Article

Lane-Based Saturation Degree Estimation for Signalized Intersections Using Travel Time Data

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Abstract

Saturation degree estimation is a vital problem of signal timing optimization. However, classic loop-detector-based algorithms are not capable to capture the severity of oversaturation, since detectors are located in front of stop lines, and also cannot distinguish the saturated degree in different lane groups if detectors are located at an upstream position. In this paper, we present a new method to estimate the lane-based saturation degree using travel times. The method is simple and mainly depends on the parameters of signal cycles and the corresponding virtual cycles. The virtual cycle parameters are extracted by analyzing the data on travel times using the K-mean cluster analysis. Then, two models for the traffic demand saturated degree (TDSD) and the effectively used green time saturation degree (EUGTSD) are presented based on the traffic flow conservation during one signal cycle and the corresponding virtual cycle. The new method can overcome the defects of loop-detector-based algorithms, and it can be used to optimize the TDSD and the EUGTSD simultaneously. Finally, the precision of the two types of models is evaluated using field survey data. The results show that the new method has a higher precision for the TDSD and the same accuracy level for the EUGTSD compared to the existing methods. The findings of this paper have potential applicability to signal control systems.

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... Recently, the issue on the need and use of systems to overcome the problem of vehicle collisions becomes more prevalent. The Road Traffic Safety Act clearly states that if a driver drives more than 4 hours without a break, it will be considered as tired while driving [29]. Factors found to be influencing fatigue have been explored in several studies particularly on drivers who were involved in longdistance passenger and shipping transportation due to requirement that need to continuously driving for a long journey from time to time to finish work responsibly. ...
... The Road Safety Law of China states that if a person drives for more than 4 hours without taking any break during this period, he will be regarded as fatigue [2], [3]. If anyone is found doing so, he can be fined by the traffic control department and his license can be deduced as well. ...
... With the rapid development of economy and the acceleration of urbanization, traffic pollution, traffic congestion, and other related problems have become social hotspots [1][2][3][4]. In order to solve these problems, traffic scholars and engineers have proposed a variety of traffic control strategies. ...
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... However, due to sample sizes, differences in driving styles [21], and the discrete characteristics of accident occurrence, current accident analysis models are only appropriate for certain countries and regions, and are difficult to transfer across them [22,23]. Shew et al. [24] established an accident prediction model based on the accident data of U.S. Highway 101 in California, and tested the prediction accuracy using I-880 highway accident data. ...
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... These detectors capture passing vehicles and record the information on them, such as license plate number, vehicle type, driving lane, and time of detection. The LPR detectors accumulate a large amount of LPR data, which can be used for estimation and prediction of traffic state parameters, as well as for analysis of vehicle travel behavior and traffic demands (Luo et al., 2019;Ma et al., 2017a;Shen et al., 2020;Wang et al., 2016). The LPR data have received widespread attention because of its wide coverage, large amount, and high quality. ...
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... ey conducted a case study to examine whether the model was feasible. Glickman considered the service demand and reversible lane allocation decision, established a mathematical model based on the minimum vehicle delay and maximum capacity, and applied it to bridges and tunnels [4][5][6][7]. Later, they extended the research to adaptive control algorithms and considered the random arrival. ...
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... Some differences in SV crashes between foggy and clear weather may exist [4,5]. e low visibility caused by fog affects driver behavior and the driving environment, which can lead to contributing effects on traffic collisions that are different from those in clear weather. ...
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Automatic license plate recognition (LPR) plays an important role in numerous applications and a number of techniques have been proposed. However, most of them worked under restricted conditions, such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. In this study, as few constraints as possible on the working environment are considered. The proposed LPR technique consists of two main modules: a license plate locating module and a license number identification module. The former characterized by fuzzy disciplines attempts to extract license plates from an input image, while the latter conceptualized in terms of neural subjects aims to identify the number present in a license plate. Experiments have been conducted for the respective modules. In the experiment on locating license plates, 1088 images taken from various scenes and under different conditions were employed. Of which, 23 images have been failed to locate the license plates present in the images; the license plate location rate of success is 97.9%. In the experiment on identifying license number, 1065 images, from which license plates have been successfully located, were used. Of which, 47 images have been failed to identify the numbers of the license plates located in the images; the identification rate of success is 95.6%. Combining the above two rates, the overall rate of success for our LPR algorithm is 93.7%.
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In this paper, we present an adaptive signal control scheme to prevent intersection traffic blockage resulted from vehicle queue spillover. A method to identify vehicle queue spillover condition through simplified shockwave analysis is developed. Instead of measuring the vehicle queue length or locating the end of queue directly, this method relies on the vehicle speed which is more feasible to measure in practice. The adaptive traffic signal control scheme is designed to prevent potential intersection traffic blockage, and adaptively allocates green time to appropriate signal phases. At the end, a simulation study is carried out to evaluate the proposed adaptive control scheme. The results show that the scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.
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In this paper, we present an adaptive signal control scheme to prevent intersection traffic blockage resulted from vehicle queue spillover. A method to identify vehicle queue spillover condition through simplified shockwave analysis is developed. Instead of measuring the vehicle queue length or locating the end of queue directly, this method relies on the vehicle speed which is more feasible to measure in practice. The adaptive traffic signal control scheme is designed to prevent potential intersection traffic blockage, and adaptively allocates green time to appropriate signal phases. At the end, a simulation study is carried out to evaluate the proposed adaptive control scheme. The results show that the scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.
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City-wide control and coordination of traffic flow can improve efficiency, fuel consumption, and safety. We consider the problem of controlling traffic lights under fixed and adaptive routing of vehicles in urban road networks. Multicommodity back-pressure algorithms, originally developed for routing and scheduling in communication networks, are applied to road networks to control traffic lights and adaptively reroute vehicles. The performance of the algorithms is analyzed using a microscopic traffic simulator. The results demonstrate that the proposed multicommodity and adaptive routing algorithms provide significant improvement over a fixed schedule controller and a single-commodity back-pressure controller in terms of various performance metrics, including queue length, trips completed, travel times, and fair traffic distribution.
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In this paper, performance of fuzzy c-means clustering method in specifying flow patterns, which are reconstructed by a macroscopic flow model, is sought using microwave radar data on fundamental variables of traffic flow. Traffic flow is simulated by the cell transmission model adopting a two-phase triangular fundamental diagram. Flow dynamics specific to the selected freeway test stretch are used to determine prevailing traffic conditions. The performance of fuzzy c-means clustering is evaluated in two cases, with two assumptions. The procedure fuzzy clustering method follows is systematically dynamic that enables the clustering, and hence partitions, over the fundamental diagram specific to selected temporal resolution. It is seen that clustering simulation with dynamic pattern boundary assumption performs better for almost all the steps of data expansion when considered to simulation with the corresponding static case.
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In this paper, we evaluate the performance of a dynamic approach to classifying flow patterns reconstructed by a switching-mode macroscopic flow model considering a multivariate clustering method. To remove noise and tolerate a wide scatter of traffic data, filters are applied before the overall modeling process. Filtered data are dynamically and simultaneously input to the density estimation and traffic flow modeling processes. A modified cell transmission model simulates traffic flow to explicitly account for flow condition transitions considering wave propagations throughout a freeway test stretch. We use flow dynamics specific to each of the cells to determine the mode of prevailing traffic conditions. Flow dynamics are then reconstructed by neural methods. By using two methods in part, i.e., dynamic classification and nonhierarchical clustering, classification of flow patterns over the fundamental diagram is obtained by considering traffic density as a pattern indicator. The fundamental diagram of speed-flow is dynamically updated to specify the current corresponding flow pattern. The dynamic classification approach returned promising results in capturing sudden changes on test stretch flow patterns as well as performance compared to multivariate clustering. The dynamic methods applied here are open to use in practice within intelligent management strategies, including incident detection and control and variable speed management.
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The exploration of the potential correlations of traffic conditions between roads in large urban networks, which is of profound importance for achieving accurate traffic prediction, often implies high computational complexity due to the implicated network topology. Hence, focal methods are required for dealing with the urban network complexity, reducing the performance requirements that are associated to the classical network search techniques (e.g., Breadth First Search). This paper introduces a graph-theory-based technique for managing spatial dependence between roads of the same network. In particular, after representing the traffic network as a graph, the local neighbors of each road are extracted using Breadth First Search graph traversal algorithm and a lower complexity variant of it. A Pearson product–moment correlation-coefficient-based metric is applied on the selected graph nodes for a prescribed number of level sets of neighbors. In order to evaluate the impact of the new method to the traffic prediction accuracy achieved, the most correlated roads are used to build a STARIMA model, taking also into account the possible time delays of traffic conditions between the interrelated roads. The proposed technique is benchmarked using traffic data from two different cities: Berlin, Germany, and Thessaloniki, Greece. Benchmark results not only indicate significant improvement on the computational time required for calculating traffic correlation metric values but also reveal that a different variant works better in different network topologies, after comparison to third-party approaches.
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A Bluetooth traffic monitoring system (BTMS) is capable of identifying vehicles and estimate their travel time (TT) in a route. This information is key for intelligent transportation systems. Although BTMSs are currently deployed in several cities throughout the world, there is no formal methodology for the TT estimation they generate. In this paper, we first analyze the specific features of the Bluetooth technology that affect the TT estimation. In particular, we study the reliability of the measurements, the representativeness of the estimates, and the issues regarding multiple detections and outliers. Based on this knowledge, we propose a comprehensive methodology for the TT estimation that considers exclusively information from vehicles. We filter these vehicles through a simple process that uses the available dedicated inquiry access code. In order to illustrate our proposal, we performed an experiment deploying commercial Bluetooth detectors on a freeway under real traffic conditions. The resulting BTMS provided highly reliable TT estimations with a 5-min resolution.
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In this paper, we develop rule- and model-based approaches for the real-time estimation of lane-to-lane turning flows. Our aim is to determine the turning proportions of vehicles based on detector information at isolated signalized junctions and thereby establish effective control strategies for adaptive traffic control systems. The key concept involves identifying the entrance lane of a vehicle detected in an exit lane at the signalized junction. Lane-to-lane turning flows are estimated by tracing the corresponding entrance lanes of the vehicle based on the detector and signal information from the set of potential entrance lanes at the junction. In the rule-based approach, the entrance lane of a vehicle detected in an exit lane is identified according to a set of specified rules. The model-based approach, which is based on utility maximization, is used to identify the most probable turns in a set of potential upstream entrance lanes. Both computer simulations and real-world traffic data show that the model-based approach outperforms the rule-based approach, particularly when turning on red is allowed, and is capable of accurate estimation under a wide range of traffic conditions in real time. However, the rule-based approach is simpler and does not require calibration, which are positive assets when no prior data are available for calibration.
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In this paper, a dynamic approach to specify flow pattern variations simulated by a multimode macroscopic flow model is followed, incorporating the neural network theory to reconstruct real-time traffic dynamics. In order to deal with the noise in and the wide scatter of traffic data, filtering is applied prior to overall modeling process. Filtered data are dynamically and simultaneously input to neural density estimation and traffic flow modeling processes. Traffic flow is simulated by modifying the cell transmission model in order to explicitly account for flow condition transitions considering wave propagations. Cell-specific flow dynamics are used to determine the mode of prevailing traffic conditions, which are, in turn, sought to be reconstructed by neural methods. The classification of flow patterns over the fundamental diagram is obtained by considering traffic density as a pattern indicator. The fundamental diagram of speed-density is updated to specify the current corresponding flow pattern. The modified classification returned promising results in capturing sudden changes on test stretch flow patterns that are simulated by the switching multimode discrete macroscopic model.
