Congestion In Cities Research Articles

SOLUTION TO THE CONGESTION OF URBAN PUBLIC PARKING IN ECUADOR

An administrative approach is designed to solve the congestion of urban rotating public parking in Ecuadorian cities, mediating a case study that demonstrates its feasibility of implementation from the administrative, organizational, and financial aspects. The absence of designs, projects and proposals related to the problem of vehicular growth and congestion in Ecuadorian cities, together with the obsolescence of current ordinances at the national level, implies a critical problem. The current situation of the case study is described and detailed, proposing to incorporate computer tools associated with the geographic information system, as well as the adoption of information and communication technologies. The new approach to guidelines for a new ordinance and application that improves the interaction between users and workers is added. The research, of a non-experimental nature, declares exploratory, explanatory, documentary, case study and projective types. It is declared as a feasible project and scientific methods of analysis of information sources, experts, and deductive, inductive, empirical, mathematical statistics are appealed to. It is concluded that the proposal enjoys the relevance and feasibility to be applied in Ecuadorian cities that use the urban public parking system.

Optimizing Traffic Flow in Smart Cities: Soft GRU-Based Recurrent Neural Networks for Enhanced Congestion Prediction Using Deep Learning

Recently, different techniques have been applied to detect, predict, and reduce traffic congestion to improve the quality of transportation system services. Deep learning (DL) is becoming increasingly valuable for solving critiques. DL applications in transportation have been collected in several recently published surveys over the last few years. The existing research has discussed the cloud environment, which does not provide timely traffic forecasts, which is the cause of frequent traffic accidents. Thus, a solid understanding of the difficulties in predicting congestion is required because the transportation system varies widely between non-congested and congested states. This research develops a bi-directional recurrent neural network (BRNN) using Gated Recurrent Units (GRUs) to extract and classify traffic into congested and non-congested. This research uses a bidirectional recurrent neural network to simulate and forecast traffic congestion in smart cities (BRNN). Urban regions worldwide struggle with traffic congestion, and conventional traffic control techniques have failed miserably. This research suggests a data-driven approach employing BRNN for traffic management in smart cities, which uses real-time data from sensors and linked devices to control traffic more efficiently. The primary measures include predicting traffic metrics such as speed, weather, current, and accident probability. Congestion prediction performance has also been improved by extracting more features such as traffic, road, and weather conditions. The proposed model achieved better measures than the existing state-of-the-art methods. This research also explores an overview and analysis of several early initiatives that have shown promising results; moreover, it explores two potential future research approaches to increase the accuracy and efficiency of large-scale motion prediction.

Urban air mobility (UAM) in the metropolitan region of São Paulo: Potential and threats

Purpose: The increase in congestion and pollution in large cities combined with the innovation of air transport technology (drones and vertical takeoff and electric propulsion vehicles – eVTOL) and with the advances in autonomous technology have stimulated this research on the potential of Urban Air Mobility – UAM as an alternative to improve mobility conditions in urban centers. In this research, the evaluation of the UAM's potential was carried out with a focus on the metropolitan region of São Paulo (RMSP), the largest metropolitan region in Brazil, with about 22 million inhabitants, and one of the ten most populous metropolitan regions in the world.Design/methodology/approach: In addition to evaluating challenges such as vehicle certification, infrastructure and ATM, an assessment of public acceptance of UAM (based on a survey) was carried out. The survey identified the perceptions of potential customers regarding the gains of the air mobility alternative, as well as the restrictions and fears in relation to the UAM. The opinion poll was carried out along the same lines as the work carried in Europe, allowing the comparison of results between the two markets.Findings: The main results highlighted the identification of the biggest problems for the implementation of UAM services in the metropolitan region of São Paulo, as well as the expectations and restrictions of customers, which will be important subsidies for the development of public policies to improve conditions mobility in the region.Originality/value: UAM is a new, disruptive mobility technology that can improve travel conditions in highly congested urban centers, such as the RMSP. The success of this new technology depends on understanding the challenges and opportunities, but mainly on the perception and expectations of potential customers.

