Emerging inter-datacenter applications require massive loads of data transfer which makes them sensitive to packet drops, high latency, and fair resource sharing. However, current congestion control (CC) protocols do not guarantee the optimal outcome of these metrics. In this paper, we introduce a new CC technique, Machine Learning Aided Congestion Control (MLACC), that combines heuristics and machine learning (ML) to improve these three network metrics. The proposed technique achieves a high level of fairness, minimum latency, and minimum drop rate. ML is utilized to estimate the ratio of the available bandwidth of the bottleneck link while the heuristic uses this ratio to enable end-points to cooperatively limit the shared bottleneck link utilization under a predefined threshold in order to minimize latency and drop rate. The key to achieving the desired fairness is using the gradient of the link utilization to control the sending rate. We compared MLACC to BBR (which is at least on par with the state-of-the-art ML-based techniques) as a base case in different network settings. The results show that MLACC can achieve lower and more stable end-to-end latency (25% to 52% latency saving). It also significantly reduces packet drop rates while attaining a higher fairness level. The only cost for these advantages is a small throughput reduction of less than 3.5%.

Intelligent Transportation Systems (ITS) rely on connected vehicles to overcome problems such as occlusions and potential accidents, due to non-line-of-sight (NLoS) and other perception challenges. These challenges are magnified when explored in conjunction with communication network limitations, such as limited coverage (e.g., base station limitations) or simple packet collisions. Regardless of the reason behind information loss, the successfully received information should be prioritized to allow successful cooperative perception and accident avoidance. We address these issues by proposing a clustering algorithm that considers information relevance to the receivers and requires no extra communication overhead or network infrastructure. Four different information scoring functions are explored to reorganize data based on its perception relevance in the different clusters, with collision awareness being the focal metric for cluster formation. Our proposed technique achieves the best reduction in the number of packets used compared to existing state-of-the-art: ETSI CPM rules, Look Ahead, and Redundancy Mitigation algorithms. Moreover, thanks to its packet prioritization and reordering, the proposed algorithm outperforms these approaches in terms of the number of packets successfully received by more than 25%. Additionally, it achieves 13.1% and 19.8% enhancement in newly perceived objects, compared to the CPM rules, for the urban and highway scenarios, respectively. Lastly, due to a 6X and 5X improvement in information quality, based on the developed information scoring functions, compared to the baselines for the urban and highway scenarios, respectively.

Road traffic control systems can change vehicles’ speeds, density, and distribution in spatial and temporal dimensions, which may have significant impacts on the performance of pre-established cellular networks and Vehicular Ad-hoc Networks (VANETs). Identifying these impacts is crucial for satisfying service requirements, especially for the future of connected autonomous vehicles. Despite the extensive research that studied the impact of mobility on communication, the impact of traffic control on communication has not been addressed. Therefore, in this paper, we attempt to understand how traffic control strategies can affect communication network performance. We focus on vehicle navigation techniques because of their global network impacts that can significantly affect the load and handover rate on base stations. In this paper, we compare the Dynamic Shortest Path Routing (DSPR) to the state-of-the-art vehicle routing techniques, namely, the K-Shortest Path Routing (K-SPR) and Travel Time System Optimum Navigation (TTSON). We build a real network with calibrated traffic and use a microscopic traffic simulator as a testbed to measure the load and handover rates on base stations. Moreover, we developed and validated an analytical model to compute the packet drop probability based on the base station normalized load in the Fifth Generation New Radio (5G-NR) cellular networks. The developed model is integrated into the testbed to evaluate the reliability of the three traffic control systems. The analysis shows that road traffic load-balancing achieved by both TTSON and K-SPR improves communication performance in the simulated network.

Cooperative driving is a promising technology in the future Connected Autonomous Vehicles (CAV) because of its benefits to safety and fuel efficiency. However, since CAV will be relying heavily on wireless communication to cooperatively coordinate road maneuvering, latency and reliability of communication still pose a challenge. In this paper, we propose a novel scheme based on deep learning prediction to enhance the uplink resource allocation process in 5G C-V2X. The proposed scheme enables the base station to predict vehicle maneuvers, subsequently, assign it the required resource in advance without the need for scheduling request and granting process. This scheme improved the ability of 5G NR to support cooperative driving requirements. Moreover, we compare both traditional and proposed schemes discussing issues that arise from the introduction of prediction models and possible approaches for further enhancements in the future.

Connected Autonomous Vehicles (CAV) utilize vehicular communication to collect information about the surrounding environment to make informed decisions about speed and maneuvering. This enables safe driving and decreases the number of accidents and thereby the associated fatalities. However, vehicular communication may suffer from high latency and low reliability, especially in dense vehicle environments, which may negatively affect the safety of CAVs. Therefore, it is crucial to study the impact of these metrics on the safety application performance while taking into account realistic CAV kinematics and dynamics. In this paper, we address this problem by comparing the performance of the Short Range Communication (DSRC) to that of the Fifth-Generation New Radio (5G-NR) and their impacts on the safety applications in the CAV environment under different settings. We develop a full-fledged simulation framework that can realistically model both vehicular mobility and communication and can capture the impact of communication on safety applications. Within this framework, we implement an important CAV's safety application, namely, the forward collision avoidance system, in which following vehicles use vehicular communications to gather information from leading vehicles to compute the safe speed and avoid collisions. We then use this framework to study and compare the performance safety of the forward collision avoidance system using both DSRC and 5G-NR communications. The results show that the packet delays and drops in communication networks can adversely affect CAV safety. The results also demonstrate that 5G is more capable of supporting the safety requirements under higher packet traffic loads and vehicle densities.

Driver distraction identification is crucial to improve road safety. Through vehicular communications, vehicles can exchange driver behavior information, based on which distracted drivers can be identified, and all drivers can be notified, which can mitigate the impact of driver distraction on road safety. To build such systems, it is essential to understand the different types of distraction, and how they affect driver behavior and their relationship to crashes or near-crashes. This understating should be based on real datasets, which are very limited. Therefore, in this paper, we cover this gap by building a data-driven simulation model to quantify the impact of realistic driver distraction on traffic safety. In particular, we use the 2nd Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) dataset to develop a simulation framework for driver distraction. First, we pre-process and analyze the dataset for different types of distractions. The analysis shows that the data can not be fitted to any of the known distributions. Therefore, we use the Gaussian Mixture Model (GMM) to represent the distraction intervals for the different distraction types. We then use these GMM models and the statistics collected from the data to realistically simulate the driver distraction using the Simulation for Urban MObility (SUMO) software. Finally, we use this framework to simulate the driver distraction in a real network. The data analysis and simulation results revealed important and interesting conclusions, such as decreasing the crash ratio when roads become congested.

Unmanned Aerial Vehicles (UAV) are expected to play a crucial role in the future of 5G and beyond. However, designing efficient routing protocols for UAV is challenging due to the mobility and energy constraints. This problem becomes harder in UAV-assisted 5G networks because of its impact on the time slot assignment for the uplink and downlink in Time Division Duplex (TDD) frame structure. Thus, in this paper, we propose a new optimum routing technique for UAV-assisted TTD 5G networks, the Optimized Load-Balancing Routing (OLBR). The optimum routing problem is formulated in such a way that the decision variables are used to compute the time slot assignment in the 5G connection between UAV nodes. The objective of the optimization model is to minimize the network-wide delay. By distributing traffic across different alternative routes, OLBR minimizes network congestion, resulting in shorter queuing delays. Such a load-balancing also decreases the possibility of node failure due to energy depletion. The proposed OLBR is compared to the shortest path routing using Monte Carlo simulation on two different network topologies at different network traffic loads. The simulation results show that the OLBR produces significant savings in network-wide packet delay compared to the shortest path.

Recently, there had been several proposals to use deep-learning based prediction models in estimating channel state information (CSI). However, all of these proposals were investigated under a fixed indoor-outdoor environment. In this paper, we propose two models to perform uplink CSI prediction in dynamic vehicular environments. One of these models is a tailoring of an existing state-of-the-art deep learning model, based on a combination of convolutional and recurrent neural networks (CNN-RNN), so as to suit the mobility factor in vehicular environments. The other model is a proposed simpler artificial neural network (ANN) model, again tailored to cope with the vehicular settings. We perform a comparative sensitivity analysis of the two models, in which we investigate the effect of changing vehicle speed, prediction horizon, and history horizons on the performance of both models. Interestingly, we have show that the simpler ANN model performs much better than the more sophisticated CNN-RNN model at broad range of vehicular driving speeds as long as the prediction horizon is smaller than the history horizon in the prediction process. The CNN-RNN becomes naturally more efficient in the opposite scenarios.

Large sport and entertainment events such as soccer games or concerts attract an immense number of fans, most of whom use personal vehicles to get to the event. Such a large number of cars presents a “vehicular crowd” that needs to leave in an organized, timely, and safe manner after the event. Combining vehicular crowds with a constrained road networks raises the need for efficient techniques for vehicular crowd management which is a fundamental building block in smart cities. We introduce a novel Vehicle Departure Control (VDC) and navigation system to clear the network in a shorter time and reduce network congestion and system-wide travel time. The proposed system collects network information from a variety of sensory devices: connected vehicles, smartphones, and traffic cameras. Then, it fuses this data to compute the current state conditions of each road link. Based on these parameters, the VDC module determines the allowable vehicle departure rates, and the navigation module computes the system-optimum routes for drivers to take. The proposed system is implemented in a microscopic simulator. The FIFA World Cup 2022 is used as a case study. We compare the proposed system to the Sup-population Dynamic Time-dependent Incremental Traffic Assignment (SFDTIA) which is a typical real-time navigation system that is currently in use by commercial systems. The results show that our optimum navigation and departure control reduced the network clearance time on average by 16%, and by 37% in certain extreme conditions.

Traffic forecasting is imperative to Intelligent Transportation Systems (ITS), and it has always been considered as a challenging research topic, due to the complex topological structure of the urban road network and the temporal stochastic nature of dynamic change. Popular sports events attract vast numbers of spectators travelling to the event, which will have a substantial effect on ITS, showing peaks on the network that can collapse a smart city's ITS. In this paper, we tackle traffic forecasting and use the Doha network in Qatar and the FIFA World Cup 2022 (FWC 2022) event as a case study. We propose a novel technique for embedding road network graphs into a Temporal-Graph Convolutional Network. The embedding process includes a modification to the graph weights based on graph theory and the properties of the line graph. Extensive simulations are carried out on a real-world calibrated dataset from Doha's road network. Our Temporal Line Graph Convolutional Network (TLGCN) proposal shows outstanding performance when compared to state-of-the-art techniques, not only for huge special events but also for the regular daily traffic.

Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter producing acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with high-fidelity simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA.

Non-recurrent events (e.g. football events or evacuation in case of natural disasters) pose great challenges to vehicle routing and traffic management in Intelligent Transportation Systems (ITSs). The high traffic demand during such events, combined with the road network resource constraints, bring forward the need for efficient and smart management techniques that better utilize the network facilities while maintaining a certain performance level. Vehicular Ad-hoc Networks (VANETs) and the advancement in information technology bring new opportunities to address such problems. This paper utilizes VANETs to build a vehicular crowd management system that utilizes system-optimum stochastic routing. The objective is to clear the network in a shorter time by better utilizing the available network resources. To build this system, vehicles are used as sensors that communicate the network state information to the Traffic Management Center (TMC) in real time. A linear programming model is developed to minimize the network-wide travel time constrained by the road link capacities based on the collected information. The results show that the proposed system decreases the network-wide travel time and is successful in clearing the network earlier by up to 38% compared to deterministic user-equilibrium traffic assignment.

Global warming, environmental pollution, and fuel shortage are currently major worldwide challenges. Ecorouting is one of several tools that attempt to address this challenge by minimizing network-wide vehicle fuel consumption and emission levels. Eco-routing systems select the most environmentally friendly route. The subpopulation feedback eco-routing (SPF-ECO) algorithm that is implemented in the INTEGRATION software can produce a reduction in fuel consumption levels by approximately 17%. However, in some cases, due to delayed updates or the lack for updates, its performance degrades. In this paper, we propose the ant colony based eco-routing technique (ACO-ECO), which is a novel feedback eco-routing and cost updating algorithm to overcome these shortcomings. In the ACO-ECO algorithm, real-time performance measures on various roadway links are shared. Vehicles build their minimum path routes using the latest real-time information to minimize their fuel consumption and emission levels. ACO-ECO is also able to capture randomness in route selection, pheromone updating, and pheromone evaporation. The results show that the ACO-ECO algorithm and SPF-ECO have similar performances in normal cases. However, in the case of link blocking, the ACO-ECO algorithm reduces the network-wide fuel consumption and CO2 emission levels in the range of 2.3% to 6.0%. It also reduces the average trip time by approximately 3.6% to 14.0%.

In this paper, we present a new carpooling recommendation system whose main objective is to find the best carpool matchings and recommend individuals to join their friends during trips or travels. The proposed recommendation system utilizes user's mobility history and user social network information to find carpool matchings. The proposed system employs a probabilistic model based on continuous time Markov chain to model user's mobility and to predict user future movements. Moreover, it uses two similarity measures (interest based similarity and friendship based similarity) to find the similarities between users. The interest based similarity uses a weighted bipartite graph between users and places, where the edges are weighted by the term frequency-inverse document frequency. The friendship based similarity uses the common friends as a similarity measure. The proposed system is evaluated using the number of carpool matchings that can be found, this number gives an indication of the reduction that can be made in vehicular traffic congestion, pollutant emissions and energy consumption.

The paper introduces a Vehicular Network Integrated Simulator (VNetIntSim) that integrates transportation modelling with Vehicular Ad Hoc Network (VANET) modelling. Specifically, VNetIntSim integrates the OPNET software, a communication network simulator, and the INTEGRATION software, a microscopic traffic simulation software. The INTEGRATION software simulates the movement of travellers and vehicles, while the OPNET software models the data exchange through the communication system. Information is exchanged between the two simulators as needed. As a proof of concept, the VNetIntSim is used to quantify the impact of mobility parameters (traffic stream speed and density) on the communication system performance, and more specifically on the data routing (packet drops and route discovery time).

Geo-social information can be utilized for user benefits in many applications. Social interaction in vehicular ad hoc networks (VANETs) is an important source for this type of information. In this paper, we first propose and describe a general architecture of the social VANET system (S-VANET) that supports social interaction through vehicular networks. Then, we present a new carpooling recommendation system that works as S-VANET application. The main objective is to recommend individuals to join their friends during trips or travels. The proposed recommendation system uses check-in history and home location to model users, and utilizes Fast Fourier transform to represent user check-ins and find the similarity between users. The system uses hierarchical clustering with weighted center of mass method to estimate the user home location.