Evaluation of Outlier Filtering Algorithms for Accurate Travel Time Measurement Incorporating Lane-Splitting Situations

Malaysia has a high percentage of motorcycles. Due to lane-splitting, travel times of motorcycles are less than passenger cars at congestion. Because of this, collecting travel times using the media access control (MAC) address is not straightforward. Many outlier filtering algorithms for travel time datasets have not been evaluated for their capability to filter lane-splitting observations. This study aims to identify the best travel time filtering algorithms for the data containing lane-splitting observations and how to use the best algorithm. Two stages were adopted to achieve the objective of the study. The first stage validates the performance of the previous algorithms, and the second stage checks the sensitivity of the algorithm parameters for different days. The analysis uses the travel time data for three routes in Kuala Lumpur collected by Wi-Fi detectors in May 2018. The results show that the Jang algorithm has the best performance for two of the three routes, and the TransGuide algorithm is the best algorithm for one route. However, the parameters of Jang and TransGuide algorithms are sensitive for different days, and the parameters require daily calibration to obtain acceptable results. Using proper calibration of the algorithm parameters, the Jang and TransGuide algorithms produced the most accurate filtered travel time datasets compared to other algorithms

Why Differential Data Work

ABSTRACT This article explains the features of differential data that make them attractive, their shortcomings, and the situations for which they are best suited. The use of differential data is ubiquitous in the seismological community, in which they are used to determine earthquake locations via the double-difference method and the Earth’s velocity structure via geotomography; furthermore, they have important applications in other areas of geophysics, as well. A common assumption is that differential data are uncorrelated and have uniform variance. We show that this assumption is well justified when the original, undifferenced data covary with each other according to a two-sided exponential function. It is not well justified when they covary according to a Gaussian function. Differences of exponentially correlated data are approximately uncorrelated with uniform variance when they are regularly spaced in distance. However, when they are irregularly spaced, they are uncorrelated with a nonuniform variance that scales with the spacing of the data. When differential data are computed by taking differences of the original, undifferenced data, model parameters estimated using ordinary least squares applied to the differential data are almost exactly equal to those estimated using weighed least squares applied to the original, undifferenced data (with the weights given by the inverse covariance matrix). A better solution only results when the differential data are directly estimated and their variance is smaller than is implied by differencing the original data. Differential data may be appropriate for global seismic travel-time data because the covariance of errors in predicted travel times may have a covariance close to a two-sided exponential, on account of the upper mantle being close to a Von Karman medium with exponent κ≪12.

Data-Driven Multi-Agent Vehicle Routing in a Congested City

Navigation in a traffic congested city can prove to be a difficult task. Often a path that may appear to be the fastest option is much slower due to congestion. If we can predict the effects of congestion, it may be possible to develop a better route that allows us to reach our destination more quickly. This paper studies the possibility of using a centralized real-time traffic information system containing travel time data collected from each road user. These data are made available to all users, such that they may be able to learn and predict the effects of congestion for building a route adaptively. This method is further enhanced by combining the traffic information system data with previous routing experiences to determine the fastest route with less exploration. We test our method using a multi-agent simulation, demonstrating that this method produces a lower total route time for all vehicles than when using either a centralized traffic information system or direct experience alone.

Evaluating Commercial Probe Data Quality on Arterial Facilities: Insights From Multi-Year Cross-Vendor Validation

This paper summarizes the findings from five years of commercial probe data validation conducted in the United States through the I-95 Corridor Coalition Vehicle Probe Project (VPP), focusing specifically on how travel time data quality on arterial facilities has changed since an initial study evaluated it during 2013 and 2014. Thirteen separate arterial validation efforts were conducted from 2014 to 2018 as part of VPP Phase II (VPPII), and data quality from three commercial probe vendors was evaluated through comparison with reference travel time data obtained via re-identification technology. Using two evaluation techniques—a traditional analysis that summarizes the accuracy of precision and bias error metrics, and a slowdown analysis that quantifies each vendor’s ability to capture major slowdown events—the results from VPPII studies are compared with data quality previously observed from nine validation efforts during Phase I (VPPI) from 2013 to 2014. The results show clear improvement in VPPII accuracy levels and suggest that commercial travel time data sets are suitable for many planning and operations applications.

An MFV-based image processing filter and its application to seismic tomographic images

In the tomographic reconstruction of seismic travel time data, care must be taken to keep the propagation of data errors to the model space under control. The non-Gaussian noise distribution—especially the outliers in the data sets- can cause appreciable distortions in the tomographic imaging. To reduce the noise sensitivity well-developed tomography algorithms can be used. On the other hand, the quality of the tomogram can further be improved by using image processing tools. In the paper, a newly developed robust filter is presented, in which the Most Frequent Value (MFV) method developed by Steiner is applied. To analyze the noise reduction capability of the new filter (called Steiner-filter) and to compare it to smoothing filters based on arithmetic- and binomial mean, as well as median, medium-sized tomographic images are used. The MFV-based filter is successfully tested also in edge detection procedures.

