike taxis), into the simulation. This was one of the first studies to explore informal transport integration.

Subsequent research has shown that MATSim is versatile in a variety of scenarios. For example, refs. [16,17] used MATSim to simulate mobility in South Africa, demonstrating its flexibility to the particular issues of emerging cities. Similarly, Reference [18] used the platform to analyze the Swiss transportation networks, demonstrating its accuracy in capturing complex mobility patterns. Developing cities, particularly in Africa and Asia, frequently confront unique issues, such as informal transportation, limited data availability, and increasing urbanization. According to [19], ABMs like MATSim can solve these complications by combining data from many sources, such as GPS traces, surveys, and OpenStreetMap data. Furthermore, ABMs have shown efficacy in assessing initiatives targeted at improving public transportation, reducing congestion, and encouraging sustainable mobility habits in low-income communities.

Ref. [20] incorporated informal matatus (shared minibuses) into MATSim, yielding useful insights into route optimization and fare structures. This study revealed MATSim’s ability to handle non-standard transit networks. After the Great East Japan earthquake, MATSim was used to model emergency evacuations and assess the recovery of transportation infrastructure. Reference [21] underlined the usefulness of agent-based models in catastrophe resilience planning. Reference [22] employed MATSim to simulate evacuation tactics during wildfires. Their results revealed that coordinated evacuation preparations might considerably minimize fatalities and road congestion. Reference [23] investigated the integration of shared autonomous vehicles (AVs) into Tokyo’s transportation network. They used MATSim to explore the effects of the introduction of SAV to commuters traveling in a central Tokyo district with a well-developed rail network, resulting in increased efficiency and reduced traffic congestion.

These examples demonstrate MATSim’s flexibility and adaptability in diverse contexts. Each scenario serves as a platform for further investigation and demonstrates the efficacy of agent-based simulations in addressing complicated transportation difficulties. Lessons from these studies can help to create realistic models and scenario-based analyses that are customized to Aswan’s unique urban and socioeconomic situation.

3. Methodology

MATSim requires multiple files for simulation. Though not all are always required, basic simulations only need a configuration file, a network description, and population data with agents’ plans. For advanced features like public transport simulation, extra files may be necessary. This section describes how to prepare the required files (input data and optional data) to run the Aswan City scenario using the MATSim software.

3.1. Steps to Create a Network File for the Aswan Scenario

The network file defines the infrastructure for agents’ or vehicles’ movement. Represented as nodes (intersections) and links (road segments). Each node and link is defined by a specific ID. Each node has a unique ID and X and Y coordinates, while links are defined by start and end nodes, describing the network’s geometry. Additional attributes are the detailed traffic aspects. To create a MATSim network file for Aswan, a geographic data source, OpenStreetMap (OSM), is used and converted into MATSim’s XML format. MATSim provides several Java tools that can convert OSM data into MATSim’s XML format. The MATSim’s core library and the converter osm2matsim are used for this process. Each <node> element represents an intersection or endpoint with a unique ID and coordinates. Figure 1 shows a sample of links of the Aswan City scenario; each <link> element represents a road segment connecting two nodes, defined by the following:

• ID: Unique identifier for the link.
• From and to: IDs of the start and end nodes.
• Length: link length (in meters).
• Free speed: Free-flow speed (in meters per second).
• Capacity: Max vehicles per hour.
• Perm lanes: lanes count.

Aswan City has an inhabited area of 375.4 km² with a population of 408,842 people. The map was edited using Java Open Street Map (JOSM), an extensible editor for OSM compatible with Java 11+. It supports loading GPX tracks, background imagery, and OSM data from local and online sources, enabling editing of OSM data (nodes, links, and relations) and their metadata tags. JOSM is an open-source and licensed GPL. For the Aswan network, only OSM tags attached to streets and links are needed.

Table 2. Attributes used for the links of the Aswan City network.

Type of Road	Capacity Veh/h	Free Speed m/s	Mode After Change
trunk	2000	22.222	Car, pt
primary	1500	22.222	Car, pt
secondary	1000	16.667	Car, pt
residential	600	8.333	Car, pt
tertiary	600	12.5	Car, pt

shows the attributes used for the links of the Aswan City network, particularly, the ‘highway’ tag, which defines street types. After generating the network, realistic values for free speed, capacity, and lane count based on road types (highways, local roads, etc.), local speed limits, and average traffic flow were adjusted to accurately represent Aswan’s traffic environment.

