Hossam Abdelgawad

• PhD, P.Eng.
• University of Toronto

The advent of autonomous vehicles (AVs), connected vehicles (CVs), electric vehicles (EVs), not to mention the intertwined synergies among them – connected autonomous electric vehicles, has merely disrupted the transport sector at multiple fronts. The safety of AVs is essential to their success in the marketplace. The tandem effect of accepting lon...

The significant growth of many urban areas comes at a cost of increasing demand for mobility and traffic congestion in large-scale urban environments. As congestion levels soar to unprecedented levels, and researchers and governments are challenged addressing the basic needs for transportation and mobility; solutions are becoming more complex and u...

Automated vehicles have begun to receive tremendous interest among researchers and decision-makers because of their substantial safety and mobility benefits. Although much research has been reported regarding the implications of Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) technologies for highway capacity, to our kn...

This article introduces a methodology for determining the optimal number and locations of roadside equipment (RSE) units for travel time estimation in vehicle-to-infrastructure and vehicle-to-vehicle communication environments. The developed approach is a novel technique for modeling RSE placement to optimize the number and positions of RSE units w...

This paper presents how big data could be utilized in preparing for smart cities. Within this context, smart cities require intelligent decisions in real time, while processing large amount of data. One big component that relates to smart cities in ITS applications is using artificial intelligent techniques that rely heavily on simulation environme...

Congestion pricing is one of the widely contemplated methods to manage traffic congestion. The purpose of congestion pricing is to manage traffic demand generation and supply allocation by charging fees (i.e., tolling) for the use of certain roads in order to distribute traffic demand more evenly over time and space. This study presents a framework...

The assessment of real-time intelligent transportation system (ITS) applications, such as traffic management and adaptive route guidance systems, requires the use of fast and near real-time dynamic traffic simulation models. Even off-line applications, used for testing planning scenarios, often require fast-enough traffic simulation models that ena...

Connected vehicle is a rapidly emerging paradigm aiming at deploying and developing a fully connected transportation system that enables data exchange among vehicles, infrastructure, and mobile devices to improve mobility, enhance safety, and reduce the adverse environmental impacts of the transportation systems. This study focuses on micro modelin...

Disturbances in roadway networks due to increases in demand or drops in network capacity can severely degrade the performance of the system. The robustness of a roadway network to such disturbances has been investigated using a variety of methods leading to disparate robust network designs. This paper introduces a unifying framework for understandi...

In this paper, we introduce a simulation testbed framework to evaluate the performance of a self-learning adaptive traffic signal control system. The core contribution of this paper is the assessment of the system’s two modes of operations (independent versus coordinated) under different congestion levels and network configurations. The insights an...

Analysis and literature review have indicated that transportation agencies often report significant percentage of missing values of traffic volumes collected at permanent data collection stations (PDCS). These missing values can be as high as 60% in some cases. Although invested significantly in legacy systems that rely primarily on inductive loop...

In the new digital age, the pace and volume of growing transportation related data is exceeding our ability to manage and analyze it. In this position paper, we present a data engine, Godzilla, to ingest real-time traffic data and support analytic and data mining over traffic data. Godzilla is a multi-cluster approach to handle large volumes of gro...

This paper introduces a framework for inferring activity-travel given nearby land-use information that can be obtained from location-based social network (LBSN) such as Foursquare. The first component of the framework implements a generic method to acquire land use data from LBSN services which is a prerequisite for the inference algorithm. Three i...

The emerging automotive, information and communication technologies have made cooperative and autonomous automated vehicles the focus of many current and upcoming research studies. In recent literature, there is evident redundancy, overlap and inconsistency in the use of the terms connected vehicles, automated vehicles and autonomous vehicles. In t...

Dynamic traffic control measures provide a set of cost effective congestion mitigation solutions for the escalating congestion problems of metropolitan areas, among which ramp metering (RM) is an effective approach. While independently controlling on-ramps can effectively prevent freeway breakdown, this may sacrifice the users of the on-ramp for th...

The significant growth of many urban centers comes at a cost of increasing demand for mobility and traffic congestion in major urban environments. As congestion levels soar to unprecedented levels and governments are challenged meeting the basic needs for transportation/mobility using traditional funding and infrastructure mechanisms; solutions are...

This paper introduces a framework for inferring activity travel given nearby land use information that can be obtained from a location-based social network (LBSN) such as Foursquare. The first component of the framework implements a generic method for acquiring land use data from LBSN services, which is a prerequisite for the inference algorithm. T...

This paper presents a micro-simulation modeling framework for evaluating pedestrian–vehicle conflicts in crowded crossing areas. The framework adopts a simulation approach that models vehicles and pedestrians at the microscopic level while satisfying two sets of constraints: (1) flow constraints and (2) non-collision constraints. Pedestrians move a...

Adaptive traffic signal control (ATSC) is a promising technique to alleviate traffic congestion. This article focuses on the development of an adaptive traffic signal control system using Reinforcement Learning (RL) as one of the efficient approaches to solve such stochastic closed loop optimal control problem. A generic RL control engine is develo...

Connected vehicle is a rapidly emerging paradigm aiming at developing and deploying a fully connected transportation system that enables data exchange among vehicles, infrastructure, and mobile devices to improve safety, mobility and reduce the adverse environmental impacts of the transportation systems. This research focuses on micro-modelling and...

Road agencies typically collect travel time information from their network to identify traffic bottlenecks and to quantify the effects of road improvement investments in terms of travel time improvements. Road agencies can benefit from newly emerging automated data collection technologies that acquire travel time information for a large geographica...

Ramp metering (RM) is the most effective dynamic traffic measure in response to growing congestion in urban freeway networks. Among the extensive RM methods available, those based on optimal control theory have shown strong potential in improving freeway performance. However, these algorithms require an accurate traffic model that limits their appl...

This paper presents a data collection framework and its prototype application for personal activity-travel surveys through the use of smartphone sensors. The core components of the framework run on smartphones backed by cloud-based (online) services for data storage, information dissemination, and decision support. The framework employs machine-lea...

http://amonline.trb.org/2vejo7/2vejo7/1

Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., mi...

Public transportation systems play a significant role in emergency evacuation. Therefore, this paper is geared towards harnessing subway and bus transit to alleviate congestion pressure during evacuation of busy urban areas. Routing and scheduling of transit vehicles and subway operation is envisioned as a new variant of the well-established vehicl...

In this paper we introduce a new approach to Freeway Ramp Metering (RM) based on Reinforcement Learning (RL) with focus on real-life experiments in a case study in the City of Toronto. Typical RL methods consider discrete state representation that lead to slow convergence in complex problems. Continuous representation of state space has the potenti...

This paper assesses the impact of exclusive truck facilities on urban freeway performance. A large-scale regional microscopic traffic simulation model is developed for morning and afternoon peak hours and is used to model two alternative truckway configurations in the Greater Toronto Area (GTA), including a truck-only highway and a truck lane conve...

This paper proposes a multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. The multiobjective approach optimizes the multimodal evacuation framework by investigating three objectives: minimizing in-vehicle travel time, minimizing at-origiin waiting time, and minimizing fleet cost in the case o...

This article presents the development of a novel framework that optimizes the evacuation of large cities using multiple modes including vehicular traffic, rapid transit, and mass-transit shuttle buses. A large-scale evacuation model is developed for the evacuation of the City of Toronto in case of emergency. A demand estimation model is first desig...

Planning for emergency evacuation has evolved and matured substantially during the last two decades. The significant rise in natural and man-made disasters in recent years created a surge in the need for improved emergency evacuation planning. Voluminous studies, formulations, and control approaches have been presented in the literature with the co...

Advancements in Intelligent Transportation Systems (ITS), communication and information technologies have the potential to considerably reduce delay and congestion through an array of network-wide traffic control and management strategies. At the University of Toronto's ITS Centre, a comprehensive ITS research and teaching program was initiated in...