A WAVE Based and Collaboration Driven Framework for Reduced Traffic Congestion in Smart Cities

This study employs the PTV VISSIM simulation software to investigate the impact of increasing traffic volumes on conventional vehicles and autonomous vehicles (AVs) with distinct behavioural traits: cautious, normal, and aggressive. The simulations cover a range of traffic volumes, from 100 to 600 vehicles, and measure the effects on travel time, emissions (CO, NOX, VOC), and fuel consumption. The results show that with increasing penetration rates of AVs, travel times generally decrease, with aggressive AVs achieving the shortest times, followed by normal, then cautious AVs. Emissions and fuel consumption also tend to decrease as the penetration rate of AVs increases. Notably, the results demonstrate that aggressive AVs excel in reducing travel time, while normal AVs consistently balance between efficiency and reduced emissions, and cautious AVs emphasize safety and lower emissions. Despite the differing behavioural traits, all AV types exhibit a marked improvement over conventional vehicles in terms of travel time, emissions, and fuel consumption. At every penetration rate, AVs lead to shorter travel times and lower emissions, with aggressive AVs being the most efficient, followed by normal and then cautious AVs. These findings emphasize the potential benefits of integrating autonomous vehicles into transportation networks. They suggest that optimizing AV behaviour, depending on the context and objectives, can lead to more efficient, environmentally friendly traffic systems. The study offers valuable insights for policymakers, urban planners, and researchers aiming to leverage the distinct strengths of each AV behaviour to create a more sustainable and efficient future for autonomous transportation.

Road congestion is estimated to cost the United Kingdom £307 billion by 2030. Furthermore, congestion contributes enormously to damaging the environment and people's health. In an attempt to combat the damage congestion is causing, new technologies are being developed, such as intelligent infrastructures and smart vehicles. The aim of this study is to develop a fuzzy system that can classify congestion using a real-world dataset referred to as Manchester Urban Congestion Dataset, which contains data similar to that collected by connected and autonomous vehicles. A set of fuzzy membership functions and rules were developed using a road congestion ontology and in conjunction with domain experts. Experiments are conducted to evaluate the fuzzy system in terms of its precision and recall in classifying congestion. Comparisons are made in terms of performance with traditional classification algorithms decision trees and Naïve Bayes using the Red, Amber, and Green classification methods currently implemented by Transport for Greater Manchester to label the dataset. The results have shown the fuzzy system has the ability to predict road congestion using volume and journey time, outperforming both decision trees and Naïve Bayes.

Traffic congestion has become increasingly prevalent in many urban areas, and researchers are continuously looking into new ways to resolve this pertinent issue. Autonomous vehicles are one of the technologies expected to revolutionize transportation systems. To this very day, there are limited studies focused on the impact of autonomous vehicles in heterogeneous traffic flow in terms of different driving modes (manual and self-driving). Autonomous vehicles in the near future will be running parallel with manual vehicles, and drivers will have different characteristics and attributes. Previous studies that have focused on the impact of autonomous vehicles in these conditions are scarce. This paper proposes a new cellular automata model to address this issue, where different autonomous vehicles (cars and buses) and manual vehicles (cars and buses) are compared in terms of fundamental traffic parameters. Manual cars are further divided into subcategories on the basis of age groups and gender. Each category has its own distinct attributes, which make it different from the others. This is done in order to obtain a simulation as close as possible to a real-world scenario. Furthermore, different lane-changing behavior patterns have been modeled for autonomous and manual vehicles. Subsequently, different scenarios with different compositions are simulated to investigate the impact of autonomous vehicles on traffic flow in heterogeneous conditions. The results suggest that autonomous vehicles can raise the flow rate of any network considerably despite the running heterogeneous traffic flow.

On ride-pooling and traffic congestion

Traffic congestion is a problem in day to day life, especially in big cities. Various traffic control infrastructure systems have been deployed to monitor and improve the flow of traffic across cities. Real-time congestion detection can serve for many useful purposes that include sending warnings to drivers approaching the congested area and daily route planning. Most of the existing congestion detection solutions combine historical data with continuous sensor readings and rely on data collected from multiple sensors deployed on the road, measuring the speed of vehicles. While in our work we present a framework that works in a pure streaming setting where historic data is not available before processing. The traffic data streams, possibly unbounded, arrive in real-time. Moreover, the data used in our case is collected only from sensors placed on the intersections of the road. Therefore, we investigate in creating a real-time congestion detection and reduction solution, that works on traffic streams without any prior knowledge. The goal of our work is 1) to detect traffic jams in real-time, and 2) to reduce the congestion in the traffic jam areas.In this work, we present a real-time traffic jam detection and congestion reduction framework: 1) We propose a directed weighted graph representation of the traffic infrastructure network for capturing dependencies between sensor data to measure traffic congestion; 2) We present online traffic jam detection and congestion reduction techniques built on a modern stream processing system, i.e., Apache Flink; 3) We develop dynamic traffic light policies for controlling traffic in congested areas to reduce the travel time of vehicles. Our experimental results indicate that we are able to detect traffic jams in real-time and deploy new traffic light policies which result in 27% less travel time at the best and 8% less travel time on average compared to the travel time with default traffic light policies. Our scalability results show that our system is able to handle high-intensity streaming data with high throughput and low latency.

In densely populated cities, severe vehicle congestion is an obstacle due to the massive increase of vehicles and bottleneck of roads. Traffic congestion brings severe economic and environmental hazards. Although it is hard to eliminate, it can be mitigated by finding the occurrence of the congestion well in advance to initiate appropriate control measures. Accurately estimating traffic speed, travel time, and other external factors is mandatory for forecasting congestion levels in a dynamic road network. In recent years, sensor-based traffic detection technologies have evolved to achieve accuracy and safety in intelligent transportation systems (ITS). However, some of them need more reliability, have weak usage in different visibility conditions, are hard to set, and have a high cost. Alternatively, modern navigation systems are developed to find the fastest, alternative routes, travel times, and congestion levels for a given source to destination. These systems use Global Positioning Systems (GPS) data to provide the routing information. Due to inaccurate/ inadequate GPS data samples, sometimes these systems may give misleading information. This work presents Long-Range Wide Area Network (LoRaWAN) architecture to overcome the abovementioned limitations. In our proposed case study, we have used the Dragino LoRaWAn Gps sensor (IN865), air quality sensor (MQ135), temperature and humidity sensor (SHT31), and other sensors to extract direct and indirect factors that influence traffic congestion. Haversine formula and hyper-heuristic-based Encoder-Decoder using Gated Recurrent Units with attention mechanism (ED-GRUAT) are used to estimate the segment-based distance, speed, vehicle travel times, and congestion levels. Our case study helps to calculate accurate traffic speed, travel times, and congestion levels.

The Autonomous Vehicle (AV) technology allows people to travel without intervening. The quick progress in this technology and the efforts of manufacturers companies to reach fully Autonomous Vehicles (AVs) using artificial intelligent, forecast a bright future. Researches have estimated that on average, one passenger in a vehicle traveling one kilometer in Lebanon costs about $ 48, including externality components of crashes, travel time, congestion and pollution. Thus, the main purpose of this research is to highlight the opportunities of implementing AVs in Lebanon, such as reduction in traffic accidents and congestion, improvement of accessibility, human health, economic and social life. Moreover, this paper highlight the barriers standing in front of implementing this technology, like the missing of Intelligent Transportation System (ITS) and the funds needed for its execution, and gives some policy recommendations. Finally, the authors present their statistics conducted in Lebanon about ethical decisions that AVs should take particularly in case of a crash in different scenarios through the participants’ responses.

Road traffic congestion is a very serious concern for urbanization. The economy of a country is also affected by this. Most traffic congestions are caused due to the unplanned road networks, high volume of vehicles on the roads, and presence of critical congestion areas. Urbanization is also playing an important role to increase the traffic jams and accidents in the urban areas. Severe traffic jams lengthen the journey time because of which productivity and business are badly affected. Moreover, it also increases the fuel consumption as well as air pollutions in the urban areas. So, road traffic congestion poses a serious challenge for all growing cities. Hence, a lot of researchers have focused on the problem of monitoring and management of vehicular congestion in the urban areas. The effective management of road vehicular congestion via adaptive traffic signal scheduling policy, on the other hand, depends on proper measuring and assessment of the vehicular congestion. Hence, a significant number of road traffic congestion monitoring, predicting, and assessment techniques using various types of information and communication technologies (ICTs) have been devised over the past few decades. Thus, this chapter extensively reviews various road traffic congestion measuring and assessment techniques proposed in the literature over the past few decades.

