Download or read book The Use of Bluetooth and Smartphone GPS Data to Investigate Factors Associated to Variations of Travel Times and Delays written by Taras Romancyshyn and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Similar to other market segments, transportation technologies continue to emerge, advance and blossom affording researchers and practitioners the ability to challenge established principles and discover new horizons. This thesis aims to investigate the use of Bluetooth and smartphone Global Positioning System (GPS) data to compute travel times (or delays) and analyze the influence of some factors such as weather and the built environment. This work demonstrates the practical applicability of the new sources of data against more traditional technologies and methods.First, Bluetooth data collected along a major arterial during the winter period was used to assess the impact of weather variables on travel speeds. This research is based on data from 2013-2015 collected from permanent sensors installed along one of the main urban arterials in Montreal, Canada. The effects were modeled at an hourly level using a mixed regression model considering the temporal variation of speed throughout a day. The results confirmed the major importance of snow as a hindrance to travel with a speed drop greater than 1.5 kilometers per hour (km/h) for an average snowfall. As predicated in the literature, the effect of rain had the next highest negative impact with a decrease in speed of 0.46-0.67 km/h for an average hourly rainfall. The impacts of temperature and visibility had positive benefits to speed while increasing wind speeds expectedly reduced speeds although; the magnitudes of their effects were small. Overall, the effects of the weather variables were slightly smaller than what has been reported in the literature which has typically been focused on highway environments.Second, an investigation was launched into whether Bluetooth / WiFi duration times at intersections could be used to estimate vehicle delay using GPS and manually processed video data. All data were collected over four days in May and June 2016. Average delay times from the video and sensor data were then computed for 15, 30 and 60 minute intervals. First-order regression models from the GPS data revealed fairly high fit scores of 68 - 96 % indicating that the sensor duration times do match actual driver delay. Similarly, regression models between the video and duration data resulted in fairly high fit scores of 70 % at the 15 minute level indicating that the sensors can competently monitor the temporal variation of delay. These results are promising given the low costs and ease of installation of these technologies, but need further validation to illustrate their practical potential to estimate intersection delay. Lastly, driver GPS data collected from a smartphone application activated from a large sample of drivers in Quebec City was used to compare the driving costs of travelling between different neighborhood typologies. The driving costs were defined as travel time, fuel consumption and greenhouse gas emissions. The four neighborhood typologies defined were developed using four built environment attributes: Population density, employment density, land-use mix, and transit accessibility. An analysis of the monetary travel costs revealed that trips originating from the rural / suburban region had trip costs 24-30% higher than other regions." --

Download or read book Traveler Satisfaction Surveys Meet Mobile Phone and Vehicle Tracking written by André Laurent Carrel and published by . This book was released on 2015 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smartphones are becoming an increasingly interesting survey medium for behavioral research due to their value for collecting long-term panel observations and supplementary data on the choice environment. Thanks to the sensor data, it becomes possible to survey participants based on whether or not a certain activity has been carried out. By fusing the phone-generated sensor data and survey responses with data from outside sources, substantial data sets can be generated which can be used to investigate choices in complex environments. Computational systems for behavior research take advantage of automation and scalability opportunities, thereby building also on pertinent bodies of literature regarding machine learning on large data sets and crowdsourcing. The importance of comprehensive, long-term data sets in understanding behavior has been highlighted in the choice theory literature, specifically with respect to capturing an individual decision-maker’s history of choices and personal experiences with those choices. To date, however, relatively few studies have capitalized on emerging technologies to create or analyze such data sets. Rich data sets which combine panel information on the decision-maker with information on the choice environment can support the study of dynamic phenomena, which is especially important in a rapidly changing world where behavioral adaptation can take place on a relatively small time scale and, once habits are formed, have long-lasting effects. Some examples of pressing questions in the field of transportation involve understanding how travelers are responding to the emerging sharing economy, to new ride sharing services and new information systems, how time use and travel patterns will change due to automated vehicles, and how more sustainable travel behavior can be promoted through incentive or pricing strategies. This dissertation aims to support the adoption of smartphone-based survey technology in travel behavior research in order to lay the groundwork for research aimed at answering the above questions. It describes the design and implementation of a smartphone-based study, presents a system for fusing smartphone data with externally acquired data, and demonstrates how these ample data sets can be leveraged to generate new behavioral insights. The problem chosen for study is the link between transit service quality, rider satisfaction and ridership retention on public transit. This is motivated by the fact that many transit agencies in the United States continue to see large rates of ridership turnover, and that to date, very little is known about what drives transit use cessation. The six-week San Francisco Travel Quality Study (SFTQS) was conducted in autumn 2013. It collected a data set that included high-resolution phone locations, a number of daily mobile surveys on specific trip experiences, responses to online entry and exit surveys, and transit vehicle locations. By fusing the phone location data with transit vehicle locations, individual-level automatic transit travel diaries could be created without the need to ask participants. The reduced respondent burden, in turn, facilitated a longer term data collection. Initial recruitment proved to be challenging, with response rates to some of the email and direct mailing lists around 1%, and response rates to in-person recruiting between 8 and 15%. On the other hand, attrition was lower than expected, considering the length of the study: The initial enrollment was 856 participants, of which 555 (65%) participants completed all required surveys and 637 (74%) completed the entry and exit survey as well as at least one daily mobile survey. Interestingly, 36% of participants later stated they would have preferred to fill out mobile surveys more frequently (e.g., one per trip rather than one per day) than what was required in the study. A central part of the computational infrastructure used to collect the data was the system of integrated methods to reconstruct and track travelers’ usage of transit at a detailed level by matching location data from smartphones to automatic transit vehicle location (AVL) data and by identifying all out-of-vehicle and in-vehicle portions of the passengers’ trips. This system is presented in detail in this dissertation, where it is shown how high-resolution travel times and their relationships with the timetable are derived. Approaches are presented for processing relatively sparse smartphone location data in dense transit networks with many overlapping bus routes, distinguishing waits and transfers from non-travel related activities, and tracking underground travel in a metro network. While transit agencies have increasingly adopted systems for collecting data on passengers and vehicles, the ability to derive high-resolution passenger trajectories and directly associate them with vehicles has remained a challenge. The system presented in this dissertation is intended to remedy this situation, and it enables a range of different analyses and applications. Results are presented from an implementation and deployment of the system during the SFTQS. An analysis of out-of-vehicle travel times shows that (a) longer overall travel times in trips involving a transfer are strongly driven by transfer times, and (b) median wait times at the origin stops are consistently low regardless of the headway. The latter can be seen as an effect of real-time information, as it appears that wait times are increasingly spent at locations other than the stop and that passengers time their arrivals at the stop. Given these shifts, the traditional assumption that the average wait time at a transit stop of a high-frequency route is half the headway due to random arrivals may need to be revisited. This dissertation presents two applications to derive new behavioral insights from the SFTQS data set and to demonstrate the power and value of these new types of data. The analyses were based on participants’ individual history of transit usage and experiences with service quality. The first analysis used the data from the daily mobile surveys to model the link between participants' reported satisfaction with travel times on specific trips (i.e., their subjective assessment) and objective measures of those travel times. Thanks to the tracking data, it was possible to decompose observed travel times into their in-vehicle and out-of-vehicle components, and to compare the observed in-vehicle travel times to scheduled in-vehicle travel times to identify delays suffered while the participant was on board. The estimation results show that on average, a minute of delay on board a vehicle contributed more to passenger dissatisfaction than a minute of waiting time either at the origin stop or at a transfer stop, and that delays on board metro trains are perceived as more onerous than delays on board buses. Furthermore, the models included participants' baseline satisfaction levels as reported in the entry survey and a daily measure of their subjective well-being. Both variables are relatively new elements in travel surveys, and both are seen to be significant in the estimation results. These results indicate that satisfaction with travel times may be composed of a baseline satisfaction level and a variable component that depends on daily experiences, and that there may be non-negligible interactions between subjective well-being and travel satisfaction. Therefore, it is recommended that future survey designs should include measures for both these variables. The second application builds on the results of the first to empirically investigate the causes for cessation of transit use, with a specific focus on the influence of personal experiences that users have had in the past, on resulting levels of satisfaction, and subsequent behavioral intentions. A latent variable choice model is developed to explain the influence of satisfaction with travel times, including wait times at the origin stop, in-vehicle travel times, transfer times and overall reliability, and satisfaction with the travel environment on behavioral intentions. The group of variables summarized as ``travel environment'' includes crowding, cleanliness, the pleasantness of other passengers, and safety. Satisfaction is modeled as a latent variable, and the choice consists of participants’ stated desire and intention to continue using public transportation in the future. In addition to the delay types captured in the first analysis, a set of negative critical incidents is included, namely being left behind at stops and arriving late to work, school or a leisure activity. The results of the model and descriptive analysis show that operational problems resulting in delays and crowding are much stronger drivers of overall dissatisfaction and cessation than variables related to the travel environment. The importance of baseline satisfaction, mood and the relatively larger impact of in-vehicle delays are confirmed by this model. Thanks to the framework, the critical incidents can be expressed in terms of equivalent delay minutes. For instance, being left behind at a bus stop is found to cause the same amount of dissatisfaction as approximately 18 minutes of wait time. Furthermore, the effect of delays or incidents on ridership can be quantified, as is demonstrated in a set of simulations using the San Francisco transit network (Muni) as a basis. It is shown that if all passengers were subjected to one hypothetical on-board delay of 10 minutes per person, the resulting loss of riders would account for approximately 9.5% of Muni's yearly ridership turnover. In summary, the contributions and impact of this dissertation are as follows: It presents a framework and system that allows the.

Download or read book Knowledge Inference from Smartphone GPS Data written by Mohsen Rezaie and published by . This book was released on 2018 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of the incorporation of GPS receivers and then GPS-enabled smartphones in transportation data collection, many studies have looked at how to infer meaningful information from this data. Research in this field has concentrated on the use of heuristics and supervised machine learning methods to detect trip ends, trip itineraries, travel mode and trip purpose. Until now approaches to inference have relied on the use of fully-validated data. However, respondent burden associated with validation lowers participation rates and reduces the amount of precisely validated data because some people do not validate their trips or misreport them. This thesis consists of two studies. In the first study I propose the application of a semi-supervised method to mode detection from smartphone travel survey data. Semi-supervised methods let researchers and planners use both validated and un-validated data. I compare the accuracy of three popular supervised methods (Decision Tree, Random Forest and Logistic Regression) with a simple semi-supervised method (Label Propagation with KNN kernel). Simple features such as speed, duration and length of trip and closeness of start and end points to transit network are used for model estimation. The results show that the semi-supervised method outperforms the supervised methods in the presence of high proportions of un-validated data and better predicts the observations in the test set. Furthermore, the run-time of the best model among the supervised methods was on average almost 16 times longer than the average run-times of the semi-supervised method. In the second study, I develop a method to infer transit itineraries from smartphone travel survey data. Since the application of semi-supervised algorithms in travel surveys and transit itinerary detection are both in the early stages of development, a supervised approach is taken to tackle the problem of transit itinerary detection. To this end, trip features were extracted from smartphone collected data and transit network information available in the General Transit Feed Specification (GTFS) format. Based on these features, a Random Forest model was trained. Using the model, transit routes for 62% of trip segments was correctly detected.

Download or read book Real time Estimation of Travel Time Using Low Frequency GPS Data from Moving Sensors written by Irum Sanaullah and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Travel time is one of the most important inputs in many Intelligent Transport Systems (ITS). As a result, this information needs to be accurate and dynamic in both spatial and temporal dimensions. For the estimation of travel time, data from fixed sensors such as Inductive Loop Detectors (ILD) and cameras have been widely used since the 1960 s. However, data from fixed sensors may not be sufficiently reliable to estimate travel time due to a combination of limited coverage and low quality data resulting from the high cost of implementing and operating these systems. Such issues are particularly critical in the context of Less Developed Countries, where traffic levels and associated problems are increasing even more rapidly than in Europe and North America, and where there are no pre-existing traffic monitoring systems in place. As a consequence, recent developments have focused on utilising moving sensors (i.e. probe vehicles and/or people equipped with GPS: for instance, navigation and route guidance devices, mobile phones and smartphones) to provide accurate speed, positioning and timing data to estimate travel time. However, data from GPS also have errors, especially for positioning fixes in urban areas. Therefore, map-matching techniques are generally applied to match raw positioning data onto the correct road segments so as to reliably estimate link travel time. This is challenging because most current map-matching methods are suitable for high frequency GPS positioning data (e.g. data with 1 second interval) and may not be appropriate for low frequency data (e.g. data with 30 or 60 second intervals). Yet, many moving sensors only retain low frequency data so as to reduce the cost of data storage and transmission. The accuracy of travel time estimation using data from moving sensors also depends on a range of other factors, for instance vehicle fleet sample size (i.e. proportion of vehicles equipped with GPS); coverage of links (i.e. proportion of links on which GPS-equipped vehicles travel); GPS data sampling frequency (e.g. 3, 6, 30, 60 seconds) and time window length (e.g. 5, 10 and 15 minutes). Existing methods of estimating travel time from GPS data are not capable of simultaneously taking into account the issues related to uncertainties associated with GPS and spatial road network data; low sampling frequency; low density vehicle coverage on some roads on the network; time window length; and vehicle fleet sample size. Accordingly this research is based on the development and application of a methodology which uses GPS data to reliably estimate travel time in real-time while considering the factors including vehicle fleet sample size, data sampling frequency and time window length in the estimation process. Specifically, the purpose of this thesis was to first determine the accurate location of a vehicle travelling on a road link by applying a map-matching algorithm at a range of sampling frequencies to reduce the potential errors associated with GPS and digital road maps, for example where vehicles are sometimes assigned to the wrong road links. Secondly, four different methods have been developed to estimate link travel time based on map-matched GPS positions and speed data from low frequency data sets in three time windows lengths (i.e. 5, 10 and 15 minutes). These are based on vehicle speeds, speed limits, link distances and average speeds; initially only within the given link but subsequently in the adjacent links too. More specifically, the final method draws on weighted link travel times associated with the given and adjacent links in both spatial and temporal dimensions to estimate link travel time for the given link. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-Berkeley s Mobile Century Project. The original GPS dataset which was broadcast on a 3 second sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds so as to evaluate the performance of each travel time estimation method at low sampling frequencies. The results were then validated against reference travel time data collected from 4,126 vehicles by high resolution video cameras, and these indicate that factors such as vehicle sample size, data sampling frequency, vehicle coverage on the links and time window length all influence the accuracy of link travel time estimation.

Download or read book An Investigation of Bluetooth Technology for Measuring Travel Times on Arterial Roads written by Trung Vo and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the field of travel time measurement using Bluetooth technology has been an area of great interest in recent years as transportation professionals strive to increase the cost-effectiveness, accuracy, anonymity, and safety of travel time data collection methods. Commonly used travel time data collection methods include the use of inductive loops, video cameras, and probe vehicles. However, Bluetooth, a globally accepted wireless technology, serves as the medium being utilized by more and more transportation consultants, public agencies, and academics in the collection of travel time data. This study seeks to develop a methodology for measuring travel times on arterial roads using Bluetooth technology. A literature review of general travel time methods and Bluetooth travel time methods was conducted to provide the context for a Bluetooth field deployment development and implementation. The study presents the deployment plan and data analysis of a case study conducted on Spring Street in Atlanta, Georgia. Variable heights, Bluetooth to Bluetooth interference, and detection of Bluetooth devices in probe vehicles are investigated and recommendations are suggested for future Bluetooth travel time studies.

Download or read book Exploring Travel Time Reliability Using Bluetooth Data Collection written by Krista Marie Purser and published by . This book was released on 2016 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bluetooth technology applications have improved travel time data collection efforts and allowed for collection of large data sets at a low cost per data unit. Mean travel times between pairs of points are available, but the primary value of this technique is the availability of the entire distribution of travel times throughout multiple days and time periods, allowing for a greater understanding of travel time variations and reliability. The use of these data for transportation planning, engineering and operations continues to expand. Previous applications of similar data sources have included travel demand and simulation model validation, work zone traffic patterns, transit ridership and reliability, pedestrian movement patterns, and before-after studies of transportation improvements. This thesis investigates the collection and analysis of Bluetooth-enabled travel time data along a multimodal arterial corridor in San Luis Obispo, California. Five BlueMAC devices collected multimodal travel time data in January and February 2016 along Los Osos Valley Road. These datasets were used to identify and process known sources of error such as occasions where vehicles using the roadway turn off and make an intermediate stop and multiple reads from the same vehicle; quantify travel time performance and reliability along arterial streets; and compare transit, bicycle, and pedestrian facility performance. Additionally, a travel time model was estimated based on segment characteristics and Bluetooth data to estimate average speeds and travel time distributions.

Download or read book Applying GPS Data to Understand Travel Behavior written by Jean Louise Wolf and published by . This book was released on 2014 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: "TRB's National Cooperative Highway Research Program (NCHRP) Report 775: Applying GPS Data to Understand Travel Behavior, Volume I: Background, Methods, and Tests describes the research process that was used to develop guidelines on the use of multiple sources of Global Positioning System (GPS) data to understand travel behavior and activity. The guidelines, which are included in NCHRP Report 775, Volume II are intended to provide a jump-start for processing GPS data for travel behavior purposes and provide key information elements that practitioners should consider when using GPS data." -- Publisher's note.

Download or read book Short Term Travel Time Prediction on Freeways written by WENFU. WANG and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Evaluation of Signal Retiming Measures Using Bluetooth Travel Time Data written by Cameron Berko and published by . This book was released on 2015 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Signal retiming is an appealing strategy for improving network performance because it does not require the addition of new roadway capacity. The emergence of Bluetooth technology presents an alternative method of collecting travel time data to evaluate the implementation of signal retiming measures along an arterial corridor via a before-and-after study. As opposed to the industry standard of collecting a limited number of travel times via dedicated travel time runs using vehicles equipped with GPS data loggers, Bluetooth technology allows for a much greater number of travel times to be collected from a wider range of vehicles and drivers. However, the need persists for a practitioner-ready methodology that details how data collected in this manner should be used to evaluate signal retiming measures. This need formed the basis upon which this investigation was conducted. Both field and simulated arterial corridors were examined in this research. The field corridor consisted of a 15.1-kilometre long section of Victoria Park Avenue located in Toronto, Ontario that contained 37 signalized intersections. Seven Bluetooth detectors were deployed to collect data, meaning that the corridor was divided into six links. GPS probe runs were also available for comparison. The simulated corridor consisted of a 4.8-kilometre long section of Hespeler Road in Cambridge, Ontario that contained 12 signalized intersections. Three Bluetooth detectors were deployed to collect data, meaning that the corridor was divided into two links. GPS probe runs were also simulated. Bluetooth travel times were available at the path level (i.e. travel times of vehicles that traversed the entire length of the arterial corridor) and at the link level (i.e. travel times of vehicles that traversed only part of the corridor). To develop measures of effectiveness for evaluating signal retiming measures, the merits of each of these data sets for this purpose were first identified. Through statistical testing, it was found to be infeasible to use the travel times of vehicles that traversed the entire corridor for signal retiming evaluation due to the small number of travel times collected. Instead, a corridor should be subdivided into links through the placement of multiple Bluetooth detectors to increase the number of travel times collected. Next, recommendations regarding the characteristics of a signal retiming study were proposed. A regression model was developed using the field data to allow a practitioner to estimate the duration of the data collection period based on the characteristics of the corridor. Using the results produced by applying this regression model to the field data, recommendations were provided for the spacing of detectors. Next, measures of effectiveness to assess the impacts of signal retiming were developed. The recommended measures incorporated the difference in the means of the Before and After travel time data, the number of vehicles that traversed each link of the corridor, and statistical significance of the difference in the means. These measures provide a practitioner with an idea of the travel time savings or losses produced for the corridor, the degree to which these savings or losses were experienced by vehicles that traversed the corridor, and whether or not these savings or losses were statistically significant. The proposed measures were applied to both the field and simulated Bluetooth travel time data. These results were then compared to the results obtained by applying these measures to the GPS probe runs and to the true changes in travel time for the simulated corridor. Since one commonly cited weakness of Bluetooth travel time data is the presence of outliers in the measured travel times, the sensitivity of the proposed measure of effectiveness to the presence of outliers (i.e. travel times whose magnitudes were not representative of the traffic stream for which signal retiming was intended) was examined using the field Bluetooth travel time data. It was demonstrated that the developed measures are not significantly influenced by the presence of outliers. This investigation provides a practitioner with guidance on how to perform a before-and-after evaluation of a signal retiming study using Bluetooth travel time data. This investigation demonstrated that the division of an arterial corridor into smaller segments produces enough data to be able to statistically differentiate between travel times collected before and after signal retiming measures have been implemented. Guidance is also provided regarding the duration of the data collection period and how to divide the corridor into links through detector spacing. Finally, the developed measures of effectiveness provide concise evidence of the success or failure of signal retiming that a practitioner can present to stakeholders and policymakers with ease.

Download or read book Error Modeling and Analysis for Travel Time Data Obtained from Bluetooth MAC Address Matching written by Yinhai Wang and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Travel time data had been very difficult to collect until recently. Current attempts at exploiting short-range communication protocols that rely on unique identifiers, primarily Bluetooth, have significantly simplified the travel time collection task. Many transportation agencies are now considering using Bluetooth travel time estimates to feed a variety of applications, such as user information systems. As Bluetooth-based travel time data collection increases in popularity, investigating the errors that are characteristic of this detection type becomes more important. A Bluetooth sensor, called the Media Access Control Address Detection (MACAD) system, was developed for travel time data collection in this study to facilitate testing system configurations and allow for future deployments. Three types of antennae and three different sensor arrangements were tested to determine the effects of these variables on travel time error. The collected travel time data were compared to license plate reader data, which, because of their relatively small detection zone for vehicle license plate recognition, were taken as the ground truth travel time. A regression model was used to investigate whether travel time error can be predicted with observable explanatory variables. Descriptive statistical analysis was also employed to evaluate the impacts of individual variables on the travel time error. The results suggested that a combination of sensors is desirable, despite the potential loss of accuracy, as the higher matching rates obtained by the system will improve sample size and reduce random error rates. Findings of this study are helpful to transportation professionals attempting to understand the errors associated with the Bluetoothbased travel time data collection technology and to configure the sensors to mitigate the errors.

Download or read book A Comparitive Assessment of Crowded Source Travel Time Estimates written by and published by . This book was released on 2014 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Travel time is one of the most widely used measures of traffic performance monitoring for the transportation systems. It is a simple concept that refers to the time required to traverse between two points of interest. Travel time is communicated and used by a wide variety of audience such as commuters, media reporters, and transportation engineers and planners. Recent developments within the wireless communication area made it possible to collect travel time data at a relatively low cost. These emerging technologies include mobile phone based technologies, in-vehicle navigation technologies and automatic vehicle identification technologies. Although these technologies offer a great collection source for travel time data, they have different levels of accuracy. In this research two sources of travel time data were evaluated. These sources of data were the INRIX travel time data and the Bluetooth travel time data. The granularity of the INRIX and the Bluetooth data were high in which travel time estimates were reported at a one minute interval. A total of 42 GPS vehicle probe surveys were carried out in three different days to evaluate the accuracy of the INRIX and the Bluetooth travel time estimates. Statistical measures such as the mean absolute error (MAE) and the mean absolute percent error (MAPE) were calculated for a total of 6 segments and 3 time periods (midday, pm peak, and weekend). The INRIX estimates during the midday were either within 0.36 minutes or 22% of the ground truth probe runs, while the Bluetooth estimates during the pm peak were either within 1 minute or 24% of the ground truth probe runs. In addition to hypothesis testing for 13,541 matched-pairs observation, correlation testing was carried out to evaluate the behavior of the Bluetooth and INRIX time series.

Download or read book Applying GPS Data to Understand Travel Behavior written by Jean Louise Wolf and published by . This book was released on 2014 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: "TRB's National Cooperative Highway Research Program (NCHRP) Report 775: Applying GPS Data to Understand Travel Behavior, Volume I: Background, Methods, and Tests describes the research process that was used to develop guidelines on the use of multiple sources of Global Positioning System (GPS) data to understand travel behavior and activity. The guidelines, which are included in NCHRP Report 775, Volume II are intended to provide a jump-start for processing GPS data for travel behavior purposes and provide key information elements that practitioners should consider when using GPS data." -- Publisher's note.

Download or read book Evaluation of Vehicle Positioning Accuracy Using GPS enabled Smartphones in Traffic Data Capturing written by Na Yin and published by . This book was released on 2014 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connected Vehicle (CV) technology aims to improve transportation management and system performance by incorporating advanced detection and communication system such as Global Positioning System (GPS), and smart devices to make roads and vehicles better equipped to exchange important information regarding road and travel conditions. GPS have emerged as the leading technology to provide location information to various location based services. With an increasing smartphone penetration rate, as well as expanding spatial and network coverage, the idea of combining GPS positioning functions with smartphone platforms to perform GPS-enabled smartphone-based traffic management and data monitoring is promising. This study presents a field experiment conducted along Whitemud Drive (a section of Connected Vehicle Test Bed in Edmonton, Alberta, Canada), Queen Elizabeth Highway, and various urban arterial roadways using a GPS-enabled smartphone, cellular positioning technique, professional GPS handset and combination of smartphone and Geofence. The relative positioning errors and the data collection performances using the aforementioned technologies were evaluated and compared. The characteristics and the relationships between the positioning errors and traffic related factors are investigated using regression analysis. The results indicate that GPS-enabled smartphones are capable of correctly positioning 92% of the roadway segments to Google Earth, while achieving accuracy of less than 10 meters for 95% of the data. Using a cellular positioning technique, cell-IDs were correctly identified in repeatable trials with accuracy levels much lower than the smartphone-GPS positioning. Using combination of smartphone positioning and Geofence are promising in finding accurate positions and timestamps. In all scenarios, the use of four data source for obtaining location and traffic condition is feasible; and particularly, using GPS-enabled smartphones and/or its combination with Geofences can provide good accuracy level for location and traffic state parameter estimates.

Download or read book Popular Science written by and published by . This book was released on 2002-12 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Popular Science gives our readers the information and tools to improve their technology and their world. The core belief that Popular Science and our readers share: The future is going to be better, and science and technology are the driving forces that will help make it better.

Download or read book Travel Speed and Delay Study written by Pima Association of Governments and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Travel Time and Delay Data Collection Using Global Positioning Systems and Geographic Information Systems written by Bruce Griesenbeck and published by . This book was released on 1998 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Utilization of the Travel Time and Delay Data from GPS for Optimal Routing written by Shilpa Mallem and published by . This book was released on 2005 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: