Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data

The file attached to this record is the author's final peer reviewed version.Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles

A conceptual framework for using feedback control within adaptive traffic control systems

Existing adaptive traffic control strategies lack an effective evaluation procedure to check the performance of the control plan after implementation. In the absence of an effective evaluation procedure, errors introduced in the system such as inaccurate estimates of arrival flows, are carried forward in time and reduce the efficiency of the traffic flow algorithms as they assess prevalent traffic conditions. It is evident that the feed-forward nature of these systems cannot accurately update the estimated quantities, especially during oversaturated conditions. This research is an attempt to develop a conceptual framework for the application of feedback control within the basic operation of existing adaptive traffic control systems to enhance their performance. The framework is applied to three existing adaptive traffic control strategies (SCOOT, SCATS, and OPAC) to enable better demand estimations and queue management during oversaturated condition. A numerical example is provided to test the performance of an arterial in a feedback environment. The example involves the design and simulation test of Proportional (P) and Proportional-Integral (P1) controllers and their adaptability to adequately control the arterial. A sensitivity analysis is further performed to justify the use of a feedback control system on arterials and to choose the type of controller best suited under given demand conditions. The simulation results indicated that for the studied arterial, the P1 controller can handle demand estimation and queuing better than P controllers. It was determined that a well designed feedback control system with a PI controller can effectively overcome some of the deficiencies of existing adaptive traffic control systems

Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.Comment: 8 page

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

AN INTEGRATED CONTROL MODEL FOR FREEWAY INTERCHANGES

This dissertation proposes an integrated control framework to deal with traffic congestion at freeway interchanges. In the neighborhood of freeway interchanges, there are six potential problems that could cause severe congestion, namely lane-blockage, link-blockage, green time starvation, on-ramp queue spillback to the upstream arterial, off-ramp queue spillback to the upstream freeway segments, and freeway mainline queue spillback to the upstream interchange. The congestion problem around freeway interchanges cannot be solved separately either on the freeways or on the arterials side. To eliminate this congestion, we should balance the delays of freeways and arterials and improve the overall system performance instead of individual subsystem performance. This dissertation proposes an integrated framework which handles interchange congestion according to its severity level with different models. These models can generate effective control strategies to achieve near optimal system performance by balancing the freeway and arterial delays. The following key contributions were made in this dissertation: 1. Formulated the lane-blockage problem between the movements of an arterial intersection approach as an linear program with the proposed sub-cell concept, and proposed an arterial signal optimization model under oversaturated traffic conditions; 2. Formulated the traffic dynamics of a freeway segment with cell-transmission concept, while considering the exit queue effects on its neighboring through lane traffic with the proposed capacity model, which is able to take the lateral friction into account; 3. Developed an integrated control model for multiple freeway interchanges, which can capture the off-ramp spillback, freeway mainline spillback, and arterial lane and link blockage simultaneously; 4. Explored the effectiveness of different solution algorithms (GA, SA, and SA-GA) for the proposed integrated control models, and conducted a statistical goodness check for the proposed algorithms, which has demonstrated the advantages of the proposed model; 5. Conducted intensive numerical experiments for the proposed control models, and compared the performance of the optimized signal timings from the proposed models with those from Transyt-7F by CORSIM simulations. These comparisons have demonstrated the advantages of the proposed models, especially under oversaturated traffic conditions

Optimisation of Signal Timing at Intersections with Waiting Areas

Unconventional geometric designs such as continu-ous-flow intersections, U-turns, and contraflow left-turn lanes have been proposed to reduce left-turn conflicts and improve intersection efficiency. Having a waiting area at a signalised intersection is an unconventional de-sign that is used widely in China and Japan to improve traffic capacity. Many studies have shown that waiting areas improve traffic capacity greatly, but few have con-sidered how to improve the benefits of this design from the aspect of signal optimisation. Comparing the start-up process of intersections with and without waiting areas, this work explores how this geometric design influenc-es vehicle transit time, proposes two signal optimisation strategies, and establishes a unified capacity calculation model. Taking capacity maximisation as the optimisation function, a cycle optimisation model is derived for over-saturated intersections. Finally, the relationship among waiting-area storage capacity, cycle time, and traffic ca-pacity is discussed using field survey data. The results of two cases show that optimising the signal scheme helps reduce intersection delays by 10–15%

Synchro Software-Based Alternatives for Improving Traffic Operations at Signalized Intersections

Traffic congestion is a considerable problem in urban arterials, especially at signalized intersections. Signalized intersections are critical elements of the highway system, thus improving their performance would significantly influence the overall operating performance of the system in terms of delay and level of service (LOS). The aim of this study is to assess the capacity performance of two signalized intersections in Duhok city, namely, Zari land intersection and Salahaddin Mosque intersection using the procedure in the Highway Capacity Manual and Synchro software. Total intersection delay, LOS, and volume to capacity ratio (v/c) were the measures of effectiveness used for comparison purposes. Different optimization alternatives have been tested to improve current and future performance. The results have shown that the Zari land intersection is currently operating at LOS F with an average delay of 590 s/veh and high values of v/c at specific movements. Results of optimization show that the scenario of creating an overpass with a change in cycle length and adding one additional lane in each direction is the best alternative to improve its performance to the LOS D with the maximum v/c ratio of 0.86. For Salahaddin Mosque intersection, the delay can be reduced from 544 s/veh (LOS F) with high values of v/c at the major street through movement to an average delay of 70 s/veh (LOS E) and maximum v/c ratio of 1, when cycle length and geometrics are changed, and approaching traffic from the minor street is prohibited

Neurofuzzy control to address stochastic variation in actuated-coordinated systems at closely-spaced intersections

This dissertation documents a method of addressing stochastic variation at closely-spaced signalized intersections using neurofuzzy control. Developed on the conventional actuated-coordinated control system, the neurofuzzy traffic signal control keeps the advantage of the conventional control system. Beyond this, the neurofuzzy signal control coordinates the coordinated phase with one of the non-coordinated phases with no reduction of the green band assigned to the coordination along the arterial, reduces variations of traffic signal times in the cycle caused by early return to green , hence, makes more sufficient utilization of green time at closely-spaced intersections. The neurofuzzy signal control system manages a non-coordinated movement in order to manage queue spillbacks and variations of signal timings.Specifically, the neurofuzzy controller establishes a secondary coordination between the upstream coordinated phase (through phase) and the downstream non-coordinated phase (left turn phase) based on real-time traffic demand. Under the fuzzy logic signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left turn lane and be served, and hence, less possibly be blocked on the downstream left turn lane. This secondary coordination favors left turn progression and, hence, reduces the queue spillbacks. The fuzzy logic method overcomes the natural disadvantage of currently widely used actuated-coordinated traffic signal control in that the fuzzy logic method could coordinate a coordinated movement with a non-coordinated movement. The experiment was conducted and evaluated using a simulation model created using the microscopic simulation program - VISSIM.The neurofuzzy control algorithm was coded with MATLAB which interacts with the traffic simulation model via VISSIM\u27s COM interface. The membership functions in the neurofuzzy signal control system were calibrated using reinforcement learning to further the performance. Comparisons were made between the trained neurofuzzy control, the untrained neurofuzzy control, and the conventional actuated-coordinated control under five different traffic volumes. The simulation results indicated that the trained neurofuzzy signal control outperformed the other two for each traffic case. Comparing to the conventional actuated-coordinated control, the trained neurofuzzy signal control reduced the average delay by 7% and the average number of stops by 6% under the original traffic volume; as traffic volume increasing to 120%, the reductions doubled

Dynamic green split optimization in intersection signal design for urban street network

In the past few decades, auto travel demand in the United States has significantly increased, but roadway capacity unfortunately has not expanded as quickly, which has led to severe levels of highway traffic congestion in many areas. In theory, the problem of congestion addressed through demand management and roadway expansion. However, system expansion in urban areas is difficult due to the extremely high cost of land; therefore, maximizing the existing capacity therefore often is considered the most realistic option. In urban areas, most of the traffic congestion and delays typically occur at signalized intersections. This thesis aims to prove the hypothesis that it is possible to increase capacity by establishing traffic signal timing plans that are more effective than existing plans. A new methodology is introduced in this thesis for dynamic green split optimization as a part of intersection signal-timing design to achieve maximized reduction in overall delay at all the intersections within an urban street network. The measurement of effectiveness in this new method is reduction in the average delay per vehicle per signal cycle. This thesis used data from 143 signalized intersections and 334 street segments in the Chicago Loop area street network to demonstrate the proposed methodology. The results suggest that it is possible to reduce delay by approximately 35% through the optimization of signal green splits for the four-hour AM and four-hour PM peak periods of a typical da