Neda Masoud

• Doctor of Philosophy
• Professor (Associate) at University of Michigan

Autonomous vehicles are expected to shift not only the driving paradigms but also the notion of vehicle ownership. Although autonomous vehicles are believed to introduce many safety, mobility, and environmental benefits, they will be initially priced relatively highly. This paper assesses the potential for circumventing this barrier by promoting a...

In the future, autonomous vehicles are expected to safely move people and cargo around. However, as of now, automated entities do not necessarily outperform human drivers under all circumstances, particularly under certain road and environmental factors such as bright light, heavy rain, poor quality of road and traffic signs, etc. Therefore, in cer...

To date, three major hurdles have hindered widespread adoption of electric vehicles (EVs): the high cost of batteries, insufficient public charging infrastructure, and the limited driving range of EVs. This study overcomes these three hurdles by introducing a new concept of vehicle-to-vehicle wireless power transfer (V2V WPT) between EVs to facilit...

In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinea...

Automated vehicles are envisioned to be an integral part of the next generation of transportation systems. Whether it is striving for full autonomy or incorporating more advanced driver assistance systems, high-accuracy vehicle localization is essential for automated vehicles to navigate the transportation network safely. In this paper, we propose...

The growing diversity and intensity of curb space demand, driven by technological advancements and new mobility systems, require strategic infrastructure development planning. However, uncertainty in future curb space demand and resource constraints pose significant challenges to resilient planning. This paper introduces a stochastic optimization m...

Reliable collision avoidance under extreme situations remains a critical challenge for autonomous vehicles. While large language models (LLMs) offer promising reasoning capabilities, their application in safety-critical evasive maneuvers is limited by latency and robustness issues. Even so, LLMs stand out for their ability to weigh emotional, legal...

Human-machine shared control in critical collision scenarios aims to aid drivers' accident avoidance through intervening only when necessary. Existing methods count on replanning collision-free trajectories and imposing human-machine tracking, which usually interrupts the driver's intent and increases the risk of conflict. Additionally, the lack of...

Performing drift maneuvers during high-speed cornering poses significant challenges for vehicle control, yet it offers the potential to minimize turning time and enhance driving dynamics. While reinforcement learning (RL) has achieved promising results in simulation, the gap between simulators and real-world conditions has limited its practical dep...

Executing drift maneuvers during high-speed cornering presents significant challenges for autonomous vehicles, yet offers the potential to minimize turning time and enhance driving dynamics. While reinforcement learning (RL) has shown promising results in simulated environments, discrepancies between simulations and real-world conditions have limit...

This paper introduces a Simulation of Urban MObility (SUMO) microscopic simulation model to examine curb space interactions and policy impacts. The model investigates user behaviors under different operational scenarios and user characteristics. Our study focuses on heterogeneous curb space zones derived from empirical observations in Ann Arbor, Mi...

The evolving transportation system has intensified the demand for diverse uses of curb space in urban areas, emphasizing the critical need for effective curb space management. In this paper, we discuss a spatiotemporal pricing strategy for curb infrastructure designed to enhance the utility for both curb space operators and users. We introduce a St...

The importance of curbside management is quickly growing in a modernized urban setting. Dynamic allocation of curb space to different usages and dynamic pricing for those usages can help meet the growing demand for curb space more effectively and promote user turnover. To model curbside operations, we formulate a Stackelberg leader-follower game be...

Autonomous vehicles are an essential component of the intelligent transportation system, and their safe operation depends on reliable data from their sensors. However, these vehicles are vulnerable to cyberattacks and sensor failures that can generate anomalous data and potentially result in fatal crashes. Therefore, there is a critical need for a...

The importance of curbside management is quickly growing in a modernized urban setting. Dynamic allocation of curb space to different usages and dynamic pricing for those usages can collectively attract more curb space users and promote user turnover while eliminating the need for high capital investments for reconstruction. To model curbside opera...

Vehicle behavior prediction in complex urban scenarios with traffic signals and interactive agents is an important yet complicated task for autonomous vehicles (AVs). In this work, a hierarchical vehicle behavior prediction framework is proposed to incorporate the traffic signal information and model the interaction between vehicles. The framework...

In this work we put forward a predictive trajectory planning framework to help autonomous vehicles plan future trajectories. We develop a partially observable Markov decision process (POMDP) to model this sequential decision making problem, and a deep reinforcement learning solution methodology to learn high-quality policies. The POMDP model utiliz...

To assure the successful operation of connected and automated vehicles, it is critical to detect and isolate anomalous and/or faulty information in a timely manner. To do so, anomaly detection techniques should be implemented in real-time where if the probability of anomalous information exceeds a certain threshold, the information is dealt with ac...

The past few years have been witness to an increase in autonomous vehicle (AV) development and testing. However, even with a fully developed and comprehensively tested AV technology, AVs are anticipated to share the roadway network with human drivers for the unforeseeable future. In such a mixed environment, we use naturalistic driving data from th...

In this paper we propose an unsupervised learning framework to predict risky driving at intersections in a connected vehicle environment. The proposed framework uses time series k-means to categorize multi-dimensional time series trajectories into several context-aware driving patterns. Dynamic time warping (DTW) is implemented within the time seri...

In order to accomplish diverse tasks successfully in a dynamic (i.e., changing over time) construction environment, robots should be able to prioritize assigned tasks to optimize their performance in a given state. Recently, a deep reinforcement learning (DRL) approach has shown potential for addressing such adaptive task allocation. It remains una...

In this study, we develop a comprehensive framework to model the impact of cyberattacks on safety, security, and head-to-tail stability of connected and automated vehicular platoons. First, we propose a general platoon dynamics model with heterogeneous time delays that may originate from the communication channel and/or vehicle onboard sensors. Bas...

Traffic congestion during peak periods has become a serious issue around the globe, mainly due to the high number of single-occupancy commuter trips. Peer-to-peer (P2P) ridesharing platforms can present a suitable alternative for serving commuter trips. However, they face a major obstacle that prevents them from being a viable mode of transportatio...

In this paper we propose a deep learning model, which we call step attention, for pedestrian trajectory prediction. The proposed model has a special architecture which consists of recurrent neural networks, convolutional neural networks, and an augmented attention mechanism. Rather than developing architectures to model factors that may affect the...

Traffic congestion has become a serious issue around the globe, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation, including the lack of a guarantee fo...

Autonomy and connectivity are expected to enhance safety and improve fuel efficiency in transportation systems. While connected vehicle-enabled technologies, such as coordinated cruise control, can improve vehicle motion planning by incorporating information beyond the line of sight of vehicles, their benefits are limited by the current shortsighte...

Autonomous vehicles are an essential component of the intelligent transportation system, and their safe operation depends on reliable data from their sensors. However, these vehicles are vulnerable to cyberattacks and sensor failures that can generate anomalous data and potentially result in fatal crashes. Therefore, there is a critical need for a...

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the cloud will be substituted by the crowd where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This parad...

A cooperative truck platoon is a set of virtually linked trucks driving with a small intra-vehicle headway enabled by connected and automated vehicle technologies. One of the primary benefits of truck platooning is energy savings due to the reduction of aerodynamic drag on the platooned vehicles. The focus of this paper is on scheduling travel itin...

Autonomy and connectivity are considered among the most promising technologies to improve safety and mobility and reduce fuel consumption and travel delay in transportation systems. In this paper, we devise an optimal control-based trajectory planning model that can provide safe and efficient trajectories for the subject vehicle while incorporating...

We extend the adversarial/non-stochastic multi-play multi-armed bandit (MPMAB) to the case where the number of arms to play is variable. The work is motivated by the fact that the resources allocated to scan different critical locations in an interconnected transportation system change dynamically over time and depending on the environment. By mode...

With ridesourcing services gaining popularity in the past few years, there has been growing interest in algorithms that could enable real-time operation of these systems. As ridesourcing systems rely on independent entities to build the supply and demand sides of the market, they have been shown to operate more successfully in metropolitan areas wh...

In this paper we present a learning-based trajectory prediction method for different road users, including vehicles, pedestrians, and cyclists. The model uses history position information of traffic agents, and predicts future positions of subjects within a finite horizon. Instead of developing different model architectures for different agent type...

The problem of dispatching shuttles to serve trip requests can be mathematically formulated as a dial-a-ride problem (DARP). With on-demand mobility services gaining more popularity due to the recent developments in gig economy, communication technologies, and urbanization, the real-time application of DARP is attracting ever more interest. However...

Traffic congestion during peak periods has become a serious issue around the globe, mainly due to the high number of single-occupancy commuter trips. Peer-to-peer (P2P) ridesharing platforms can present a suitable alternative for serving commuter trips. However, they face a major obstacle that prevents them from being a viable mode of transportatio...

Efficient collaboration among various stakeholders is important for the successful completion of a construction project. However, stakeholders in construction are fragmented, which in turn hinders accountable information sharing. To address this issue, the authors aim to develop and test an integrated digital twin and blockchain framework for trace...

The problem of dispatching shuttles to serve trip requests can be mathematically formulated as a dial-a-ride problem (DARP). With on-demand mobility services gaining more popularity due to the recent developments in the gig economy, communication technologies, and urbanization, the real-time application of DARP is attracting ever more interest. How...

