Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

Heavy Vehicle Performance During Recovery From Forced-Flow Urban Freeway Conditions Due To Incidents, Work Zones and Recurring Congestion

Information contained in the Highway Capacity Manual on the influence heavy vehicles have on freeway traffic operations has been based on few field data collection efforts and relied mostly on traffic simulation efforts. In the 2010 Manual heavy vehicle impact is evaluated based on “passenger car equivalent” values for buses, recreational vehicles and trucks. These values were calibrated for relatively uncongested freeway conditions (levels of service A through C) since inadequate field data on heavy vehicle behavior under congested conditions were available. A number of field data collection efforts, that were not included in deriving the passenger car equivalent values used in the Highway Capacity Manual, indicated that heavy vehicle impacts on traffic operations may increase as freeway congestion levels increase and freeways operate under unstable flow conditions. The goal of the present effort was to collect and analyze field data with an emphasis on heavy vehicle behavior under lower speeds and derive passenger car equivalent values under such conditions

Real-time Queue Length Estimation Applying Shockwave Theory at Urban Signalized Intersections

Signal control is a basic need for urban traffic control; however, it is a very rough intervention in the free flow of traffic, which often results in queues in front of signal heads. The general goal is to reduce the delays caused, and to plan efficient traffic management on the network. For this, the exact knowledge of queue lengths on links is one of crucial importance. This article presents a link-based methodology for real-time queue length estimation in urban signalized road networks. The model uses a Kalman Filter-based recursive method and estimates the length of the queue in every cycle. The input of the filter, i.e. the dynamics of queue length is described by the traffic shockwave theory and the store and forward model. The method requires one loop-detector per link placed at the appropriate position, for which the article also provides suggestions

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

Real-Time Vehicle Emission Estimation Using Traffic Data

The current state of climate change should be addressed by all sectors that contribute to it. One of the major contributors is the transportation sector, which generates a quarter of greenhouse gas emissions in North America. Most of these transportation related emissions are from road vehicles; as result, how to manage and control traffic or vehicular emissions is therefore becoming a major concern for the governments, the public and the transportation authorities. One of the key requirements to emission management and control is the ability to quantify the magnitude of emissions by traffic of an existing or future network under specific road plans, designs and traffic management schemes. Unfortunately, vehicular traffic emissions are difficult to quantify or predict, which has led a significant number of efforts over the past decades to address this challenge. Three general methods have been proposed in literature. The first method is for determining the traffic emissions of an existing road network with the idea of measuring the tail-pipe emissions of individual vehicles directly. This approach, while most accurate, is costly and difficult to scale as it would require all vehicles being equipped with tail-pipe emission sensors. The second approach is applying ambient pollutant sensors to measure the emissions generated by the traffic near the sensors. This method is only approximate as the vehicle-generated emissions can easily be confounded by other nearby emitters and weather and environmental conditions. Note that both of these methods are measurement-based and can only be used to evaluate the existing conditions (e.g., after a traffic project is implemented), which means that it cannot be used for evaluating alternative transportation projects at the planning stage. The last method is model-based with the idea of developing models that can be used to estimate traffic emissions. The emission models in this method link the amount of emissions being generated by a group of vehicles to their operations details as well as other influencing factors such as weather, fuel and road geometry. This last method is the most scalable, both spatially and temporally, and also most flexible as it can meet the needs of both monitoring (using field data) and prediction. Typically, traffic emissions are modelled on a macroscopic scale based on the distance travelled by vehicles and their average speeds. However, for traffic management applications, a model of higher granularity would be preferred so that impacts of different traffic control schemes can be captured. Furthermore, recent advances in vehicle detection technology has significantly increased the spatiotemporal resolutions of traffic data. For example, video-based vehicle detection can provide more details about vehicle movements and vehicle types than previous methods like inductive loop detection. Using such detection data, the vehicle movements, referred to as trajectories, can be determined on a second-by-second basis. These vehicle trajectories can then be used to estimate the emissions produced by the vehicles. In this research, we have proposed a new approach that can be used to estimate traffic generated emissions in real time using high resolution traffic data. The essential component of the proposed emission estimation method is the process to reconstruct vehicle trajectories based on available data and some assumptions on the expected vehicle motions including cruising, acceleration and deceleration, and car-following. The reconstructed trajectories containing instantaneous speed and acceleration data are then used to estimate emissions using the MOVES emission simulator. Furthermore, a simplified rate-based module was developed to replace the MOVES software for direct emission calculation, leading to significant improvement in the computational efficiency of the proposed method. The proposed method was tested in a simulated environment using the well-known traffic simulator - Vissim. In the Vissim model, the traffic activities, signal timing, and vehicle detection were simulated and both the original vehicle trajectories and detection data recorded. To evaluate the proposed method, two sets of emission estimates are compared: the “ground truth” set of estimates comes from the originally simulated vehicle trajectories, and the set from trajectories reconstructed using the detection data. Results show that the performance of the proposed method depends on many factors, such as traffic volumes, the placement of detectors, and which greenhouse gas is being estimated. Sensitivity analyses were performed to see whether the proposed method is sufficiently sensitive to the impacts of traffic control schemes. The results from the sensitivity analyses indicate that the proposed method can capture impacts of signal timing changes and signal coordination but is insufficiently sensitive to speed limit changes. Further research is recommended to validate the proposed method using field studies. Another recommendation, which falls outside of this area of research, would be to investigate the feasibility of equipping vehicles with devices that can record their instantaneous fuel consumption and location data. With this information, traffic controllers would be better informed for emission estimation than they would be with only detection data

Antifragile Control Systems: The case of an oscillator-based network model of urban road traffic dynamics

Existing traffic control systems only possess a local perspective over the multiple scales of traffic evolution, namely the intersection level, the corridor level, and the region level respectively. But luckily, despite its complex mechanics, traffic is described by various periodic phenomena. Workday flow distributions in the morning and evening commuting times can be exploited to make traffic adaptive and robust to disruptions. Additionally, controlling traffic is also based on a periodic process, choosing the phase of green time to allocate to opposite directions right of the pass and complementary red time phase for adjacent directions. In our work, we consider a novel system for road traffic control based on a network of interacting oscillators. Such a model has the advantage to capture temporal and spatial interactions of traffic light phasing as well as the network-level evolution of the traffic macroscopic features (i.e. flow, density). In this study, we propose a new realization of the antifragile control framework to control a network of interacting oscillator-based traffic light models to achieve region-level flow optimization. We demonstrate that antifragile control can capture the volatility of the urban road environment and the uncertainty about the distribution of the disruptions that can occur. We complement our control-theoretic design and analysis with experiments on a real-world setup comparatively discussing the benefits of an antifragile design for traffic control

Doctor of Philosophy

dissertationAs the nation's traffic system becomes more congested for various periods of the day, more research in the area of intelligent transportation systems is needed. Traditional solutions of adding more highways and widening the existing system are not feasible anymore due to rapidly increasing demand and lack of room for expansion. The national interest is therefore focused on congestion mitigation methods that promote efficient use of existing infrastructure. Some of the key aspects of congestion management techniques include Intelligent Transportation Systems (ITS) elements. These ITS elements can play a role in drivers' interaction, route choice, and traffic controls. Combined Traffic Assignment and Control (CTAC) framework-based models can capture the ITS elements- based control-driver interaction in traffic systems. The CTAC method has been the topic of scientific research for the last three decades. Several solution algorithms, model formulations, and implementation efforts have been well documented. Although proven in research, the use of the combined traffic assignment and control modeling framework is rare in practice. Typically, the engineering practice tends to keep Traffic Assignment and Control Optimization processes separate. By doing so, the control-driver interaction in the traffic system is ignored. Previous research found that CTAC models could capture the control-driver interaction and the combined modeling framework should be used in practice

Inteligentno upravljanje prometom uz dodjelu prioriteta vozilima žurnih službi

Advanced traffic management systems in city traffic (traffic light management) give possibility to give priority of passage to selected type of users, such as public transport, VIP users, and emergency services. In Republic of Croatia at present time there is no existent developed adaptive algorithms that can give priority to vehicles of Emergency services through the intersection. During this research solution to give priority passage Emergency vehicles in city traffic, benefit of such advances system will be investigated and proved with a simulation model. In same project cooperative concept will be evaluated (regarding emergency services) which includes a real time vehicle to vehicle and vehicle to infrastructure communication.Napredni sustavi upravljanja gradskom prometnom mrežom (semaforiziranim raskrižjima) omogućuju prioritetni prolazak određenom tipu korisnika kao npr. javni gradski prijevoz, VIP korisnici, žurne službe. U Republici Hrvatskoj za sada nema razrađenih adaptivnih upravljačkih algoritama prema kojima vozila žurnih službi mogu prioritetno proći raskrižjem. Kroz ovo istraživanje razmotrit će se mogućnost prioritetnog prolaska žurnih službi u gradskom prometu, te će se na temelju simulacijskog modela dokazati korist takvog unaprijeđenog sustava. Također, dodatno će se razmotriti i mogućnosti kooperativnog koncepta u odnosu na vozila žurnih službi, što uključuje komunikaciju između vozila i vozila i infrastrukturu stvarnom vremenu

Inteligentno upravljanje prometom uz dodjelu prioriteta vozilima žurnih službi

Advanced traffic management systems in city traffic (traffic light management) give possibility to give priority of passage to selected type of users, such as public transport, VIP users, and emergency services. In Republic of Croatia at present time there is no existent developed adaptive algorithms that can give priority to vehicles of Emergency services through the intersection. During this research solution to give priority passage Emergency vehicles in city traffic, benefit of such advances system will be investigated and proved with a simulation model. In same project cooperative concept will be evaluated (regarding emergency services) which includes a real time vehicle to vehicle and vehicle to infrastructure communication.Napredni sustavi upravljanja gradskom prometnom mrežom (semaforiziranim raskrižjima) omogućuju prioritetni prolazak određenom tipu korisnika kao npr. javni gradski prijevoz, VIP korisnici, žurne službe. U Republici Hrvatskoj za sada nema razrađenih adaptivnih upravljačkih algoritama prema kojima vozila žurnih službi mogu prioritetno proći raskrižjem. Kroz ovo istraživanje razmotrit će se mogućnost prioritetnog prolaska žurnih službi u gradskom prometu, te će se na temelju simulacijskog modela dokazati korist takvog unaprijeđenog sustava. Također, dodatno će se razmotriti i mogućnosti kooperativnog koncepta u odnosu na vozila žurnih službi, što uključuje komunikaciju između vozila i vozila i infrastrukturu stvarnom vremenu

Relieving the Impact of Transit Signal Priority on Passenger Cars through a Bilevel Model

Transit signal priority (TSP) is an effective control strategy to improve transit operations on the urban network. However, the TSP may sacrifice the right-of-way of vehicles from side streets which have only few transit vehicles; therefore, how to minimize the negative impact of TSP strategy on the side streets is an important issue to be addressed. Concerning the typical mixed-traffic flow pattern and heavy transit volume in China, a bilevel model is proposed in this paper: the upper-level model focused on minimizing the vehicle delay in the nonpriority direction while ensuring acceptable delay variation in transit priority direction, and the lower-level model aimed at minimizing the average passenger delay in the entire intersection. The parameters which will affect the efficiency of the bilevel model have been analyzed based on a hypothetical intersection. Finally, a real-world intersection has been studied, and the average vehicle delay in the nonpriority direction decreased 11.28 s and 22.54 s (under different delay variation constraint) compared to the models that only minimize average passenger delay, while the vehicle delay in the priority direction increased only 1.37 s and 2.87 s; the results proved the practical applicability and efficiency of the proposed bilevel model