K. Larry Head

CONTROLLED OPTIMIZATION OF PHASES AT AN INTERSECTION

This paper presents a general purpose algorithm for real-time traffic control at an intersection. Our methodology, based on dynamic programming, allows optimization of a variety of performance indices such as delay, stops and queue lengths. Furthermore, optimal phase sequencing is a direct by-product of this new approach. These features make the new methodology a powerful tool for intersection control. We demonstrate the usefulness of the approach by a simulation experiment in which our intersection control algorithm is interfaced with a well established simulation package called TRAF-NETSIM. Our study compares the controlled optimization of phases methodology with fully-actuated as well as semi-actuated control. We show that consistent reductions in delay may be possible by adopting the new algorithm.

Heuristic Algorithm for Priority Traffic Signal Control

A heuristic algorithm is presented for traffic signal control with simultaneous multiple priority requests at isolated intersections in the context of vehicle-to-infrastructure communications being available on priority vehicles, such as emergency vehicles and transit buses. This heuristic algorithm can achieve near-optimal signal timing when all simultaneous requests are considered and can be visualized in a phase–time diagram. First, the problem with the control of multiple priority traffic signals is transformed into a network cut problem that is polynomial solvable under some reasonable assumptions. Second, a phase–time diagram is presented to visualize and evaluate priority delay given a signal plan and a collection of priority request arrival times. Microscopic traffic simulation is used to compare the heuristic with the state-of-the-practice algorithms for transit signal priority. The proposed heuristic algorithm could reduce average bus delay in congested conditions by about 50%, especially with a high frequency of conflicting priority requests.

Optimal Intersection Operation with Median U-Turn: Lane-Based Approach

The median U-turn intersection is a potential method for relieving traffic congestion at intersections. Median U-turn intersections can be classified into six types, according to the prohibited traffic movements. Although much is known about the technology of the median U-turn, efforts have focused on its geometric design, safety analysis, and evaluation. In this paper, a lane-based optimization model is proposed for the integrated design of median U-turn intersection types, lane markings, distances between the median crossovers and the main intersection, and signal timings. The capacity maximization problem is considered, and a set of constraints is determined that ensures the feasibility and safety of the optimization results. The optimization is formulated as a multi-objective mixed-integer nonlinear programming problem. It is solved by transformation to a series of mixed-integer linear programming models. The latter can be solved by the standard branch-and-bound technique. Extensive numerical analysis and a case study have shown the effectiveness of the proposed method. In addition, the method can assist transportation professionals in the proper selection of U-turn types and in the design of intersections with median U-turns.

Heuristic algorithms to solve 0–1 mixed integer LP formulations for traffic signal control problems

In this paper, three heuristic solution algorithms, (the Dive-and-Fix method, the Ratio-cluster method, and the Cumulative-departure method) are specially designed to solve the traffic signal control problem formulated as a 0–1 mixed-integer linear programming problem with cell transmission model. These three solution algorithms are based on two fundamental approaches. First, the 0–1 mixed-integer linear program is solved via linear relaxation (LR). Second, the non-integer solutions obtained from the LR are converted into the integer solutions by taking advantage of the underlying physical mechanism embedded in the LR solutions that lead to the optimal signal control. In particular, proportional capacities for different approaches and the cumulative exit flow at each intersection obtained from the LR solutions are utilized to determine green time allocation for each approach. It is demonstrated that the near-optimal solutions obtained with these algorithms are very close to the optimal solutions under both uncongested and congested traffic conditions.

Efficient Priority Control Model for Multimodal Traffic Signals

The paper presents a model for multimodal traffic signal priority control. The approach is based on an analytical model and a flexible implementation algorithm that considers real-time vehicle actuation. The analytical model considers the needs of different modes in a real-time connected vehicle environment. The model provides an optimal signal schedule that minimizes the total weighted priority request delay. The flexible implementation algorithm is designed to guarantee that the optimal signal schedule is applied with minimum negative impact on regular vehicles. The model has been tested in a simulation framework on two networks: San Mateo, California, and Anthem, Arizona. The simulation experiments showed that the model, when compared with fully actuated control, was able to reduce average delay and travel times for priority vehicles without a significant negative impact on passenger cars. The field results of implementing the priority framework in the nationally affiliated connected vehicles test bed in Anthem showed the effectiveness of the model in the real world.

Dynamic Turning Restriction Management for Signalized Road Network

Turning restrictions are a commonly used strategy for improving the capacity of signalized intersections in an urban network. To meet the needs of the pronounced variability of volumes in the network and to extend the application range of the turning restriction strategy, a dynamic turning restriction optimization method is developed for multiple signalized intersections. A lane-based optimization model is proposed for the integrated operation of prohibited movements, detour routes, the layout of the intersections, and signal timings. The capacity maximization problem is considered. A set of constraints is determined; the set meets the characteristics of dynamic control and ensures feasibility and safety of the optimization results. The optimization is formulated as a mixed-integer linear programming problem, which can be solved with the standard branch-and-bound technique. The results of numerical analysis show the effectiveness of the proposed method, as well as the promising property of helping transportation professionals make a proper selection of turning restriction types and avoid causing new bottleneck points.

Unified route choice framework: Specification and application to urban traffic control

The route choice system and the traffic control system (TCS) constitute two major approaches to mitigating congestion in urban road networks. The interaction between signal control and route choice is considered from a narrower route choice system perspective, with the focus on route choice models for operational purposes. The goal is to analyze the relative performance of alternative route choice models as different assumptions are made about the type of TCS in use. To this end, an agent-based framework for formulating different route choice models is defined, and this framework is integrated with a microscopic traffic simulation environment. Within the framework, each agents memory is updated repeatedly (daily) to reflect available prior individual and social experience, and then a route is chosen by a probabilistic sequential decision-making process. Several previously developed route choice models from the literature are implemented with the framework, and their performance, along with some additional hybrid models that are suggested by the modeling framework, is evaluated on two simulated real-world systems: a 32-intersection road network in Pittsburgh, Pennsylvania, running with a SYNCHRO-generated coordinated timing plan and the same road network running with the scalable urban traffic control (SURTRAC) adaptive TCS. The results show that specific route choice models perform differentially when applied in conventional and adaptive traffic control settings and that better overall network performance for all route choice models is achieved in the adaptive control setting. This unified framework also makes it possible to analyze the performance impact of route choice model components and to formulate better-performing hybrid models.

Connected vehicle-based adaptive signal control and applications

Basic signal operation strategies allocate green time to different traffic movements to control the flow at an intersection. Signal control applications consider different objectives, such as coordination with multiple intersections, multimodal priority, and safety. Real-time signal control applications rely mainly on infrastructure-based detection data. With the emergence of connected vehicle technology, high-resolution data from connected vehicles will become available for signal control. This paper presents a framework that uses connected vehicle data for adaptive signal control and considers dilemma zone protection, multimodal signal priority, and coordination. Initially, the market penetration rate of connected vehicles will be low, so infrastructure-based detector actuation logic is integrated into the framework to improve performance. Simulation analysis demonstrated good results when the penetration rate was medium to high and that the actuation logic was necessary when the penetration rate was low.

Adaptive Coordination Based on Connected Vehicle Technology

This paper presents a methodology that integrates coordination with adaptive signal control in a connected vehicle environment. The model consists of two levels of optimization. At the intersection level, an adaptive control algorithm allocates the optimal green time to each phase in real time by using dynamic programming that considers coordination constraints. At the corridor level, a mixed-integer linear program is formulated on the basis of data from the intersection level to optimize offsets along the corridor. After the corridor-level algorithm solves the optimization problem, the optimized offsets are sent to the intersection-level algorithm to update the coordination constraints. The model was compared with actuated–coordinated signal control by means of Vissim simulation. The results indicate that the model can reduce average delay and average number of stops for both coordinated routes and the entire network.

Improving the operational performance of two-quadrant parclo interchanges with median U-turn concept

ABSTRACT Parclo (partial cloverleaf) interchange, connecting major freeways and surface streets with one or two loop ramps and two closely spaced intersections, is one of the most commonly used interchange types in urban and suburban areas. The two-ramp terminal intersections are often the critical bottlenecks for the parclo interchange system, especially when the turning volumes are high. In this study, we propose a design in which the median U-turn technology is applied to the parclo interchanges to relieve the congestion problem. A mixed-integer nonlinear program is developed to integrate the selection of prohibited movements with detour routes, lane markings, and signal timings for three types of parclo interchange. The results from extensive numerical analyses and a case study reveal that the proposed design is promising as it could increase the capacity. The capacity improvement, when the proposed design is applied, is larger with the increase in the proportion of the turning movements. Moreover, a relatively stable capacity improvement (approximately 8.33%) could be obtained when our design is applied to cases where the volumes between the two sub-intersections are unbalanced.