2013 Session: 360

• Detailed Analysis of Travel Time Reliability Performance Measures from Empirical Data
  Abstract: Research on measuring travel time reliability has increased heavily in recent years. A host of measures have been proposed and researched as well as adopted by management agencies. For this study, 13 reliability measures are calculated for 983 freeway segments from 15 minute space mean speed data. Statistical tests are performed to determine how consistently the measures rank the segments at different times of day and how well correlated the measures are to the average. Analysis of the change in measures over the day as well as each measure’s relationship with the average is discussed. Temporal sampling by time of day has a large effect on the travel time reliability measures calculated. This means effects like directional demand peaks can affect direct comparisons across segment types. Ideal comparisons of reliability measures should include all 24 hours of the day, and time of day analysis can identify time periods where management strategies can have the most effect on reliability.No single measure was identified as ideal, though the semi-standard deviation performed well on most tests. It also reports values in reference to the free flow travel time, eliminating the false negative issue where the standard deviation may be very low while the average travel time is high. It is recommended that full distributions be compared where appropriate. With increasing exposure to these distributions and careful explanations as to what they represent, decision makers can effectively prioritize traffic management and geometric improvements.
  Authors: Chase, R. Thomas; Williams, Billy M.; Rouphail, Nagui M.
  Authors: Chase, R. Thomas; Williams, Billy M.; Rouphail, Nagui M.
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-0226
• Arterial Incident Detection Procedures Utilizing Real-Time Vehicle Reidentification Travel Time Data
  Abstract: Travel time data obtained from vehicle re-identification systems is becoming increasingly available due to the implementation of various technologies such as license plate recognition, automatic toll collection systems, inductive loop signature systems, and Bluetooth-based wireless vehicle identification. Travel time data obtained in real-time from such systems is used to update estimated travel times displayed on variable message signs, and research has also been conducted that utilizes travel time data as inputs to incident detection algorithms. Implementation of such systems and prior research has primarily focused on freeways and other free-flowing roads. However, such systems for travel time data collection are also being implemented on arterials. In this research an incident detection procedure that utilizes point-to-point travel time data obtained from an arterial vehicle re-identification system is developed and evaluated. Historical travel time data provided by a Bluetooth-based travel time data collection system, and reported incident data are utilized to evaluate the procedure. The results show that the procedure provides a good balance of detection and false alarm rates.
  Authors: Kim, David Sungsup; Park, SeJoon; Ko, Seng-Seok; Yu, Wooyeon
  Authors: Kim, David Sungsup; Park, SeJoon; Ko, Seng-Seok; Yu, Wooyeon
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-0161
• Modewise Travel Time Estimation on Urban Arterials Using Transit Buses as Probes
  Abstract: The accurate estimation of travel time of different types of vehicles in a traffic stream has always been of interest in various stages of planning, design, operations and evaluation of transportation systems. The traditional way of travel time data collection by means of active test vehicles or license plate matching techniques has its own limitations in terms of cost, manpower, geographic coverage, sample size and accuracy. With the growing need for real-time travel time data, the passive probe vehicles with onboard Global Positioning Systems (GPS) is increasingly being used. However, due to privacy issues and participation requirements, the public transit vehicles are the only ones which can be equipped with GPS devices and this could possibly be used as a source to estimate the travel time of other types of vehicles. The present study is an attempt in this direction. Two approaches have been proposed: one based on the ratio of the section travel times of public transit to other vehicles and other one based on the quantifiable relationship between the public transit and other vehicles section travel times. As the dwell time at bus stops is a unique characteristic of transit buses when compared to other vehicles in the stream, a methodology has been proposed to find the dwell times based on the approaching and departing speeds at bus stops. The results showed that the second approach based on relationship between the bus and other vehicles section travel times performs better.
