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Author: J. W. C. van Lint
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 332
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Author: J. W. C. van Lint
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 332
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Author: WENFU. WANG
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Short-term travel time prediction supports the implementation of proactive traffic management and control strategies to alleviate if not prevent congestion and enable rational route choices and traffic mode selections to enhance travel mobility and safety. Over the last decade, Bluetooth technology has been increasingly used in collecting travel time data due to the technology's advantages over conventional detection techniques in terms of direct travel time measurement, anonymous detection, and cost-effectiveness. However, similar to many other Automatic Vehicle Identification (AVI) technologies, Bluetooth technology has some limitations in measuring travel time information including 1) Bluetooth technology cannot associate travel time measurements with different traffic streams or facilities, therefore, the facility-specific travel time information is not directly available from Bluetooth measurements; 2) Bluetooth travel time measurements are influenced by measurement lag, because the travel time associated with vehicles that have not reached the downstream Bluetooth detector location cannot be taken at the instant of analysis. Freeway sections may include multiple distinct traffic stream (i.e., facilities) moving in the same direction of travel under a number of scenarios including: (1) a freeway section that contain both a High Occupancy Vehicle (HOV) or High Occupancy Toll (HOT) lane and several general purpose lanes (GPL); (2) a freeway section with a nearby parallel service roadway; (3) a freeway section in which there exist physically separated lanes (e.g. express versus collector lanes); or (4) a freeway section in which a fraction of the lanes are used by vehicles to access an off ramp. In this research, two different methods were proposed in estimating facility-specific travel times from Bluetooth measurements. Method 1 applies the Anderson-Darling test in matching the distribution of real-time Bluetooth travel time measurements with reference measurements. Method 2 first clusters the travel time measurements using the K-means algorithm, and then associates the clusters with facilities using traffic flow model. The performances of these two proposed methods have been evaluated against a Benchmark method using simulation data. A sensitivity analysis was also performed to understand the impacts of traffic conditions on the performance of different models. Based on the results, Method 2 is recommended when the physical barriers or law enforcement prevent drivers from freely switching between the underlying facilities; however, when the roadway functions as a self-correcting system allowing vehicles to freely switching between underlying facilities, the Benchmark method, which assumes one facility always operating faster than the other facility, is recommended for application. The Bluetooth travel time measurement lag leads to delayed detection of traffic condition variations and travel time changes, especially during congestion and transition periods or when consecutive Bluetooth detectors are placed far apart. In order to alleviate the travel time measurement lag, this research proposed to use non-lagged Bluetooth measurements (e.g., the number of repetitive detections for each vehicle and the time a vehicle spent in the detection zone) for inferring traffic stream states in the vicinity of the Bluetooth detectors. Two model structures including the analytical model and the statistical model have been proposed to estimate the traffic conditions based on non-lagged Bluetooth measurements. The results showed that the proposed RUSBoost classification tree achieved over 94% overall accuracy in predicting traffic conditions as congested or uncongested. When modeling traffic conditions as three traffic states (i.e., the free-flow state, the transition state, and the congested state) using the RUSBoost classification tree, the overall accuracy was 67.2%; however, the accuracy in predicting the congested traffic state was improved from 84.7% of the two state model to 87.7%. Because traffic state information enables the travel time prediction model to more timely detect the changes in traffic conditions, both the two-state model and the three-state model have been evaluated in developing travel time prediction models in this research. The Random Forest model was the main algorithm adopted in training travel time prediction models using both travel time measurements and inferred traffic states. Using historical Bluetooth data as inputs, the model results proved that the inclusion of traffic states information consistently lead to better travel time prediction results in terms of lower root mean square errors (improved by over 11%), lower 90th percentile absolute relative error ARE (improved by over 12%), and lower standard deviations of ARE (improved by over 15%) compared to other model structures without traffic states as inputs. In addition, the impact of traffic state inclusion on travel time prediction accuracy as a function of Bluetooth detector spacing was also examined using simulation data. The results showed that the segment length of 4~8 km is optimal in terms of the improvement from using traffic state information in travel time prediction models.
