Wan Li

Oak Ridge National Laboratory | ORNL

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinea...

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sus...

This paper studies the traffic delay prediction modeling for multiple signalized intersections along the Ala Moana Boulevard and Nimitz Highway in Hawaii. Several machine learning (ML) based approaches have been studied in the literature, and most of them focused on prediction accuracy rather than the end use of real-time control and implementation...

This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel con...

Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two...

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they requir...

Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed, which achieved high accuracy and large-scale prediction. However, existing studies...

Recently, the emergence of deep learning has facilitated many research fields including transportation, especially traffic pattern recognition and traffic forecasting. While many efforts have been made in the exploration of new models for higher accuracy and larger scale, few existing studies focus on learning higher-resolution traffic patterns. Th...

Signalized intersections play an important role in transportation efficiency and vehicle fuel economy in urban areas. This paper proposes a cooperative method of traffic signal control and vehicle speed optimization for connected automated vehicles, which optimizes the traffic signal timing and vehicles' speed trajectories at the same time. The met...

Adaptive control software lite (ACS-Lite) is a traffic signal timing optimization system that dynamically adjusts traffic signal timing to meet current traffic demands. In this study, we conduct a “before and after” analysis to evaluate the ACS-Lite adaptive traffic control system recently deployed on a congested urban corridor in Albany, New York....

This study introduces an agent-based dynamic feedback-control toll pricing strategy that accounts for the trip purpose, travel time reliability, departure time choice and level of income such that the toll revenue is maximized while maintaining a minimum desired level of service on the managed lanes. An agent-based modelling was applied to simulate...

This paper aims to develop a modeling framework for optimizing the timing of a set of traffic signals by considering individual vehicle characteristics (such as fuel consumption and travel time). Through the Vehicle to Infrastructure (V2I) communications, such individual vehicle information is available for the infrastructure center to produce opti...

Adaptive traffic signal control technologies have been increasingly deployed in real world situations. The objective of this project was to develop a decision-making tool to guide traffic engineers and decision-makers who must decide whether or not adaptive control is better suited for a given traffic corridor and/or intersections than the existing...

This paper proposes a dynamic traffic signal timing optimization strategy (DTSTOS) based on various vehicle fuel consumption and dynamic characteristics to minimize the combined total energy consumption and traffic delay for vehicles passing through an intersection. With increasing penetration of new vehicle types and configurations, vehicle fuel c...

Congestion pricing is an effective management policy to reduce traffic congestion on freeways. This study accounted for the travel time difference and reliability on managed and general-purpose lanes in a modi- lied approach to determining toll rates recently developed by others. The original approach was modified by developing an agent-based toll...