  Authors: Kumar, Vasantha; Vanajakshi, Lelitha Devi
  Authors: Kumar, Vasantha; Vanajakshi, Lelitha Devi
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-1022
• Vehicle Re-identification for Travel Time Measurement Based on Loop Detectors Considering Lane Changes
  Abstract: This study develops the vehicle re-identification algorithm for travel time measurement on expressway based on loop detector data. Travel time is one of the most valuable information among traffic information and is necessary for the effective traffic operation and management, though travel time measurement is still challenging. In this study, we propose the methodology that the lack of vehicle signature information from loop detectors is complemented and enriched by models. Concretely, lane-changing models estimating the probabilities that a vehicle at upstream station will change the lane and be observed in the other lane at downstream station and that a gap observed at upstream will be occupied by some vehicles from the other lanes are established. Then, the estimation of these probabilities as well as the similarity of vehicle signatures is simultaneously considered in the re-identification algorithm. As a result of the application to the expressway in Japan where loop detectors are installed with at most 2 km distance, it was revealed that by considering these probabilities, the accuracy of vehicle re-identification and measured travel time much improved. In addition, though it is critical for the algorithm to give the appropriate parameters to the model, the parameters which were adjusted to the specific traffic situation could robustly present the good-fit results even in the other traffic situation.
  Authors: Shiomi, Yasuhiro; Ogawa, Takayuki; Uno, Nobuhiro; Shimamoto, Hiroshi
  Authors: Shiomi, Yasuhiro; Ogawa, Takayuki; Uno, Nobuhiro; Shimamoto, Hiroshi
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-1294
• Improving Accuracy of Bluetooth-Based Travel Time Estimation Using Low-Level Sensor Data
  Abstract: Bluetooth sensors have a large detection zone compared to other static Vehicle Re-Identification Systems (VRIS). Although larger detection zone increases the probability of detecting a Bluetooth-enabled device in a fast moving vehicle, but it increases the probability of multiple detection-events triggered by a single device. This could lead to location ambiguity and reduces the accuracy of travel time estimation. Therefore, the accuracy of travel time estimations by Bluetooth Technology (BT) depends upon how location ambiguity is handled by the estimation method. The issue of multiple detection-events in the context of travel time estimation by BT has been considered by various researchers. However, the treatment of this issue has remained simplistic so far. Most previous studies simply used the first detection-event (Enter-Enter) as the best estimate. No systematic analysis for exploring the most accurate method of estimating travel time using multiple detection-events has been conducted. In this study different aspects of BT detection zone including the size and its impacts on the accuracy of travel time estimation are discussed. Moreover, four alternative methods are applied namely, Enter-Enter, Leave-Leave, Peak-Peak and Combined to estimate travel time. These methods are developed based upon various technical considerations related to multiple detection-events. A controlled field experiment is conducted to evaluate the accuracy of alternative methods through comparison with the ground truth travel-time data measured by GPS. The Results show that the accuracy of Combined and Peak-Peak methods are higher than others and employment of first detection-event does not necessarily yield the best travel time estimation.
  Authors: Namaki Araghi, Bahar; Tørholm Christensen, Lars; Krishnan, Rajesh; Hammershøj Olesen, Jonas; Lahrmann, Harry
  Authors: Namaki Araghi, Bahar; Tørholm Christensen, Lars; Krishnan, Rajesh; Hammershøj Olesen, Jonas; Lahrmann, Harry
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-1922
• Exploring the Effects of Sensor Data Aggregation on Measuring Arterial Performance
  Abstract: Modern loop detector technology offers operations staff a wealth of data about their facilities. It should be possible to use these data to monitor, analyze, and communicate an arterial's performance, but dealing with gaps in the detector network, aggregated vehicle counts, and lags in receiving data can pose a challenge. This paper proposes six methods of using data from loop detectors to derive useful performance measures. A simulated arterial is employed to investigate each methods' accuracy under various conditions. Estimates are compared to simulated data visually, with input/output diagrams; and statistically, with simulated ground truth travel times. Methods for estimating travel time are applied to aggregated data and to varying detector densities. It is found that data from detectors combined with information about signal timing, saturation headways, and free flow travel times can be used by two of the methods described in the paper to provide accurate and useful estimates of average vehicle delay and average travel time, even in conditions where detectors are missing from intersections or detector data are aggregated.
  Authors: Wolfe, Michael; Monsere, Christopher M.; Bertini, Robert Lawrence
  Authors: Wolfe, Michael; Monsere, Christopher M.; Bertini, Robert Lawrence
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-2707
• Weather’s Impact on Travel Time and Travel Time Variability in New York City
  Abstract: In this study, the impact of weather conditions on travel time and travel time variability in New York City is investigated using Classification and Regression Trees (C&RT). For this purpose, taxi GPS data provided by the New York City Taxi and Limousine Commission (TLC) with more than 370 million records is merged with historical weather data. For all day-of-week (DOW), time-of-day (TOD) and weather condition categories, the impact of weather on the mean and mode of travel time distributions and on the coefficient of variation as a measure for variability are analyzed. It is found that the level of travel time variability changes across DOW-TOD-weather categories and that weather has a higher impact on travel time and variability during less congested periods. The literature has shown that inclement weather slows traffic: a major finding of this study is that it also reduces traffic variability, a finding that would seem counter-intuitive. Using a rich dataset and the appropriate analytical methods, the present study contributes valuable insights to the understanding of travel time variability in an urban context.
