Download or read book Freeway Travel Time Estimation Using Limited Loop Data written by Silin Ding and published by . This book was released on 2008 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing drivers with real-time, good-quality traveling information is becoming increasingly important as congestion increases in cities across the United States. Studies have shown as congestion increases, travel time reliability decreases. Travelers would like to have information about certain traffic conditions as particularly detours causing time delays, delays because of road constructions, and delays due to accidents. Since congestion is treated as a major factor influencing travel decisions, some metropolitan areas are providing travel time information to motorists through dynamic message signs (DMS), 511 programs, the Internet, highway advisory radio, and other sources. Traffic conditions are affected by current events and established travel patterns. Today, travel time data can be gathered from microwave radar, automatic vehicle tag matching, video detection, license plate matching, and most commonly, inductive loops. Loop detectors are placed in individual lanes to provide volume, occupancy and local speed information. Although closely spaced loop detectors are helpful to system operation, they are expensive to install and to maintain. With the proliferation of cell phone usage, loop detector data is no longer critical to incident detection. The effectiveness of using loop detector data to reliably estimate travel time has yet to be proved. In recent years, researchers discussed the pros and cons of detector spacing. This discussion is necessary and timely because of the widespread use of the loop detection system today. The focal point of the discussion is to determine the appropriate detector spacing needed for various applications while maintaining the same level of data quality for all users. This thesis studied different freeway travel time estimation methods and explored the impact of loop detector spacing on travel time estimation. The analysis was performed on a sixteen-mile stretch of I-75 in Cincinnati, Ohio and used both simulation and field tests to evaluate the results. First, the commonly used midpoint method for travel time estimation was examined under various traffic and roadway conditions. Starting with the existing 1/3 mile spacing, spacing was increased by using fewer detectors to obtain data for analysis. Then, enhancements were introduced over the midpoint method using different data processing methods reported by other researchers to improve its performance. Preliminary results showed that by using the midpoint method, different detector spacings result in different levels of accuracy and generally the estimation error increases with the detector spacing. Moreover, with increasing traffic congestion, the travel time errors from the existing methods increased significantly. After a congestion based error correction term is introduced, the improved midpoint method is able to make more accurate travel time estimates at larger spacings under work zone and incident conditions. The work was also tested against field data collected through probe vehicles. Based on field data, the estimated travel times from the improved method matches closely with those measured by the floating cars; the differences between the travel time are within 10%. Results from this study showed that a larger detector spacing than the commonly used 1/3 mile does not worsen the estimation results. Overall, the one-mile spacing scheme has outperformed the other tested alternatives in the testbed area. This thesis also studied the reliability of the probe vehicle technique. License Plate Matching Survey was conducted to carry out the analysis. The results showed that the accuracy of probe vehicle travel time is affected by the standard deviation of travel time and different analysis periods. Minimum sample size was examined as the last part of the thesis.

Download or read book Innovative Methods for Calculation of Freeway Travel Time Using Limited Data written by Ping Yi and published by . This book was released on 2008 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Description: Travel time estimations created by processing of simulated freeway loop detector data using proposed method have been compared with travel times reported from VISSIM model. An improved methodology was proposed to estimate freeway corridor travel time under congested traffic. Field data were also collected using the floating car method and comparison of the estimated with the field measured travel times was made.

Download or read book Highway Travel Time Estimation With Data Fusion written by Francesc Soriguera Martí and published by Springer. This book was released on 2015-11-30 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Reliable Travel Time Prediction for Freeways written by J. W. C. van Lint and published by . This book was released on 2004 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Travel Time Estimation for Northeast Illinois Expressways written by Abolfazl Mohammadian and published by . This book was released on 2020 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimation and Prediction of Travel Time from Loop Detector Data for Intelligent Transportation Systems Applications written by Lelitha Devi Vanajakshi and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of Advanced Traveler Information Systems (ATIS), short-term travel time prediction is becoming increasingly important. Travel time can be obtained directly from instrumented test vehicles, license plate matching, probe vehicles etc., or from indirect methods such as loop detectors. Because of their wide spread deployment, travel time estimation from loop detector data is on of the most widely used methods. However, the major criticism about loop detector data is the high probability of error due to the prevalence of equipment malfunctions. This dissertation presents methodologies for estimating and predicting travel time from the loop detector data after correcting for errors. The methodology is a multi-stage process, and includes the correction of data, estimation of travel time and predictions of travel time, and each stage involves the judicious use of suitable techniques. The various techniques selected for each of the stages are detailed below. The test sites are from the freeways in San Antonio, Texas, which are equipped with dual inductance loop detectors and AVI. Constrained non-linear optimization approach by Generalized Reduced Gradient (GRG) method for data reduction and quality control, which included a check for the accuracy of data from a series of detectors for conservation of vehicles, in addition to the commonly adopted checks. A theoretical model based on traffic flow theory for travel time estimation for both off-peak and peak traffic conditions using flow, occupancy and speed values obtained from detectors. Application of a recently developed technique called Support Vector Machines (SVM) for travel time prediction. An Artificial Neural Network (ANN) method is also developed for comparison. Thus, a complete system for the estimation and prediction of travel time from loop detector dats is detailed in this dissertation. Simulated data from CORSIM simulation software is used for the validation of the results.

Download or read book Travel Time Estimation from Loop Detector Data for Advanced Traveler Information Systems Applications written by and published by . This book was released on 1994 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Arterial Link Travel Time Estimation Using Loop Detector Data written by H. Michael Zhang and published by . This book was released on 1997 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Freeway Travel Time Prediction Using Single Loop Detector and Incident Data written by Jingxin Xia and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Improved Estimates of Travel Time from Real Time Inductance Loop Sensors written by Daniel J. Dailey and published by . This book was released on 1993 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Estimating Arterial Link Travel Time Using Loop Detector Data Phase II written by H. Michael Zhang and published by . This book was released on 1998 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes efforts towards developing an arterial travel time model using data from inductive loop detectors and traffic controllers. The model consists of two parts. including the speed estimated from the volume and occupancy measured by detectors and the speed estimated based on critical volume/capacity ratio.

Download or read book Travel Time Prediction under Egypt Heterogeneous Traffic Conditions using Neural Network and Data Fusion written by Mohamed Zaki and published by Infinite Study. This book was released on with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cairo is experiencing traffic congestion that places it among the worst in the world. Obviously, it is difficult if not impossible to solve the transportation problem because it is multi-dimensional problem but it's good to reduce this waste of money and the associated waste of time resulting from congestion.

Download or read book An Investigation of Travel Time Estimation Based on Point Sensors written by Russell Bartlett Holt and published by . This book was released on 2003 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: Several transportation agencies are currently estimating freeway travel times using data provided by inductive loop detectors. These point detectors typically report only aggregated values of volume, lane occupancy, and time-mean-speed at relatively short polling intervals. Travel time estimation methods that assume speeds measured at points are representative of actual travel speeds over segments of roadway are called extrapolation methods. While the literature indicates that extrapolation methods should not be used during congested traffic conditions, little research has been completed to quantify the nature of the specific estimation errors. Meanwhile, travel times estimated and predicted using these methods continue to be disseminated in real-time to motorists along major freeways in several large U.S. cities. The goals of this research are to examine the prevailing issues that reduce the accuracy of extrapolation-based travel time estimation methods and to quantify the errors resulting from these driving implementation issues. First, the three primary sources of error that routinely threaten the accuracy of extrapolation methods are identified and critically examined. Next, a microscopic traffic simulation model is used to develop a generic half mile freeway link that allows for the quantification of (1) the discrepancies between space mean-speeds and time-mean-speeds (as measured at the point detectors), and (2) the typical extrapolation travel time estimation errors that can be expected if detector stations are located within different regions of the half-mile freeway link. Finally, the link-based findings are applied to both a simulated freeway corridor and a field data set to demonstrate the limitations of using extrapolation methods during time periods when recurring and nonrecurring (incident-based) congestion exists. The findings reveal that although extrapolation methods can be used with sufficient accuracy during free-flow (uncongested) traffic conditions, the use of these methods during time periods when congestion is present will result in large errors between estimated travel times and actual experienced travel times. Specifically, the results show that the half-mile link travel time estimates consistently underestimate actual link travel times by more than 30% when the traffic demand exceeds 85% of the capacity of the freeway link. The application of the link-based findings to longer freeway corridors also suggests that estimation errors over each individual detector station influence area tend to sum together when extrapolation methods are used along lengthy corridors experiencing heavy congestion.

Download or read book An Application of Regression Tree Methodology in Freeway Travel Time Estimation Using Speed as a Proxy written by Lijuan Wang and published by . This book was released on 2009 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate freeway travel time estimation is of increasing importance for the travelers' information and route guidance system. A non-parametric statistical methodology known as regression trees is deployed in this research for dynamically and accurately estimating freeway travel times for the I5-I205 loop in the Portland Metro area of Oregon using speed as a proxy. In the absence of historical travel time data on PORTAL (Portland Oregon Regional Transportation Archive Listing), which is the source of data collection in this research, regression tree models are built to predict speeds first and the predicted speeds are in turn used to estimate travel times by mid-point algorithm. The regression tree models in this research are built based on historical data sets, including not only the traffic flow data but also the incident related data, weather data and time of day. This ensures the models will maintain stable prediction ability under both free flow conditions and non-free flow conditions on freeways. Model construction and validation are implemented in the statistical software package S-PLUS. A full regression tree model is constructed on one test data set including 227 daily test data sets randomly selected from the total of 342 daily test data sets collected in the entire year of 2005. To determine what kind of regression tree model should be selected to predict speed or estimate travel time for a certain day under dynamic conditions, a characterization approach is deployed and four characterization standards are setup to track the characteristics of both test data sets and validation data sets. Two experimental designs are constructed to evaluate and compare the performances of eleven regression tree models - the full regression tree model and the ten characterization regression tree models. The results show that these eleven tree models possess the ability to accurately predict speeds or estimate travel times. In addition, meaningful results are obtained showing which of these eleven tree models are best to choose for dynamically estimating travel times for a future day.

Download or read book Freeway Travel Time Prediction Using Data from Mobile Probes written by Pedram Izadpanah and published by . This book was released on 2010 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is widely agreed that estimates of freeway segment travel times are more highly valued by motorists than other forms of traveller information. The provision of real-time estimates of travel times is becoming relatively common in many of the large urban centres in the US and overseas. Presently, most traveler information systems are operating based on estimated travel time rather than predicted travel time. However, traveler information systems are most beneficial when they are built upon predicted traffic information (e.g. predicted travel time). A number of researchers have proposed different models to predict travel time. One of these techniques is based on traffic flow theory and the concept of shockwaves. Most of the past efforts at identifying shockwaves have been focused on performing shockwave analysis based on fixed sensors such as loop detectors which are commonly used in many jurisdictions. However, latest advances in wireless communications have provided an opportunity to obtain vehicle trajectory data that potentially could be used to derive traffic conditions over a wide spatial area. This research proposes a new methodology to detect and analyze shockwaves based on vehicle trajectory data and will use this information to predict travel time for freeway sections.

Download or read book Real Time Prediction of Traffic Speed and Travel Time Characteristics on Freeways written by Reza Noroozisanani and published by . This book was released on 2017 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Travel Time Estimation Using Cell Phones TTECP for Highways and Roadways written by and published by . This book was released on 2007 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: