This paper proposes an algorithm for traffic signal optimization that utilizes data from Connected and Autonomous Vehicles (CAVs). The algorithm developed in this paper improves upon an existing algorithm by optimizing the departure sequence of vehicle platoons, instead of individual vehicles at the intersection, which significantly reduces the overall computational effort required. Additionally, autonomous vehicle trajectory design is improved by explicitly accounting for acceleration and deceleration processes that these vehicles will experience and only designing the trajectories of the lead vehicle in each platoon. The latter provides even further computational benefits, which combined with the platoon-based departure sequence optimization, allows the algorithm to be extended to more realistic intersection configurations that would otherwise be infeasible for real-time traffic control (specifically, intersections with four-approaches and multiple lanes per approach). The algorithm is tested in a simulation environment and the results reveal that computational savings of over 90% can be achieved with the platoon-based approach with negligible changes in overall operational efficiency measured in car delays or number of stopping maneuvers. Additional results show that the benefits of the algorithm increase with increasing ratios of CAVs in the vehicle fleet, but the marginal benefits are small after the fleet is composed of 40% of CAVs.

Connected vehicle-based signal priority control is widely regarded as an advanced method for improving travel efficiency of an emergency vehicle when passing through intersections. However, the Vehicle-to-everything (V2X) communication delay is a critical factor affecting the performance of signal request and has rarely been considered in existing studies. This paper conducted a comprehensive delay analytics of the preemption signal request and its influences on the practical preemption control for the emergency vehicle. First of all, a general end-to-end delay decomposition model is formulated to analyze significant delay uncertainties from different sources. Then, a compensated distance strategy is adopted for cooperative preemption control to ensure the reliability of preemption control and minimizes impacts on performances caused by communication delay. Based on the analysis of field data and numerical results, the proposed model is able to reveal characteristics of communication delay for multi-modal traffic signal control with priority. The proposed communication delay compensation strategy shows clear benefits in improving the performance of signal preemption control priority for an emergency vehicle at intersections and hence has potential to enhance V2X applications in Connected Vehicle Environment.

Establishment of effective cooperation between vehicles and transportation infrastructure improves travel reliability in urban transportation networks. Lack of collaboration, however, exacerbates congestion due mainly to frequent stops at signalized intersections. It is beneficial to develop a control logic that collects basic safety message from approaching connected and autonomous vehicles and guarantees efficient intersection operations with safe and incident free vehicle maneuvers. In this paper, a signal-free intersection control logic is formulated into a dynamic programming model that aims to maximize the intersection throughput. A stochastic look-ahead technique is proposed based on Monte Carlo tree search algorithm to determine the near-optimal actions (i.e., acceleration rates) over time to prevent movement conflicts. Our numerical results confirm that the proposed technique can solve the problem efficiently and addresses the consequences of existing traffic signals. The proposed approach, while completely avoids incidents at intersections, significantly reduces travel time (ranging between 52.7% and 82.3% when compared to fixed-time and fully-actuated control strategies) at intersections under various demand patterns.

This paper presents an arterial-level traffic progression optimization model (ALTPOM). The model addresses traffic congestion on signalized arterials when deployment of connected vehicle technologies is rudimentary (i.e. penetration rate of connected vehicle is not 100%). ALTPOM improves traffic progression by optimizing offsets for an entire signalized arterial simultaneously. To optimize these offsets, splits of coordinated intersections are first adjusted to balance predicted upcoming demands of all approaches at individual intersections.

An open source traffic simulator, Enhanced Transportation Flow Open-source Microscopic Model (ETFOMM), was selected to implement and evaluate the performance of ALTPOM. The case studies’ field signal timing plans were coordinated and optimized using TRANSYT-7F as the benchmark. ALTPOM was implemented with connected vehicles penetration rates at 25% and 50%, and ALTPOM significantly outperforms TRANSYT-7F with at least 26.0% reduction of control delay (sec/vehicle) and 4.4% increase of throughput for both directions of major and minor streets. This technique differs from traditional traffic coordination, which prioritizes major street traffic and thereby generally results in degrading performance on minor streets. ALTPOM also provides smooth traffic progression for the coordinated direction with little impact on the opposite direction. The performance of ALTPOM improves as the penetration rate of connected vehicles increases.

The Texas A&M Transportation Institute (TTI) has developed an augmented reality environment where real entities (vehicles and traffic signal operation) are combined with simulated traffic in a platform to test connected vehicle (CV) applications and technologies. The first-of-its-kind approach — called CONVAS (CONnected Vehicle Assessment Simulation) — marries the cost effectiveness of computer simulation with actual roadway operations to produce an efficient, yet dependable evaluation mechanism in a controlled environment for the Federal Highway Administration (FHWA).

The project team developed an enhanced hardware-in-the-loop (HITL) simulation in this project by incorporating an actual, connected vehicle on a roadway network into a simulation model and displaying simulated connected vehicles inside the real vehicle at the same time. This platform also provides SPaT (signal phasing and timing) message signal status and the MAP information to the CV through a tablet display. This enables development and testing of advanced CV applications or strategies by allowing assessments of how connected vehicles respond to each other and other entities like pedestrians, emergency vehicles, and transit vehicles in a controlled environment.

The CONVAS system integrates a traffic signal controller and a real CV with an On Board Unit (OBU) with the VISSIM simulation model to form the HITL platform. In addition, the HITL platform consists of a road side unit (RSU) and an integrated infrastructure to vehicle prototype (IVP) system to facilitate communication between RSU, OBU, external signal controller and VISSIM. This paper describes the various components of this platform and how they interact and how to use the platform to evaluate a CV application.

At intersections where left turn bays do not have sufficient lengths or left turn volume is relative high, left turn vehicles may spill back and block the adjacent through traffic. This paper aims to develop quantitative measure of left turn spillback and relevant signal control strategy. First, an improved queue length estimation method involving vehicles in left turn bays is developed based on Comert and Cetin’s queue length estimation method with connected vehicles. Next a probabilistic model for left turn spillback estimation is proposed to measure left turn spillback percentage at an intersection in a connected environment. The model accuracy is validated with results from microscopic simulation. The effect of different bay lengths is also studied. Finally a signal control demonstration is presented to show that the proposed method has good value in signal control strategy adjustment.

We study the traffic signal control problem in the connected vehicle (CV) environment by ensuring a fixed cycle length so that the proposed model can be extended readily for the coordination of multiple signals. The signal control problem can be first formulated as a mixed-integer nonlinear program, by considering information of individual vehicles’ trajectories and their realistic driving/car-following behavior. Due to the large dimension of the problem and the complexity of the nonlinear car-following model, we reformulate the problem as a Dynamic programming (DP) model by dividing the timing decisions into stages (one stage for a phase) and approximating the fuel consumption and travel time of a stage as functions of the state and decision variables of the stage. We then develop a two-step method to ensure that the obtained optimal solution can lead to the fixed cycle length. Numerical experiments are provided to test the performance of the proposed model using data generated from traffic simulation.

Traffic congestion results in increasing delay in daily commutes and unreliable transportation services. Traffic signal control on arterial corridors attracts strong research and implementation attention due to its advancement on direct traffic management and coordination for congestion mitigation. With increasing penetration rates of connected vehicles, signals are able to interact with real-time traffic conditions and to be operated more proactively with traffic prediction models to mitigate road congestion, to improve road capacities, and to enhance transportation network efficiency. This paper develops a proactive signal control system based on connected vehicles to minimize vehicle delay at signalized intersections. The system utilizes connected vehicles to make a short-term prediction about the overall vehicle delays at different movements accurately. The optimal signal phasing and timing plan is estimated to minimize the total vehicle delay at the intersection. Two road segments with one isolated intersection and three consecutive intersections are simulated with the INTEGRATION
microscopic traffic simulator to validate the proposed system. The result obtained under the proactive control system is compared to a well-tuned fully actuated control. Applying the proactive control
system, the total vehicle delay can be reduced by up to 59%, the vehicle stops by 40%, and the fuel consumption by 18%.

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 ten years or longer, the CV penetration rate is expected to remain at a very low level. This paper presents a real-time detector-free adaptive signal control with very low penetration of CVs (smaller or equal to 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, then 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 in terms of 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.

This paper presents a condensed look at our discovery research project in examining the comprehensive mobility, safety, fuel consumption and emission benefits on early deployment of connected vehicle infrastructure at signalized intersection.  A model to predicts queue length based on different connected vehicle penetration rates and vehicle arrival time stamp from detector is first established. Offsets, common cycle lengths and splits are adjusted or optimized to clear or minimize the predicted queue length of all phases.  An open source traffic simulation engine ETFOMM that is supported by U.S. DOT was selected for implementation and case study. A case study is presented with five different penetration rates and different control strategies. With a penetration rate of merely 10%, the proposed model and algorithm can reduce 13.6% control delay, 45% conflicts, around 8% fuel and emission. That bring tangible at infant. The proposed algorithm improves its efficiency with the increase of penetration rate with witch the mobility, safety, fuel and emission benefits continue to grow. That creates a remarkable scenario of both delay reduction and capacity (throughput) gains. The model can reduce 30.9% control delay and increase 8.2% throughput at high penetration rate (70%). In addition, the fuel consumption of total network can improve 18.9% and emission can improve 18.7%.

One of the frontier of studies on Connected and Automated Vehicle (CAV) systems is to examine the impacts of CAVs on joint optimization of individual vehicle trajectories and traffic signals. This research paper is the first stage of a series of research with the goal of reaching to a practice-ready signal-vehicle cooperation system where vehicles are partially CAV and partially human driven vehicles. The main objective of this paper is to develop an eco-traffic signal system that can improve fuel consumption and delays at an isolated intersection using Connected and Automated Vehicle Technology. The proposed system consists of an eco-driving process and a mixed-integer linear traffic signal optimization model. The system was tested in an eight-movement single-lane intersection with 100% CAV. The preliminary result showed that the signal optimization model reduces the average vehicles delay by 22% when it is compared to actuated signal control.

Crowdsourced vehicle trajectory data, e.g., from connected vehicles or ride-hailing service providers such as Uber in the U.S. or Didi in China, are increasingly available. These trajectory data would potentially revolutionize traffic signal operation. Different from conventional detector data, trajectory data could serve as a low-cost, continuous and reliable data source, which could advance conventional detector-based signal control to a detector-free signal control scheme. Using trajectory data as the only input, this research develops a system that can optimize fixed-time traffic signal systematically. The proposed system is an integrated platform for performance evaluation and parameter optimization, with three main components: 1. traffic demand estimation, 2. performance visualization and 3. parameter optimization. It optimizes a set of signal parameters, including Time of Day (TOD) schedule, cycle length, offset and green split. The system has been implemented in the City of Jinan, China, using vehicle trajectory data collected from Didi vehicles. Two case studies on arterial signal optimization are presented. In both cases, the system considerably improved the performance of signal operation, reducing delay by 5-20%. During peak-hour periods with severe oversaturation, the number of spillover vehicles on main street during red signal also decreased substantially, indicating reduction of likelihood of intersection gridlock. The effectiveness of the developed system demonstrates the potential for large scale deployment.

Advancement in connected vehicle technology has created opportunities for researchers to develop safety critical and assistive applications for drivers. These applications do not only ensure drivers’ safety and assistance services but also benefit the environment by reducing fuel consumption and CO2 emission. An intersection approach advisory system, which is one of the connected vehicle applications, utilizes the dedicated short range communications (DSRC) technology for V2X communications and National Transportation Communications for ITS Protocol (NTCIP) protocol reading SPaT data to provide intersection related information to drivers. In this paper, we discuss such a system that provides drivers the live signal information and recommends speeds through visual and auditory means in a less distractive way to make informed decisions before reaching to an intersection for different traffic scenarios. We also discuss the in-depth analysis of the implementation issues and challenges of such a system.

Connected vehicle technology has shown great potential to improve traffic operations and safety. Intersections are among locations in urban areas with a high number of crashes between vehicles and pedestrians. This study utilizes the vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and vehicle to pedestrian (V2P) communications to develop algorithms to predict unsafe conditions at signalized intersections, determine an appropriate crash remedial decision, and communicate it with appropriate vehicles. The proposed algorithms are examined in a simulation testbed developed in VISSIM under various demand patterns, connected vehicle penetration rates, and driver compliance with the crash remedial decisions. Case study results show that increasing the traffic demand and reducing driver compliance rate are associated with higher number of conflicts and potential crashes. Furthermore, increasing the market penetration rates is shown to be associated with less number of conflicts. Moreover, adding V2I communication to V2V and V2P communications helped decrease the number of conflicts significantly.