Toward Greening City Logistics: A Systematic Review on Corporate Governance and Social Responsibility in Managing Urban Distribution Centers

Background: The ramifications of climate change are rampant: All stakeholders must act effectively and swiftly. Unsustainable and increased urbanization adds additional strain on combatting environmental degradation. Since the last decade, urban distribution centers (UDCs) have emerged in response to the steep rise in urban freight transportation and its negative impact on city congestion and air quality. Methods: In this paper, we conduct a comprehensive review of the performance of UDCs and investigate its alignment with the corporate governance (CG) and corporate social responsibility (CSR) initiatives, including the shareholders’ governance strategies and policies, as well as environmental, social, and economic measures. Our systematic literature review consists of multiple phases: In the first one, we utilize bibliometric tools to implement a quantitative analysis of the extant literature. Next, a cluster-based network analysis complements this analysis to describe the evolution of research in this area. Results: Our descriptive analysis categorizes existing research on UDCs based on CG- and CSR-compliant themes. We classify pertinent peer-reviewed articles into topical clusters and offer research opportunities related to improving the performance of UDCs. Conclusions: This study aims to stimulate further scholarly inquiry into sustainable city logistics and provides a knowledge-based guide for academicians and practitioners, logistics service providers, policymakers, and customers.

Place Centered Social-Economic, Cultural and Environmental Influence in the Making of Namatala Slums in Mbale Municipality, Uganda

The increase in the levels of urbanization and congestion in cities like Mbale can be attributed to the lack of proper policies and planning to regulating or managing the socio-economic and environmental factors that otherwise exacerbate occurrence of slums. In this paper focus is drawn on these factors which are thought to have largely influenced formation of Namatala slum in Mbale Municipality. While this slum exhibits the challenges presented by typical most African slums, this study argues that linkages between the social, economic, environmental, and cultural dimensions of Namatala slum formed the basis of understanding the challenges particular to its populace. The study used mixed methods research design that entailed collection of data through household survey questionnaires, key stakeholder interviews and focus group discussions. The study established that Namatala is a significantly disadvantaged location, wrought with poverty, inadequate social amenities, poor housing, lack of solid waste management, lack of essential infrastructure, and inadequate access to clean water, safe sanitation facilities and security of tenure. This problem can be attributed to a lack of place-specific policy implementation of the national policies and guidelines as well as lack of intervention by the government, inappropriate regulation, dysfunctional land markets, unresponsive financial systems, and a fundamental lack of political will.

Exploring the Potentials of Physical Appropriations in Leftover Spaces in Downtown Cairo

Cairo comprises many lost spaces between buildings. These spaces have significant potential to accommodate various activities. The potential of these spaces in a commercial district is the domain of the present study as the users provide these spaces with their special meanings through physical appropriations. It is believed that they might be developed to realize better functional values. The research aims to investigate the relation between the users' appropriations and the potential of the leftover spaces, as well as the degree to which this may enhance the role of these spaces in congested cities. The study begins with a review of the classifications of leftover spaces and a theoretical background focused on users' appropriations to achieve this goal. The outcome is then empirically examined in one leftover space in Downtown Cairo. The research adopts a qualitative approach. It utilizes direct observation and semi-structured open-ended interviews to investigate people's appropriations in such areas, together with the meanings of such appropriations to people's everyday life, to set an understanding of this relationship in light of the prior literature. The study concludes with a model for investigating the relationship between space's physical qualities and users' appropriations. It showed that leftover spaces could be appropriated by people's everyday practices creating new functions and meanings in dynamic and creative ways. This experience can improve the built environment and the quality of life for residents in other underutilized spaces in the city/ other cities.

Improving Attitudes toward Cycling in a Middle East and North Africa Region City

This paper aims to investigate the impact of different parameters on promoting the role of cycling as a daily mode of transport. In the first step of the analyses, binary logistic regression was used to examine the impact of different parameters on using or not using the bicycle as a transportation mode in weekly trips. Then by text mining, the main reasons for not using a bicycle in weekly trips are outlined. Finally, for those who use bicycles for at least one utilitarian trip a week, the effect of different factors on the popularity of this mode is investigated by structural equation modeling. Tehran, as a big city in the Middle East and North Africa (MENA) region, was considered as the case study. The results suggest that it is necessary to work on social norms about cycling, especially among those with higher education levels and income. Women use bicycles less than men and it is also necessary to rethink attitudes and regulations in relation to women cycling in Islamic countries. Bicycle promotion should aim to facilitate more positive attitudes among women. Providing more facilities such as safe bicycle paths, bicycle parking, and bike-sharing facilities have significant impacts on using this mode and its popularity. In highly congested cities, alongside facilitating cycling, it is important to set restrictions on private car use.

A Review of Blood Delivery for Sustainable Supply Chain Management (BSCM)

The management and supply of blood are critical yet considered a challenging exercise within the healthcare industry. The inventory managers within the sector concerned with blood storage and transportation always focus on their consumers’ effective and timely responses. A lack of blood during emergencies significantly impacts patients’ lives needing a blood transfusion. On the other hand, blood transfusion to needy patients must be effective and timely, requiring adequate transportation and supply chain policies. A weak transportation system could lead to blood shortages, delivery inefficiencies, and even contamination during the process. There are few blood donors globally; therefore, any wastage due to a poor delivery solution is ineffective. The current review aims to offer a comparative study of blood delivery within crowded cities, specifically using cars and motorcycles as the leading delivery solutions. Based on the systematic literature review, the most effective and responsive blood delivery system in a congested city may be identified considering SC costs, time availability, and emergency severity. The present research provides a comparative analysis of the available blood delivery systems in a congested city. It also helps the stakeholders to take quick and responsive decisions quickly.

Effect of the trip-length distribution on network-level traffic dynamics: Exact and statistical results

This paper presents additional results of the generalized bathtub model for urban networks, including a simpler derivation and exact solutions for uniformly distributed trip lengths. It is shown that in steady state this trip-based model is equivalent to the more parsimonious accumulation-based model, and that the trip-length distribution has merely a transient effect on traffic dynamics, which converge to the same point in the macroscopic fundamental diagram (MFD). To understand the statistical properties of the system, a queueing approximation method is proposed to compute the network accumulation variance. It is found that (i) the accumulation variance is much larger than predicted by traditional queueing models, due to the nonlinear dynamics imposed by the MFD, (ii) the trip-length distribution has no effect on the accumulation variance, indicating that a proposed formula for the variance might be universal, and (iii) the system exhibits critical behavior near the capacity state where the accumulation variance diverges. This indicates that the tools from critical phenomena and phase transitions might be useful to understand congestion in cities.

COVID-19 pandemic, port congestion, and air quality: Evidence from China

The emergency of COVID-19 leads to almost all unnecessary activities being banned because of city lockdowns, which results in the economy and human mobility being strictly restricted. While affecting economic development, it has brought some environmental benefits. As a critical link to collection and distribution, ports have been deeply impacted by COVID-19, including quarantine time and operational efficiency, and even cause unexpected port congestion. This study empirically examines the relationship between the COVID-19 pandemic, port congestion and air quality in Chinese port cities using classical and system panel models. We find that the COVID-19 pandemic and port congestion significantly influence air quality in port cities. Managerial implications include the ensuring of port workers’ shifts, the unblocking of port logistics, and the cooperation between transportation, customs, and quarantine departments, which can reduce the time of ships at berths and improve the air quality in port cities.

Coordinating Multi-party Vehicle Routing with Location Congestion via Iterative Best Response

This work is motivated by a real-world problem of coordinating B2B pickup-delivery operations to shopping malls involving multiple non-collaborative logistics service providers (LSPs) in a congested city where space is scarce. This problem can be categorized as a vehicle routing problem with pickup and delivery, time windows and location congestion with multiple LSPs (or ML-VRPLC in short), and we propose a scalable, decentralized, coordinated planning approach via iterative best response. We formulate the problem as a strategic game where each LSP is a self-interested agent but is willing to participate in a coordinated planning as long as there are sufficient incentives. Through an iterative best response procedure, agents adjust their schedules until no further improvement can be obtained to the resulting joint schedule. We seek to find the best joint schedule which maximizes the minimum gain achieved by any one LSP, as LSPs are interested in how much benefit they can gain rather than achieving a system optimality. We compare our approach to a centralized planning approach and our experiment results show that our approach is more scalable and is able to achieve on average 10% more gain within an operationally realistic time limit.

Measuring Spatializing Inequalities of Transport Accessibility and Urban Development Patterns: Focus on Megacity Urbanization, Thailand

The metropolis of Bangkok is characterized as a primate city because of its role as the capital of Thailand. Its suburbanization spreads to five surrounding provinces. Compared to other provinces in Thailand, it has highly concentrated urban development, without disparity between urban and rural areas. Furthermore, the travel volume in Bangkok and its surrounding areas is the highest in the country, with the majority related to private vehicle usage. This is why Bangkok is ranked as the world’s most congested city. To solve this problem and sustain the urbanization of the capital, it is necessary to understand the urban development patterns in Bangkok and their associated factors in measuring the accessibility of transportation. This research applied factor analysis and cluster analysis to characterize the different district contexts of the Bangkok Metropolitan Region, by selecting the Bangkok Metropolitan Administration (BMA) as the capital city area and Pathum Thani Province as a suburbanized area. Consequently, their transport accessibility could be represented by the level of accessibility in terms of the inequalities in the existing transportation system. Furthermore, by clustering the districts according to their economic and social factors, the causes of these inequalities could be identified by spatializing and geographically highlighting them. These findings should be integrated into the urban planning and development policies to overcome urban development challenges and create a city with more accessible and affordable public transport opportunities.