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This paper presents a method to identify spillovers based on upstream fixed detector data, using occupancy per cycle as the determination index. The key idea of this new method is that when the queues extend to the detector position, there will be unusable green time to a certain degree, and the occupancy will be greater than a particular threshold. Firstly, this paper introduces traffic wave models modified by a kinematic equation, and provides a calculation method for the occupancy per cycle under different traffic conditions, based on the relationship between the three basic traffic flow parameters, speed, traffic flow, and density. Secondly, the threshold of occupancy, which characterizes the appearance of spillovers, is determined by the premise that the stopping and starting waves have the same speed, and then the accuracy of the new method are verified by VISSIM simulation, using the ratio of misjudgment as the evaluation index. Finally, the precision stability of the method is analyzed, and the results show that the precision of this method is affected by the the detector location and bus ratio insignificantly.
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The emergence of new technologies allows better monitoring of traffic conditions and understanding of urban network dynamics. Bluetooth technology is becoming widespread, as it represents a cost-effective means for capturing road traffic in both arterials and motorways. Although the extraction of travel time from Bluetooth data is fairly straightforward, data reliability and processing is still challenging with the issues of penetration rate, mode discrimination, and detection quality. This paper presents a methodological contribution to the use of Bluetooth data for the spatiotemporal analysis of a large urban network (Brisbane, Australia). It introduces the concept of the Bluetooth origin–destination (B-OD) matrix, which is built from a network of 79 Bluetooth detectors located within the Brisbane urban area. The B-OD matrix describes the dynamics of a subpopulation of vehicles, between pairs of detectors. The results show that the characteristics of urban networks can be effectively represented through B-OD matrices. A comparison with loop detector data enables an assessment of the results' significance. Then, the spatiotemporal structure of the network is analyzed with two different clustering analyses, namely, latent Dirichlet allocation (LDA) and $K$-means. While LDA is used to detect a temporal pattern, the $K$-means algorithm highlights Bluetooth fundamental diagram (BFD) classes. The results show that Bluetooth data has the potential to be a reliable data source for traffic monitoring. By highlighting hidden structures of a large area, the algorithm outputs allow us to provide the road operators with a fine spatiotemporal analysis of their network, in terms of traffic conditions.
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Analyses on the dynamics of traffic flow, ranging from intersection flows to network‐wide flow propagation, require accurate information on time‐varying local traffic flows. To effectively determine the flow performance measures and consequently the congestion indicators of segmented road pieces, the ability to process such data in real time is out of the question. In this article, a dynamic approach to specify flow pattern variations is proposed mainly concentrating on the incorporation of neural network theory to provide real‐time mapping for traffic density simultaneously in conjunction with a macroscopic traffic flow model. To deal with the noise and the wide scatter of raw flow measures, a filtering is applied prior to modeling processes. Filtered data are dynamically and simultaneously input to processes of neural density mapping and traffic flow modeling. The classification of flow patterns over the fundamental diagram, which is dynamically plotted with the outputs of the flow modeling subprocess, is obtained by considering the density measure as a pattern indicator. Densities are mapped by selected neural approximation method for each simulation time step considering explicitly the flow conservation principle. Simultaneously, mapped densities are matched over the fundamental diagram to specify the current corresponding flow pattern. The approach is promising in capturing sudden changes on flow patterns and is open to be utilized within a series of intelligent management strategies including especially nonrecurrent congestion effect detection and control.
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This paper presents a framework for online highway travel-time prediction using traffic measurements that are likely to be available from vehicle infrastructure integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety. In the proposed intelligent VII system, two artificial intelligence (AI) paradigms, i.e., artificial neural networks (ANNs) and support vector regression (SVR), are used to determine future travel time based on such information as the current travel time and VII-enabled vehicles' flow and density. The development and performance evaluation of the VII-ANN and VII-SVR frameworks, in both the traffic and communications domains, were conducted using an integrated simulation platform for a highway network in Greenville, SC. In particular, the simulation platform allows for implementing traffic surveillance and management methods in the traffic simulator PARAMICS and for evaluating different communication protocols and network parameters in the communication network simulator, Network Simulator version 2 (ns-2). This paper's findings reveal that the designed communications system can support the travel-time prediction functionality. The findings also demonstrate that the travel-time prediction accuracy of the VII-AI framework was superior to a baseline instantaneous travel-time prediction algorithm, with the VII-SVR model slightly outperforming the VII-ANN model. Moreover, the VII-AI framework was shown to perform reasonably well during nonrecurrent congestion scenarios, which have traditionally challenged sensor-based highway travel-time prediction methods.
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This paper investigates the performance of a flow model in providing efficient travel-time estimation for varying flow patterns of freeway traffic by adopting a two-phase fundamental diagram. The model follows a discrete-packet-based mesoscopic simulation approach that explicitly considers both the anisotropic property of traffic flow in packet state updating and the uniform speed differentiation of vehicle packets at each discrete time step. The measure of travel time is obtained as a link performance resulting from a simplified dynamic network loading process. The spatiotemporal flow propagation on a selected freeway segment is simulated comparatively by incorporating both the proposed model and a linear-travel-time-function-based link performance model. Performance of the flow model in travel-time estimation is sought, considering actual measures obtained by a probe vehicle. The main improvement on estimating the travel-time process is that the employed model considers different speed and acceleration levels on different discrete time intervals and satisfies the anisotropy property by consistently simulating flow propagation within the dynamic network modeling frame. In contrast to the vast data need and computational burden of trajectory-based methods, the employed flow-based model requires only the time-varying inflow profiles to estimate spatially and temporally varying travel times by artificially segmenting freeway routes.
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Quick and reliable identification of the traffic state is of critical importance to traffic control systems, especially when spillovers appear. Firstly, a calculation method for the occupancy per cycle under different traffic conditions were presented, based on the relationship between the three basic traffic flow parameters, speed, traffic flow and density. Secondly, the times at which the stopping and starting waves approach a loop detector were confirmed using the traffic wave models modified by a kinematic equation. Then, the threshold of occupancy, which characterizes the appearance of spillovers, was determined by the premise that the stopping and starting waves had the same speed. At last, the accuracy and usability of the new method were verified by VISSIM simulation, using the ratio of misjudgment as the evaluation index. The results show that the ratio of misjudgment of the new method is about 11.36% compared to 17.65% of the previous method.
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The present study summarises the travel time reconstruction performance of a network flow model by explicitly analysing the adopted fundamental diagram relation under congested and un-congested traffic patterns. The incorporated network flow model uses a discrete meso-simulation approach in which the anisotropic property of traffic flow and the uniform acceleration of vehicle packets are explicitly considered. The flow performances on link-route dynamics have been derived by reasonably alternating the adopted two-phase, i.e., congested and un-congested, fundamental relation of traffic flow. The linear speed–density relation with the creeping speed assumption is substituted with the triangular flow–density relation in order to investigate the performance of the network flow model in varying flow patterns. Applying the anisotropic mesoscopic model, the measure of travel time is obtained as a link performance from a simplified dynamic network loading process. Travel time reconstruction performance of the network flow model is sought considering the actual measures that are obtained by a probe vehicle, in addition to reconstructions by a macroscopic network flow model. The main improvements on travel time reconstruction process are encountered in terms of the computation load within the explicit analyses by the alternation of adopted two-phase fundamental diagram. Although the accuracies of the flow model with the adoption of two different fundamental diagrams are hard to differentiate, the computational burden of the simulation process by the triangular fundamental diagram is found to be considerably different.
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Optimal control of oversaturated signals is one of the major concerns of traffic engineering practice today. Although some control strategies have been proposed in the past, due to their limitations they have not been used in practice. One of their major deficiencies is that the effects of queue length constraints on the signal operation have been inadequately investigated. Thus, the optimal control strategy with state variable constraints is studied in this paper. The optimal policy minimizing intersection delay subject to queue length constraints is to switch the signals as soon as the queues are at their limits so that the input and output flows are balanced. Cycle length can remain constant if only one queue length constraint is imposed but it must be free to vary if constraints are imposed on more than one queue. The conditions under which the problem is impossible are stated. A numerical solution method is proposed for determining the optimal control for the case in which the intersection demand is predictable for the entire control period.
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In 2005, the Cobb County Department of Transportation, Cobb County, GA, conducted an adaptive signal control pilot study implementing the Sydney Coordinated Adaptive Traffic System (SCATS) on 15 intersections. This paper presents the results of a before-and-after probe-vehicle-based operational comparison of optimized time-of-day (i.e., before control) and SCATS (i.e., after control) traffic control system performance. The focus of this operational analysis is the typical operating performance during the weekday peak, weekday off-peak, and weekend travel periods. Travel time data were collected using Global-Positioning-System (GPS)-equipped test vehicles. The results showed that both systems provided good performance, whereas neither the before time-of-day or after SCATS is clearly dominant, except on Cumberland Parkway, where SCATS control consistently provides equivalent or superior performance to that of the time-of-day control.
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A real-time adaptive control algorithm for tuning traffic signal offsets in a coordinated traffic signal system is presented. The algorithm described here is used in the Adaptive Control Software-Lite (ACS-Lite) adaptive control system. The algorithm uses a statistical average flow profile of traffic on the coordinated approaches to an intersection to assess when vehicles are arriving during the signal cycle. Alternative offset adjustments are evaluated by calculating how much of this flow profile is being captured by the green phase serving each coordinated approach. The algorithm considers the impact of the offset adjustment on traffic at the local intersection as well as on traffic at adjacent intersections that are also under ACS-Lite control. Simulation tests have quantitatively shown that this tuning approach can improve arterial progression performance relative to the quality of the initial baseline fixed offsets used in a traffic pattern. Several issues and areas for improvement of the algorithm are also identified and discussed.
Article
ACS-Lite is being developed by FHWA to be a cost-effective solution for applying adaptive control system (ACS) technology to current, state-of-the-practice closed-loop traffic signal control systems. This effort is intended to make ACS technology accessible to many jurisdictions without the upgrade and maintenance costs required to implement ACS systems that provide optimized signal timings on a second-by-second basis. The ACS-Lite system includes three major algorithmic components: a time-of-day (TOD) tuner, a run-time refiner, and a transition manager. The TOD tuner maintains plan parameters (cycle, splits, and offsets) as the long-term traffic conditions change. The run-time refiner modifies the cycle, splits, and offsets of the plan that is currently running based on observation of traffic conditions that are outside the normal bounds of conditions this plan is designed to handle. The run-time refiner also determines the best time to transition from the current plan to the next plan in the schedule, or, like a traffic-responsive system, it might transition to a plan that is not scheduled next in the sequence. The transition manager selects from the transition methods built in to the local controllers to balance the time spent out of coordination with the delay and congestion that is potentially caused by getting back into step as quickly as possible. These components of the ACS-Lite algorithm architecture are described and the similarities and differences of ACS-Lite with state-of-the-art and state-of-the-practice adaptive control algorithms are discussed. Closed-loop control system characteristics are summarized to give the context in which ACS-Lite is intended to operate.
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a b s t r a c t Conceptually, an oversaturated traffic intersection is defined as one where traffic demand exceeds the capacity. Such a definition, however, cannot be applied directly to identify oversaturated intersections because measuring traffic demand under congested conditions is not an easy task, particularly with fixed-location sensors. In this paper, we circumvent this issue by quantifying the detrimental effects of oversaturation on signal operations, both temporally and spatially. The detrimental effect is characterized temporally by a residual queue at the end of a cycle, which will require a portion of green time in the next cycle; or spatially by a spill-over from downstream traffic whereby usable green time is reduced because of the downstream blockage. The oversaturation severity index (OSI), in either the temporal dimension (T-OSI) or the spatial dimension (S-OSI) can then be mea-sured using high-resolution traffic signal data by calculating the ratio between the unus-able green time due to detrimental effects and the total available green time in a cycle. To quantify the T-OSI, in this paper, we adopt a shockwave-based queue estimation algo-rithm to estimate the residual queue length. S-OSI can be identified by a phenomenon denoted as ''Queue-Over-Detector (QOD)", which is the condition when high occupancy on a detector is caused by downstream congestion. We believe that the persistence dura-tion and the spatial extent with OSI greater than zero provide an important indicator for measuring traffic network performance so that corresponding congestion mitigation strat-egies can be prepared. The proposed algorithms for identifying oversaturated intersections and quantifying the oversaturation severity index have been field-tested using traffic signal data from a major arterial in the Twin Cities of Minnesota.
Article
Link travel times are crucial for advanced traveler information systems and traffic management applications. However, current systems for estimating them still have shortcomings that need to be addressed. In this paper, we propose a novel framework for vehicle reidentification via signature matching using signal processing techniques and a travel time estimation algorithm that is robust to potential (and often inevitable) vehicle misidentifications. Individual vehicles are matched between well-separated stations in a road transportation network using signatures captured by embedded roadway sensors. Statistical and multirate signal processing methods are used to develop data-postprocessing algorithms that are critical to the subsequent signature-matching problem, which is formulated using optimal techniques from communication theory. A probabilistic modeling of the generated matching assignments and an unsupervised data-clustering technique are then used to devise a travel time estimation algorithm. The proposed method is tested under a real traffic scenario, and accurate link travel time measures are reported.
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A practical system is described for the real-time estimation of travel time across an arterial segment with multiple intersections. The system relies on matching vehicle signatures from wireless sensors. The sensors provide a noisy magnetic signature of a vehicle and the precise time when it crosses the sensors. A match (re-identification) of signatures at two locations gives the corresponding travel time of the vehicle. The travel times for all matched vehicles yield the travel time distribution. Matching results can be processed to provide other important arterial performance measures including capacity, volume/capacity ratio, queue lengths, and number of vehicles in the link. The matching algorithm is based on a statistical model of the signatures. The statistical model itself is estimated from the data, and does not require measurement of ‘ground truth’. The procedure does not require measurements of signal settings; in fact, signal settings can be inferred from the matched vehicle results. The procedure is tested on a 1.5 km (0.9 mile)-long segment of San Pablo Avenue in Albany, CA, under different traffic conditions. The segment is divided into three links: one link spans four intersections, and two links each span one intersection.
Article
In this paper, a neural network (NN) approximator, integrated to a dynamic network loading (DNL) process, is utilized to model delays and to solve the DNL problem at an unsignalized highway node. First, a dynamic node model (DNM) is set out to compute the time-varying traffic flows conflicting at the node. The presented DNM has two components: a link model set with a linear travel time function and an algorithm written with a set of node rules considering the constraints of conservation, flow splitting rates and non-negativity. Each of the selected NN methods, feed-forward back-propagation NN, radial basis function NN, and generalized regression NN, are utilized one by one in the NN approximator that is integrated with the proposed DNM, and, hence, three DNL processes are simulated. Delays forming as a result of capacity constraint and flow conflicting at the node are calculated with selected NN configurations after calibrating the NN component with conical delay function formulation. The results of the model structure, run solely with the conventional delay function, are then compared to evaluate the performance of the models supported with NNs relatively.
Article
This paper presents a new methodology for optimiz- ing the signal timing controls of oversaturated networks based on the cell transmission model and a goal programming tech- nique with multiple objectives. The proposed model accounts for intersection spillovers, equity in delays, and system throughputs. This new formulation is solved by genetic algorithms to obtain signal timing plans. A case study with a nine-intersection network and a comparison between the proposed model and the through- put-maximizing strategy are examined. It is found that the new method can efficiently minimize spillovers, balance delay equity, and provide reasonable system throughputs in their respective or- der for oversaturated networks. The result also indicates that the throughput-maximizing strategy does not always yield minimum spillovers for oversaturated networks and occasionally provides a larger difference in average link delay at a spillover intersection than the proposed model does.
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In this paper, we propose a fast greedy search algorithm for optimal single-cycle signal timing at individual oversaturated intersections. We illustrate the efficiency of the algorithm with a numerical example in the literature.
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