  Authors: Yazici, M. Anil; Kamga, Camille; Singhal, Abhishek
  Authors: Yazici, M. Anil; Kamga, Camille; Singhal, Abhishek
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-3137
• Optimal Number and Location of Node-Based Sensors for Travel Time Data Collection in Networks
  Abstract: Travel time prediction is highly used in traffic management and planning and its accuracy relies on the accuracy of travel time data. Various methods are being used in collecting travel time data using different types of sensors such as link-based and node-based sensors. Recently, a new method in collecting travel time data is introduced that is called Bluetooth technology which detects Bluetooth devices in the vehicles to determine their travel time. Bluetooth sensors are generally node-based sensors.Despite the amount of literature available in sensor location problem, a few discuss node-based sensors with the application of collecting travel time. Different projects in collecting travel time data using Bluetooth Sensors motivated the study of sensor location problem for installing Bluetooth sensors and in a general sense, node-based sensors. The goal of this study is to find the optimal number of node-based sensors and their deployment location in a network in order to collect travel time data with a high reliability. Two formulations are proposed for modeling this problem. The formulations consider a new set of reliability factors. Using these formulations, sensor location problem can be solved optimally for large networks. The proposed formulations are not restricted to Bluetooth sensors and can also be applied to any node-based sensor location problem. Various case studies using real world networks are conducted to compare the results obtained from both proposed formulations with available mothodologies in the literature. Findings of the case studies are reported in the paper.
  Authors: Asudegi, Mona; Haghani, Ali
  Authors: Asudegi, Mona; Haghani, Ali
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-3128
• Traffic Flow Estimation Using Higher-Order Speed Statistics
  Abstract: In this article, we consider the problem of estimating traffic flow on a multi-lane road using a set of point speeds, either crowd-sourced or collected from the fixed infrastructure. We specifically investigate the relation between higher-order speed moments and the expected value of traffic flow. The algorithm proposed is based on the selection of optimal covariates constructed as speed moments, for a class of conditional mean predictors. The second contribution of this article consists in the analysis of specific components of the speed moments with significant correlation with flow values. In particular, we show that for more than 75\% of the fixed sensing devices considered, the correlation coefficient between the inter-lanes speed variance and the aggregate flow is more than 0.75. Additionally, for more than 70\% of these fixed sensing devices the lane speed variance increases with flow. The third contribution of this article consists of identifying the explanatory features for the high correlation between speed moments and flow values. The algorithms presented in this article are trained and tested on a large dataset from the Mobile Millennium system, collected in the Bay Area from August 2009 to October 2009.
  Authors: Bulteau, Edouard; Leblanc, Romain; Blandin, Sebastien; Bayen, Alexandre
  Authors: Bulteau, Edouard; Leblanc, Romain; Blandin, Sebastien; Bayen, Alexandre
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-3307
• Estimating Link Travel Time from Low-Frequency GPS Data
  Abstract: 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, vehicle coverage on the network, time window length and vehicle sample size. This paper reports the results of a research study that sought to better estimate travel time using vehicle trajectory data from moving sensors (i.e. probe vehicles equipped with GPS) in ‘near’ real-time.In the proposed methodology, accurate locations of vehicles on a link are first determined by map-matching (MM) so as to reduce the potential positioning errors associated with GPS and digital road map. Two mathematical methods are then developed to estimate link travel time from map-matched GPS fixes, vehicle speed and network connectivity information with a special focus on sampling frequency, vehicle sample size and time window length. 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 that was in 3 sec sampling frequency has been extracted at different sampling frequencies such as 6, 30, 60 and 120 seconds. This facilitates to evaluate the performance of a travel time estimation method at different sampling frequencies. The results are then validated against reference travel time data collected from high resolution video cameras. The results indicates 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. The performance has found to be better in the 5 minutes time window length for 60 sec GPS sampling frequency.
