New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs  Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data.  It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit truck

Probabilistic travel time progression and its application to automatic vehicle identification data

Travel time has been identified as an important variable to evaluate the performance of a transportation system. Based on the travel time prediction, road users can make their optimal decision in choosing route and departure time. In order to utilise adequately the advanced data collection methods that provide real-time different types of information, this paper is aimed at a novel approach to the estimation of long roadway travel times, using Automatic Vehicle Identification (AVI) technology. Since the long roads contain a large number of scanners, the AVI sample size tends to reduce and, as such, computing the distribution for the total road travel time becomes difficult. In this work, we introduce a probabilistic framework that extends the deterministic travel time progression method to dependent random variables and enables the off-line estimation of road travel time distributions. In the proposed method, the accuracy of the estimation does not depend on the size of the sample over the entire corridor, but only on the amount of historical data that is available for each link. In practice, the system is also robust to small link samples and can be used to detect outliers within the AVI data

Travel time measure specification by functional approximation: application of radial basis function neural networks

AbstractIn this study, in the purpose of providing a dynamic procedure for reliable travel time specification, the performance of a neural functional approximation method is analysed. The numerical analyses are carried out on the succeeding sections of a freeway segment inputting data obtained from microwave radar sensor units located successively at the cross-sections of a freeway segment of approximately 4km. Measurements on traffic variables, i.e., vehicle counts, speed, and occupancy, for the reference time periods are processed. The structure of the employed radial basis function neural networks are configured considering the data of a three-lane freeway segment obtained by succeeding sensors located in side-fired position. Travel time measures approximated by the neural models are compared with the corresponding field measurements obtained by probe vehicle. Results prove neural model's performance in representing spatiotemporal variation of flow dynamics as well as travel times. Adaptability of the proposed travel time specification procedure to real-time intelligent control systems is a possible future extension

Measuring and visualizing space–time congestion patterns in an urban road network using large-scale smartphone-collected GPS data

Congestion is a dynamic phenomenon with elements of space and time, making it a promising application of probe vehicles. The purpose of this paper is to measure and visualize the magnitude and variability of congestion on the network scale using smartphone GPS travel data. The sample of data collected in Quebec City contained over 4000 drivers and 21,000 trips. The congestion index (CI) was calculated at the link level for each hour of the peak period and congestion was visualized at aggregate and disaggregate levels. Results showed that each peak period can be viewed as having an onset period and dissipation period lasting one hour. Congestion in the evening is greater and more dispersed than in the morning. Motorways, arterials, and collectors contribute most to peak period congestion, while residential links contribute little. Further analysis of the CI data is required for practical implementation in network planning or congestion remediation

A Comprehensive Study on the Estimation of Freeway Travel Time Index and the Effect of Traffic Data Quality

Travel time reliability aims to quantify the variation of travel time by using the entire range of travel times for a given trip, for a selected time period over a selected horizon. A trip can occur over a segment, facility or any subset of the transportation network, for the purpose of calculating travel time reliability. As one of the most important performance measures, travel time reliability reports the number of trips that fail or succeed according to a predetermined standard. Unreliability is usually caused by the interaction of factors that influence travel times, such as fluctuations in demand due to daily or seasonal variation, or special events, traffic control devices, traffic incidents, inclement weather, work zones, and physical capacity. These factors collectively produce travel times that can be better presented by a probability distribution. A well-accepted measure of travel time reliability is the Travel Time Index (TTI) formulated as the ratio of travel time in the peak period to the travel time at free-flow conditions. In this thesis, the Travel Time Index values were calculated and compared from two different kinds of data sources: probe vehicles and fixed location detectors. Speed from vehicle probe data can be retrieved from the National Performance Management Research Dataset (NPMRDS) and the freeway segment speed can be calculated by dividing the segment length by the total travel time. Spot speed from fixed location detectors can be retrieved from the Wisconsin’s Archived Data Management Systems (ADMS), V-SPOC (Volume, Speed and Occupancy) which measures the speed at certain locations of a segment. The free flow speed also varies by data source. In the V-SPOC data, the posted speed limit is considered to be the free flow speed and in the NPMRDS data, the reference speed which is the 85th percentile speed of all observed sample speeds is considered to be the free flow speed. The effect of data quality on the TTI values is also examined in the thesis. Inductive loop detectors are a major source of traffic information, but they are often criticized for generating missing and faulty data which compromise real-time traffic control, operations, and management. There is no doubt that the quality of data will affect the accuracy of the calculation of Travel Time Index and its influence needs to be quantified. This study area was chosen to be the one that contains all different kinds road segments like basic, weaving, on ramp and off ramp segments. The result shows that the removal of invalid data improves the TTI index in the congested traffic conditions. Lastly, a traffic simulation application, FREEVAL-RL tool, was applied to calculate the Travel Time Index. The sensitivity analysis of some important parameters used in the FREEVAL-RL Tool was performed. Calibration procedure was designed and carried out for the tool to reflect the real-world scenarios such as are Capacity Adjustment Factor, jam density and capacity drop. The outcome of the calibrated model was consistently matched to the travel time distribution in terms of mean, 50th percentile, 80th percentile, 95th percentile Travel Time Index (TTI) reported in the NPMRDS data

