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This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

1. An Analytical Framework for Traffic Signal Control

The research team developed a unified framework for optimizing traffic lights accounting for traffic dynamics and real-time traffic flow. In the problem, the decision variable is binary determining which phase to be activated at any simulation interval. The output from the proposed model provides optimal signal timing plans that minimizes the intersection delay and total system travel time. The objective function explicitly considers intersection delay and loss time due to phase switches in addition to traditional travel time objective. Moreover, the formulation increases its applicability by means of considering flexible cycle lengths. Two test networks are used to demonstrate the applicability of the proposed model. Results show better performance of the models when compared to pre-timed optimal signal plans. Figure 1 presents one sample result on the cycle length variation.

2. Adaptive signal control algorithms using real-time data

The research team developed and implemented two adaptive signal control algorithms: Enhanced Longest Queue First (ELQF) and Maximal Weight Matching (MWM) within the agent based traffic simulator. The research is motivated to address limitations of the original Longest Queue First (LQF) algorithm. In LQF, one approach with high traffic volume will get green successively. This creates unreasonable delay for vehicles on other approaches. In the ELQF algorithm, we introduce the concept of second best queue and maximum green. The key idea is related to service provisioning for special type of vehicles (e.g. transit, EMS, fire trucks). The algorithm assigns more weight on certain vehicle types (emergency vehicles, buses etc) as compared to other vehicles. The MWM algorithm uses the cumulative value of weights of vehicles in queue to make control decisions.

3. Network traffic control accounting for fairness across users

Traditional signal optimization algorithms focus on local information and aim to minimize average delay, number of stops, queue size, etc. for a particular intersection. Instead of looking at the delay incurred in one intersection in the route, our proposed algorithm accounts for the historical delay information of individual vehicles and seeks to minimize the stopped delay for the entire trip. This research proposes trip delay based control algorithms that accounts for the fairness across road users with respect to intersection delay. The fairness objective is to minimize the variation of trip intersection delay for a population of road users within the defined temporal and spatial boundaries. Figure 2 provides one sample result for the ELQF algorithm.

4. A Bi-level Formulation for the Dynamic Equilibrium based Traffic Signal Control

The signal settings influence the route choices and departure time choice made by road users and the resulting flow redistribution influences the signal settings. Therefore, it is necessary to consider the interaction between signal settings and route choice behavior of the road users. In this study, we formulate and solve the combined dynamic user equilibrium and signal control (DUESC) as a bi-level optimization problem. The bi-level problem has two basic components: the dyn...

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