Recent evidence suggests hospitals fail to meet guideline specified time to percutaneous coronary intervention (PCI) for a proportion of ST elevation myocardial infarction (STEMI) presentations. Implicit in achieving this time is the rapid assembly of crucial catheter laboratory staff. As a proof-of-concept, we set out to create regional maps that graphically show the impact of traffic congestion and distance to destination on staff recall travel times for STEMI, thereby producing a resource that could be used by staff to improve reperfusion time for STEMI. Travel times for staff recalled to one inner and one outer metropolitan hospital at midnight, 6 p.m., and 7 a.m. were estimated using Google Maps Application Programming Interface. Computer modeling predictions were overlaid on metropolitan maps showing color coded staff recall travel times for STEMI, occurring within non-peak and peak hour traffic congestion times. Inner metropolitan hospital staff recall travel times were more affected by traffic congestion compared with outer metropolitan times, and the latter was more affected by distance. The estimated mean travel times to hospital during peak hour were greater than midnight travel times by 13.4 min to the inner and 6.0 min to the outer metropolitan hospital at 6 p.m. (p < 0.001). At 7 a.m., the mean difference was 9.5 min to the inner and 3.6 min to the outer metropolitan hospital (p < 0.001). Only 45% of inner metropolitan staff were predicted to arrive within 30 min at 6 p.m. compared with 100% at midnight (p < 0.001), and 56% of outer metropolitan staff at 6 p.m. (p = 0.021). Our results show that integration of map software with traffic congestion data, distance to destination and travel time can predict optimal residence of staff when on-call for PCI.

Abstract: As urban populations grow and cities face increasing challenges in traffic congestion, energy management, and environmental sustainability, Quantum Artificial Intelligence (Quantum AI) is emerging as a transformative technology to optimize urban systems. This book, "Quantum AI for Smart Cities: Optimizing Traffic and Urban Sustainability," explores how the convergence of quantum computing and AI-driven analytics can revolutionize traffic flow management, smart grids, and sustainable urban planning. It delves into quantum-enhanced predictive modeling, real-time optimization algorithms, and autonomous mobility solutions, enabling cities to reduce congestion, lower emissions, and improve resource allocation. The book also examines Quantum AI’s role in energy-efficient infrastructure, climate adaptation strategies, and smart governance models, demonstrating its potential to create data-driven, intelligent, and eco-friendly urban environments. Additionally, it addresses ethical, security, and regulatory considerations, providing insights into quantum cybersecurity, AI governance frameworks, and policy recommendations for responsible adoption. Designed for urban planners, AI researchers, policymakers, and technology leaders, this book serves as a comprehensive guide to leveraging Quantum AI for the next-generation evolution of smart, sustainable, and efficient cities. Keywords: Quantum AI, smart cities, traffic optimization, urban sustainability, quantum computing, artificial intelligence, machine learning, predictive analytics, smart mobility, autonomous vehicles, intelligent transportation, energy management, smart grids, renewable energy, climate adaptation, IoT, big data analytics, sustainable urban planning, ethical AI, AI governance, cybersecurity, quantum encryption, urban infrastructure, smart governance, environmental sustainability, digital transformation.

A lot of research work has been carried out on the smart city and its applications in last two decades. Smart transportation is an application of smart city and also an important factor in building smart cities. Due to the complexity in passenger's traffic in big cities, a smart transportation system is needed to resolve the issue. This paper examines the concept of a vision-based transportation system to resolve the complexities in passenger's traffic and improving the productivity of existing transportation system. The vision-based transportation systems are expected to have positive mobility effects and be able to subsidize to the green environment by improving air quality and energy conservation for planning and developing a smart city. Thus, vision-based transportation is possibly an important approach for supporting and improving the transportation system. Vision-based transportation is a set of technologies applied to transportation infrastructure and vehicles to improve their performance. More specifically, vision-based transportation involves the application of established communications, controls, electronics, computer hardware, and software technologies in the transportation system. The paper examines in detail a broad range of vision-based transportation technologies and the expected benefits. These benefits include improved transportation system, travel time, throughput, cost savings, and most importantly safety and security. The paper considers a set of barriers that are required for the improvement of autonomous vehicles.

Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles

Over the past decade, Smart cities have been advanced and minimizing traffic congestion become the main concern in the progression of smart cities. The rapid growth in the number of road vehicles has raised the number of road accidents and traffic congestion. To Solve this problem, Vehicular Network (VN) formulated several novel concepts which include traffic control, vehicular communications, and navigation. Machine Learning (ML) will be an effective technique for identifying hidden insights into ITS without being programmed clearly by learning from data. This article develops an Optimal Extreme Learning Machine based Traffic Congestion Control System (OELM-TCCS) in vehicular networks. The presented OELM-TCCS technique mainly focuses on the identification and recognition of traffic congestion in VANET. To perform this, the presented OELM-TCCS technique primarily designs a new ELM model to carry out the classification process. In addition, the presented OELM-TCCS technique executes the satin bowerbird optimization (SBO) algorithm for parameter tuning of the ELM method. To demonstrate the boosted performance of the OELM-TCCS model, a series of experiments were executed. The experimental outcomes indicate the betterment of the OELM-TCCS method over recent methods with maximum accuracy of 99.17%.

The integration of autonomous vehicles (AVs) into public transportation systems has the potential to transform urban mobility by enhancing efficiency, accessibility, and sustainability. As cities continue to expand, traditional transit systems face challenges such as traffic congestion, high operational costs, and limited coverage. AVs, with their ability to optimize routes, reduce human error, and operate continuously, offer a promising solution to improve public transit networks. The adoption of AV technology in public transportation can redefine mobility patterns, particularly by bridging first- and last-mile connectivity gaps, enhancing commuter convenience, and improving mobility for elderly and disabled passengers. Unlike conventional transit modes, autonomous buses, shuttles, and taxis can dynamically adjust routes based on real-time data, reducing travel time and increasing overall system efficiency. Additionally, AVs can function seamlessly with multi-modal transport networks, integrating with metro, rail, and bus services to create a more cohesive and accessible urban transit system. Beyond improving passenger experience, AVs offer significant economic and environmental benefits. Their reduced dependence on human drivers leads to lower labor costs, while advanced energy-efficient systems contribute to lower fuel consumption and carbon emissions. Autonomous public transit solutions also help alleviate traffic congestion by minimizing unnecessary stops and optimizing roadway usage through vehicle-to-infrastructure (V2I) and vehicle-to vehicle (V2V) communication. However, the transition to a fully autonomous public transport system is not without challenges. Cities and governments must address issues such as infrastructure readiness, regulatory frameworks, liability concerns, cyber security risks, and public acceptance to ensure a smooth and secure transition. By analyzing real-world case studies and pilot projects, this chapter provides insights into how different cities worldwide are integrating AVs into their transit systems. Several pilot programs have demonstrated the feasibility of self driving shuttle services, particularly in urban areas with high commuter demand and limited transit accessibility. Furthermore, technological advancements in artificial intelligence (AI), machine learning, and sensor-based navigation continue to refine AV capabilities, making them increasingly viable for large-scale deployment in public transportation. This chapter also explores future trends, policy recommendations, and best practices that can accelerate AV adoption in public transport. Governments, urban planners, and transportation authorities must collaborate to design regulatory policies that encourage innovation while ensuring passenger safety and data security. Investment in smart infrastructure, including intelligent traffic management systems and dedicated AV lanes, will be crucial for optimizing AV operations in public transit. Ultimately, the successful integration of AVs into public transportation can lead to a more efficient, inclusive, and sustainable urban mobility ecosystem. With the right combination of technological innovation, policy support, and public engagement, AVs have the potential to redefine how people commute in the cities of the future, offering safe, affordable, and environmentally friendly solutions.

Novel Use of Integrated Map Software and Traffic Congestion Data Affecting Staff Travel Time to PCI for STEMI