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- Multiobjective Optimization, Transportation Planning, Transportation Economics, Spatial cognition, Urban design and planning, Sustainable Transportation, and 7 moreDriving Behaviour Modelling and Analysis, Planning, Transportation network modeling, Networks, Intelligent Transportation Systems, Travel Demand Modeling, and Transport Network Optimizationedit
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In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM... more
In this paper, a spatial information-theoretic model is proposed to locate sensors for detecting source-to-target patterns of special nuclear material (SNM) smuggling. In order to ship the nuclear materials from a source location with SNM productions to a target city, the smugglers must employ global and domestic logistics systems. This paper focuses on locating a limited set of fixed and mobile radiation sensors in a transportation network, with the intent to maximize the expected information gain and minimize the estimation error for the subsequent nuclear material detection stage. A Kalman filtering-based framework is adapted to assist the decision-maker in quantifying the network-wide information gain and SNM flow estimation accuracy.
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A computational approach for incorporating the effects of short left turn pockets on sustained service rates in a mesoscopic modeling environment is presented. Mesoscopic models, intended to handle fairly large networks while maintaining... more
A computational approach for incorporating the effects of short left turn pockets on sustained service rates in a mesoscopic modeling environment is presented. Mesoscopic models, intended to handle fairly large networks while maintaining individual vehicle identity, provide a detailed yet efficient alternative to estimate sustained service rates. However, mesoscopic models typically ignore mid-link perturbations and queuing, thus limiting their reliability in the presence of a short left turn pockets at signalized intersections. The model presented herein relies upon a gating mechanism situated at the entry point to the left turn pocket. Through a series of logical triggers, the gating mechanism allows for the formation of a (vertical) queue of vehicles upstream of the pocket when arrivals exceed storage capacity. The method satisfies all assumed requirements for integrating the effects of short left turn bays, which include pocket spillback, pocket starvation and sensitivity to signal timing and phase sequence. The approach has been implemented within the mesoscopic modeling platform DYNASMART for both single and multiple left turn pockets, using varying pocket lengths and demand volumes. The resulting sustained service rates favorably compared to those generated by a representative micro-simulation model (VISSIM). Further comparisons of the proposed approach against empirical observations are planned.
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Under the existing loosely-distributed sensoring environment with heterogeneous data sources, transportation planning and management agencies have found a critical need for efficiently storing, processing and extracting network-level... more
Under the existing loosely-distributed sensoring environment with heterogeneous data sources, transportation planning and management agencies have found a critical need for efficiently storing, processing and extracting network-level information. The emerging practice of cloud computing provides a revolutionary solution platform to meet the above mentioned needs. A specific distributed computing framework, MapReduce, is introduced in this paper to design data-intensive software systems for managing and manipulating a large volume of data. Focusing on a traffic-oriented data-intensive application, this research designed and implemented a travel time reliability-based traveler information provision system, which leverages the unified data storage and computing platform provided by the cloud computing architecture.
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This paper presents a single-level nonlinear optimization model to estimate dynamic origin-destination (OD) demand. The model is a path flow-based optimization model, which incorporates heterogeneous sources of traffic measurements and... more
This paper presents a single-level nonlinear optimization model to estimate dynamic origin-destination (OD) demand. The model is a path flow-based optimization model, which incorporates heterogeneous sources of traffic measurements and does not require explicit dynamic link-path incidences. The objective is to minimize (i) the deviation between observed and estimated traffic states and (ii) the deviation between aggregated path flows and target OD flows, subject to the dynamic user equilibrium (DUE) constraint represented by a gap-function-based reformulation. A Lagrangian relaxation-based algorithm which dualizes the difficult DUE constraint to the objective function is proposed to solve the model. This algorithm integrates a gradient-projection-based path flow adjustment method within a column generation-based framework. Additionally, a dynamic network loading (DNL) model, based on Newell’s simplified kinematic wave theory, is employed in the DUE assignment process to realistically capture congestion phenomena and shock wave propagation. This research also derives analytical gradient formulas for the changes in link flow and density due to the unit change of time-dependent path inflow in a general network under congestion conditions. Numerical experiments conducted on three different networks illustrate the effectiveness and shed some light on the properties of the proposed OD demand estimation method.
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This article focuses on optimizing a passenger train timetable in a heavily congested urban rail corridor. When peak-hour demand temporally exceeds the maximum loading capacity of a train, passengers may not be able to board the next... more
This article focuses on optimizing a passenger train timetable in a heavily congested urban rail corridor. When peak-hour demand temporally exceeds the maximum loading capacity of a train, passengers may not be able to board the next arrival train, and they may be forced to wait in queues for the following trains. A binary integer programming model incorporated with passenger loading and departure events is constructed to provide a theoretic description for the problem under consideration. Based on time-dependent, origin-to-destination trip records from an automatic fare collection system, a nonlinear optimization model is developed to solve the problem on practically sized corridors, subject to the available train-unit fleet. The latest arrival time of boarded passengers is introduced to analytically calculate effective passenger loading time periods and the resulting time-dependent waiting times under dynamic demand conditions. A by-product of the model is the passenger assignment with strict capacity constraints under oversaturated conditions. Using cumulative input-output diagrams, we present a local improvement algorithm to find optimal timetables for individual station cases. A genetic algorithm is developed to solve the multi-station problem through a special binary coding method that indicates a train departure or cancellation at every possible time point. The effectiveness of the proposed model and algorithm are evaluated using a real-world data set.
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Using a sample-based representation scheme to capture spatial and temporal travel time correlations, this article constructs an integer programming model for finding the a priori least expected time paths. We explicitly consider the... more
Using a sample-based representation scheme to capture spatial and temporal travel time correlations, this article constructs an integer programming model for finding the a priori least expected time paths. We explicitly consider the non-anticipativity constraint associated with the a priori path in a time-dependent and stochastic network, and propose a number of reformulations to establish linear inequalities that can be easily dualized by a Lagrangian relaxation solution approach. The relaxed model is further decomposed into two sub-problems, which can be solved directly by using a modified label-correcting algorithm and a simple single-value linear programming method. Several solution algorithms, including a sub-gradient method, a branch and bound method, and heuristics with additional constraints on Lagrangian multipliers, are proposed to improve solution quality and find approximate optimal solutions. The numerical experiments investigate the quality and computational efficiency of the proposed solution approach.
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Traffic state estimation on freeway segments is widely studied as a complex non-linear and stochastic estimation problem. By capturing the essential forward and backward wave propagation characteristics through cumulative flow count... more
Traffic state estimation on freeway segments is widely studied as a complex non-linear and stochastic estimation problem. By capturing the essential forward and backward wave propagation characteristics through cumulative flow count variables, this paper develops a unified representation with a parsimonious explanation for traffic observations under free-flow, congested and dynamic transient conditions. New formulations are presented to utilize Bluetooth vehicle identification records and GPS vehicle location data on a freeway corridor with a merge/diverge. By further adding non-negativity and maximum discharge rate constrains, we construct a computationally efficient linear programming model to estimate traffic states, namely, density and traffic flow, through cumulative flow counts at each second. The proposed model is implemented and tested systematically based on a real-world NGSIM data set.
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Simultaneous train rerouting and rescheduling on an N-track network: A model reformulation with network-based cumulative flow variables
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Accessibility emerges as the transportation performance measure that emphasizes the benefits of the transportation system users, capturing more than the speed of travel. Transit accessibility shows how easy it is for an individual to... more
Accessibility emerges as the transportation performance measure that emphasizes the benefits of the transportation system users, capturing more than the speed of travel. Transit accessibility shows how easy it is for an individual to travel to a desired destination using public transit. However, in order for transit to be considered as an option in mode choice at all, there has to be a feasible transit route leading from given origin to desirable destination within the available time frame. This paper uses spatial and temporal constraints, and a set of transit features that impact access to transit, to develop a conceptual framework for transit accessibility measurements in the potential Transit Oriented Development (TOD) location in West Valley City, Utah. As this network develops more transit friendly features, both temporal and spatial accessibility indicators will provide useful information on the opportunities the users can reach using transit. The proposed methodology builds upon the traffic and transit data from the case study network, and uses an open source tool to perform transit accessibility measurements by calculating the number of accessible transit stops from each origin. The methodology considers network features, acceptable walking time, available time budget, transit schedule variability and spatial constraints as impact factors in accessibility measurements. The goal of the paper is to establish a feasible set of transit accessibility indicators that would be used for both the case study street network and transit service modifications into a transit friendly and eventually a TOD environment.
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The extension of a simplified, data-driven car-following model introduced in “Simplified, data-driven, errorable car-following model to predict the safety effects of distracted driving” presented in the IEEE ITSC Conference is presented... more
The extension of a simplified, data-driven car-following model introduced in “Simplified, data-driven, errorable car-following model to predict the safety effects of distracted driving” presented in the IEEE ITSC Conference is presented in this paper. The model was developed to predict the situational risk associated with distracted driving. To obtain longitudinal driving patterns, this paper analyzed and synthesized the NGSIM naturalistic driver and traffic database to identify essential driver behavior and characteristics. Cognitive psychology concepts, distracted driving simulator, and experimental data were adapted to examine the probabilistic nature of distracted driving due to internal vehicle distractions. An extended microscopic car-following model was developed and validated, which can be fully integrated with the naturalistic data and incorporate the probabilities of driver distraction.
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Dynamic origin–destination (OD) estimation and prediction is an essential support function for real-time dynamic traffic assignment model systems for ITS applications. This paper presents a structural state space model to systematically... more
Dynamic origin–destination (OD) estimation and prediction is an essential support function for real-time dynamic traffic assignment model systems for ITS applications. This paper presents a structural state space model to systematically incorporate regular demand pattern information, structural deviations and random fluctuations. By considering demand deviations from the a priori estimate of the regular pattern as a time-varying process with smooth trend, a polynomial trend filter is developed to capture possible structural deviations in real-time demand. Based on a Kalman filtering framework, an optimal adaptive procedure is further proposed to capture day-to-day demand evolution, and update the a priori regular demand pattern estimate using new real-time estimates and observations obtained every day. These models can be naturally integrated into a real-time dynamic traffic assignment system and provide an effective and efficient approach to utilize the real-time traffic data continuously in operational settings. A case study based on the Irvine test bed network is conducted to illustrate the proposed methodology.
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Research Interests: Engineering, Modeling, Case Studies, Network Analysis, Intelligent Transportation Systems, and 23 moreIntelligent Transport System, Traffic Management, Case Study, Variational Inequality Problems, Reliability, Discrete choice models, Urban Transport, Minimum Travel Cost, Travel Time, Bus Rapid Transit, Stochastic dynamics, Dynamic Traffic Assignment, Time Dependent, Route Choice, Supply, Demand Management, Transportation Network, Transportation Networks, Large Scale, Shortest Path, Multi Dimensional, Utility Maximization, and User Equilibrium
Research Interests: Applied Mathematics, Traffic Simulation, Column Generation, Minimum Travel Cost, Dynamic Traffic Assignment, and 10 moreROAD NETWORK, Boolean Satisfiability, Flow Pattern, Large Scale Simulation, Dynamic Networks, Direct Method, Traffic Assignment, User Equilibrium, Optimal Solution, and Objective function
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Emergency evacuations arising from hurricane disasters in the Gulf Coast region have aroused increasing attention of transportation agencies. These emergency evacuations caused significant congestions in the disastrous area, along the... more
Emergency evacuations arising from hurricane disasters in the Gulf Coast region have aroused increasing attention of transportation agencies. These emergency evacuations caused significant congestions in the disastrous area, along the evacuation routes, and at the evacuation destinations. Although most studies focus on traffic problems near the disastrous area, this study investigates effective strategies of managing traffic at the evacuation destination.
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A key foundation for developing strategies aimed at improving the efficiency and reliability of an urban transportation network is identifying the locations and impact of system bottlenecks. Although free-flow capacity and queue discharge... more
A key foundation for developing strategies aimed at improving the efficiency and reliability of an urban transportation network is identifying the locations and impact of system bottlenecks. Although free-flow capacity and queue discharge rates at system bottlenecks have traditionally been modeled as fixed values, they are in fact random variables. Therefore, assessing the operational impact of network bottlenecks requires reliable and realistic tools that account for stochasticity in prebreakdown flow rates and queue ...