Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version

Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems

Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic congestion and emission of greenhouse gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), particulate matter (PM), and oxides of nitrogen (NOx). Intelligent transport system (ITS) technologies can be implemented to lower pollutant emissions and reduction of fuel consumption. This paper investigates the ITS techniques and technologies for the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption and exhaust pollutant in the urban environment

Modelling Road Congestion using a Fuzzy System and Real-World Data for Connected and Autonomous Vehicles

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

Simulation Exploration of the Potential of Connected Vehicles in Mitigating Secondary Crashes

Secondary crashes (SCs) on freeways are a major concern for traffic incident management systems. Studies have shown that their occurrence is significant and can lead to deterioration of traffic flow conditions on freeways in addition to injury and fatalities, albeit their magnitudes are relatively low when compared to primary crashes. Due to the limited nature of crash data in analyzing freeway SCs, surrogate measures provide an alternative for safety analysis for freeway analysis using conflict analysis. Connected Vehicles (CVs) have seen compelling technological advancements since the concept was introduced in the 1990s. In recent years, CVs have emerged as a feasible application with many safety benefits especially in the urban areas, that can be deployed in masses imminently. This study used a freeway model of a road segment in Florida’s Turnpike system in VISSIM microscopic simulation software to generate trajectory files for conflict analysis in SSAM software, to analyze potential benefits of CVs in mitigating SCs. The results showed how SCs could potentially be reduced with traffic conflicts being decreased by up to 90% at full 100% composition of CVs in the traffic stream. The results also portrayed how at only 25% CV composition, there was a significant reduction of conflicts up to 70% in low traffic volumes and up to 50% in higher traffic volumes. The statistical analysis showed that the difference in average time-to-collision surrogate measure used in deriving conflicts was significant at all levels of CV composition

VANET-enabled eco-friendly road characteristics-aware routing for vehicular traffic

There is growing awareness of the dangers of climate change caused by greenhouse gases. In the coming decades this could result in numerous disasters such as heat-waves, flooding and crop failures. A major contributor to the total amount of greenhouse gas emissions is the transport sector, particularly private vehicles. Traffic congestion involving private vehicles also causes a lot of wasted time and stress to commuters. At the same time new wireless technologies such as Vehicular Ad-Hoc Networks (VANETs) are being developed which could allow vehicles to communicate with each other. These could enable a number of innovative schemes to reduce traffic congestion and greenhouse gas emissions. 1) EcoTrec is a VANET-based system which allows vehicles to exchange messages regarding traffic congestion and road conditions, such as roughness and gradient. Each vehicle uses the messages it has received to build a model of nearby roads and the traffic on them. The EcoTrec Algorithm then recommends the most fuel efficient route for the vehicles to follow. 2) Time-Ants is a swarm based algorithm that considers not only the amount of cars in the spatial domain but also the amoumt in the time domain. This allows the system to build a model of the traffic congestion throughout the day. As traffic patterns are broadly similar for weekdays this gives us a good idea of what traffic will be like allowing us to route the vehicles more efficiently using the Time-Ants Algorithm. 3) Electric Vehicle enhanced Dedicated Bus Lanes (E-DBL) proposes allowing electric vehicles onto the bus lanes. Such an approach could allow a reduction in traffic congestion on the regular lanes without greatly impeding the buses. It would also encourage uptake of electric vehicles. 4) A comprehensive survey of issues associated with communication centred traffic management systems was carried out

Road Traffic Congestion Analysis Via Connected Vehicles

La congestion routière est un état particulier de mobilité où les temps de déplacement augmentent et de plus en plus de temps est passé dans le véhicule. En plus d’être une expérience très stressante pour les conducteurs, la congestion a également un impact négatif sur l’environnement et l’économie. Dans ce contexte, des pressions sont exercées sur les autorités afin qu’elles prennent des mesures décisives pour améliorer le flot du trafic sur le réseau routier. En améliorant le flot, la congestion est réduite et la durée totale de déplacement des véhicules est réduite. D’une part, la congestion routière peut être récurrente, faisant référence à la congestion qui se produit régulièrement. La congestion non récurrente (NRC), quant à elle, dans un réseau urbain, est principalement causée par des incidents, des zones de construction, des événements spéciaux ou des conditions météorologiques défavorables. Les opérateurs d’infrastructure surveillent le trafic sur le réseau mais sont contraints à utiliser le moins de ressources possibles. Cette contrainte implique que l’état du trafic ne peut pas être mesuré partout car il n’est pas réaliste de déployer des équipements sophistiqués pour assurer la collecte précise des données de trafic et la détection en temps réel des événements partout sur le réseau routier. Alors certains emplacements où le flot de trafic doit être amélioré ne sont pas surveillés car ces emplacements varient beaucoup. D’un autre côté, de nombreuses études sur la congestion routière ont été consacrées aux autoroutes plutôt qu’aux régions urbaines, qui sont pourtant beaucoup plus susceptibles d’être surveillées par les autorités de la circulation. De plus, les systèmes actuels de collecte de données de trafic n’incluent pas la possibilité d’enregistrer des informations détaillées sur les événements qui surviennent sur la route, tels que les collisions, les conditions météorologiques défavorables, etc. Aussi, les études proposées dans la littérature ne font que détecter la congestion ; mais ce n’est pas suffisant, nous devrions être en mesure de mieux caractériser l’événement qui en est la cause. Les agences doivent comprendre quelle est la cause qui affecte la variabilité de flot sur leurs installations et dans quelle mesure elles peuvent prendre les actions appropriées pour atténuer la congestion.----------ABSTRACT: Road traffic congestion is a particular state of mobility where travel times increase and more and more time is spent in vehicles. Apart from being a quite-stressful experience for drivers, congestion also has a negative impact on the environment and the economy. In this context, there is pressure on the authorities to take decisive actions to improve the network traffic flow. By improving network flow, congestion is reduced and the total travel time of vehicles is decreased. In fact, congestion can be classified as recurrent and non-recurrent (NRC). Recurrent congestion refers to congestion that happens on a regular basis. Non-recurrent congestion in an urban network is mainly caused by incidents, workzones, special events and adverse weather. Infrastructure operators monitor traffic on the network while using the least possible resources. Thus, traffic state cannot be directly measured everywhere on the traffic road network. But the location where traffic flow needs to be improved varies highly and certainly, deploying highly sophisticated equipment to ensure the accurate estimation of traffic flows and timely detection of events everywhere on the road network is not feasible. Also, many studies have been devoted to highways rather than highly congested urban regions which are intricate, complex networks and far more likely to be monitored by the traffic authorities. Moreover, current traffic data collection systems do not incorporate the ability of registring detailed information on the altering events happening on the road, such as vehicle crashes, adverse weather, etc. Operators require external data sources to retireve this information in real time. Current methods only detect congestion but it’s not enough, we should be able to better characterize the event causing it. Agencies need to understand what is the cause affecting variability on their facilities and to what degree so that they can take the appropriate action to mitigate congestion

