Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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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

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

A Microscopic Simulation Laboratory for Evaluation of Off-street Parking Systems

The parking industry produces an enormous amount of data every day that, properly analyzed, will change the way the industry operates. The collected data form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as how to set specific parking policies (e.g. electrical charging only parking space) to achieve the sustainable and eco-friendly parking. However, there lacks an intelligent tool to assess the layout design and operational performance of parking lots to reduce the externalities and increase the revenue. To address this issue, this research presents a comprehensive agent-based framework for microscopic off-street parking system simulation. A rule-based parking simulation logic programming model is formulated. The proposed simulation model can effectively capture the behaviors of drivers and pedestrians as well as spatial and temporal interactions of traffic dynamics in the parking system. A methodology for data collection, processing, and extraction of user behaviors in the parking system is also developed. A Long-Short Term Memory (LSTM) neural network is used to predict the arrival and departure of the vehicles. The proposed simulator is implemented in Java and a Software as a Service (SaaS) graphic user interface is designed to analyze and visualize the simulation results. This study finds the active capacity of the parking system, which is defined as the largest number of actively moving vehicles in the parking system under the facility layout. In the system application of the real world testbed, the numerical tests show (a) the smart check-in device has marginal benefits in vehicle waiting time; (b) the flexible pricing policy may increase the average daily revenue if the elasticity of the price is not involved; (c) the number of electrical charging only spots has a negative impact on the performance of the parking facility; and (d) the rear-in only policy may increase the duration of parking maneuvers and reduce the efficiency during the arrival rush hour. Application of the developed simulation system using a real-world case demonstrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating smart parking facilities

Development of a Mode Choice Model to understand the potential impacts of LRT on Mode Shares in the Region of Waterloo

A new light rail transit system (LRT), ION, began operations in the Region of Waterloo in the June of 2019, and the second phase is yet to begin construction. The main thrust of this growth management project for the region was achieving sustainability goals by promoting denser development and boosting transit ridership. The LRT is integrated within the existing transit system, and this study intends to understand its impact on transit mode shares. To understand the potential impact of introduction of a new transit system on mode shares, an analytical modelling approach is required. This research conducts spatial analysis in ARCMap to describe the current commuting patterns in the region of Waterloo, highlighting the spatial distribution of modal shares, top trip origins and destinations and trip distribution patterns by different modes. Furthermore, many nested logit and multinomial logit models were estimated to relate various socio economic, spatial and trip attributes to mode choice behaviour. The nested logit models did not prove to be a good fit for the available data, and the best multinomial logit model was finally used to understand the potential impacts of LRT. The final model estimation projects 0.09% increase in commuter transit ridership for the estimated average decrease of 0.14% in travel time, which is a result of both, introduction of ION and realignment of transit routes. It is however, essential to note that ION is a driver of urban growth and development with potential to attract denser and mixed land use developments, which are both key to increasing transit ridership. This study is a start towards understanding the impacts of LRT and findings may prove to be a valuable resource to discerning spatial distribution of commuter trips in the region. Additionally, the model serves as flexible template, which can be employed to assess the impacts of LRT in the future and inform transit policy decisions

Congestion Control in Vehicular Ad Hoc Networks

RÉSUMÉ Les réseaux Véhiculaires ad hoc (VANets) sont conçus pour permettre des communications sans fil fiables entre les nœuds mobiles à haute vitesse. Afin d'améliorer la performance des applications dans ce type de réseaux et garantir un environnement sûr et confortable pour ses utilisateurs, la Qualité de Service (QoS) doit être supportée dans ces réseaux. Le délai ainsi que les pertes de paquets sont deux principaux indicateurs de QoS qui augmentent de manière significative en raison de la congestion dans les réseaux. En effet, la congestion du réseau entraîne une saturation des canaux ainsi qu’une augmentation des collisions de paquets dans les canaux. Par conséquent, elle doit être contrôlée pour réduire les pertes de paquets ainsi que le délai, et améliorer les performances des réseaux véhiculaires. Le contrôle de congestion dans les réseaux VANets est une tâche difficile en raison des caractéristiques spécifiques des VANets, telles que la grande mobilité des nœuds à haute vitesse, le taux élevé de changement de topologie, etc. Le contrôle de congestion dans les réseaux VANets peut être effectué en ayant recours à une stratégie qui utilise l'un des paramètres suivants : le taux de transmission, la puissance de transmission, la priorisation et l’ordonnancement, ainsi que les stratégies hybrides. Les stratégies de contrôle de congestion dans les réseaux VANets doivent faire face à quelques défis tels que l'utilisation inéquitable des ressources, la surcharge de communication, le délai de transmission élevé, et l'utilisation inefficace de la bande passante, etc. Par conséquent, il est nécessaire de développer de nouvelles approches pour faire face à ces défis et améliorer la performance des réseaux VANets. Dans cette thèse, dans un premier temps, une stratégie de contrôle de congestion en boucle fermée est développée. Cette stratégie est une méthode de contrôle de congestion dynamique et distribuée qui détecte la congestion en mesurant le niveau d'utilisation du canal. Ensuite, la congestion est contrôlée en ajustant la portée et le taux de transmission qui ont un impact considérable sur la saturation du canal. Ajuster la portée et le taux de transmission au sein des VANets est un problème NP-difficile en raison de la grande complexité de la détermination des valeurs appropriées pour ces paramètres. Considérant les avantages de la méthode de recherche Tabou et son adaptabilité au problème, une méthode de recherche multi-objective est utilisée pour trouver une portée et un taux de transmission dans un délai raisonnable. Le délai et la gigue, fonctions multi-objectifs de l'algorithme Tabou, sont minimisés dans l'algorithme proposé. Par la suite, deux stratégies de contrôle de congestion en boucle ouverte sont proposées afin de réduire la congestion dans les canaux en utilisant la priorisation et l'ordonnancement des messages. Ces stratégies définissent la priorité pour chaque message en considérant son type de contenu (par exemple les messages d'urgence, de beacon, et de service), la taille des messages, et l’état du réseau (par exemple, les métriques de la vélocité, la direction, l'utilité, la distance, et la validité). L'ordonnancement des messages est effectué sur la base des priorités définies. De plus, comme seconde technique d'ordonnancement, une méthode de recherche Tabou est employée pour planifier les files d'attente de contrôle et de service des canaux de transmission dans un délai raisonnable. A cet effet, le délai et la gigue lors de l'acheminement des messages sont minimisés. Enfin, une stratégie localisée et centralisée qui utilise les ensembles RSU fixés aux intersections pour détecter et contrôler de la congestion est proposée. Cette stratégie regroupe tous les messages transférés entre les véhicules qui se sont arrêtés à une lumière de signalisation en utilisant les algorithmes de Machine Learning. Dans cette stratégie, un algorithme de k-means est utilisé pour regrouper les messages en fonction de leurs caractéristiques (par exemple la taille des messages, la validité des messages, et le type de messages, etc.). Les paramètres de communication, y compris le portée et le taux de transmission, la taille de la fenêtre de contention, et le paramètre AIFS (Arbitration Inter-Frame Spacing) sont déterminés pour chaque grappe de messages en vue de minimiser le délai de livraison. Ensuite, les paramètres de communication déterminés sont envoyés aux véhicules par les RSUs, et les véhicules opèrent en fonction de ces paramètres pour le transfert des messages. Les performances des trois stratégies proposées ont été évaluées en simulant des scénarios dans les autoroutes et la circulation urbaine avec les simulateurs NS2 et SUMO. Des comparaisons ont aussi été faites entre les résultats obtenus à partir des stratégies proposées et les stratégies de contrôle de congestion communément utilisées. Les résultats révèlent qu’avec les stratégies de contrôle de congestion proposées, le débit du réseau augmente et le taux de perte de paquets ainsi que de délai diminuent de manière significative en comparaison aux autres stratégies. Par conséquent, l'application des méthodes proposées aide à améliorer la performance, la sureté et la fiabilité des VANets.----------ABSTRACT Vehicular Ad hoc Networks (VANets) are designed to provide reliable wireless communications between high-speed mobile nodes. In order to improve the performance of VANets’ applications, and make a safe and comfort environment for VANets’ users, Quality of Service (QoS) should be supported in these networks. The delay and packet losses are two main indicators of QoS that dramatically increase due to the congestion occurrence in the networks. Indeed, due to congestion occurrence, the channels are saturated and the packet collisions increase in the channels. Therefore, the congestion should be controlled to decrease the packet losses and delay, and to increase the performance of VANets. Congestion control in VANets is a challenging task due to the specific characteristics of VANets such as high mobility of the nodes with high speed, and high rate of topology changes, and so on. Congestion control in VANets can be carried out using the strategies that can be classified into rate-based, power-based, CSMA/CA-based, prioritizing and scheduling-based, and hybrid strategies. The congestion control strategies in VANets face to some challenges such as unfair resources usage, communication overhead, high transmission delay, and inefficient bandwidth utilization, and so on. Therefore, it is required to develop new strategies to cope with these challenges and improve the performance of VANets. In this dissertation, first, a closed-loop congestion control strategy is developed. This strategy is a dynamic and distributed congestion control strategy that detects the congestion by measuring the channel usage level. Then, the congestion is controlled by tuning the transmission range and rate that considerably impact on the channel saturation. Tuning the transmission range and rate in VANets is an NP-hard problem due to the high complexity of determining the proper values for these parameters in vehicular networks. Considering the benefits of Tabu search algorithm and its adaptability with the problem, a multi-objective Tabu search algorithm is used for tuning transmission range and rate in reasonable time. In the proposed algorithm, the delay and jitter are minimized as the objective functions of multi-objective Tabu Search algorithm. Second, two open-loop congestion control strategies are proposed that prevent the congestion occurrence in the channels using the prioritizing and scheduling the messages. These strategies define the priority for each message by considering the content of messages (i.e. types of the messages for example emergency, beacon, and service messages), size of messages, and state of the networks (e.g. velocity, direction, usefulness, distance and validity metrics). The scheduling of the messages is conducted based on the defined priorities. In addition, as the second scheduling technique, a Tabu Search algorithm is employed to schedule the control and service channel queues in a reasonable time. For this purpose, the delay and jitter of messages delivery are minimized. Finally, a localized and centralized strategy is proposed that uses RSUs set at intersections for detecting and controlling the congestion. These strategy clusters all the messages that transferred between the vehicles stopped before the red traffic light using Machine Learning algorithms. In this strategy, a K-means learning algorithm is used for clustering the messages based on their features (e.g. size of messages, validity of messages, and type of messages, and so on). The communication parameters including the transmission range and rate, contention window size, and Arbitration Inter-Frame Spacing (AIFS) are determined for each messages cluter based on the minimized delivery delay. Then, the determined communication parameters are sent to the vehicles by RSUs, and the vehicles operate based on these parameters for transferring the messages. The performances of three proposed strategies were evaluated by simulating the highway and urban scenarios in NS2 and SUMO simulators. Comparisons were also made between the results obtained from the proposed strategies and the common used congestion control strategies. The results reveal that using the proposed congestion control strategies, the throughput, packet loss ratio and delay are significantly improved as compared to the other strategies. Therefore, applications of the proposed strategies help improve the performance, safety, and reliability of VANets

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

The End of Traffic and the Future of Access: A Roadmap to the New Transport Landscape

In most industrialized countries, car travel per person has peaked and the automobile regime is showing considering signs of instability. As cities across the globe venture to find the best ways to allow people to get around amidst technological and other changes, many forces are taking hold — all of which suggest a new transport landscape. Our roadmap describes why this landscape is taking shape and prescribes policies informed by contextual awareness, clear thinking, and flexibility