Visualising corridor performance with probe vehicle speed and crash data

This is the data set associated with the journal article titled 'Effects of Semi-trailer Modifications on HGV Fuel Consumption'. The data files are in MATLAB file format. Figure 4 was generated using the vehicle speed profile during a coast-down test conducted at MIRA-HORIBA twin-straight test track at Nuneaton, UK. Figures 5(a) and 5(b) were generated using vehicle speed and acceleration data from a coast-down test. Figure 5(b) was plotted after compensating for the wind speed. Figure 6 was generated using the time and vehicle speed data from a cost-down test. The modelled legend shows the simulated vehicle speed profile with the estimated vehicle parameters, i.e. the estimated coefficients of aerodynamic drag and rolling resistance. Figures 8(a), 8(b), 8(c) and 8(d) were generated using a simulation model with different combinations of the estimated vehicle parameters as mentioned in the legends. They show the fuel consumption benefit as a percentage. Figures 9(a) and 9(b) were generated using a simulation model with different combinations of the estimated vehicle parameters as mentioned in the legends, and they show the absolute fuel economy. All the files were generated using MATLAB software package from Mathworks.

Road diets provide a means to reduce traffic speeds and promote alternative travel modes within the United States. Throughout the state of New Jersey there are approximately 50 existing road diets with more planned. While research has shown that road diets improve safety, no defined means to measure their long-term impact on traffic flow were found. This paper explores some new and existing means for applying intelligent transportation techniques to quantify long-term congestion changes through commercial anonymous probe vehicle (APV) speed data, the annual average daily traffic and other factors. Two case studies are used to evaluate congestion performance metrics, analysing 8 million APV speed records. Although there are a number of variables to consider, this preliminary research establishes a base measure for evaluating the pre- and post-road-diet impact on traffic flow.

The large transmission of COVID-19 has resulted in a deep impact on the surrounding urban environments, especially on air quality and traffic flows. The objective of this study was to analyze air pollutant concentrations (PM10, SO2, NO2, CO, and O3) and traffic volumes at five congested districts (Bundaran HI, Kelapa Gading, Jagakarsa, Lubang Buaya, and Kebon Jeruk) within Jakarta city impacted by the large-scale social restriction (LSSR) policy. Air quality data during three periods; before, during, and after the LSSR at five observed districts was obtained from the Department of Environment of Jakarta using the Air Quality Monitoring (AQMS) tool. While vehicle speed data were obtained from the waze data during the study period. In this study, air pollutant data during three periods; before, during, and after the LSSR were compared with vehicle speed and meteorological data using a statistical analysis. Results revealed the mean traffic volume at all five districts has greatly reduced by 19% from before to during the LSSR period. It was consistent with the mean PM10, NO2, CO, and SO2 concentrations which also dropped about 46%, 45%, 30%, and 23% respectively. In contrast, the concentrations of air pollutants significantly increased after the LSSR period. During the LSSR period, the traffic volume was negatively associated with the O3 concentration ( r = −.86, p < .01), it was different with before the LSSR periods where the traffic volume associated with CO ( r = .88, p < .01) and NO2 ( r = .89, p < .01). The broad analysis of changes in air pollutants and traffic volumes can be used by the authorities to arrange a good air quality management and an effective way for current and future scenarios.

An investigation into the effect of road gradient and driving style on [formula omitted] emissions from a diesel vehicle driven on urban roads

The study investigated vehicle speed changes during winter weather events. A total of 954 winter maintenance logs in 24 counties over three winter seasons were analyzed. Several variables of interest were developed, such as storm duration, maintenance operation hour, crew delayed, maximum speed reduction, and the storm start and end times. Also, t1, which represents the start of vehicle speed reduction, and t3, the time at which vehicle speeds recover to normal winter driving speeds, were measured from automatic traffic recorder data. The study confirms that vehicle speed seems to be a good measure of representing driving conditions during winter weather events and winter maintenance performance alike. Speed recovery duration was found to be a dependent variable, defined as a possible evaluation of winter maintenance operations using vehicle speed data.

Probe vehicles and loop detectors collect traffic data for empirical MFD estimation. The accuracy of MFD estimation is contingent upon the percentage of road network links covered (road coverage) and the extent of information (penetration rate) from each link. While the previous research has explored the effects of loop detector road coverage and probe vehicle penetration rates, the impact of probe vehicle road coverage remains unaddressed. Furthermore, the unequal distribution and low penetration rates of probe vehicles undermine the reliability of the data obtained from them. Therefore, this study aimed to (i) examine the impact of probe vehicle road coverage on MFD estimation, (ii) enhance the reliability of the spatial data by using speed data reconstruction, (iii) highlight the increased role of spatial data in the accuracy of MFD estimation, and (iv) develop a performance factor of speed data quality to demonstrate that the quality of spatial data is more susceptible to the penetration rate than road coverage. The results underlined the role of FCD road coverage in MFD estimation, and the proposed methodology significantly improves spatial data quality. The study concluded that spatial and temporal data affect the MFD estimation differently, and the proposed performance factor increases the role of spatial data in MFD estimation.