  Authors: Sanaullah, Irum; Quddus, Mohammed A.; Enoch, Marcus Paul
  Authors: Sanaullah, Irum; Quddus, Mohammed A.; Enoch, Marcus Paul
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-3909
• Travel Time Distributions on Urban Streets: Estimation with Hierarchical Bayesian Mixture Model and Application to Traffic Analysis with High-Resolution Bus Probe Data
  Abstract: This paper develops a hierarchical Bayesian mixture travel time model to capture the interrupted nature of urban traffic flows. It uses high-resolution bus probe data to estimate travel times on urban streets for short links rather than for long paths, and reveals predominantly bimodal travel time distributions at the link level, with one mode corresponding to travels without delays and the other travels with delays. This bimodal travel time distribution is then used to analyze traffic operations and identify congestion. The advantage of the mixture model is demonstrated using empirical bus probe data, and the results are encouraging.
  Authors: Ji, Yuxiong; Zhang, H. Michael
  Authors: Ji, Yuxiong; Zhang, H. Michael
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4377
• Data-Driven Particle Filter for Travel Time Prediction
  Abstract: The research presented in this paper develops a data-driven particle filter to predict travel times by sampling from historical data. In the proposed method, each particle corresponds to a travel time sequence from a database of historical data. The particle weight is calculated using a dissimilarity measure between measurement and particle sequences. A resampling method is developed in the data-driven particle filter to eliminate particles with low weights and re-select samples according to the probability of each track. Travel time predictions are computed by aggregating the weighted travel times of each particle. A freeway stretch from Newport News to Virginia Beach is selected to test the proposed algorithm using five-minute aggregated traffic data in 2010 provided by INRIX. The travel time prediction results during the summer season demonstrate that the proposed method outperforms two Kalman filter methods by reducing the prediction error by 30% and 57%.
  Authors: Chen, Hao; Rakha, Hesham
  Authors: Chen, Hao; Rakha, Hesham
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4392
• Using GPS Data to Calculate Delay Time on Classified Roads in Houston-Galveston-Brazoria Area
  Abstract: Congestion is an ongoing issue in the Houston-Galveston-Brazoria area. Until recently, traffic analyses were conducted by manual vehicle counts, videotaping and other dated methods that resulted in inaccurate results. This research focuses on using GPS data to calculate delay time on classified roads in the HGB region. GPS devices provide local time and date, location, speed, and elevation at one second intervals. This type of data is a more accurate method of analyzing traffic volume because it (1) analyzes a complete route and (2) focuses on exact times and locations. With the availability of second-by-second speed, acceleration and deceleration trends can be established and delay duration can be analyzed.The primary objective of this research is to analyze traffic volume and calculate delay time on major freeways in the HGB area. This research is a pilot test, thus minimal data was acquired. A more in depth study will be conducted in the near future that is aimed to calculate delay time on all of the major freeways in the HGB area.
  Authors: Hoover, Chelse
  Authors: Hoover, Chelse
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4858
• Evaluating the Performance of Travel Time Outlier Detection Algorithms
  Abstract: A number of technologies are available to acquire vehicle travel times including automatic number plate recognition (ANPR), cell phone probes, dedicated GPS probes, automatic identification of vehicles equipped with transponders or toll tags, and Bluetooth detectors. The travel time measurements from all of these technologies contain various sources of errors and biases. As a result, a number of filtering and outlier detection algorithms have been proposed to identify erroneous data and exclude them from the analysis. However, it is difficult to assess the performance of a given outlier detection algorithm in either absolute terms or with relative to any other algorithm using field data because the true travel times are unknown. Furthermore, it is not possible to identify how well the algorithm can identify any given source of outliers.In this paper we propose a framework for evaluating travel time outlier detection algorithms. The framework can be customized to address the specific characteristics of any travel time sensor technology. However, in this paper we demonstrate the framework for application to travel times acquired by Bluetooth detectors on arterial roadways. We use the framework to evaluate the performance characteristics of two outlier detection algorithms proposed by Dion and Rakha. The results clearly show the performance characteristics of both algorithms for a wide range of operating conditions. It is demonstrated that one of the algorithms has an approximately 30% likelihood of providing results that are worse than not using any outlier detection algorithm at all. The other algorithm is shown to provide improvements under almost all conditions with a relative improvement of up to 60%.
