The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to...The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used.The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers＇ information and vehicles＇ parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively.The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.展开更多
Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could ...Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could be transmitted to navigation systems such as Waze. This study reports on the deployment and impact evaluation of digital alerts on motorist’s assistance patrols and 19 Queue trucks in Indiana. The motorist assistance patrol evaluation is provided qualitatively. A novel analysis of queue warning trucks equipped with digital alerts was conducted during the months of May-July in 2021 using connected vehicle data. This new data set reports locations of anonymous hard-braking events from connected vehicles on the Interstate. Hard-braking events were tabulated for when queueing occurred with and without the presence of a queue warning truck. Approximately 370 hours of queueing with queue trucks present and 58 hours of queueing without queue truck<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> present were evaluated. Hard-braking events were found to decrease approximately 80% when queue warning trucks were used to alert motorists of impending queues.</span>展开更多
Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper pre...Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper presents several tools using CV data to evaluate traffic progression quality along a signalized corridor. These include both performance measures for high-level analysis as well as visualizations to examine details of the coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor. Results for the real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). Comparing flow visualizations developed with different included O-D paths reveals several features, such as the presence of secondary and tertiary platoons on certain sections that cannot be seen when only end-to-end journeys are included. In addition, speed heat maps are generated, providing both speed performance along the corridor and locations and the extent of the queue. The proposed visualization tools portray the corridor’s performance holistically instead of combining individual signal performance metrics. The techniques exhibited in this study are compelling for identifying locations where engineering solutions such as access management or timing plan change are required. The recent progress in infrastructure-free sensing technology has significantly increased the scope of CV data-based traffic management systems, enhancing the significance of this study. The study demonstrates the utility of CV trajectory data for obtaining high-level details of the corridor performance as well as drilling down into the minute specifics.展开更多
The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008-2009. Since then, many new data sources, including connec...The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008-2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics, have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023-2024 winter season, 26 unique MDSS forecast data attributes were collected at 0, 1, 3, 6, 12 and 23-hour intervals from the observed storm time for 6 roadway segments during 13 individual storms. In total, over 888,000 MDSS data points were archived for this evaluation. This study developed novel visualizations to compare MDSS forecasts to multiple other independent data sources, including connected vehicle data, National Oceanic and Atmospheric Administration (NOAA) weather data, road friction data and snowplow telematics. Three Indiana storms, with varying characteristics and severity, were analyzed in detailed case studies. Those storms occurred on January 6th, 2024, January 13th, 2024 and February 16th, 2024. Incorporating these visualizations into winter weather after-action reports increases the robustness of post-storm performance analysis and allows road weather stakeholders to better understand the capabilities of MDSS. The results of this analysis will provide a framework for future MDSS evaluations and implementations as well as training tools for winter operation stakeholders in Indiana and beyond.展开更多
This paper outlines research findings from an investigation into a range of options for generating vehicle data relevant to traffic management systems.Linking data from freight vehicles with traffic management systems...This paper outlines research findings from an investigation into a range of options for generating vehicle data relevant to traffic management systems.Linking data from freight vehicles with traffic management systems stands to provide a number of benefits.These include reducing congestion,improving safety,reducing freight vehicle trip times,informing alternative routing for freight vehicles,and informing transport planning and investment decisions.This paper will explore a number of different methods to detect,classify,and track vehicles,each having strengths and weaknesses,and each with different levels of accuracy and associated costs.In terms of freight management applications,the key feature is the capability to track in real time the position of the vehicle.This can be done using a range of technologies that either are located on the vehicle such as GPS(global positioning system)trackers and RFID(Radio Frequency Identification)Tags or are part of the network infrastructure such as CCTV(Closed Circuit Television)cameras,satellites,mobile phone towers,Wi-Fi receivers and RFID readers.Technology in this space is advancing quickly having started with a focus on infrastructure based sensors and communications devices and more recently shifting to GPS and mobile devices.The paper concludes with an overview of considerations for how data from freight vehicles may interact with traffic management systems for mutual benefit.This new area of research and practice seeks to balance the needs of traffic management systems in order to better manage traffic and prevent bottlenecks and congestion while delivering tangible benefits to freight companies stands to be of great interest in the coming decade.This research has been developed with funding and support provided by Australia’s SBEnrc(Sustainable Built Environment National Research Centre)and its partners.展开更多
The accurate prediction of battery pack capacity in electric vehicles(EVs)is crucial for ensuring safety and optimizing performance.Despite extensive research on predicting cell capacity using laboratory data,predicti...The accurate prediction of battery pack capacity in electric vehicles(EVs)is crucial for ensuring safety and optimizing performance.Despite extensive research on predicting cell capacity using laboratory data,predicting the capacity of onboard battery packs from field data remains challenging due to complex operating conditions and irregular EV usage in real-world settings.Most existing methods rely on extracting health feature parameters from raw data for capacity prediction of onboard battery packs,however,selecting specific parameters often results in a loss of critical information,which reduces prediction accuracy.To this end,this paper introduces a novel framework combining deep learning and data compression techniques to accurately predict battery pack capacity onboard.The proposed data compression method converts monthly EV charging data into feature maps,which preserve essential data characteristics while reducing the volume of raw data.To address missing capacity labels in field data,a capacity labeling method is proposed,which calculates monthly battery capacity by transforming the ampere-hour integration formula and applying linear regression.Subsequently,a deep learning model is proposed to build a capacity prediction model,using feature maps from historical months to predict the battery capacity of future months,thus facilitating accurate forecasts.The proposed framework,evaluated using field data from 20 EVs,achieves a mean absolute error of 0.79 Ah,a mean absolute percentage error of 0.65%,and a root mean square error of 1.02 Ah,highlighting its potential for real-world EV applications.展开更多
In order to provide important parameters for schedule designing, decision-making bases for transit operation management and references for passengers traveling by bus, bus transit travel time reliability is analyzed a...In order to provide important parameters for schedule designing, decision-making bases for transit operation management and references for passengers traveling by bus, bus transit travel time reliability is analyzed and evaluated based on automatic vehicle location （AVL） data. Based on the statistical analysis of the bus transit travel time, six indices including the coefficient of variance, the width of travel time distribution, the mean commercial speed, the congestion frequency, the planning time index and the buffer time index are proposed. Moreover, a framework for evaluating bus transit travel time reliability is constructed. Finally, a case study on a certain bus route in Suzhou is conducted. Results show that the proposed evaluation index system is simple and intuitive, and it can effectively reflect the efficiency and stability of bus operations. And a distinguishing feature of bus transit travel time reliability is the temporal pattern. It varies across different time periods.展开更多
In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze ...In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.展开更多
Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities.Considering the variability of emissions caused by vehicles,roads,and traffic,the 24-hour change characteristics ...Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities.Considering the variability of emissions caused by vehicles,roads,and traffic,the 24-hour change characteristics of air pollutants(CO,HC,NO_(X),PM_(2.5))on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July 2018.The results showed that the spatial diversity of emissions caused by the unbalanced economywas obvious.The vehicle emissions in the Pearl River Delta region(PRD)with a higher economic level were approximately 1–2 times those in the non-Pearl RiverDelta region(non-PRD).Provincial roads with high loads became potential sources of high emissions.Therefore,emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater reduction.Gasoline passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions,contributing more than 50%in the daytime(7:00–23:00)and higher than 26%at night(0:00–6:00).Diesel trucks made up 10%of traffic,but were the dominant player at night,contributed 50%–90%to NO_(X) and PM_(2.5) emissions,with amarked 24-hour change rule of more than 80%at night(23:00–5:00)and less than 60%during daytime.Therefore,targeted control measures by time-section should be set up on collaborative control.These findings provide time-varying decision support for variable vehicle emission control on a large scale.展开更多
Commercially available connected vehicle (CV) probe data has been demonstrated to provide scalable and near-real-time methodologies to evaluate the performance of road networks for various applications. However, one o...Commercially available connected vehicle (CV) probe data has been demonstrated to provide scalable and near-real-time methodologies to evaluate the performance of road networks for various applications. However, one of the major concerns of probe data for agencies is data sampling, particularly during low-volume overnight hours. This paper reports on an evaluation that looked at both connected passenger cars and connected trucks. This study analyzed 40 continuous count stations in Indiana that recorded more than 10.8 million vehicles and more than 13 million trips (3 billion records) from CV data over a 1-week period from May 9<sup>th</sup> to 15<sup>th</sup> in 2022. The average truck penetration was observed to be 3.4% during overnight hours from 1 AM to 5 AM when the connected passenger car penetration was at the lowest. When both connected trucks and connected car penetration were analyzed, the overall CV penetration was 6.32% on interstates and 5.30% on non-interstate roadways. The paper concludes by recommending that both connected car and connected truck data be used by agencies to increase penetration and reduce the hourly variation in CV penetration. This is particularly important during overnight hours.展开更多
Historically, researchers and practitioners have utilized spot speeds and microscopic simulation methodologies to evaluate the operational impact of differential or uniform speed limits for trucks and passenger vehicl...Historically, researchers and practitioners have utilized spot speeds and microscopic simulation methodologies to evaluate the operational impact of differential or uniform speed limits for trucks and passenger vehicles. This paper presents a methodology that uses connected truck data to develop a statistical characterization of both passenger car and truck speeds. These techniques were applied to three adjacent states, Illinois, Indiana and Ohio. Illinois and Ohio have 70 mph speed limits for both trucks and cars. Indiana has a differential speed limit for heavy trucks (65 mph) and passenger cars (70 mph). The statistical distribution of truck speeds was then compared among Illinois, Indiana and Ohio. These speeds were derived from over 8 million connected truck records traveling along Interstate 70 in Illinois, Indiana and Ohio during a one-week period from May 8-14, 2022. Statistical test results over selected 20-mile sections in each state showed that median truck speeds in Indiana with its differential speed limit of 65 mph were only 1 - 2 mph lesser than the neighboring states of Illinois and Ohio who observe a uniform speed limit of 70 mph for all traffic.展开更多
Connected vehicle data is an important assessment tool for agencies to evaluate the performance of freeways and arterials, provided there is sufficient penetration to provide statistically robust performance measures....Connected vehicle data is an important assessment tool for agencies to evaluate the performance of freeways and arterials, provided there is sufficient penetration to provide statistically robust performance measures. A common concern by agencies interested in using crowd sourced probe data is the penetration rate across different types of roads, different hours of the day, and different regions. This paper describes and demonstrates a methodology that uses data from state highway performance monitoring systems in Indiana, Ohio<span style="font-family:;" "=""> </span><span style="font-family:Verdana;">and Pennsylvania. The study analyzes 54 locations over the 3 states for select Wednesdays and Saturdays in 2020 and 2021. Overall, across all locations and dates, the median penetration was approximately 4.5%. The median penetration for August 2020 for Indiana, Ohio, and Pennsylvania was 4.6%, 4.3%, and 4.0%, respectively. The median penetration for those same states in August 2020 on interstates and non-interstates was 3.9% and 4.6%, respectively. Additionally, the study conducted a longitudinal evaluation of Indiana penetration for selected months between January 2020 </span><span style="font-family:Verdana;">and</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This pap</span><span style="font-family:Verdana;">er concludes by recommending that the techniques described in this paper</span><span style="font-family:Verdana;"> be scaled to other states so that traffic engineers can make informed decisions on the use and limitations of connected vehicle data for various use cases.</span></span>展开更多
As an independent navigation method,inertial navigation system(INS)has played a huge advantage in a lot of special conditions.But its positioning error will accumulate with time,so it is difficult to work independentl...As an independent navigation method,inertial navigation system(INS)has played a huge advantage in a lot of special conditions.But its positioning error will accumulate with time,so it is difficult to work independently for a long time.The vehicle loaded with the inertial navigation system usually drives on the road,so the high precision road data based on geographic information system(GIS)can be used as a bind of auxiliary information,which could correct INS errors by the correlation matching algorithm.The existing road matching methods rely on mathematical models,mostly for global positioning system(GPS)trajectory data,and are limited to model parameters.Therefore,based on the features of inertial navigation trajectory and road,this paper proposes a road data aided vehicle inertial navigation method based on the learning to rank and iterative closest contour point(ICCP)algorithm.Firstly,according to the geometric and directional features of inertial navigation trajectory and road,the combined feature vector is constructed as the input value;Furthermore,the scoring function and RankNet neural network based on the features of vehicle trajectory data and road data are constructed,which can learn and extract the features;Then,the nearest point of each track point and its corresponding road data set to be matched is calculated.The average translation between the two data sets is calculated by using the position relationship between each group of track points to be matched and road points;Finally,the trajectory data set is iteratively translated according to the translation amount,and the matching track point set is obtained when the trajectory error converges to complete the matching.During experiments,it is compared with other algorithms including the hidden Markov model(HMM)matching method.The experimental results show that the algorithm can effectively suppress the divergence of trajectory error.The matching accuracy is close to HMM algorithm,and the computational efficiency can meet the requirements of the traditional matching algorithm.展开更多
The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1...The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.展开更多
At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from a...At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.展开更多
Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surroun...Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surrounding road users during a conflict is helpful in analyzing the causal mechanisms of collisions,thus providing insights into roadway safety improvement countermeasures.This study utilized the NGSIM vehicle trajectory datasets to investigate the causal factors in conflicts at intersections by analyzing roadway-to-vehicle and vehicle-to-vehicle interactions.In order to remove the outliers and white noise existing in the raw data,vehicle trajectories were reconstructed by apply-ing discrete wavelet transform and Kalman filtering(KF).Generalized time-to-collision was adopted to detect and measure the severity of conflicts,by which 1127 conflict events were extracted.Path analysis(PA)models were then established to determine in exactly which ways the roadway-to-vehicle and vehicle-to-vehicle interactions were related to conflict severity.Various roadway and environmental characteristics such as traffic flow average speed,percentage of trucks,and intersection skew angle were included in the models.The results indicate that the roadway and environmental characteristics have both direct and indirect effects on conflict severity.In the indirect effects,the kinematics of conflicting vehicles such as the average and standard deviation of speed,plays an intermediate role in linking roadway factors and conflict outcome.The framework of this study can be used to assess roadway readiness for both human-driven and automated vehicles.展开更多
For some space radio telescopes,the orbit determination accuracy is not high enough,the time synchronization accuracy provided by the satellite platforms is low,and GNSS devices are not available.As a result,a traditi...For some space radio telescopes,the orbit determination accuracy is not high enough,the time synchronization accuracy provided by the satellite platforms is low,and GNSS devices are not available.As a result,a traditional method that relies on GNSS devices to obtain an initial clock offset followed by performing correlation with the calibration source may fail to obtain fringes.Moreover,a brutal force search across the 2D clock offset and fringe rate search plane is computationally expensive.In light of these challenges,we propose a novel time synchronization method that utilizes the spacecraft’s telemetry tone signal.This method employs frequency polynomials derived from Doppler tracking for fringe rotation during the correlation process.By aligning the frequency of the target station precisely with that of the reference station,it is only necessary to split the clock offset search range into multiple time windows,perform correlation for each window,and identify the window with the highest signal-to-noise ratio(SNR).The precise clock offset is determined by combining the residual delay with the initial offset.To validate the method,we observe the Tianwen-1 telemetry signal with the 4.5 m small telescope in the Tianma campus of Shanghai Astronomical Observatory and 40 m telescope in Kunming.The results demonstrate that our method can accurately determine clock offset for a time range as wide as±10 ms,with an SNR slightly higher than that achieved with the delay model.This method is suitable for wide-range time synchronization for space Very Long Baseline Interferometry observations,especially in scenarios involving small antennas with low sensitivity and poor orbit determination accuracy.展开更多
The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since ...The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since winter weather varies considerably, allocation of snow removal and deicing resources are highly decentralized to facilitate agile response. Historically, real-time two-way radio communication with drivers has been the primary monitoring system, but with 6 districts, 29 subdistricts, and over one hundred units it does not scale well for systematic data collection. Emerging technology such as real-time truck telematics, hi-resolution NOAA data, dash camera imagery, and crowdsourced traffic speeds can now be fused into dashboards. These real-time dashboards can be used for systematic monitoring and allocation of resources during critical weather events. This paper reports on dashboards used during the 2020-2021 winter season derived from that data. Nearly 13 million location records and 11 million dash camera images were collected from telematics onboard 1105 trucks. Peak impact of nearly 1570 congested miles and 610 trucks deployed was observed for a winter storm on February 15<sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;">, 2021 chosen for further analysis. In addition to tactical adjustments of resources during storms, this system-wide collection of resources allows agencies to monitor multiple seasons and make long</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">term strategic asset allocation decisions. Also, from a public information perspective, these resources were found to be very useful to agencies that interface with the media (and social media) during large storms to provide real-time visual updates on conditions throughout the state from pre-treatment, through cleanup.</span>展开更多
Data acquisition and preprocessing is a core course on digital intelligence at Wuhan University that is designed to cultivate students’understanding of data sources and improve preprocessing methods.The course aims a...Data acquisition and preprocessing is a core course on digital intelligence at Wuhan University that is designed to cultivate students’understanding of data sources and improve preprocessing methods.The course aims at fostering digital thinking and literacy and enhancing intelligent computing skills.This study examined digital intelligence education and reform practices integrated into the data acquisition and preprocessing course,which covered web data,social sensing data,remote sensing data,sensor network data,unmanned aerial vehicle data,and 3D data.Moreover,the study explored the development and implementation of the course’s teaching platform,which was based on the open geospatial engine.展开更多
Many studies have been carried out using vehicle trajectory to analyze traffic conditions, for instance, identifying traffic congestion. However, there is a lack of a systematic study on the appropriate number of prob...Many studies have been carried out using vehicle trajectory to analyze traffic conditions, for instance, identifying traffic congestion. However, there is a lack of a systematic study on the appropriate number of probe vehicles and their sampling interval in order to identify traffic congestion accurately. Moreover, most of related studies ignore the streaming feature of trajectory data. This paper first represents a novel method of identifying traffic congestion considering the stream feature of vehicle trajectories. Instead of processing the whole data stream, a series of snapshots are extracted. Congested road segments can be identified by analyzing the clusters＇ evolution among a series of adjacent snapshots. We then calculated a series of parameters and their corresponding congestion identification accuracy. The results have implications for related probe vehicle deployment and traffic analysis; for example, when 5% of probe vehicles are available, 85% identification accuracy can be reached if the sampling time interval is 10 s.展开更多
基金supported by the project "Research on the Traffic Environment Carrying Capacity and Feedback Gating Based Dynamic Traffic Control in Urban Network" which is funded by the China Postdoctoral Science Foundation (No. 2013M540102)supported by the Open Foundation of smart-city research center of Hangzhou Dianzi University, smart-city research center of Zhejiang Province
文摘The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used.The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers＇ information and vehicles＇ parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively.The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.