A Comprehensive Study on the Estimation of Freeway Travel Time Index and the Effect of Traffic Data Quality

Travel time reliability aims to quantify the variation of travel time by using the entire range of travel times for a given trip, for a selected time period over a selected horizon. A trip can occur over a segment, facility or any subset of the transportation network, for the purpose of calculating travel time reliability. As one of the most important performance measures, travel time reliability reports the number of trips that fail or succeed according to a predetermined standard. Unreliability is usually caused by the interaction of factors that influence travel times, such as fluctuations in demand due to daily or seasonal variation, or special events, traffic control devices, traffic incidents, inclement weather, work zones, and physical capacity. These factors collectively produce travel times that can be better presented by a probability distribution. A well-accepted measure of travel time reliability is the Travel Time Index (TTI) formulated as the ratio of travel time in the peak period to the travel time at free-flow conditions. In this thesis, the Travel Time Index values were calculated and compared from two different kinds of data sources: probe vehicles and fixed location detectors. Speed from vehicle probe data can be retrieved from the National Performance Management Research Dataset (NPMRDS) and the freeway segment speed can be calculated by dividing the segment length by the total travel time. Spot speed from fixed location detectors can be retrieved from the Wisconsin’s Archived Data Management Systems (ADMS), V-SPOC (Volume, Speed and Occupancy) which measures the speed at certain locations of a segment. The free flow speed also varies by data source. In the V-SPOC data, the posted speed limit is considered to be the free flow speed and in the NPMRDS data, the reference speed which is the 85th percentile speed of all observed sample speeds is considered to be the free flow speed. The effect of data quality on the TTI values is also examined in the thesis. Inductive loop detectors are a major source of traffic information, but they are often criticized for generating missing and faulty data which compromise real-time traffic control, operations, and management. There is no doubt that the quality of data will affect the accuracy of the calculation of Travel Time Index and its influence needs to be quantified. This study area was chosen to be the one that contains all different kinds road segments like basic, weaving, on ramp and off ramp segments. The result shows that the removal of invalid data improves the TTI index in the congested traffic conditions. Lastly, a traffic simulation application, FREEVAL-RL tool, was applied to calculate the Travel Time Index. The sensitivity analysis of some important parameters used in the FREEVAL-RL Tool was performed. Calibration procedure was designed and carried out for the tool to reflect the real-world scenarios such as are Capacity Adjustment Factor, jam density and capacity drop. The outcome of the calibrated model was consistently matched to the travel time distribution in terms of mean, 50th percentile, 80th percentile, 95th percentile Travel Time Index (TTI) reported in the NPMRDS data

Multimodal Data at Signalized Intersections: Strategies for Archiving Existing and New Data Streams to Support Operations and Planning & Fusion and Integration of Arterial Performance Data

There is a growing interest in arterial system management due to the increasing amount of travel on arterials and a growing emphasis on multimodal transportation. The benefits of archiving arterial-related data are numerous. This research report describes our efforts to assemble and develop a multimodal archive for the Portland-Vancouver region. There is coverage of data sources from all modes in the metropolitan region; however, the preliminary nature of the archiving process means that some of the data are incomplete and samples. The arterial data sources available in the Portland-Vancouver region and that are covered in this report include data for various local agencies (City of Portland, Clark County, WA, TriMet and C-TRAN) covering vehicle, transit, pedestrian, and bicycle modes. We provide detailed descriptions of each data source and a spatial and temporal classification. The report describes the conceptual framework for an archive and the data collection and archival process, including the process for extracting the data from the agency systems and transferring these data to our multimodal database. Data can be made more useful though the use of improved visualization techniques. Thus as part of the project, a number of novel, online visualizations were created and implemented. These graphs and displays are summarized in this report and example visualizations are shown. As with any automated sensor system, data quality and completeness is an important issue and the challenge of automating data quality is large. Preliminary efforts to validate and monitor data quality and automate data quality processing are explored. Finally, the report presents efforts to combine transit and travel time data and signal timing and vehicle count data to generate some sample congestion measures

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

UC-ITS-2017-36This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up-and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks