Papers by Saif Eddin Jabari
Transportation Research Record: Journal of the Transportation Research Board
The fundamental diagram (FD) describes the functional relationship among macro parameters of traf... more The fundamental diagram (FD) describes the functional relationship among macro parameters of traffic flow (e.g., volume, density, and space mean speed). A well-established FD is crucial for traffic operations and management (e.g., traffic estimation and control). However, there is still lacking an efficient method to select a FD and evaluate the fitting performance with empirical data. In this paper, we propose a novel evaluation approach to the FD using a linear transformation method, which can evaluate the fitting performance in the whole density region. It can also provide directions for further optimization of the FD model by performing the same transformation on the empirical dataset and the selected FD. Furthermore, we propose a quantitative indicator, called the weighted coefficient of determination, which can better evaluate the fitting performance of different FDs. The proposed method is tested with freeway field data from loop detectors. The results show that the proposed ...
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)
The Adaptive Smoothing Method (ASM) is a data-driven approach for traffic state estimation. It in... more The Adaptive Smoothing Method (ASM) is a data-driven approach for traffic state estimation. It interpolates unobserved traffic quantities by smoothing measurements along spatio-temporal directions defined by characteristic traffic wave speeds. The standard ASM consists of a superposition of two a priori estimates weighted by a heuristic weight factor. In this paper, we propose a systematic procedure to calculate the optimal weight factors. We formulate the a priori weights calculation as a constrained matrix completion problem, and efficiently solve it using the Alternating Direction Method of Multipliers (ADMM) algorithm. Our framework allows one to further improve the conventional ASM, which is limited by utilizing only one pair of congested and free flow wave speeds, by considering multiple wave speeds. Our proposed algorithm does not require any field-dependent traffic parameters, thus bypassing frequent field calibrations as required by the conventional ASM. Experiments using NGSIM data show that the proposed ADMM-based estimation incurs lower error than the ASM estimation.
100th Annual Meeting of the Transportation Research Board, Washington D.C., 2021
HAL (Le Centre pour la Communication Scientifique Directe), Sep 16, 2019
ArXiv, 2020
Autonomous Vehicles (AVs) with Deep Reinforcement Learning (DRL)-based controllers are used for r... more Autonomous Vehicles (AVs) with Deep Reinforcement Learning (DRL)-based controllers are used for reducing traffic jams. AVs trained with such deep neural networks render them vulnerable to machine learning-based attacks. In this work, we explore the backdooring of a DRL-based AV controller in a standard traffic scenario. The AV exhibits intended operation of reducing congestion during genuine observations, but when a particular set of observations appears, the AV can be triggered to either decelerate to cause congestion (congestion attack) or to accelerate and crash into the vehicle in front (insurance attack). These backdoors in AVs may be engineered to pose serious threats to human lives.
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2017
Physical Review E
Kinetics of dilute heterogeneous traffic on a two lane road is formulated in the framework of Ben... more Kinetics of dilute heterogeneous traffic on a two lane road is formulated in the framework of Ben-Naim Krapivsky model and stationary state properties are analytically derived in the asymptotic limit. The heterogeneity is introduced into the model as a quenched disorder in desired speeds of vehicles. The two-lane model assumes that each vehicle/platoon in a lane moves ballistically until it approaches a slow moving vehicle/platoon and then joins it. Vehicles in a platoon are assumed to escape the platoon at a constant rate by changing lanes after which they continue to move at their desired speeds. Each lane is assumed to have a different escape rate. As the stationary state is approached, the platoon density in the two lanes become equal, whereas the vehicle densities and fluxes are higher in the lane with lower escape rate. A majority of the vehicles enjoy a free-flow if the harmonic mean of the escape rates of the lanes is comparable to average initial flux on the road. The average platoon size is close to unity in the free-flow regime. If the harmonic mean is lower than the average initial flux, then vehicles with desired speeds lower than a characteristic speed v * still enjoy free-flow while those vehicles with desired speeds that are greater than v * experience congestion and form platoons behind the slower vehicles. The characteristic speed depends on the mean of escape times (R = (R 1 + R ā1)/2) of the two lanes (represented by 1 and-1) as v * ā¼ R ā 1 Āµ+2 , where Āµ is the exponent of the quenched disorder distribution for desired speed in the small speed limit. The average platoon size in a lane, when v * 1, is proportional to R Āµ+1 Āµ+2 plus a lane dependent correction. Equations for the kinetics of platoon size distribution for two-lane traffic are also studied. It is shown that a stationary state with platoons as large as road length can occur only if the mean escape rate is independent of platoon size.