Probe vehicle speed data are being used to evaluate traffic congestion characteristics, resiliency, and network response at local, corridor, and regional levels. A better understanding of changes in traffic characteristics, 24-hours a day, 7 days a week, can be realized through the analysis of spatially located, temporal speed data. This paper explores the possibility to use probe vehicle data sets to quantify the impact of Coronavirus (COVID-19), which closed New Jersey State schools and buildings starting around March 18, 2020. The preliminary research analyzed about 500,000 speed records over a 21 week period at two intersections in Northern New Jersey to numerically and visually characterize the speed patterns. A simplistic comparison of average speeds binned in 15, 10, and 1-minute increments was conducted to quantify the change in vehicle travel speeds. Although further research and statistical analysis is necessary to evaluate the data as it relates to the New Jersey State pandemic policies, the preliminary results indicate that school closures and stay-at-home orders effectively increased speeds at the two study sites. Based on these results a statewide analysis, with evolving performance metrics, will be conducted.

Estimating the effect of vehicle speeds on bicycle and pedestrian safety on the Georgia arterial roadway network

Maximum speed limits continue to be an important policy issue. Research has consistently shown speeds to increase following speed limit increases. These increases have also generally coincided with increases in both the frequency and severity of crashes. However, research has been more limited as to the direct relationship between speed and safety. To this end, the present study evaluates the effects of increasing speed limits on the safety of rural limited access freeways while accounting for contemporaneous changes in the speed profiles of these same roadways. The speed limit increases in Michigan occurred between May 1 and June 12 of 2017, when the maximum speed limits were increased from 70 to 75 mph on approximately 600 miles of freeways. The maximum speed limits for trucks were also increased from 60 to 65 mph on all freeways state-wide at this same time. Speed data were obtained for the Michigan rural freeway network through probe vehicles. These data are merged with pertinent roadway data, as well as police-reported crash data at various levels of injury severity. The impacts of the speed limit increase on safety were evaluated by estimating random effects negative binomial models as part of a case-control before-after study design. The results show that the locations where the speed limits were increased experienced a 5% increase in crashes, while a marginal reduction in crashes was observed where speed limits did not increase. Interestingly, mean speeds were found to be negatively associated with crash frequency, while the standard deviation of speed was found to exhibit a positive relationship. Several site-specific characteristics were also found to be strong predictors of crash frequency. The results provide important insights into the nature of the relationship between speed and safety and will help to guide subsequent speed limit policy decisions.

Big data from probe vehicles is increasingly becoming an important contributor for determining the regional performance of a transportation roadway network. Recent research has applied aggregated speed data from probe vehicles to quantify travel time variations as a result of recurring congestion, incidents, weather events and other non-recurring congestion. Through the establishment of a base travel time for all roadway segments in a region, any increase in travel time characteristics in the regional networks can be quantified temporally and spatially. This characterization is especially important when determining a region’s congestion resiliency, which is being defined as the ability of a roadway network accommodate failures and return to a baseline congestion after a major capacity reduction to the roadway network. This paper demonstrates how aggregated big data on vehicle speeds obtained from regionally deployed probe vehicles could be used to characterize and visualize the interdependent congestion impacts between regions and across roadway types (interstate, arterial, and local). To demonstrate the models and methodologies, an in-depth analysis of the I-276 Bridge closure incident in Burlington County, NJ near Philadelphia, PA was conducted. The bridge was clzosed after a routine inspected identified a crack in one of the structural members. In total, 90 days of data, which included 90-million speed records, were commercially collected for 1765 roadway segments, was analyzed. A novel performance metric was developed to allow an impact analysis by comparing Burlington County to two adjacent counties, Mercer and Camden. The results showed that the bridge closure did have a definitive, quantifiable impact on the primary road network of the adjacent counties. Subsequent analysis identified specific roadways that were most impacted by the closure. Although this research explores historic speed data, the methodologies presented can be applied to real-time speed data to assist in developing efficient traffic operation plans during major incidents, lane closures and weather events.