Using machine learning approach to construct the people flow tracking system for smart cities

In the crowd congestion in smart cities, the people flow statistics is necessary in public areas to reasonably control people flow. The You Only Look Once-v3 (YOLOv3) algorithm is employed for pedestrian detection, and the Smooth_L1 loss function is introduced to update the backpropagation parameters to ensure the stability of the object detection model. After the pedestrian is detected, tracking the pedestrian for a certain time is necessary to count out the specific number of pedestrians entering and leaving. Specifically, the Mean Shift algorithm is combined with the Kalman filter to track the target. When the target is lost, the Mean Shift algorithm is used for iterative tracking, and then the Kalman prediction is updated. In the experiment, 7,000 original images are collected from the library, mentioning 88 people of which 82 are recognized, and the detection accuracy reaches 93.18%. The 12,200 original images collected in the teaching building include149 people, of which 139 are recognized, with the detection accuracy reaching 93.29%. Therefore, the people flow statistics system based on machine vision and deep learning can detect and track pedestrians effectively, which is of great significance for the people flow statistics in public areas in smart cities and for the smooth development of various activities.

Research on Traffic Congestion Management in Small and Medium-sized Cities ——Taking the Central Urban Area of Huangshi City as an Example

Taking Huangshi City as an example, this article analyzes the current situation, causes, and strategies for managing traffic congestion in small and medium-sized cities. It puts forward measures for managing traffic congestion applicable to Huangshi City and other similar cities. These measures include strengthening road traffic planning and management and accelerating the construction of multi-level traffic facilities in traffic supply management. In traffic demand management, it proposes implementing a public transportation priority strategy, strengthening traffic diversion and illegal management.

IMPROVED TRAFFIC CONTROL SYSTEM THROUGH DETECTION AND CLASSIFICATION OF VEHICLES USING CAMERA BASED CROWD SENSING WITH DEEP LEARNING APPROACH.

Abstract - Intelligent traffic light control system can be considered as one of the main aspects of a smart city. There is significant increase in the number of People that own vehicles and this leads to traffic congestion in urban cities of developing countries. Traffic congestion is one of the highly challenging problems people face in their daily lives which affect their working productivity, causes accident, unnecessary delays and possibly loss of lives. The control of traffics based on the densities and classes of the vehicles using deep learning approach proved to be a better approach in handling traffic jams according to the current state of the studies. However, the current state of the studies did not have special consideration for the class of special vehicles (VIPs) which need special consideration due to their importance and tight schedules. Also, it did not consider allocating time based on the classification. Thus, a model to improve the detection and classification of vehicles on side of intersection as well as special consideration for VIPs was proposed. The proposed model was built using Faster R-CNN based on Tensorflow machine learning framework on a Google Colab environment. The model was able to detect and classify vehicles as “special vehicles (i.e. VIP), Emergency (EV) and Others” classes of vehicles, a time allocation simulator was proposed based on the specified classes with special timing for VIPs. Results show that using the proposed method, 94.43% accuracy was achieved.

Development and Implementation of a Smart Parking Spot Allocation SystemBased on the User’s Category and Priority using Verilog HDL

Finding parking spotsfor automobiles is a major issue in many large and congested cities. Usually, drivers lose time searching for parking spots, especially during peak hours, which increases traffic congestion and makes drivers frustrated and annoyed. Large building parking areascould also become dangerous to women, pregnant women,and the elderly, as several criminal cases in the parking area,werereported. In this project, a prototype of a smart parking spot allocation system based on the user’s category and priority was developed. The choiceofusercategories ispeople with disabilities (OKU), pregnant women/elderly, women,and normal users. Thehighest priority is assigned to OKU, followed by pregnant women/elderly, followed by women and the lowest priority is assigned tonormal users. The parking spots for the highest priority category are placed near building entrances such as mall entrances. The controller for the automatic parking spot allocation system wasdeveloped using Verilog HDL code and the prototype wasimplemented on FPGA DE2-115. The controller is programmed to process the user’s category which is selected by the userat the second entrance and assign a specific parking spot number according to the category’s priority. The prototype was tested with multiple parking spots condition with different userinputsfor different usercategories. The system was able to allocate parking spotsbased on the user’s category depending on the parking spot availablefor the selected categorywith 75% out of 12 tests correct.However, all 12 tests,or 100% recorded accurate allocation based on the expected output of the system design.Ina conclusion,this proposed system would beable to cater to the issue offinding parking spots hence directly avoidingtraffic congestion and frustration among users. In addition, this system can indirectly reduce crime cases in the parking area due to parking spaces that prioritize categories of users needingto be parked near the entrance.