The network file is loaded into MATSim and checked for accuracy by verifying node connections and ensuring no nodes are disconnected. Once the network file is created and validated, it can be loaded into MATSim for further scenario design and simulation. This process generates a customized network file for Aswan City, ready for traffic simulation and analysis in MATSim. To improve the simulation performance, a network can often be simplified.

3.2. Steps to Create a Plan File for the Aswan Scenario

The plan file in MATSim contains details about agents and their daily schedules. It specifies activity types, durations, and locations, as well as the modes and routes for each trip. The plans.xml contains a file list of the daily plans for the entire population. Each agent has several plans, each consisting of a series of activities and legs. A MATSim plan file is an XML file containing personal elements, each representing an agent with a unique ID and a corresponding plan. Each plan contains a sequence of activities, such as home, work, or shopping, with coordinates (x, y) and its time to end. It also includes leg components that describe travels between activities, with the mode determining the form of transit (for example, vehicle, bus, walk). Optional considerations include age, car availability, and income level. The main means of transport used for public transport in Aswan City is the minibus. It is an informal, market-oriented, self-organizing public transport system. There are mainly six lines operating with 14- to 16-seater vehicles on fixed routes, but without a fixed schedule.

3.2.1. Origin–Destination Matrix Preparation

Developing a detailed, agent-based demand model that accurately mirrors real-world conditions is difficult without a full census of the study area. To overcome this matter, a pre-defined Origin–Destination (OD) matrix was utilized. Although there is a variation in the overall number of trips, the pre-specified OD matrix may serve as a measure of workplace appeal for each zone. A passenger traffic study was undertaken in Aswan utilizing mini-buses, adopting a tabular approach to record passenger pickups and drop-offs at stopping points. Additionally, a questionnaire-based survey was carried out among city inhabitants, with results gathered at bus stops and via Google Forms. In total, 833 respondents completed the survey and were considered in the analysis. Our model does not include other trip types, such as educational, commercial, freight, and through traffic. Additionally, it does not consider secondary trips that take place after work-related activities.

Table 3. Questionnaire data summary.

Socio-Demographic Variables	%
Age
<18	9
18–40	27
40–60	59
≥60	5
Gender
Male	69
Female	31
Means of transport used
car	30
pt	70
Is it possible to use public transportation (and temporarily give up your car) if the public transportation network is conveniently planned?
Yes	21
No	79
Occupation
Student	21
Office employee	46
Free actions	28
Other	5
Activity start timing
7.00 to 9.30 a.m.	100
Activity end timing
1.00 to 4.00 p.m.	100
Time taken to get to the bus stop to and from home or work
5 to 30 min	100
Do you find the following factors a reason not to use public transport to move around the city?
Take a long time	38
Do not reply regularly	22
Very crowded	20
Uncomfortable public transport vehicles	18
Financially expensive	2
Repeat the journey
Daily	95
Randomly	5
Using more than one means of transportation to reach the desired location
Yes, I prefer	9
No, I do not prefer	91

shows the questionnaire data of the utilized survey.

The survey provides a detailed dataset capturing the socio-demographic characteristics and travel behavior of Aswan City residents. It identifies key factors influencing transportation planning and usage patterns. The majority of the population falls within the 40–60 age range (59%), indicating a dominant working-age group. This shows an emphasis on commuting-related transportation demands. A male-dominated demography (69%) may reflect occupational trends, cultural standards, or survey participation bias. Analyzing gender dynamics is vital for customizing mobility solutions to individual needs.

Public transport is the dominant mode, used by 70% of respondents. However, 30% continue to use private vehicles, which might be owing to convenience, a lack of access to effective public transportation, or personal preferences. Even if public transportation is improved, the vast majority (79%) are hesitant to switch. This exposes long-standing hurdles, such as ingrained automobile use patterns, perceived public transportation inefficiencies, or socioeconomic issues. Nearly half of the respondents (46%) are office workers, reflecting peak-hour commute habits. Students (21%) and informal laborers (28%) contribute to the variation in travel times and destinations.

For activity, the start and finish hours follow a steady pattern of morning departures and early afternoon returns. This coincides with work and school schedules and shows a significant morning peak in transportation demand. Quoted distance for reaching bus stops is appropriate for metropolitan locations. However, a large range (5–30 min) may influence public transportation’s appeal, particularly for those at the upper end. Long journey times, unreliability, and overcrowding are the most important impediments to using public transportation. Financial costs are the least important consideration, implying that enhancing service quality might alleviate consumer unhappiness. Most respondents travel every day, underlining the significance of a dependable and efficient transportation infrastructure to satisfy frequent commuting demands. The vast majority choose single-mode transit (91%), presumably owing to the hassle or lack of smooth communication across modes.