IEEE Access, 2020
This paper investigates the vulnerability of urban traffic networks to cyberattacks on traffic li... more This paper investigates the vulnerability of urban traffic networks to cyberattacks on traffic lights. We model traffic signal tampering as a bi-objective optimization problem that simultaneously seeks to reduce vehicular throughput in the network over time (maximize impact) while introducing minimal changes to network signal timings (minimize noticeability). We represent the Spatio-temporal traffic dynamics as a
static network flow problem on a time-expanded graph. This allows us to reduce the (non-convex) attack problem to a tractable form, which can be solved using traditional techniques used to solve linear network programming problems. We show that minor but objective adjustments in the signal timings over time can severely impact traffic conditions at the network level. We investigate network vulnerability by examining the concavity of the Pareto-optimal frontier obtained by solving the bi-objective attack problem. Numerical experiments are carried to illustrate the types of insights that can be extracted from the Pareto-optimal frontier. For instance, our experiments suggest that the vulnerability of a traffic network to signal tampering is independent of the demand levels.
Physical Review E, 2020
We study heterogeneous traffic dynamics by introducing quenched disorders in all the parameters o... more We study heterogeneous traffic dynamics by introducing quenched disorders in all the parameters of Newell's car-following model. Specifically, we consider randomness in the free-flow speed, the jam density, and the backward wave speed. The quenched disorders are modeled using beta distributions. It is observed that, at low densities, the average platoon size and the average speed of vehicles evolve as power-laws in time as derived by Ben-Naim, Krapivsky, and Redner (BKR). No power-law behavior has been observed in the time evolution of the second moment of density and density distribution function indicating no equivalence between the present system and the sticky gas. As opposed to a totally asymmetric simple exclusion process (TASEP), we found no power-law behavior in the stationary gap distribution and the transition from the platoon forming phase to the laminar phase coincides with the free-flow to congestion transition and is always of first-order, independent of the quenched disorder in the free-flow speed. Using mean-field theory, we derived the gap distribution of vehicles and showed that the phase transition is always of first-order, independent of the quenched disorder in the free-flow speed corroborating the simulation results. We also showed that the transition density is the reciprocal of the average gap of vehicles in the platoon in the thermodynamic limit.
IEEE Access, 2019
This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal... more This paper presents a mesoscopic traffic flow model that explicitly describes the spatio-temporal evolution of the probability distributions of vehicle trajectories. The dynamics are represented by a sequence of factor graphs, which enable learning of traffic dynamics from limited Lagrangian measurements using an efficient message passing technique. The approach ensures that estimated speeds and traffic densities are non-negative with probability one. The estimation technique is tested using vehicle trajectory datasets generated using an independent microscopic traffic simulator and is shown to efficiently reproduce traffic conditions with probe vehicle penetration levels as little as 10%. The proposed algorithm is also compared with state-of-the-art traffic state estimation techniques developed for the same purpose and it is shown that the proposed approach can outperform the state-of-the-art techniques in terms reconstruction accuracy. INDEX TERMS Stochastic traffic dynamics, conditional random fields, Markov random fields, factor graphs, traffic state estimation.
Transportation Research Part B: Methodological, 2019
Decentralized intersection control techniques have received recent attention in the literature as... more Decentralized intersection control techniques have received recent attention in the literature as means to overcome scalability issues associated with network-wide intersection control. Chief among these techniques are backpressure (BP) control algorithms, which were originally developed of for large wireless networks. In addition to being lightweight computationally, they come with guarantees of performance at the network level, specifically in terms of network-wide stability. The dynamics in backpressure control are represented using networks of point queues and this also applies to all of the applications to traffic control. As such, BP in traffic fail to capture the spatial distribution of vehicles along the intersection links and, consequently, spill-back dynamics. This paper derives a position weighted backpressure (PWBP) control policy for network traffic applying continuum modeling principles of traffic dynamics and thus capture the spatial distribution of vehicles along network roads and spill-back dynamics. PWBP inherits the computational advantages of traditional BP. To prove stability of PWBP, (i) a Lyapunov functional that captures the spatial distribution of vehicles is developed; (ii) the capacity region of the network is formally defined in the context of macroscopic network traffic; and (iii) it is proved, when exogenous arrival rates are within the capacity region, that PWBP control is network stabilizing. We conduct comparisons against a real-world adap-tive control implementation for an isolated intersection. Comparisons are also performed against other BP approaches in addition to optimized fixed timing control at the network level. These experiments demonstrate the superiority of PWBP over the other control policies in terms of capacity region, network-wide delay, congestion propagation speed, recov-erability from heavy congestion (outside of the capacity region), and response to incidents.
Transportation Research Part C: Emerging Technologies , 2019
This paper proposes a cooperative lane changing strategy using a transferable utility games frame... more This paper proposes a cooperative lane changing strategy using a transferable utility games framework. This allows vehicles to engage in transactions where gaps in traffic are created in exchange for monetary compensation. The proposed approach is best suited to discretionary lane change maneuvers. We formulate gains in travel time, referred to as time differences, that result from achieving higher speeds. These time differences, coupled with value of time, are used to formulate a utility function, where utility is transferable. We also allow for games between connected vehicles that do not involve transfer of utility. We apply Nash bargaining theory to solve the latter. A cellular automaton is developed and utilized to perform simulation experiments that explore the impact of such transactions on traffic conditions (travel-time savings, resulting speed-density relations and shock wave formation) and the benefit to vehicles. The results show that lane changing with transferable utility between drivers can help achieve win-win results, improve both individual and social benefits without resulting in any adverse effects on traffic characteristics in general and, in fact, result in slight improvement at traffic densities outside of free-flow and (bumper-to-bumper) jammed traffic.
Transportation Research Part B, 2016
First-order network flow models are coupled systems of differential equations which describe the ... more First-order network flow models are coupled systems of differential equations which describe the build-up and dissipation of congestion along network road segments, known as link models. Models describing flows across network junctions, referred to as node models, play the role of the coupling between the link models and are responsible for capturing the propagation of traffic dynamics through the network. Node models are typically stated as optimization problems, so that the coupling between the link dynamics is not known explicitly. This renders network flow models analytically intractable. This paper examines the properties of node models for urban networks. Solutions to node models that are free of traffic holding, referred to as holding-free solutions , are formally defined and it is shown that flow maximization is only a sufficient condition for holding-free solutions. A simple greedy algorithm is shown to produce holding-free solutions while also respecting the invariance principle. Staging movements through nodes in a manner that prevents conflicting flows from proceeding through the nodes simultaneously is shown to simplify the node models considerably and promote unique solutions. The staging also models intersection capacities in a more realistic way by preventing unrealistically large flows when there is ample supply in the downstream and preventing artificial blocking when some of the downstream supplies are restricted.
Transportation Research Part B, 2018
This paper proposes a new stochastic model of traffic dynamics in La-grangian coordinates. The so... more This paper proposes a new stochastic model of traffic dynamics in La-grangian coordinates. The source of uncertainty is heterogeneity in driving behavior , captured using driver-specific speed-spacing relations, i.e., parametric uncertainty. It also results in smooth vehicle trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby, overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. We utilize ensemble filtering techniques for data assimilation (traffic state estimation), but derive the mean and covariance dynamics as the ensemble sizes go to infinity, thereby bypassing the need to sample from the parameter distributions while estimating the traffic states. As a result, the estimation algorithm is just a standard Kalman-Bucy algorithm, which renders the proposed approach amenable to real-time applications using recursive data. Data assimilation examples are performed and our results indicate good agreement with out-of-sample data.
Transportation Research Part B, 2014
Probabilistic models describing macroscopic traffic flow have proven useful both in practice and ... more Probabilistic models describing macroscopic traffic flow have proven useful both in practice and in theory. In theoretical investigations of wide-scatter in flow-density data, the statistical features of flow density relations have played a central role. In real-time estimation and traffic forecasting applications, probabilistic extensions of macroscopic relations are widely used. However, how to obtain such relations, in a manner that results in physically reasonable behavior has not been addressed. This paper presents the derivation of probabilistic macroscopic traffic flow relations from Newell's simplified car-following model. The probabilistic nature of the model allows for investigating the impact of driver heterogeneity on macroscopic relations of traffic flow. The physical features of the model are verified analytically and shown to produce behavior which is consistent with well-established traffic flow principles. An empirical investigation is carried out using trajectory data from the New Generation SIMulation (NGSIM) program and the model's ability to reproduce real-world traffic data is validated.
This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The sou... more This paper proposes a new stochastic model of traffic dynamics in Lagrangian coordinates. The source of uncertainty in the proposed model is parametric. Specifically, we assume that drivers vary in free-flow (desired) speeds, minimum preferred safety distances from their leaders
(when stationary), and reaction times. Consequently, uncertainty in the model can be interpreted as capturing heterogeneity in the driver population. It also results in smooth trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby,
overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. A stochastic version of Newells car-following model is utilized. The mean dynamics of the model are presented as the limiting dynamics of an ensemble averaged process as the ensemble size goes to infinity. Covariance dynamics are also presented using a Gaussian approximation of the stochastic system. Numerical examples are provided to illustrate convergence of ensemble averaged process to the mean dynamics, as well as to illustrate the behavior
of covariance dynamics. A data assimilation example is also given. Results show that the model can be used to estimate aggregated measures of traffic conditions with reasonable accuracy.
This paper presents a probabilistic approach to reconstruct vehicle trajectories from GPS probe d... more This paper presents a probabilistic approach to reconstruct vehicle trajectories from GPS probe data on arterials. By combining car-following concepts with machine learning algorithms, we overcome the drawbacks of pure statistical modeling to investigate the question of adequate probe penetration levels on single-lane roads. Although the parameters of the traffic state estimation model are learned from historical data, the proposed algorithm is found to be robust to unpredictable conditions. The estimation algorithm is tested using a vehicle trajectory dataset generated using microsimulation software. The results highlight the need to take into account the randomness of the spatio-temporal coverage associated with probe data for reliable state estimation algorithms.
We propose an algorithm for real-time optimization of traffic lights in urban road networks. The ... more We propose an algorithm for real-time optimization of traffic lights in urban road networks. The algorithm is based on maximizing controller flow subject to a macroscopic traffic dynamics model (the cell transmission model). We make simplifications to this formulation that preserve its effectiveness at optimizing traffic lights while decreasing the computational cost by an order of magnitude. The resulting algorithm is based on alternately solving a number of continuous knapsack problems (which are computationally cheap) and simulating traffic dynamics (also cheap). Our algorithm is centralized, allowing coordination between traffic lights, however computationally cheap enough for use in real-time on road networks of moderate size. Numerical results are presented which support this claim and demonstrate the effectiveness of our algorithm at maximizing flow and minimizing delay.
We address two shortcomings in present travel time distribution estimation methods, which specifi... more We address two shortcomings in present travel time distribution estimation methods, which specifically apply to the case of congested stop-and-go traffic for which the probability densities tend to be multi-modal. The first shortcoming is related to the determination of the number of modes, which can change from one location in the network to another, as well as by time of day. The second one is the wide-spread use of mixtures of Gaussian probability densities, which can assign positive probabilities to negative travel times and offer too little flexibility because of their symmetric shape. These drawbacks of the existing approaches have been tackled in this paper through the use of a sparse kernel density estimation (KDE) technique using asymmetric Gamma kernels. The sparse modeling techniques have the additional capability to automatically infer the minimum number of modes from the data, thereby avoiding the need to predefine the number of mixture components. The use of asymmetric gamma kernels ensures nonnegative supports while also providing increased flexibility in the shapes of the distributions. Experimental results using high-dimensional simulated and real-world travel time data illustrate the efficacy of the proposed method, and further illustrate that Gamma kernels indeed outperform the classical Gaussian kernels in terms of estimation accuracy with as few elements as possible.
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Papers by Saif Eddin Jabari
static network flow problem on a time-expanded graph. This allows us to reduce the (non-convex) attack problem to a tractable form, which can be solved using traditional techniques used to solve linear network programming problems. We show that minor but objective adjustments in the signal timings over time can severely impact traffic conditions at the network level. We investigate network vulnerability by examining the concavity of the Pareto-optimal frontier obtained by solving the bi-objective attack problem. Numerical experiments are carried to illustrate the types of insights that can be extracted from the Pareto-optimal frontier. For instance, our experiments suggest that the vulnerability of a traffic network to signal tampering is independent of the demand levels.
(when stationary), and reaction times. Consequently, uncertainty in the model can be interpreted as capturing heterogeneity in the driver population. It also results in smooth trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby,
overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. A stochastic version of Newells car-following model is utilized. The mean dynamics of the model are presented as the limiting dynamics of an ensemble averaged process as the ensemble size goes to infinity. Covariance dynamics are also presented using a Gaussian approximation of the stochastic system. Numerical examples are provided to illustrate convergence of ensemble averaged process to the mean dynamics, as well as to illustrate the behavior
of covariance dynamics. A data assimilation example is also given. Results show that the model can be used to estimate aggregated measures of traffic conditions with reasonable accuracy.
static network flow problem on a time-expanded graph. This allows us to reduce the (non-convex) attack problem to a tractable form, which can be solved using traditional techniques used to solve linear network programming problems. We show that minor but objective adjustments in the signal timings over time can severely impact traffic conditions at the network level. We investigate network vulnerability by examining the concavity of the Pareto-optimal frontier obtained by solving the bi-objective attack problem. Numerical experiments are carried to illustrate the types of insights that can be extracted from the Pareto-optimal frontier. For instance, our experiments suggest that the vulnerability of a traffic network to signal tampering is independent of the demand levels.
(when stationary), and reaction times. Consequently, uncertainty in the model can be interpreted as capturing heterogeneity in the driver population. It also results in smooth trajectories in a stochastic context, which is in agreement with real-world traffic dynamics and, thereby,
overcoming issues with aggressive oscillation typically observed in sample paths of stochastic traffic flow models. A stochastic version of Newells car-following model is utilized. The mean dynamics of the model are presented as the limiting dynamics of an ensemble averaged process as the ensemble size goes to infinity. Covariance dynamics are also presented using a Gaussian approximation of the stochastic system. Numerical examples are provided to illustrate convergence of ensemble averaged process to the mean dynamics, as well as to illustrate the behavior
of covariance dynamics. A data assimilation example is also given. Results show that the model can be used to estimate aggregated measures of traffic conditions with reasonable accuracy.