Deep Reinforcement Learning for Smart Traffic Management in Indian Cities

Indian cities are plagued by traffic congestion, leading to significant economic and environmental costs. This paper explores the application of Deep Reinforcement Learning (DRL) to develop smart traffic management systems capable of optimizing traffic flow in real-time. The study evaluates the performance of DRL algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) in managing traffic signals, routing vehicles, and reducing congestion in cities like Delhi, Mumbai, and Bangalore. The research utilizes traffic simulation models that integrate DRL with real-time traffic data from sensors and cameras. Case studies from smart traffic management pilot projects in Indian cities are presented to highlight the effectiveness of these systems in reducing travel time, fuel consumption, and air pollution. The paper also discusses the challenges of deploying DRL-based traffic management systems in India, including infrastructure limitations, data accuracy, and scalability. Additionally, the ethical implications of using AI for traffic management, such as privacy concerns and algorithmic bias, are considered

MaaS Implementation in Bandung City: Big Data Collection, Integration, and Utilization

The results of the Eden Strategy Institute 2021 study show that Bandung City is ranked 28th in the world's Top 50 Smart City Government Rankings. However, from the results of the Asian Development Outlook 2019 study, Bandung City is ranked as the most congested city in Indonesia and 14th in Asia. Solutions to problems are offered in the form of Mobility as a Service (MaaS), an integrated system of transportation services, digitization, and the use of big data. The realization of MaaS requires contributions from all parties. The synergy between the government, academia, stakeholders, the media, and the public is called Pentahelix. The research stage includes the collection of primary data and secondary data by conducting a literature review. Then conduct a graphical analysis of multivariate data on Pentahelix with three assessments, namely performance, competence, and involvement. Next, modeling and loading were carried out for a pilot simulation of The Bandung City area in the eastern region using macro or meso-level traffic simulation software, namely PTV Visum 2022 (Student). After comparing with real-time analysis from the Google Maps application, comprehensive results were obtained with real conditions. As a means of controlling public transportation services, an Electronic Device Capture (EDC) hardware digital system is needed that is integrated between modes through software and artificial intelligence. The final stage of this study was carried out a risk analysis matrix for the implementation of MaaS for the city of Bandung with moderate risk results. MaaS is not a new idea, but it is a good solution to overcome congestion and change Bandung’s transportation concept.

Reintegration of Karachi port through sustainable adaptive reuse of abandoned and underused industrial buildings

The economic capital and one of Pakistan’s most important cities gain popularity due to the reputable Karachi port. Karachi was referred to as Korokala and Debal in ancient times; a city conquered by famous conquerors, including Alexander the Great and Muhammad bin Qasim due to its seaport importance. However, most of the port’s tangible and material heritage were built by the British during the 19th–20th century. The Britishers conquered the city and started developing Karachi port because of the seaport. Due to the port, the area of Karachi grew from a small fishermen’s village to a bustling metropolitan city. During the second industrial revolution in the late 19th century, the Britishers developed Indian industries so that they depended on British capital goods. To transport the goods, the British empire constructed railways and maritime routes in the mid and late nineteenth century, The port of Karachi was one of them.Karachi port remains the hub of Pakistan’s main logistic transportation, but some of its heritage buildings have been neglected on this site. After the 9/11 tragedy in the US, due to security reasons, Karachi port trust restricted the port to the public. Now, the security situation is better, and the port can be reintegrated and made accessible to the public through sustainable adaptive reuse of abandoned or underused buildings. Karachi is a congested city with few places for recreation; reintegrating this site can open opportunities for tourism while showcasing the city’s industrial heritage.This paper wants to articulate the potential of industrial heritage. But the latter in Pakistan is not yet recognized as part of a heritage discourse. The port offers a rich case study due to its layered history and various typologies of buildings on site.

Sustainability of Parcel Stations - Opportunities for Climate Protection

Growing e-commerce leads to more traffic, congestion and emissions in cities due to the increasing parcel volumes. Parcel stations can represent a sustainable form of parcel delivery to private households. This paper addresses the research question under which conditions the use of parcel stations can make a relevant contribution to climate-friendly urban logistics. First, the sustainability of parcel stations is evaluated; then, the usage behavior and the acceptance of the recipients is analyzed in order to identify fields of action for increasing acceptance and the need for further research. The data source for this work is a systematic search of current literature and research projects.