Researching Relationships between Truck Travel Time Performance Measures and On-Network and Off-Network Characteristics

Trucks serve significant amount of freight tonnage and are more susceptible to complex interactions with other vehicles in a traffic stream. While traffic congestion continues to be a significant ‘highway’ problem, delays in truck travel result in loss of revenue to the trucking companies. There is a significant research on the traffic congestion mitigation, but a very few studies focused on data exclusive to trucks. This research is aimed at a regional-level analysis of truck travel time data to identify roads for improving mobility and reducing congestion for truck traffic. The objectives of the research are to compute and evaluate the truck travel time performance measures (by time of the day and day of the week) and use selected truck travel time performance measures to examine their correlation with on-network and off-network characteristics. Truck travel time data for the year 2019 were obtained and processed at the link level for Mecklenburg County, Wake County, and Buncombe County, NC. Various truck travel time performance measures were computed by time of the day and day of the week. Pearson correlation coefficient analysis was performed to select the average travel time (ATT), planning time index (PTI), travel time index (TTI), and buffer time index (BTI) for further analysis. On-network characteristics such as the speed limit, reference speed, annual average daily traffic (AADT), and the number of through lanes were extracted for each link. Similarly, off-network characteristics such as land use and demographic data in the near vicinity of each selected link were captured using 0.25 miles and 0.50 miles as buffer widths. The relationships between the selected truck travel time performance measures and on-network and off-network characteristics were then analyzed using Pearson correlation coefficient analysis. The results indicate that urban areas, high-volume roads, and principal arterial roads are positively correlated with the truck travel time performance measures. Further, the presence of agricultural, light commercial, heavy commercial, light industrial, single-family residential, multi-family residential, office, transportation, and medical land uses increase the truck travel time performance measures (decrease the operational performance). The methodological approach and findings can be used in identifying potential areas to serve as truck priority zones and for planning decentralized delivery locations.

Representativeness of Taxi GPS-Enabled Travel Time Data Using Gamma Generalized Linear Model

The sensor-era has brought rapid changes in transportation; the abundance of data has started changing the traditional way in which planners and engineers approach mobility. Nowadays, traffic monitoring and information provision systems heavily rely on floating car data usually of special vehicles (e.g., trucks, taxi), and the question that arises is whether such sources can provide reliable data for the whole traffic in a complex urban environment. The current paper, through Thessaloniki's (GR) case study, seeks to evaluate the reliability of taxi data compared to the overall traffic. The analysis reveals that for the examined critical urban road paths, there is a strong relation among floating taxi data with the overall traffic that is additionally influenced by other significant factors (e.g., number of lanes, day, time period). Furthermore, a modelling approach with a generalized linear model (gamma with log link) seems appropriate when dealing with skewed and heteroscedastic traffic data.

Calibrating Microsimulation Parameters for Vehicular Travel Time

Travel time is one of the simplest yet the most important parameter for transportation facility users as well as transportation engineers. Travel time data is valuable for widerange of transportation analysis including congestion management, transportation planning and passenger’sdecision making.Traffic simulation models are now becoming necessary tools to understand the behavior of traffic and reduce vehicular travel times, but it is very important to calibrate these models first. Thisstudy attempts to determines the values of those parameters,using microsimulation,that significantly affect the travel time. These parameters arethenused for calibrating the traffic simulation model that results in realistic travel time.Study was conducted on an urban road andfield data was collected during weekdays for peak hours. The traffic network was modelled usingVISSIM®.The calibration parameters were desired speed distribution, number of lanes,average standstill distance and minimum headway. After calibrating the model, the travel times collected from field data and those by simulations for different modes of transportation were in close agreement.

Unveiling Tatun volcanic plumbing structure induced by post-collisional extension of Taiwan mountain belt

The Tatun Volcanic Group (TVG) is proximal to the metropolis of Taipei City (population of ca. 7 million) and has long been a major concern due to the potential risks from volcanic activity to the population and critical infrastructure. While the TVG has been previously considered a dormant or extinct volcano, recent evidence suggests a much younger age of the last eruption event (~ 6000 years) and possible existence of a magma reservoir beneath the TVG. However, the location, dimension, and detailed geometry of the magma reservoir and plumbing system remains largely unknown. To examine the TVG volcanic plumbing structure in detail, the local P-wave travel time data and the teleseismic waveform data from a new island-wide Formosa Array Project are combined for a 3D tomographic joint inversion. The new model reveals a magma reservoir with a notable P-wave velocity reduction of 19% (ca. ~ 19% melt fraction) at 8–20 km beneath eastern TVG and with possible northward extension to a shallower depth near where active submarine volcanoes that have been detected. Enhanced tomographic images also reveal sporadic magmatic intrusion/underplating in the lower crust of Husehshan Range and northern Taiwan. These findings suggest an active volcanic plumbing system induced by post-collisional extension associated with the collapse of the orogen.