Machine Learning Approaches for Traffic Flow Forecasting

Intelligent Transport Systems (ITS) as a field has emerged quite rapidly in the recent years. A competitive solution coupled with big data gathered for ITS applications needs the latest AI to drive the ITS for the smart and effective public transport planning and management. Although there is a strong need for ITS applications like Advanced Route Planning (ARP) and Traffic Control Systems (TCS) to take the charge and require the minimum of possible human interventions. This thesis develops the models that can predict the traffic link flows on a junction level such as road traffic flows for a freeway or highway road for all traffic conditions. The research first reviews the state-of-the-art time series data prediction techniques with a deep focus in the field of transport Engineering along with the existing statistical and machine leaning methods and their applications for the freeway traffic flow prediction. This review setup a firm work focussed on the view point to look for the superiority in term of prediction performance of individual statistical or machine learning models over another. A detailed theoretical attention has been given, to learn the structure and working of individual chosen prediction models, in relation to the traffic flow data. In modelling the traffic flows from the real-world Highway England (HE) gathered dataset, a traffic flow objective function for highway road prediction models is proposed in a 3-stage framework including the topological breakdown of traffic network into virtual patches, further into nodes and to the basic links flow profiles behaviour estimations. The proposed objective function is tested with ten different prediction models including the statistical, shallow and deep learning constructed hybrid models for bi-directional links flow prediction methods. The effectiveness of the proposed objective function greatly enhances the accuracy of traffic flow prediction, regardless of the machine learning model used. The proposed prediction objective function base framework gives a new approach to model the traffic network to better understand the unknown traffic flow waves and the resulting congestions caused on a junction level. In addition, the results of applied Machine Learning models indicate that RNN variant LSTMs based models in conjunction with neural networks and Deep CNNs, when applied through the proposed objective function, outperforms other chosen machine learning methods for link flow predictions. The experimentation based practical findings reveal that to arrive at an efficient, robust, offline and accurate prediction model apart from feeding the ML mode with the correct representation of the network data, attention should be paid to the deep learning model structure, data pre-processing (i.e. normalisation) and the error matrices used for data behavioural learning. The proposed framework, in future can be utilised to address one of the main aims of the smart transport systems i.e. to reduce the error rates in network wide congestion predictions and the inflicted general traffic travel time delays in real-time

Efficient medium access control protocol for vehicular ad-hoc networks

Intelligent transportation systems (ITS) have enjoyed a tremendous growth in the last decade and the advancement in communication technologies has played a big role behind the success of ITS. Inter-vehicle communication (IVC) is a critical requirement for ITS and due to the nature of communication, vehicular ad-hoc network technology (VANET) is the most suitable communication technology for inter-vehicle communications. In Practice, however, VANET poses some extreme challenges including dropping out of connections as the moving vehicle moves out of the coverage range, joining of new nodes moving at high speeds, dynamic change in topology and connectivity, time variability of signal strength, throughput and time delay. One of the most challenging issues facing vehicular networks lies in the design of efficient resource management schemes, due to the mobile nature of nodes, delay constraints for safety applications and interference. The main application of VANET in ITS lies in the exchange of safety messages between nodes. Moreover, as the wireless access in vehicular environment (WAVE) moves closer to reality, management of these networks is of increasing concern for ITS designers and other stakeholder groups. As such, management of resources plays a significant role in VANET and ITS. For resource management in VANET, a medium access control protocol is used, which makes sure that limited resources are distributed efficiently. In this thesis, an efficient Multichannel Cognitive MAC (MCM) is developed, which assesses the quality of channel prior to transmission. MCM employs dynamic channel allocation and negotiation algorithms to achieve a significant improvement in channel utilisation, system reliability, and delay constraints while simultaneously addressing Quality of Service. Moreover, modified access priority parameters and safety message acknowledgments will be used to improve the reliability of safety messages. The proposed protocols are implemented using network simulation tools. Extensive experiments demonstrated a faster and more efficient reception of safety messages compared to existing VANET technologies. Finally, improvements in delay and packet delivery ratios are presented

Dynamic management of traffic signals through social IoT

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed