Yiheng Feng

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.

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.

Estimating Freeway Travel Times Using the General Motors Model

Travel time is a key transportation performance measure because of its diverse applications. Various modeling approaches to estimating freeway travel time have been well developed due to widespread installation of intelligent transportation system sensors. However, estimating accurate travel time using existing freeway travel time models is still challenging under congested conditions. Therefore, this study aimed to develop an innovative freeway travel time estimation model based on the General Motors (GM) car-following model. Since the GM model is usually used in a microsimulation environment, the concepts of virtual leading and virtual following vehicles are proposed to allow the GM model to be used in macroscale environments using aggregated traffic sensor data. Travel time data collected from three study corridors on I-270 in Saint Louis, Missouri, were used to verify the estimated travel times produced by the proposed General Motors travel time estimation (GMTTE) model and two existing models, the instantaneous model and the time-slice model. The results showed that the GMTTE model outperformed the two existing models due to lower mean average percentage errors of 1.62% in free-flow conditions and 6.66% in two congested conditions. Overall, the GMTTE model demonstrated its robustness and accuracy for estimating freeway travel times.

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.

Estimating Vehicles in the Dilemma Zone and Application to Fixed-Time Coordinated Signal Optimization

Most traffic accidents are related to driving behaviors. Dangerous driving behaviors may occur if a vehicle is trapped in the dilemma zone. Traditional methods for calculating the number of vehicles in the dilemma zone (NVDZ) are usually based on field data collection and postanalysis and require a great deal of time and human resources. This paper proposes an analytical model for estimating NVDZ on the basis of signal timing, arterial geometry, traffic demand, and driving characteristics. Through application of Robertsons platoon dispersion model, the proposed model calculates the flow rate in the dilemma zone area at yellow onset as well as queue lengths under a discrete time horizon. A VISSIM-based microscopic simulation is then calibrated to validate the NVDZ calculation. The mathematical framework for signal optimization is implemented. Most of the current signal optimization methodologies do not consider safety measures such as NVDZ to be an objective. In this paper, delay and NVDZ are formulated as a multi-objective optimization problem addressing efficiency and safety together. Examples show that delay and NVDZ are competing objectives and cannot be optimized at the same time. Finally, an economic model is applied to quantify both delay and NVDZ in monetary values. The optimal solution considering both factors reduces NVDZ by more than 20% with a less than 2% increase in delay compared with minimizing only delay. The total cost is reduced.

Real-Time Detector-Free Adaptive Signal Control with Low Penetration of Connected Vehicles

Most of the existing connected vehicle (CV)-based traffic control models require a critical penetration rate. If the critical penetration rate cannot be reached, then data from traditional sources (e.g., loop detectors) need to be added to improve the performance. However, it can be expected that over the next 10 years or longer, the CV penetration will remain at a low level. This paper presents a real-time detector-free adaptive signal control with low penetration of CVs ( ≤ 10%). A probabilistic delay estimation model is proposed, which only requires a few critical CV trajectories. An adaptive signal control algorithm based on dynamic programming is implemented utilizing estimated delay to calculate the performance function. If no CV is observed during one signal cycle, historical traffic volume is used to generate signal timing plans. The proposed model is evaluated at a real-world intersection in VISSIM with different demand levels and CV penetration rates. Results show that the new model outperforms well-tuned actuated control regarding delay reduction, in all scenarios under only 10% penetrate rate. The results also suggest that the accuracy of historical traffic volume plays an important role in the performance of the algorithm.

Vulnerability of Traffic Control System Under Cyberattacks with Falsified Data

Existing traffic control systems are mostly deployed in private wired networks. With the development of wireless technology, vehicles and infrastructure devices will be connected through wireless communications, which might open a new door for cyberattackers. It is still not clear what types of cyberattacks can be performed through infrastructure-to-infrastructure and vehicle-to-infrastructure communications, whether such attacks can introduce critical failure to the system, and what the impacts are of cyberattacks on traffic operations. This paper investigates the vulnerability of traffic control systems in a connected environment. Four typical elements, including signal controllers, vehicle detectors, roadside units, and onboard units, are identified as the attack surfaces. The paper mainly focuses on attacking actuated and adaptive signal control systems by sending falsified data, which is considered as an indirect but realistic attack approach. The objective of an attacker is to maximize system delay with constraints such as budget and attack intensity. Empirical results show that different attack scenarios result in significant differences in delay, and some ineffective attacks may even improve the system performance. Simulation results from a real-world corridor show that critical intersections, which have a higher impact on network performance, can be identified by analyzing the attack locations. Identification of such intersections can be helpful in designing a more resilient transportation network.

New Dimensions of Intersection Control with Connected and Automated Vehicles

Connected and automated vehicle (CAV) technologies are believed to offer tremendous benefits to the urban transportation system. Intersection control will also experience a transition process from the state-of-the-practice paradigm to a fully CAV deployed environment, and new dimensions will be added to the control framework. In this paper, we envision that the transition process mainly contains three major stages, namely, the detector-free signal operation, the generalized spatiotemporal intersection control, and the infrastructure adaptation. Several key challenges are discussed.

Multimodal Data Analytics Comparative Visualization Tool: Case Study of Pedestrian Crossing Design

The purpose of this paper is to define a visualization method to evaluate the performance of a multimodal traffic signal system. Previous studies have concentrated on performance assessment for single modes, such as delay, travel time of passenger vehicles, and transit running times. The methodology presented in this paper considers an integrated approach to multimodal performance assessment. A tool, called a multimodal performance dashboard, was developed to visualize the relationship between various performance measures and multiple modes. Dashboards can be used to characterize the performance of an existing system and also for before-and-after studies when a new design is implemented. Radar diagrams are the basic element of the multimodal performance dashboard and are constructed for performance measures (e.g., passenger vehicle travel time, transit delay, pedestrian volume, and truck stops) and for each movement at an intersection. An arterial corridor in the SMARTDrive test bed of the Maricopa County, Arizona, Department of Transportation was analyzed with the Vissim microsimulation model to study the effects of different designs and signal timing strategies on several performance measures for vehicles and pedestrians. According to the results of this study, choosing an appropriate control strategy can affect the different movements of different modes (including pedestrians) in a variety of ways. The more modes involved in the system, the more challenging it is to determine the proper control strategy. Using this comparative tool, alongside statistical models, makes it easier for decision makers to understand, visualize, and analyze data.