  Authors: Salek Moghaddam, Soroush; Hellinga, Bruce
  Authors: Salek Moghaddam, Soroush; Hellinga, Bruce
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4932
• Feasibility of Using Cellular Phone Location Data in Traffic Survey on Intercity Trips
  Abstract: This research analyzed the feasibility of using cell phone locations data for collecting traffic data on inter-city trips. Cell phone positioning technologies and their penetration rates, as well as the limitations of their applications, were discussed. Algorithms for collecting traffic data between cities are proposed on the basis of the network-based cell phone location data. A cell phone location database including cities located along the Kansas Metro Corridor was used to estimate daily traffic, derive the origin-destination (O-D) traffic by time-of-day and commuting traffic along the corridor based on a five week observation period. The results found that the available cell phone network data detected about 15 percent of the daily traffic data compared to the Average Annual Daily Traffic (AADT) data along the Kansas Metro Corridor. It was also found that using the cell phone network can not only estimate a portion of ground traffic volumes, but also can reflect the variety of traffic flow due to special events. The results found that the use of cell phone network data in estimating the dynamic O-D traffic flow can reflect similar trends in actual commuting traffic on inter-city trips. Nevertheless, including more network data from other carriers and applying the appropriate data verification are needed for further study.
  Authors: Wang, Ming-Heng; Schrock, Steven D.; Vander Broek, Nate; Mulinazzi, Thomas
  Authors: Wang, Ming-Heng; Schrock, Steven D.; Vander Broek, Nate; Mulinazzi, Thomas
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4957
• Shortest Paths in Stochastic Time-Dependent Networks with Link Travel Time Correlation
  Abstract: This paper develops a simple robust framework for the problem of finding the least expected travel time route from any node to any given destination in a stochastic and time-dependent network. We consider both spatial and temporal link travel time correlations in the proposed solution based on a dynamic programming approach. In particular, the spatial correlation is represented by a Markovian property of the link states where each link is assumed to experience congested or uncongested conditions. The temporal correlation is manifested through the time-dependent expected link travel time given the condition of the link traversed. The framework enables a route guidance system where at any decision node within a network, one can make a decision based on current traffic information about which node to take next to achieve the shortest expected travel time to the destination. Numerical examples are presented to illustrate the computational steps involved in the framework of making route choice decisions and to demonstrate the effectiveness of the proposed solution.
  Authors: Dong, Wei; Vu, Hai; Nazarathy, Yoni; Vo, Quoc Bao; Li, Minyi; Hoogendoorn, Serge
  Authors: Dong, Wei; Vu, Hai; Nazarathy, Yoni; Vo, Quoc Bao; Li, Minyi; Hoogendoorn, Serge
  Year: 2013
  Document Type: Paper
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4872
• Detailed Analysis of Travel Time Reliability Performance Measures from Empirical Data
  Authors: Chase, R.
  Authors: Chase, R.
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-0226
• Improving Accuracy of Bluetooth-Based Travel Time Estimation Using Low-Level Sensor Data
  Authors: Namaki Araghi, Bahar
  Authors: Namaki Araghi, Bahar
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-1922
• Weather's Impact on Travel Time and Travel Time Variability in New York City
  Authors: Yazici, M. Anil
  Authors: Yazici, M. Anil
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-3137
• Optimal Number and Location of Node-Based Sensors for Travel Time Data Collection in Networks
  Authors: Asudegi, Mona
  Authors: Asudegi, Mona
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-3128
• Estimating Link Travel Time from Low-Frequency GPS Data
  Authors: Enoch, Marcus
  Authors: Enoch, Marcus
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-3909
• Using GPS Data to Calculate Delay Time on Classified Roads in Houston-Galveston-Brazoria Area
  Authors: Hoover, Chelse
  Keywords: poster presentation; poster design; poster template
  Authors: Hoover, Chelse
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4858
• Evaluating the Performance of Travel Time Outlier Detection Algorithms
  Authors: Salek Moghaddam, Soroush
  Authors: Salek Moghaddam, Soroush
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-4932
• Arterial Incident Detection Procedures Utilizing Real-Time Vehicle Reidentification Travel Time Data
  Authors: Kim, David
  Authors: Kim, David
  Year: 2013
  Document Type: Presentation; Poster
  Subject: Data and Information Technology
  Session: 360
  Paper Number: 13-0161