To assure the successful operation of connected and automated vehicles, it is critical to detect and isolate anomalous and/or faulty information in a timely manner. To do so, anomaly detection techniques should be implemented in real-time where if the probability of anomalous information exceeds a certain threshold, the information is dealt with ac...

This paper investigates intersection control in a fully connected and automated vehicles (CAV) environment, which aims to coordinate in real-time conflicting traffic movements under safety and kinodynamic constraints to maximize intersection throughput or minimize traffic delay. Given the problem’s NP-hardness, we leverage graph coloring to devise...

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This p...

Autonomous vehicles (AVs) are expected to be an integral part of the next generation of transportation systems, where they will share the transportation network with human-driven vehicles during the transition period. In this work, we model the interactions between vehicles (two AVs or an AV and a human-driven vehicle) in a lane changing process by...

Peer-to-peer (P2P) ridesharing is a form of shared-use mobility that has emerged in recent decades as a result of enabling of the sharing economy, and the advancement of new technologies that allow for easy and fast communication between individuals. A P2P ridesharing system provides a platform to match a group of drivers, who use their personal ve...

We extend the adversarial/non-stochastic multi-play multi-armed bandit (MPMAB) to the case where the number of arms to play is variable. The work is motivated by the fact that the resources allocated to scan different critical locations in an interconnected transportation system change dynamically over time and depending on the environment. By mode...

As a consequence of the sharing economy attaining more popularity, there has been a shift toward shared-use mobility services in recent years, especially those that encourage users to share their personal vehicles with others. To date, different variants of these services have been proposed that call for different settings and give rise to differen...

In this work we propose a vehicle-to-everything (V2X) simulator, called V2XSim, for connected vehicle (CV) environment simulation. As this field is rather new, many researchers focusing on CV-based research do not have access to real-world test-beds to validate their methodologies. As such, there is a timely need for simulation platforms that can i...

In this paper we investigate a new form of automated public transportation, named 'modular transit', configured to overcome the shortcomings of the traditional bus, including the first-and last-mile problem, low occupancy, and low levels of comfort, accessibility, and flexibility. The modular transit system consists of a set of trailer modules who...

Traffic congestion, especially during peak hours, which is mainly due to a great number of solo-driver commuting trips, has become a serious issue around the globe. Unfortunately, current public transit agencies have not been able to successfully shift commuters from solo driving to transit because of limited geographical coverage, operational wind...

In this paper, we study the Minimum Weight Pairwise Distance Preservers (MWPDP) problem. Consider a positively weighted undirected/directed connected graph $G = (V, E, c)$ and a subset $P$ of pairs of vertices, also called demand pairs. A subgraph $G'$ is a distance preserver with respect to $P$ if and only if every pair $(u, w) \in P$ satisfies $d...

Peer-to-peer (P2P) ridesharing is a form of shared-use mobility that has emerged in recent decades as a result of enabling of the sharing economy, and the advancement of new technologies that allow for easy and fast communication between individuals. A P2P ridesharing system provides a platform to match a group of drivers, who use their personal ve...

In the future, autonomous vehicles are expected to safely move people and cargo around. However, as of now, automated entities do not necessarily outperform human drivers under all circumstances, particularly under certain road and environmental factors such as bright light, heavy rain, poor quality of road and traffic signs, etc. Therefore, in cer...

A dynamic ridesharing system is a platform that connects drivers who use their personal vehicles to travel with riders who are in need of transportation, on a short notice. Since each driver/rider may have several potential matches, to achieve a high performance level, the ridesharing operator needs to make the matching decisions based on a global...

In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the sub...

The state of the art of modelling, control, and optimisation is discussed for automated road vehicles that may utilise wireless vehicle-to-everything (V2X) connectivity. The appropriate tools to address safety and energy efficiency are described and the effects on traffic dynamics are highlighted. Finally, the economical and societal impacts of the...

In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinea...

Autonomy and connectivity are considered among the most promising technologies to improve safety, mobility, fuel and time consumption in transportation systems. Some of the fuel efficiency benefits of connected and automated vehicles (CAVs) can be realized through platooning. A platoon is a virtual train of CAVs that travel together following the p...

In the U.S., in 2015 alone, there were approximately 35,000 fatalities and 2.4 million injuries caused by an estimated 6.3 million traffic accidents. In the future, it is speculated that automated systems will help to avoid or decrease the number and severity of accidents. However, before such a time, a broad range of vehicles, from non-autonomous...

This paper aims to synchronize timetables in a transit network so as to minimize the total passenger transfer waiting time. Assuming a fixed headway for each line, we first formulate the problem as an optimization problem with congruence constraints. We show that the problem is NP-hard, and investigate several special cases of the problem that are...