Public transportation is commonly used (70%), yet some impediments prevent its widespread implementation. The majority of individuals (79%) are hesitant to move from vehicles, emphasizing the necessity for focused governmental measures. The major users are office professionals and students, with regular peak-hour demand. Improving public transportation dependability, decreasing congestion, and increasing comfort levels might boost its popularity. Thus, improving public transportation quality, dependability, and capacity is critical for reducing automobile reliance and better serving present customers. The data provides a foundation for modeling treatments, such as better public transportation planning, reduced access times, and increased multimodal connections, utilizing tools such as MATSim. To encourage modal switches, effective programs must address ingrained behaviors and perceptions of public transportation inefficiencies.

This survey offers a solid foundation for defining agent attributes and activity plans in transportation simulations, offering actionable insights into Aswan’s mobility patterns.

The survey of passenger traffic was carried out in vehicles (minibuses) by a tabular method, taking into account the picking up and dropping off of passengers at stopping points. A questionnaire-based survey of residents of Aswan City was also conducted by filling out questionnaires at different stops using Google Forms.

Table 4. Number of trips from different origins to destinations per weekday.

Origin	Destination
	Al Mawkaf	Railway Station	Al Eshara	Al Sail	Al Hakroub	Al Tamen	El Nafk	Al Mahmodia
Al Mawkaf	—		377	406	60
Railway Station	550	—	1240	2254		510
Al Eshara			—					495
Al Sail	550	265	599	—
Al Hakroub	48				—
Al Tamen	112	290	600	42		—	320
El Nafk			1059			404	—	495
Al Mahmodia			421				20	—

presents an OD matrix, which provides a detailed representation of the travel patterns within a specific region on a typical weekday based on survey data. It counts the number of journeys taken between different areas, emphasizing the interaction of significant points of interest such as the Al Mawkaf, Railway Station, Al Sail, Al Tamen, Al Eshara, Al Hakroub, Al Tamen, El Nafk, and Al Mahmodia. The notable difference in total numbers arises from several factors. Firstly, our model excludes other trip types such as educational, commercial, freight, and through traffic. Secondly, it does not account for secondary trips that occur after work activities. The matrix aids in identifying high-demand travel corridors and possible bottlenecks by plotting the volume of journeys between origins (rows) and destinations (columns). For example, 550 journeys begin at Al Sail and end at the Al Mawkaf, whereas 1240 excursions are documented from the Railway Station to the Al Eshara. Empty cells in the matrix reflect places without any documented journeys, indicating probable connection gaps in Aswan’s transportation network.

3.2.2. Trip Chains of Agents

To simplify the situation, just two kinds of travel chains are selected for the synthetic population: home-to-work and work-to-home. Survey data used to create the population includes information on transit options to work. The research suggests that 30% of people utilize private cars, while 70% depend on public transit. According to MATSim, traveling by bus in the morning and returning by private car is implausible, since the car would not be accessible at the office. Due to the absence of detailed data on the number and distribution of workplaces in the area, we relied on a field survey of residential locations and their proximity to transportation options. The survey considered the walking time to the nearest bus stop, ensuring that no residence is more than 30 min away on foot.

3.2.3. Activity Duration and Constraints

MATSim’s evolutionary design allows agents to modify their departure iterations. Adjusted plans’ performance is assessed using activity-specific limitations. Home activities typically start between 7:00 a.m. and 9:30 a.m., and work activities usually end between 1:00 p.m. and 4:00 p.m.

Table 5. Activity constraints.

Trip Activity	Duration	Start Time	End Time
Home	12 h	always open	always open
Work	9 h	7:00 a.m.	6 p.m.

shows the constraints, which include the earliest and latest start times, average and minimum lengths, and facility operation hours. The model begins by assuming a normal distribution of departure times for home-to-work and work-to-home journeys during morning and evening peak hours. Table 5 describes the behavioral models and predefined constraints used in the simulation. These parameters are critical for shaping the simulation’s results and reaching a stable equilibrium. If an agent arrives at an activity location before its start time or departs after its end time, the “idle” time incurs an indirect penalty through the opportunity cost of time—utility that could have been earned by engaging in the activity for a long duration. This concept will be explored further in the re-planning and scoring section. Moreover, MATSim allows for the inclusion of additional disutility for early or late arrivals. Next, we load the plan file into MATSim and check for parsing errors. Then, we verify that each person has a complete sequence of activity and leg elements, adhering to MATSim’s requirements. After creating and validating the plan file, we integrate it with the network file in the MATSim configuration and run the simulation to assess the realism of agent behavior based on the plan. Finally, the simulation outputs can be analyzed.

4. Simulation Run and Result Analysis

Figure 1 shows the transportation network of Aswan City, represented as nodes (intersections) and links (road segments), where the characteristics of each node and link include multiple factors such as the number of lanes, free-flow speed, traffic capacity, and link length, accurately representing the traffic movement in the city’s road network.

To enhance transport system efficiency and evaluate network performance, a simulation was conducted involving the simplification of Aswan’s road network. Figure 2 shows the unused links and nodes which removed during simulation (in red color). Figure 3 presents the resulting streamlined network structure from the MATSim plugin in JOSM after removing unnecessary links and nodes.

This arrangement allows for the modeling of Aswan’s transportation network, offering insights into how to analyze and enhance the city’s transit system based on MATSim results. The simulation was performed to evaluate network performance and total traffic flow. Figure 4 presents the simulation results of the activity and trip distribution for Aswan City using MATSim. It illustrates the spatial distribution of activities and travel flows, where “h” denotes home activities and “w” denotes work activities. The results highlight key concentrations of demand and movement across the city. The work trips are the main activity during the period between 8 a.m. and 2 p.m., with 1800 trips. The number of work trips starts to increase from 7 a.m. and reaches the maximum value during the period between 8 a.m. and 2 p.m. It is reasonable according to the official work hours in Aswan City (8 a.m. to 2 p.m.).

The simulation resulted in a favorable score average, as shown in Figure 5, suggesting overall effectiveness in analyzing network performance and traffic flow. The figure illustrates the evolution of simulation score statistics across MATSim iterations for Aswan City, displaying the trends of the average best, worst, average, and executed scores. The results indicate rapid improvement during the early iterations, followed by gradual stabilization as the simulation converges, reflecting the model’s learning process and optimization toward stable travel behavior patterns. This convergence also suggests that the simulation could be terminated at this stage, since no further improvements occur beyond 100 iterations. These findings may be used to guide future choices on how to enhance the city’s transportation infrastructure.

The data provided in Figure 6 shows the distribution of passenger hours traveled by mode, giving useful information on the efficiency and attractiveness of various transportation alternatives within the city. Analyzing these patterns might help urban planners make educated choices to maximize public transportation systems and fulfill the demands of commuters. The figure presents passenger hours of travel by mode and activity type across MATSim iterations for Aswan City, including car, public transport, walking, and stage activities. The results show a clear decline in both travel and waiting times over successive iterations, followed by stabilization at lower levels, indicating the optimization of travel behavior and convergence to an equilibrium state. This trend reflects improved system efficiency, with passengers experiencing less wasted time.

Figure 7 demonstrates that the simulation outcomes for Aswan are clearly visible, where each agent and vehicle’s speeds can be seen on the road network, along with traffic congestion areas and the number of stuck agents in the network. The simulation data may also be utilized to discover areas for improvement and optimize resources for increased efficiency in the transportation system. The figure illustrates the spatial distribution of home and work activities in Aswan City alongside the transport network simulated in MATSim. Vehicle speeds are represented by a color scale, where green indicates higher speeds, yellow moderate speeds, and red lower speeds, highlighting congestion-prone areas. The visualization shows how activity locations and transport infrastructure interact with travel conditions, providing insights into network performance and areas requiring mobility improvements. It is clear from the figure that the majority of vehicles can move at higher speeds (green color that spreads all over the network), while a few vehicles move at lower speeds (red color in some areas). This result may help municipal planners make more informed judgments about infrastructure development and public transit strategies.

5. Conclusions and Future Work

This research successfully used MATSim to model Aswan City’s transportation network, revealing important information about urban mobility dynamics and traffic patterns. The simulation indicated key areas for development and demonstrated MATSim’s ability to simulate real-world urban situations. The simulation was developed using two fundamental components: the network file, which defines the road infrastructure, and the population file, which provides the demographic characteristics and activity patterns of agents. These elements serve as the backbone of any MATSim simulation framework. By expanding this framework, this study seeks to provide actionable insights for city planners and policymakers in Aswan, ensuring that the transportation network evolves to accommodate the city’s growing demands while aligning with broader goals of sustainability and efficiency. However, the current simulation was limited by the absence of comprehensive public transit data, which is a key component of Aswan’s transportation network. To address this constraint, future research will emphasize the incorporation of precise public transport data, including bus routes, timetables, and stop locations, into the MATSim model. This upgrade will improve the city’s transit simulation, enabling more thorough analysis and policy formation. By adding public transportation dynamics, the model may lead to optimized mobility and reduced congestion.