Author: Reza Noroozisanani
Publisher:
ISBN:
Category : Automobiles
Languages : en
Pages : 129
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Book Description
Travel time is one of the important transportation performance measures, and represents the quality of service for most of the facilities. In other words, one of the essential goals of any traffic treatment is to reduce the average travel time. Therefore, extensive work has been done to measure, estimate, and predict travel time. Using historical observations, traditional traffic analysis methods try to calibrate empirical models to estimate the average travel time of different transportation facilities. However, real-time traffic responsive management strategies require that estimates of travel time also be available in real-time. As a result, real time estimation and prediction of travel time has attracted increasing attention. Various factors influence the travel time of a road segment including: road geometry, traffic demand, traffic control devices, weather conditions, driving behaviors, and incidents. Consequently, the travel time of a road segment varies as a result of the variation of the influencing factors. Predicting near-future freeway traffic conditions is challenging, especially when traffic conditions are in transition from one state to another (e.g. changing from free flow conditions to congestion and vice versa). This research aims to develop a method to perform real-time prediction of near-future freeway traffic stream characteristics (namely speed) and that relies on spot speed, volume, and occupancy measurements commonly available from loop detectors or other similar traffic sensors. The framework of this study consists of a set of individual modules. The first module is called the Base Predictor. This module provides prediction for traffic variables while state of the traffic remains constant i.e free flow or congested. The Congestion Detection Module monitors the traffic state at each detector station of the study route to identify whether traffic conditions are congested or uncongested. When a congestion condition is detected, the Traffic Propagation Module is activated to update the prediction results of the Steady-State Module. The Traffic Propagation Module consists of two separate components: Congestion Spillback activates when traffic enters a congested state; Congestion Dissipation is activated when a congested state enters a recovery phase. The proposed framework of this study is calibrated and evaluated using data from an urban expressway in the City of Toronto, Canada. Data were obtained from the westbound direction of the Gardiner Expressway which has a fixed posted speed limit of 90 km/hr. This expressway is equipped with mainline dual loop detector stations. Traffic volume, occupancy and speed are collected every 20 seconds for each lane at all the stations. The data set used in this study was collected over the period from January 2009 to December 2011. For the Steady-State Module, a model based on Kalman filter was developed to predict the near future traffic conditions (speed, flow, and occupancy) at the location of fixed point detectors (i.e. loop detector in this study). Traffic propagation was proposed to be predicted based on either a static or dynamic traffic pattern. In the static pattern it was assumed that traffic conditions can be attributed based on the observed conditions in the same time of day; however, in the dynamic pattern, expected traffic conditions are estimated based on the current measurements of upstream and downstream detectors. The prediction results were compared to a naïve method, and it was shown that the average prediction error during the “change period” when traffic conditions are changing from free flow to congestion and vice versa is significantly lower when compared to the naïve method for the sample locations (approximately 25% improvement) For the Traffic Propagation Module, a model has been developed to predict the speed of backward forming and forward recovery shockwaves. Unlike classic shockwave theory which is deterministic, the proposed model expresses the spillback and recovery as a stochastic process. The transition probability matrix is defined as a function of traffic occupancy on upstream and downstream stations in a Markov framework. Then, the probability of spillback and recovery was computed given the traffic conditions. An evaluation using field data has shown that the proposed stochastic model performs better than a classical shockwave model in term of correctly predicting the occurrence of backward forming and forward recovery shockwaves on the field data from the urban expressway. A procedure has been proposed to improve the prediction error of a time series model (Steady-State Module) by using the results of the proposed Markov model. It has been shown that the combined procedure significantly reduces the prediction error of the time series model. For the real-time application of the proposed shockwave model, a module (Congestion Detection Module) is required to simultaneously work with the shockwave model, and identify the state of the traffic based on the available measurements. A model based on Support Vector Machine (SVM) was developed to estimate the current traffic state based on the available information from a fixed point detector. A binary model for the traffic state was considered i.e. free follow versus congested conditions. The model was shown to perform better compared to a Naïve model. The classification model was utilized to inform the Traffic Propagation Module. The combined model showed significant improvement in prediction error of traffic speed during the “Change Period” when traffic conditions are changing from free flow to congestion and vice versa. Variability of travel speed in the near future was also investigated in this research. A continuous-time Markov model has been developed to predict the state of the traffic for the near future. Four traffic states were considered to characterize the state of traffic: two free flow states, one transition state, and one congested state. Using the proposed model, we are able to predict the probability of the traffic being in each of the possible states in the near future based on the current traffic conditions. The predicted probabilities then were utilized to characterize the expected distribution of traffic speed. Based on historical observations, the distribution of traffic speed was characterized for each traffic state separately. Given these empirical distributions and the predicted probabilities, distribution of traffic speed was predicted for the near future. The distribution of traffic speed then was used to predict a confidence interval for the near future. The confidence interval can be used to identify the expected range of future speeds at a given confidence level.
Author: Bryan T. Nemeth
Publisher:
ISBN:
Category :
Languages : en
Pages : 108
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Author:
Publisher:
ISBN:
Category : Traffic monitoring
Languages : en
Pages : 144
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Author: Yaxin Hu
Publisher:
ISBN:
Category :
Languages : en
Pages : 164
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Book Description
There is increasing recognition among travelers, transportation professionals, and decision makers of the importance of the reliability of transportation facilities. An important step towards improving system reliability is developing methods that can be used in practice to predict freeway travel times for the near future (e.g. 5 -15 minutes). Reliable and accurate predictions of future travel times can be used by travelers to make better decisions and by system operators to engage in pre-active rather than reactive system management. Recent advances in wireless communications and the proliferation of personal devices that communicate wirelessly using the Bluetooth protocol have resulted in the development of a Bluetooth traffic monitoring system. This system is becoming increasingly popular for collecting vehicle travel time data in real-time, mainly because it has the following advantages over other technologies: (1) measuring travel time directly; (2) anonymous detection; (3) weatherproof; and (4) cost-effectiveness. The data collected from Bluetooth detectors are similar to data collected from Automatic Vehicle Identification (AVI) systems using dedicated transponders (e.g. such as electronic toll tags), and therefore using these data for travel time prediction faces some of the same challenges as using AVI measurements, namely: (1) determining the optimal spacing between detectors; (2) dynamic outlier detection and travel time estimation must be able to respond quickly to rapid travel time changes; and (3) a time lag exists between the time when vehicles enter the segment and the time that their travel time can be measured (i.e. when the vehicle exits the monitored segment). In this thesis, a generalized model was proposed to determine the optimal average spacing of Bluetooth detector deployments on urban freeways as a function of the length of the route for which travel times are to be estimated; a traffic flow filtering model was proposed to be applied as an enhancement to existing data-driven outlier detection algorithms as a mechanism to improve outlier detection performance; a short-term prediction model combining outlier filtering algorithm with Kalman filter was proposed for predicting near future freeway travel times using Bluetooth data with special attention to the time lag problem. The results of this thesis indicate that the optimal detector spacing ranges from 2km for routes of 4km in length to 5km for routes of 20km in length; the proposed filtering model is able to solve the problem of tracking sudden changes in travel times and enhance the performance of the data-driven outlier detection algorithms; the proposed short-term prediction model significantly improves the accuracy of travel time prediction for 5, 10 and 15 minutes prediction horizon under both free flow and non-free flow traffic states. The mean absolute relative errors (MARE) are improved by 8.8% to 30.6% under free flow traffic conditions, and 7.5% to 49.9% under non-free flow traffic conditions. The 90th percentile errors and standard deviation of the prediction errors are also improved.
Author: Huizhao Tu
Publisher:
ISBN:
Category : Travel time (Traffic engineering)
Languages : en
Pages : 196
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Author: Francesc Soriguera Martí
Publisher: Springer
ISBN: 3662488582
Category : Technology & Engineering
Languages : en
Pages : 226
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Book Description
This monograph presents a simple, innovative approach for the measurement and short-term prediction of highway travel times based on the fusion of inductive loop detector and toll ticket data. The methodology is generic and not technologically captive, allowing it to be easily generalized for other equivalent types of data. The book shows how Bayesian analysis can be used to obtain fused estimates that are more reliable than the original inputs, overcoming some of the drawbacks of travel-time estimations based on unique data sources. The developed methodology adds value and obtains the maximum (in terms of travel time estimation) from the available data, without recurrent and costly requirements for additional data. The application of the algorithms to empirical testing in the AP-7 toll highway in Barcelona proves that it is possible to develop an accurate real-time, travel-time information system on closed-toll highways with the existing surveillance equipment, suggesting that highway operators might provide their customers with such an added value with little additional investment in technology.
Author: National Cooperative Highway Research Program
Publisher: Transportation Research Board
ISBN: 0309117410
Category : Traffic congestion
Languages : en
Pages : 79
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Book Description
TRB¿s National Cooperative Highway Research Program (NCHRP) Report 618: Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability explores a framework and methods to predict, measure, and report travel time, delay, and reliability from a customer-oriented perspective.
Author: Prahlad D. Pant
Publisher:
ISBN:
Category : Electronic traffic controls
Languages : en
Pages : 64
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Book Description
A freeway construction work zone creates conflicts between vehicular traffic and work activity. The closure of one or more lanes of a freeway section causes a bottleneck on the freeway and reduces the capacity in the work zone, which can lead to conditions that violate the expectations of the motorists. Such a work zone situation is a challenge to one of the main objectives of a traffic management system, that is, to maintain "the safe and efficient movement of traffic." The advance warning area of a traffic control zone represents the area in which the motorists are informed as to what they can expect ahead. The information which is normally provided to the motorists include the type of construction activity, type of lane closure, extent of the work zone, and whether there are available alternate routes to avoid the construction all together. This information is given on static signs, or electronic portable changeable message signs (CMS) by way of static preprogrammed messages.