A peer to peer ridesharing system connects drivers who are using their personal vehicles to conduct their daily activities with passengers who are looking for rides. A well-designed and properly implemented ridesharing system can bring about social benefits, such as alleviating congestion and its adverse environmental impacts, as well as personal b...

CAV sensor anomaly detection dataset Please use the DOI number for citations: 10.5281/zenodo.3373615 or cite: Van Wyk, Franco, et al. "Real-time sensor anomaly detection and identification in automated vehicles." IEEE Transactions on Intelligent Transportation Systems 21.3 (2019): 1264-1276. Download from: https://github.com/next-generation-m...

Connected and automated vehicles (CAVs) are expected to revolutionize the transportation industry, mainly through allowing for a real-time and seamless exchange of information between vehicles and roadside infrastructure. Although connectivity and automation are projected to bring about a vast number of benefits, they can give rise to new challenge...

A cooperative truck platoon is a set of virtually linked trucks driving with a small intra-vehicle gap enabled by connected and automated vehicle technologies. One of the primary benefits of truck platooning is energy savings due to the reduction of aerodynamic drag on the platooned vehicles. The focus of this paper is on scheduling travel itinerar...

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims to provide a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multiple...

Connected and automated vehicles (CAVs) are expected to revolutionize the transportation industry, mainly through allowing for a real-time and seamless exchange of information between vehicles and roadside infrastructure. Although connectivity and automation are projected to bring about a vast number of benefits, they can give rise to new challenge...

Peer-to-peer (P2P) ridesharing is a relatively new concept that aims at providing a sustainable method for transportation in urban areas. Previous studies have demonstrated that a system that incorporates both P2P ridesharing and transit would enhance mobility. We develop schemes to provide travel alternatives, routes and information across multipl...

Real-time peer-to-peer ridesharing is a promising mode of transportation that has gained popularity during the recent years thanks to the widespread use of smart phones, mobile application development platforms, and online payment systems. An assignment of drivers to riders, known as the ride-matching problem, is a central component of a peer-to-pe...

One of the main obstacles that has challenged peer-to-peer (P2P) ridesharing systems in operating as stand-alone systems is reaching a critical mass of participants. Toward this goal, we propose what we call the P2P ride exchange mechanism to increase matching rate and customer retention in a ridesharing system. This mechanism gives riders the oppo...

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel by transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem,...

In this paper, we mathematically model the multi-hop Peer-to-Peer (P2P) ride-matching problem as a binary program. We formulate this problem as a many-to-many problem in which a rider can travel by transferring between multiple drivers, and a driver can carry multiple riders. We propose a pre-processing procedure to reduce the size of the problem,...

We design an economic benchmark for P2P ridesharing that takes advantage of time savings from HOV lanes. The ridesharing system is presented as an alternative mode for daily commuting, that is, we ensure a ride-back for the matched riders. The modelling is based on the Vickrey-Clarke-Groves (VCG) mechanism that is known to be efficient, incentive c...

Peer-to-peer ridesharing is a recently emerging travel alternative that can help accommodate the growth in urban travel demand and at the same time alleviate problems such as excessive vehicular emissions. Prior ridesharing projects suggest that the demand for ridesharing is usually shifted from transit, while its true benefits are realized when th...

This paper presents a matching and pricing mechanism for a peer-topeer ridesharing system that ensures a ride-back for matched riders. This service is thus presented as an alternative to driving alone for daily commuting. The matching algorithm is formulated as a minimum-cost, maximum-flow problem that is exact and quickly solvable on polynomial ti...

One of the main obstacles that has challenged peer-to-peer (P2P) ridesharing systems in operating as standalone systems is reaching a critical mass of participants. Toward this goal, the authors propose the P2P ride exchange mechanism to increase customer retention. This mechanism gives riders the opportunity to purchase other riders’ itineraries,...

Over the past decade there has been a surge of shared-use mobility concepts that are redefining how people move in urban areas. In this context, a new shared-use mobility concept, Car2work, that fills the gap between the existing approaches by integration of those approaches with the transit network is proposed. Car2work differs from the traditiona...

Several methods have been proposed to disaggregate Freight Analysis Framework (FAF) commodity flows to zonal structures of greater geographical detail. This disaggregation is usually performed on the basis of explanatory variables related to the supply and demand of goods. This paper studied a complementary procedure to determine the mode splits of...

For many manufacturers, the cost of replacing returned products may more than offset the cost of producing parts with a higher quality. This is especially true if good parts from returned products are used to remanufacture aftermarket products. Furthermore, such policy allows for satisfying a customer population with a variable expectation for acce...