Probe vehicle speed data has become an important data source for evaluating the congestion performance of highways and arterial roads. Pre-defined spatially located segments known as traffic message channels (TMCs) are linked to commercially available, temporal anonymous probe vehicle speed data. These data have been used to develop agency-wide performance measures to better plan and manage infrastructure assets. Recent research has analyzed individual as well as aggregated TMC links on roadway systems to identify congested areas along spatially defined routes. By understanding the typical congestion of all TMCs in a region as indicated by increased travel times, a broader perspective of the congestion characteristics can be gained. This is especially important when determining the impact of such occurrences in the region as a major crash event, special events, or during extreme conditions such as a natural or human-made disaster. This paper demonstrates how aggregated probe speed data can be used to characterize regional congestion. To demonstrate the methodology, an analysis of vehicle speed data during Hurricane Sandy, the second costliest hurricane in the United States, is used to show the regional impact in 2012. Further, the analysis results are compared and contrasted with comparable periods of increased congestion in 2013, 2014, and 2016. The analysis encompasses 614 TMCs, within 10 miles of the New Jersey coast. Approximately 90 million speed records covering five counties are analyzed in the study.

The development of data-driven smart arterial systems that enables reduction in delays and increases the travel time reliability is a valuable tool for improving of arterial performance. Understanding the synergies and differences between speed data sets and traffic signal controller data is necessary for the efficient deployment of Intelligent Transportation Systems (ITS). It is not always feasible to fully instrument an intersection that provides data on optimal performance metrics. Therefore, it is necessary to establish a baseline performance metric for a given corridor to develop an ITS management plan. To that effect, this study conflated crowd-sourced anonymous probe vehicle data on vehicles trajectories and speeds with high-resolution traffic signal data sets. Interdependencies between the two datasets were examined and baseline corridor performance metrics were established. The analysis included an evaluation of the data sets for a 7.3-mile corridor in Burlington County, New Jersey (AADT ranging from 10,000-45,000). A GPS-equipped test vehicle was used to establish reliability of probe-vehicle data, which was then compared to near-term performance measures derived from high-resolution traffic signal data. Based on the analysis of approximately 1.7-million probe vehicle data points using visualization tools, the study demonstrated that the integration of multiple data sets provides a viable mechanism for the development of reliable, visually intuitive, arterial performance metrics. The study results also indicate that long term speed data from anonymous probe vehicle data could be used to evaluate traffic signal data to measure arterial performance measurement.

Bayesian mixture modeling approach to account for heterogeneity in speed data

This paper proposes, develops and presents a method that may be used to adjust and correct sample estimates of the usual spearman rank correlation coefficient for the possible presence of tied observation in the sampled populations. Without these adjustments the sums of squares of ranks in the denominator of the usual expression for the estimation of spearman rank correlation coefficients are often over estimated if there are ties in the populations and no effort is made to correct and adjust for them especially if these ties are not few, resulting in inefficient, unreliable and often misleading estimates. The proposed method is shows to be easily modified and applied to estimate ties adjusted spearman partial rank correlation coefficient between any two populations holding observations from a third population at constant levels with all the populations adjusted for the possible presence of tied observations. The usual statistical tests for significance are also easily modified using the ties adjusted rank correlation coefficients in place of the unadjusted and usual rank correlation coefficients. The proposed method is illustrated with some sample data and shows to produce more efficient estimates than the usual ties unadjusted uncorrected spearman rank correlation coefficients.

Yttrium-90 ((90)Y)-microspheres administered via the hepatic artery has been used for the treatment of unresectable primary or metastatic cancer in the liver. Prior to (90)Y therapy, however, the (90)Y administered activity and the percent shunting to lungs must be determined, most commonly by gamma camera imaging of technetium-99m ((99m)Tc)-macroaggregated albumin (MAA). The purpose of the current study was to identify and evaluate an objective measure of the correlation of (90)Y and MAA activity distributions and thus assess the reliability of MAA imaging for evaluation of (90)Y administered activity and tumor and liver radiation doses. The MAA study consisted of two acquisitions. After administration of 185 MBq of MAA, a partial-body or so-called breakthrough scan was performed in order to determine the percent shunting to lungs. Immediately after a breakthrough scan, a combined single-photon emission computed tomography (SPECT)/transmission computed tomography (CT) scanner was used to image MAA distribution in order to derived the prescribed (90)Y administered activity based on tumor and liver dosimetry. (90)Y SPECT/CT was performed 2-4 weeks later and activities used were in the range of 777-2,442 MBq. In order to compare (90)Y and MAA SPECT images, first the respective CT image sets were registered using a transform based on normalized mutual information. The transform thus derived was used to align the 90Y and MAA SPECT image sets, and the Spearman's (rho) rank correlation as well as image distance (L2-norm) between the registered SPECT images were then calculated. The Spearman's rank correlation values ranged from 0.451 to 0.818 and the L2 distances from 0.626 to 2.889. Based on visual inspection, the registration of the (90)Y and MAA SPECT images appeared reasonably accurate. The regression coefficient (r) between visual scoring and the Spearman's rank correlation was 0.65 and between visual scoring and L2 distance 0.61. The Spearman's rank correlation thus appears to be more reliable than the image distance for assessing the correlation of the (90)Y and MAA images.