Efficient Transport System Research Articles

Effect of Public Transportation Accessibility on Employment Rates in Indonesia

Purpose: The aim of the study was to analyze the effect of public transportation accessibility on employment rates in Indonesia. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The effect of public transportation accessibility on employment rates in Indonesia has been shown to have a positive relationship. Improved access to public transportation enhances mobility, allowing individuals to access a wider range of job opportunities, particularly in urban areas. Research indicates that areas with better transportation infrastructure see increased employment rates, especially among lower-income groups who rely on public transit. Additionally, more efficient public transport systems can reduce travel time and costs, further encouraging job participation. Unique Contribution to Theory, Practice and Policy: Social exclusion theory, human capital theory & spatial mismatch theory may be used to anchor future studies on the effect of public transportation accessibility on employment rates in Indonesia. Transit-oriented development can encourage the creation of job hubs around public transportation stations, reducing the time spent commuting. Policies should prioritize investments in sustainable and accessible public transportation systems, particularly in areas with high unemployment rates or underserved communities.

Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM

Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation.

Artificial Intelligence in Traffic Management: A Review of Applications and Impact on Transportation Systems

Artificial Intelligence (AI) has become a transformative force in traffic management, offering innovative solutions to address congestion, safety, and efficiency in transportation systems. This literature review examines recent advancements in AI applications within traffic management, focusing on methodologies such as machine learning, computer vision, and optimization techniques. The study also evaluates the impacts of these technologies on transportation systems, including their benefits and challenges. Key applications such as traffic prediction, signal control, autonomous vehicles, and incident management are discussed. The review concludes by identifying research gaps and proposing directions for future studies

Molecular Interactions of the Antimicrobial Peptide Tritrpticin with Mixed Nanoaggregates: A Fluorescence Spectroscopy Study.

Tritrpticin (TRP3) is a peptide belonging to the cathelicidin family and has a broad spectrum of antimicrobial activity. However, this class of biomolecules can be easily degraded in the body, making it necessary to use an efficient transport system. The ability to form stable nanostructures from the interaction of glycyrrhizin saponin with the pluronic polymer F127 was demonstrated, forming mixed biopolymeric micelles, highly promising as drug carriers. The present work sought to understand the physicochemical interaction of the antimicrobial peptide TRP3 with the mixed polymeric micelle made from pluronic F127 and the saponin glycyrrhizin. The interaction of tritrpticin with mixed nanostructured micelles was evaluated through fluorescence spectroscopy and fluorescence quenching with acrylamide. The experiments were performed at room temperature (25 ± 1°C), adopting an excitation wavelength set to 280 nm and emission between 300 and 500 nm, with a slit of 5 nm. The interaction of the cationic peptide tritrpticin with the mixed biopolymeric micelles was observed through the blue shift of the fluorescence emission to shorter wavelengths, proving the change of tryptophan to a more hydrophobic environment. Through the fluorescence suppression technique, it was possible to indicate the location of the peptide in the mixed micelles, proving tritrpticin to be partially inserted inside them. It was concluded that tritrpticin interacted with mixed nanostructured micelles, forming a promising system for biotechnological applications.

Dynamic Stability Analysis and Optimization of Multi-Vehicle Systems in Heterogeneous Connected Traffic

This study investigates the stability and performance of mixed-traffic flows consisting of human-driven vehicles (HDVs), connected autonomous vehicles (CAVs), autonomous vehicles (AVs), and connected human-driven vehicles (CHVs). Recognizing the complexity introduced by multi-vehicle interactions in such heterogeneous traffic, a refined CAV car-following model that integrates multi-vehicle state information, including headway, weighted velocity differences, weighted acceleration, and optimal velocity memory effects from both front and rear vehicles, is introduced. Through theoretical analysis of the model’s linear and nonlinear stability, the key parameters that enhance flow stability in mixed environments are determined. Numerical simulations across braking, start-up, and ring road scenarios validate the proposed model’s efficacy, demonstrating that it can effectively suppress traffic congestion and reduce oscillations, thereby improving traffic flow stability. This work offers valuable insights into the behavior of connected vehicles within mixed traffic and highlights the potential for CAV-based strategies to enhance both safety and efficiency in future transportation systems.

A Novel Artificial Intelligence-Enhanced Digital Network for Prehospital Emergency Support: Community Intervention Study.

Efficient emergency patient transport systems, which are crucial for delivering timely medical care to individuals in critical situations, face certain challenges. To address this, CONNECT-AI (CONnected Network for EMS Comprehensive Technical-Support using Artificial Intelligence), a novel digital platform, was introduced. This artificial intelligence (AI)-based network provides comprehensive technical support for the real-time sharing of medical information at the prehospital stage. This study aimed to evaluate the effectiveness of this system in reducing patient transport delays. The CONNECT-AI system provided 3 key AI services to prehospital care providers by collecting real-time patient data from the scene and hospital resource information, such as bed occupancy and the availability of emergency surgeries or procedures, using 5G communication technology and internet of things devices. These services included guidance on first aid, prediction of critically ill patients, and recommendation of the optimal transfer hospital. In addition, the platform offered emergency department medical staff real-time clinical information, including live video of patients during transport to the hospital. This community-based, nonrandomized controlled intervention study was designed to evaluate the effectiveness of the CONNECT-AI system in 2 regions of South Korea, each of which operated an intervention and a control period, each lasting 16 weeks. The impact of the system was assessed based on the proportion of patients experiencing transfer delays. A total of 14,853 patients transported by public ambulance were finally selected for analysis. Overall, the median transport time was 10 (IQR 7-14) minutes in the intervention group and 9 (IQR 6-13) minutes in the control group. When comparing the incidence of transport time outliers (>75%), which was the primary outcome of this study, the rate was higher in the intervention group in region 1, but significantly reduced in region 2, with the overall outlier rate being higher in the intervention group (27.5%-29.7%, P=.04). However, for patients with fever or respiratory symptoms, the group using the system showed a statistically significant reduction in outlier cases (36.5%-30.1%, P=.01). For patients who received real-time acceptance signals from the hospital, the reduction in the percentage of 75% outliers was statistically significant compared with those without the system (27.5%-19.6%, P=.02). As a result of emergency department treatment, 1.5% of patients in the control group and 1.1% in the intervention group died (P=.14). In the system-guided optimal hospital transfer group, the mortality rate was significantly lower than in the control group (1.54%-0.64%, P=.01). The present digital emergency medical system platform offers a novel approach to enhancing emergency patient transport by leveraging AI, real-time information sharing, and decision support. While the system demonstrated improvements for certain patient groups facing transfer challenges, further research and modifications are necessary to fully realize its benefits in diverse health care contexts. ClinicalTrials.gov NCT04829279; https://clinicaltrials.gov/study/NCT04829279.

Optimizing Modular Vehicle Public Transportation Services with Short-Turning Strategy and Decoupling/Coupling Operations

In public transportation systems, the passenger demand during peak hours is characterized by over-saturation at intermediate stops and directional imbalances, and the traditional single scheduling strategy and fixed capacity cannot solve the contradiction between the demand and capacity mismatch. In order to accurately match demand and capacity, this paper proposes a method to optimize the service of a public transportation system by using a short-turning strategy combined with decoupled/coupled operation of modular vehicles (MVs). The short-turning strategy is used to alleviate the heavy passenger flow at intermediate stations, and the decoupling/coupling operations of MVs are employed to flexibly adjust the capacity levels in different directions. Considering urban space limitations, depots for storing modular units (MUs) are only set up at the starting and ending stations of bidirectional lines. MVs can not only adjust the departure capacity at the starting station but also consider whether to decouple/couple at turnaround stations for short-turning trips to achieve a more effective supply–demand match, with the decoupled/coupled MUs being deadheaded from or provided by the depot. We formulated this problem as an integer nonlinear programming (INLP) model, jointly optimizing the departure intervals of each trip, the capacity of MVs, the turnaround scheme for short-turning trips, and the decoupling/coupling scheme for MVs at turnaround stations, with the aim of minimizing passenger waiting time costs and vehicle operating costs. To facilitate a solution, we equivalently transformed some nonlinear terms in the model, which was then solved by the commercial solver Gurobi. The numerical study shows that, compared with the traditional full-length strategy combined with conventional buses, the model proposed in this paper can reduce the total system cost by about 19.59%. In particular, it can achieve precise matching between passenger demand and transport capacity, thereby reducing the passenger waiting time cost by about 29.99%. Compared with the full-length strategy combined with MVs, the total system cost is also reduced by about 14.65%. The research results contribute to enhancing the service quality and efficiency of public transportation systems, which is of great significance to the sustainable development of these systems.

Exploring the evolution and future trajectory of transportation technologies

The need for people to relocate due to the environmental difficulties they experienced centuries ago gave birth to the concept of transportation; It has become one of the indispensable needs of people such as food, drink, health and shelter. In order to meet this displacement requirement, various tools have been developed and used according to the continental or marine characteristics of the Earth, and the need for transportation has continued to develop dynamically and become an integral part of our daily lives. In this study, the development of the need to relocate, which is among the basic needs, from past to present under the umbrella of transportation, its technological evolution and popular demand factors were investigated. As a result of the study, it has been reached that electric vehicles have become popular in the 21st century with the increase in urban population, people's demand for individual transportation vehicles and the development of sustainable energy-based fuel technologies. Urban air mobility solutions, including electric vertical take-off and landing (eVTOL) aircraft and drones, have the potential to relieve congestion on roadways and offer efficient transportation options for urban commuters. Additionally, autonomous and connected vehicles have the potential to optimize traffic flow and reduce congestion, thereby improving the energy efficiency of transportation systems. However, more concrete and quantitative comparisons are required to determine their impact on fuel consumption.

Adapting the ITF's Transport Policy with a Focus on the EU's Strategy of Sustainable Mobility

Urban regions worldwide face increasing challenges in mobility planning due to transformative developments such as climate change, energy crises, migration, digitalisation, and the rise of artificial intelligence. In Europe, frameworks like the European Union's Sustainable and Smart Mobility Strategy and the International Transport Forum's (ITF) policy measures aim to create sustainable, multimodal, and efficient urban transport systems. This paper explores how these frameworks can be integrated to enhance urban mobility governance, focusing on the Brussels-Capital Region’s Good Move plan. The plan emphasises shared mobility, digitalised multimodal systems like mobility as a Service (MaaS), and a regulatory framework that balances innovation with public oversight. Through a "learning by doing" approach, this study highlights the interplay between public and private actors, effective data governance, and market regulation to establish a viable MaaS ecosystem. Key findings underline the potential of MaaS to reduce urban emissions, improve accessibility, and promote sustainable travel while addressing regulatory challenges such as data-sharing, competition safeguards, and equitable mobility subsidies. The paper concludes with actionable recommendations to optimise MaaS integration and support urban regions in achieving resilient, livable cities through sustainable mobility solutions.

An Efficient Cluster-Based Mutual Authentication and Key Update Protocol for Secure Internet of Vehicles in 5G Sensor Networks.

The Internet of Vehicles (IoV), a key component of smart transportation systems, leverages 5G communication for low-latency data transmission, facilitating real-time interactions between vehicles, roadside units (RSUs), and sensor networks. However, the open nature of 5G communication channels exposes IoV systems to significant security threats, such as eavesdropping, replay attacks, and message tampering. To address these challenges, this paper proposes the Efficient Cluster-based Mutual Authentication and Key Update Protocol (ECAUP) designed to secure IoV systems within 5G-enabled sensor networks. The ECAUP meets the unique mobility and security demands of IoV by enabling fine-grained access control and dynamic key updates for RSUs through a factorial tree structure, ensuring both forward and backward secrecy. Additionally, physical unclonable functions (PUFs) are utilized to provide end-to-end authentication and physical layer security, further enhancing the system's resilience against sophisticated cyber-attacks. The security of the ECAUP is formally verified using BAN Logic and ProVerif, and a comparative analysis demonstrates its superiority in terms of overhead efficiency (more than 50%) and security features over existing protocols. This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments.

Exploring Innovations in Smart Navigation Technology

The study on Smart Navigation Technology provides an analysis of the application of technology in the design of intelligent navigation and safety solutions. Attention has been taken to developments intended to give better comfort in driving together with enhanced assurance of the safety of both the automobile and its occupants. Various technologies considered include developments with GPS-based navigation, routing based on AI, and sensor-based emergency detection; also, alert mechanisms during emergencies based on IoT relatedness. With the use of real-time data processing, it ensures to minimize response time in emergency situations. This application highlights the role of machine learning algorithms that help analyse the driving behaviour and predict hazards. This survey identifies emerging trends, technical challenges, and potential areas for further research in smart navigation and safety solutions through comprehensive analysis of these technologies, contributing to the creation of more intelligent, efficient, and secure transportation systems

Анализ применения технологий Индустрии 4.0 в интеллектуальных транспортных системах

The intensive development of digital technologies, their active implementation in the field of transport and logistics necessitates the study of their impact on the transport system, in particular on the level of its intellectualization. This will allow us to determine which technologies of Industry 4.0 need to be implemented first of all in order to achieve the best parameters for the development of intelligent transport systems. In this regard, the paper analyzes various tech-nologies of Industry 4.0, identifies possible ways of their application to solve problems in the context of the functioning of intelligent transport systems and their elements. Based on the analysis, possible parameters of the impact of technologies on the development of intelligent transport systems are identified, which include physical and environmental safety of transport, information security, speed and accuracy of decision-making, physical safety of transport, resource efficiency, convenience and comfort, the presence of an entertainment component, etc. Based on the compari-son of the identified parameters with specific types of digital technologies, it is determined which technologies have a greater impact on the intellectualization of transport systems. These technologies include the Internet of Things, augmented reality AR and cloud solutions. The parameters that are more susceptible to the effects of digital technologies are also determined: the safety and efficiency of transport systems. The results obtained are of practical importance for the development of strategic solutions in the implementation of the concept of Industry 4.0 in the field of transport and logistics.

Scaling Laws for Optimal Herschel–Bulkley Yield Stress Fluid Flow in Self-Similar Tree-like Branching Networks

Abstract Fractal networks fluid flows have attracted significant research interest, yet most studies often assume a constant viscosity or power-law fluid flows. This research explores yield stress fluid flows in fractal tube networks, which are commonly found in engineered microfluidic devices and various industrial processes. We analyze optimal flow conditions and structures in tree-like branching networks using Herschel–Bulkley fluid model to understand yield stress materials. We focus on maximizing flow conductance under volume constraints, assuming steady, incompressible, fully developed laminar flow in circular tubes. We propose a conjecture that if the tube-wall stress, remains the same in the network for all branches, then an optimal solution exists and we derive the theoretical formulations for it. We find that the flow conductance is extremely sensitive to the geometry of the network. The effective conductance initially rises as the daughter-parent radius ratio increases, but eventually, it begins to decline. The peak conductance occurs at a specific radius ratio. We find that optimal network geometry depends on the number of branch splits $N$, and independent of the power-law index $n$ and the yield stress $\tau_y$. This optimal condition leads to an equal pressure drop across each branching level. Our results are validated with existing theory and extended to encompass shear-thinning and shear-thickening behaviors for any number of splits $N$ with and without yield stress. Additionally, we derive relationships between geometrical and flow characteristics for parent and daughter tubes, including wall stresses, length ratios. These findings provide valuable design principles for efficient transport systems involving yield stress fluids.

Prospects for Implementation of Autonomous Vehicles and Associated Infrastructure in Developing Countries

This study explores the implementation and impact of autonomous vehicle (AV) systems, particularly in developing countries. While AVs promise enhanced road safety by reducing crashes, injuries, and fatalities, their adoption faces significant challenges, including public acceptance and infrastructure readiness. A mixed methods approach was employed, combining quantitative data from surveys of approximately 1500 randomly selected individuals and qualitative insights from in-depth interviews with policymakers, traffic engineers, and industry representatives. The quantitative analysis revealed high levels of perceived usefulness (78.8%), positive attitudes (87.78%), and expected benefits (86.09%) among respondents, indicating optimism about AVs’ potential to improve traffic efficiency and safety. However, concerns about technical reliability, cybersecurity, and the cost of infrastructure upgrades persist. Comparative analysis of physical and digital infrastructure highlighted significant gaps, particularly in road quality, markings, and internet connectivity. Policy implications emphasize the need for targeted public education to build trust and address safety concerns, regulatory reforms to ensure cybersecurity and ethical compliance, and strategic investments in infrastructure to meet AV requirements. Drawing on lessons from international contexts, the study recommends proactive stakeholder engagement and community outreach to align technological advancements with societal needs. These findings provide a roadmap for policymakers to navigate the challenges of AV adoption in Ethiopia and similar contexts, ensuring the integration of automation into sustainable and efficient transportation systems.

Dynamic Road Anomaly Detection: Harnessing Smartphone Accelerometer Data with Incremental Concept Drift Detection and Classification.

Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification. As a result, there is a need for novel approaches that can adapt to changing data environments and perform effective classification without relying on pre-existing training data. This study introduces a novel, real-time road condition monitoring technique harnessing smartphone sensor data, addressing the limitations of pre-trained models that lack adaptability in dynamic environments. A hybrid anomaly detection method, combining unsupervised and supervised learning, is proposed to effectively manage concept drift, demonstrating a significant improvement in accuracy and robustness with a 96% success rate. The findings underscore the potential of incremental learning to enhance model responsiveness and efficiency in distinguishing various road anomalies, offering a promising direction for future transportation safety and resource optimization strategies.

Environmental Performance and SDGs: How Do Income Disparity, Urbanization, Resource Consumption, and Female Employers Affect the Ecological Footprint in Emerging Economies?

ABSTRACTThe significance of environmental deterioration as a pressing issue has led to its prominence in global discussions on strategies for prevention. It can be asserted that there exists a universal agreement that the enhancement of environmental quality is imperative for the promotion of sustainable development and societal well‐being, especially in emerging economies. In this perspective, the present research uses panel data of the E7 economies from 1992 to 2021 to assess the effects of income disparity, urbanization, resources consumption (nonrenewable and renewable energy consumption), and women employers on ecological footprint. This study employs second‐generation empirical techniques such as CIPS and CADF unit root tests, Westerlund bootstrap cointegration, and DFE/MG/PMG‐ARDL models to check the relationships between the studied variables. The empirical findings of this study reveal that in the E7 countries, the ecological footprint is increased with an increased income disparity and nonrenewable energy consumption. On the other hand, urbanization, renewable energy consumption, and female employers help reduce the ecological footprint. Additionally, the results of the causality measure under the Dumitrescu and Hurlin test unveil bidirectional causality between all the independent variables except nonrenewable energy and ecological footprint. From the policy perspective, the selected countries must implement policies that promote equitable income distribution, planned urbanization to promote compact cities, efficient transportation systems, and green infrastructure, and provide opportunities for women to attain higher positions in organizations, particularly at the managerial and decision‐making levels in order to address the adverse environmental consequences in emerging economies.

Unraveling the Traffic Congestion Due to Bottleneck: A Review

In the metropolitan cities across the world, traffic congestion is a major problem due to growing urbanization, vehicle growth and infrastructure constrains. Bottleneck congestion is a major problem faced in traffic management in many urban centers as it affects travel time, emissions, and fuel consumption. It accounts for nearly 40% of congestion. This extensive literature review aims to discuss the main reasons behind traffic congestion, classify their patterns, and discuss how it can be detected and prevented. This paper focuses on the economic and social consequences of traffic bottlenecks by considering several traffic congestion research. The methods namely; Manual counting, social surveys, CCTV footage analysis, Navigation, and simulation software namely VISSIM are discussed in this paper. It also offers traffic control methods and other difficulties and contemporary control measures for congestion. In addition, this review emphasizes on the need of cost effective traffic congestion management, particularly in underdeveloped countries like Nepal, where video graphic survey is recommended as the most feasible approach. The purpose is to deliver pertinent information to transportation planners and engineers to address issues concerning traffic congestion in big cities and offer ways of improving the efficiency of transport systems in urban cities.

Modern Development of Transport Infrastructure in Kyrgyzstan

The article is devoted to the analysis of the modern development of transport infrastructure in Kyrgyzstan. The work considers the key trends and directions of state policy in this area. The study shows that Kyrgyzstan has a stable positive dynamic in the development of various types of passenger and freight transportation. There is an increase in the volume of automobile, rail, air, and other transport services. Particular attention is paid to the implementation of large-scale infrastructure projects for the reconstruction and modernization of key highways connecting Kyrgyzstan with neighboring countries. This contributes to improving the quality and availability of transport communications, as well as the country's integration into regional and global logistics chains. In the aviation industry, measures are being taken to expand the route network of the national air carrier, improve the quality of airport services, and renew the aircraft fleet. Comprehensive measures in the field of transport infrastructure carried out in Kyrgyzstan are aimed at increasing the efficiency, competitiveness, and environmental friendliness of the national transport system, which plays a key role in ensuring economic growth and sustainable development of the country.

A systematic review of the interrelations of urban form and mode choice in African cities

Rapid urbanization and informal urban development sweeping across African cities underscore a complex yet inadequately explored link between urban form and mode choice. Extensively studied in Western urban settings, these dynamics when applied to African cities remain a relatively unexplored realm, necessitating an extensive empirical review. This paper explores how various aspects of urban form correlate with mode choices in African urban contexts. A systematic review approach was used to filter journal papers published between 2000 and 2023 in Africa. The application of the (filtering) methodology identified 22 studies from the African region as most suitable for review. The review reveals that high-density and mixed-use areas in African cities significantly promote non-motorized transport and public transit. Infrastructure development and improved connectivity further enhance these sustainable modes. Socio-economic factors, governance policies, and cultural norms also play pivotal roles in shaping mode choices and urban mobility patterns. However, high-density cities like Abuja and Nairobi face unique challenges such as overcrowding in public transport and walking facilities. Sprawling cities like Lagos and Johannesburg struggle with inadequate public transport infrastructure, leading to a heavy reliance on private cars. Connectivity is crucial for supporting non-motorized and public transport, particularly for lower-income groups who rely more on these modes and para-transit services than higher-income groups. The findings highlight the need for tailored urban planning strategies to address these challenges and harness existing opportunities, promoting more sustainable, inclusive, and efficient urban transportation systems in African cities. Moreover, this study discloses existing research gaps that warrant further exploration.

Relationship Between Historic Evolution, Urbanization and Mobility in Ibadan, Nigeria

Ibadan is said to be the biggest indigenous city in Nigeria with a population of about 4.04 million, making it the third largest city in the country after Lagos and Kano. Since its founding in 1829, Ibadan has experienced urban evolution that is mostly unguided. With its high fertility rate, population growth and rural-to-urban migration, Ibadan has experienced significant urban sprawl that has resulted in challenges in urban mobility especially to the lower-income earners. This study looks at the relationship between urban planning, urbanization and mobility in the city of Ibadan. The study found that lack of integrated land use planning that includes transport networks including a masterplan is one of the major issues facing mobility in the city. Moreover, unregulated sprawl mostly made of slums and informal settlements has ecological, economic and social effects. Upgrading of slums coupled with integrated urban planning and enforcement is proposed as part of the solution for an effective urban mobility in the city. Policies that encourage compact urban development, mixed land use, and the provision of efficient public transportation systems to reduce the dependence on private transportation and create more walkable, bikeable, and transit- friendly communities are recommended.