Yanfeng Geng

New “Smart Parking” System Based on Resource Allocation and Reservations

We propose a novel “smart parking” system for an urban environment. The system assigns and reserves an optimal parking space based on the drivers cost function that combines proximity to destination and parking cost. Our approach solves a mixed-integer linear programming (MILP) problem at each decision point defined in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and is updated at the next decision point with a guarantee that there is no resource reservation conflict and that no driver is ever assigned a resource with a cost function higher than this drivers current cost function value. Based on simulation results, compared with uncontrolled parking processes or state-of-the-art guidance-based systems, our system reduces the average time to find a parking space and the parking cost, whereas the overall parking capacity is more efficiently utilized. We also describe full implementation in a garage to test this system, where a new light system scheme is proposed to guarantee user reservations.

A new “smart parking” system based on optimal resource allocation and reservations

We propose a new “smart parking” system for an urban environment. The system assigns and reserves an optimal resource (parking space) for a user (driver) based on the users objective function that combines proximity to destination and parking cost, while also ensuring that the overall parking capacity is efficiently utilized. Our approach solves a Mixed Integer Linear Program (MILP) problem at each decision point in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and subject to random events such as new user requests or parking spaces becoming available. The allocation is updated at the next decision point ensuring that there is no resource reservation conflict and that no user is ever assigned a resource with higher than the current cost function value. Simulated case studies are included based on parking at part of the Boston University campus showing that we can achieve significant improvement over uncontrolled parking processes or state-of-the-art guidance-based systems. We also describe a laboratory setting where this system has been tested in real time.

Dynamic resource allocation in urban settings: A “smart parking” approach

We propose a “smart parking” system for an urban environment based on a dynamic resource allocation approach. The system assigns and reserves an optimal resource (parking space) for a user (driver) based on the users objective function that combines proximity to destination with parking cost, while also ensuring that the overall parking capacity is efficiently utilized. Our approach solves a Mixed Integer Linear Program (MILP) problem at each decision point in a time-driven sequence. The solution of each MILP is an optimal allocation based on current state information and subject to random events such as new user requests or parking spaces becoming available. The allocation is updated at the next decision point ensuring that there is no resource reservation conflict, that no user is ever assigned a resource with higher than the current cost function value, and that a set of fairness constraints is satisfied. We add an event-driven mechanism to compensate for users with no assignment that are close to their destinations. Simulation results show that using this “smart parking” approach can achieve near-optimal resource utilization and significant improvement over uncontrolled parking processes or state-of-the-art guidance-based systems.

Traffic light control using Infinitesimal Perturbation Analysis

We address the traffic light control problem for a single intersection by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimates of a cost metric with respect to the controllable green and red cycle lengths. The IPA estimators obtained require counting traffic light switchings and estimating car flow rates only when specific events occur. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach.

Predicting link travel times from floating car data

We study the problem of predicting travel times for links (road segments) using floating car data. We present four different methods for predicting travel times and discuss the differences in predicting on congested and uncongested roads. We show that estimates of the current travel time are mainly useful for prediction on links that get congested. Then we examine the problem of predicting link travel times when no recent probe car data is available for estimating current travel times. This is a serious problem that arises when using probe car data for prediction. Our solution, which we call geospatial inference, uses floating car data from nearby links to predict travel times on the desired link. We show that geospatial inference leads to improved travel time estimates for congested links compared to standard methods.

Multi-intersection Traffic Light Control Using Infinitesimal Perturbation Analysis

Abstract We address the traffic light control problem for multiple intersections in tandem by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. Using Infinitesimal Perturbation Analysis (IPA), we derive on-line gradient estimates of a cost metric with respect to the controllable green and red cycle lengths. The IPA estimators obtained require counting traffic light switchings and estimating car flow rates only when specific events occur. The estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach.

Quasi-dynamic traffic light control for a single intersection

We address the traffic light control problem for a single intersection by viewing it as a stochastic hybrid system and developing a Stochastic Flow Model (SFM) for it. We adopt a quasi-dynamic control policy based on partial state information defined by detecting whether vehicle backlog is above or below a certain threshold, without the need to observe an exact vehicle count. Using Infinitesimal Perturbation Analysis (IPA), we derive on-line gradient estimators of an average traffic congestion metric with respect to these controllable green and red cycle lengths when the vehicle backlog is above or below the threshold. The estimators are used to iteratively adjust light cycle lengths so as to improve performance and, in conjunction with a standard gradient-based algorithm, to seek optimal values which adapt to changing traffic conditions. Simulation results are included to illustrate the approach and quantify the benefits of quasi-dynamic traffic light control over earlier static approaches.

Adaptive traffic light control

We address the traffic light control problem by developing a Stochastic Flow Model (SFM) for an intersection and using a policy based on partial state information defined by detecting whether vehicle backlogs are above or below certain thresholds. Using Infinitesimal Perturbation Analysis (IPA), we derive online gradient estimators of an average traffic congestion metric with respect to the green and red cycle lengths and to the backlog thresholds. The estimators are used to adjust light cycle lengths and thresholds so as to improve performance and to seek optimal values which adapt to changing traffic conditions.

Optimal Dynamic Allocation and Space Reservation for Electric Vehicles at Charging Stations

Abstract We propose an optimal allocation and reservation system for Electric Vehicles (EVs) at charging stations distributed in an urban environment. The system assigns and reserves an optimal space at a charging station based on the users cost function that combines proximity to current location (or destination) and charging cost. Our approach is motivated by a similar system we have developed for “smart parking”, where resources are parking spaces rather than EV charging station spaces. We solve a Mixed Integer Linear Program (MILP) problem at each assignment decision point over time. The solution of each MILP is an optimal allocation based on current state information, and is updated at the next decision point. Formal guarantees are included that there is no resource reservation conflict and that no user is ever assigned a resource with a higher than this users current cost function value. Simulation results are included to illustrate how our system, compared to uncontrolled processes or guidance-based approaches, reduces the average time to find a charging space and the associated user cost, while the overall charging space capacity is more efficiently utilized.

Manufacturing schedule of dual-armed cluster tools based on heuristic search

Cluster tools are fundamental equipments in over-8-inch wafer manufacturing. Due to the toolspsila unique physical structure and particular semiconductor manufacturing demands, the scheduling of cluster tools is more complex than normal Job-Shop problems. This paper proposes a scheduling algorithm for dual-armed cluster tools based on Heuristic Search. We mainly examine the scheduling technique under the most complex situation including residency constraints and multi-visit requirements. A periodic idea that every wafer enters into cluster tools in the same interval is proved still feasible under this situation. To find a conflict-free schedule under a fundamental period (FP) in short time, we define an evaluation function to select the most proper operation sequence from all the possibilities, and some feedback information from a conflict is used to prevent over pruning. Experiments under the most complex situations demonstrate the feasibility and effectiveness of this algorithm. Other experiments with looser constraints show its expansibility and adaptability.