More Like this

1. Bilevel optimization of a housing allocation and traffic emission problem in a predictive dynamic continuum transportation system

   https://doi.org/10.1111/mice.13007  

   Yang, Liangze ; Wong, S. C. ; Ho, H. W. ; Shu, Chi‐Wang ; Zhang, Mengping ( April 2023 , Computer-Aided Civil and Infrastructure Engineering)

   In recent decades, the effects of vehicle emissions on urban environments have raised increasing concerns, and it has been recognized that vehicle emissions affect peoples’ choice of housing location. Additionally, housing allocation patterns determine people's travel behavior and thus affect vehicle emissions. This study considers the housing allocation problem by incorporating vehicle emissions in a city with a single central business district (CBD) into a bilevel optimization model. In the lower level subprogram, under a fixed housing allocation, a predictive dynamic continuum user‐optimal (PDUO‐C) model with a combined departure time and route choice is used to study the city's traffic flow. In the upper level subprogram, the health cost is defined and minimized to identify the optimal allocation of additional housing units to update the housing allocation. A simulated annealing algorithm is used to solve the housing allocation problem. The results show that the distribution of additional housing locations is dependent on the distance and direction from the CBD. Sensitivity analyses demonstrate the influences of various factors (e.g., budget and cost of housing supply) on the optimized health cost and travel demand pattern.

    

   more » « less
2. Computing Dynamic User Equilibrium on Large-Scale Networks: Algorithms and Software Implementation

   https://doi.org/902 https://doi.org/10.1007/s11067-018-9433-y  

   Han, Ke ; Eve, Gabriel ; Friesz, Terry ( January 2019 , Networks and spatial economics)

   Dynamic user equilibrium (DUE) is the most widely studied form of dynamic traffic assignment (DTA), in which road travelers engage in a non-cooperative Nash-like game with departure time and route choices. DUE models describe and predict the time-varying traffic flows on a network consistent with traffic flow theory and travel behavior. This paper documents theoretical and numerical advances in synthesizing traffic flow theory and DUE modeling, by presenting a holistic computational theory of DUE, which is numerically implemented in a MATLAB package. In particular, the dynamic network loading (DNL) sub-problem is formulated as a system of differential algebraic equations based on the Lighthill-Whitham-Richards fluid dynamic model, which captures the formation, propagation and dissipation of physical queues as well as vehicle spillback on networks. Then, the fixed-point algorithm is employed to solve the DUE problems with simultaneous route and departure time choices on several large-scale networks. We make openly available the MATLAB package, which can be used to solve DUE problems on user-defined networks, aiming to not only facilitate benchmarking a wide range of DUE algorithms and solutions, but also offer researchers a platform to further develop their own models and applications. The MATLAB package and computational examples are available online. 

   more » « less
3. Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

   https://doi.org/10.1111/mice.12439  

   Khan, Zadid ; Khan, Sakib Mahmud ; Chowdhury, Mashrur ; Safro, Ilya ; Ushijima‐Mwesigwa, Hayato ( March 2019 , Computer-Aided Civil and Infrastructure Engineering)

   This study presents the Wireless Charging Utility Maximization (WCUM) framework, which aims to maximize the utility of Wireless Charging Units (WCUs) for electric vehicle (EV) charging through the optimal WCU deployment at signalized intersections. Furthermore, the framework aims to minimize the control delay at all signalized intersections of the network. The framework consists of a two‐step optimization formulation, a dynamic traffic assignment model to calculate the user equilibrium, a traffic microsimulator to formulate the objective functions, and a global Mixed Integer Non‐Linear Programming (MINLP) optimization solver. An optimization problem is formulated for each intersection, and another for the entire network. The performance of the WCUM framework is tested using the Sioux Falls network. We perform a comparative study of 12 global MINLP solvers with a case study. Based on solution quality and computation time, we choose the Couenne solver for this framework.

    

   more » « less
4. Operations Design of Modular Vehicles on an Oversaturated Corridor with First-in, First-out Passenger Queueing

   https://doi.org/10.1287/trsc.2021.1074  

   Shi, Xiaowei ; Li, Xiaopeng ( August 2021 , Transportation Science)

   null (Ed.)

   Although urban transit systems (UTS) often have fixed vehicle capacity and relatively constant departure headways, they may need to accommodate dramatically fluctuating passenger demands over space and time, resulting in either excessive passenger waiting or vehicle capacity and energy waste. Therefore, on the one hand, optimal operations of UTS rely on accurate modeling of passenger queuing dynamics, which is particularly complex on a multistop transit corridor. On the other hand, capacities of transit vehicles can be made variable and adaptive to time-variant passenger demand so as to minimize energy waste, especially with the emergence of modular vehicle technologies. This paper investigates operations of a multistop transit corridor in which vehicles may have different capacities across dispatches. We specify skewed time coordinates to simplify the problem structure while incorporating traffic congestion. Then, we propose a mixed integer linear programming model that determines the optimal dynamic headways and vehicle capacities over the study time horizon to minimize the overall system cost for the transit corridor. In particular, the proposed model considers a realistic multistop first-in, first-out (MSFIFO) rule that gives the same boarding priority to passengers arriving at a station in the same time interval yet with different destinations. A customized dynamic programming (DP) algorithm is proposed to solve this model efficiently. To circumvent the rapid increase of the state space of a typical DP algorithm, we analyze the theoretical properties of the investigated problem and identify upper and lower bounds to a feasible solution. The bounds largely reduce the state space during the DP iterations and greatly improve the efficiency of the proposed DP algorithm. The state dimensions are also reduced with the MSFIFO rule such that all queues with different destinations at the same origin can be tracked with a single boarding position state variable at each stage. A hypothetical example and a real-world case study show that the proposed DP algorithm greatly outperforms a state-of-the-art commercial solver (Gurobi) in both solution quality and time. 

   more » « less
5. A Smartphone-Based Parking Guidance System with Predictive Parking Availability Information.

   Xiao, J. ; Lou, Y. ( January 2018 , 2018 Transportation Research Board Annual Meeting)

   Searching for parking has been a problem faced by many drivers, especially in urban areas. With an increasing public demand for parking information and services, as well as the proliferation of advanced smartphones, a range of smartphone-based parking management services began to emerge. Funded by the National Science Foundation, our research aims to explore the potential of smartphone-based parking management services as a solution to parking problems, to deepen our understandings of travelers’ parking behaviors, and to further advance the analytical foundations and methodologies for modeling and assessing parking solutions. This paper summarizes progress and results from our research projects on smartphone-based parking management, including parking availability information prediction, parking searching strategy, the development of a mobile parking application, and our next steps to learn and discover new knowledge from its deployment. To predict future parking occupancy, we proposed a practical framework that integrates machine-learning techniques with a model-based core approach that explicitly models the stochastic parking process. The framework is able to predict future parking occupancy from historical occupancy data alone, and can handle complex arrival and departure patterns in real-world case studies, including special event. With the predicted probabilistic availability information, a cost-minimizing parking searching strategy is developed. The parking searching problem for an individual user is a stochastic Markov decision process and is formalized as a dynamic programming problem. The cost-minimizing parking searching strategy is solved by value iteration. Our simulated experiments showed that cost-minimizing strategy has the lowest expected cost but tends to direct a user to visit more parking facilities compared with two greedy strategies. Currently, we are working on implementing the predictive framework and the searching algorithm in a mobile phone application. We are working closely with Arizona State University (ASU) Parking and Transit Services to implement a three-stage pilot deployment of the prototype application around the ASU main campus. In the first stage, our application will provide real-time information and we will incorporate availability prediction and searching guidance in the second and third stages. Once the mobile application is deployed, it will provide unique opportunities to collect data on parking search behaviors, discover emerging scenarios of smartphone-based parking management services, and assess the impacts of such systems. 

   more » « less