文摘Back of queue crashes on Interstates are a major concern for all state transportation departments. In 2020, Indiana DOT begin deploying queue warning trucks with message boards, flashers and digital alerts that could be transmitted to navigation systems such as Waze. This study reports on the deployment and impact evaluation of digital alerts on motorist’s assistance patrols and 19 Queue trucks in Indiana. The motorist assistance patrol evaluation is provided qualitatively. A novel analysis of queue warning trucks equipped with digital alerts was conducted during the months of May-July in 2021 using connected vehicle data. This new data set reports locations of anonymous hard-braking events from connected vehicles on the Interstate. Hard-braking events were tabulated for when queueing occurred with and without the presence of a queue warning truck. Approximately 370 hours of queueing with queue trucks present and 58 hours of queueing without queue truck<span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> present were evaluated. Hard-braking events were found to decrease approximately 80% when queue warning trucks were used to alert motorists of impending queues.</span>
文摘Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper presents several tools using CV data to evaluate traffic progression quality along a signalized corridor. These include both performance measures for high-level analysis as well as visualizations to examine details of the coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor. Results for the real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). Comparing flow visualizations developed with different included O-D paths reveals several features, such as the presence of secondary and tertiary platoons on certain sections that cannot be seen when only end-to-end journeys are included. In addition, speed heat maps are generated, providing both speed performance along the corridor and locations and the extent of the queue. The proposed visualization tools portray the corridor’s performance holistically instead of combining individual signal performance metrics. The techniques exhibited in this study are compelling for identifying locations where engineering solutions such as access management or timing plan change are required. The recent progress in infrastructure-free sensing technology has significantly increased the scope of CV data-based traffic management systems, enhancing the significance of this study. The study demonstrates the utility of CV trajectory data for obtaining high-level details of the corridor performance as well as drilling down into the minute specifics.
文摘The Indiana Department of Transportation (INDOT) adopted the Maintenance Decision Support System (MDSS) for user-defined plowing segments in the winter of 2008-2009. Since then, many new data sources, including connected vehicle data, enhanced weather data, and fleet telematics, have been integrated into INDOT winter operations activities. The objective of this study was to use these new data sources to conduct a systematic evaluation of the robustness of the MDSS forecasts. During the 2023-2024 winter season, 26 unique MDSS forecast data attributes were collected at 0, 1, 3, 6, 12 and 23-hour intervals from the observed storm time for 6 roadway segments during 13 individual storms. In total, over 888,000 MDSS data points were archived for this evaluation. This study developed novel visualizations to compare MDSS forecasts to multiple other independent data sources, including connected vehicle data, National Oceanic and Atmospheric Administration (NOAA) weather data, road friction data and snowplow telematics. Three Indiana storms, with varying characteristics and severity, were analyzed in detailed case studies. Those storms occurred on January 6th, 2024, January 13th, 2024 and February 16th, 2024. Incorporating these visualizations into winter weather after-action reports increases the robustness of post-storm performance analysis and allows road weather stakeholders to better understand the capabilities of MDSS. The results of this analysis will provide a framework for future MDSS evaluations and implementations as well as training tools for winter operation stakeholders in Indiana and beyond.
基金funding and support provided by Australia’s SBEnrc(Sustainable Built Environment National Research Centre)and its partners.
文摘This paper outlines research findings from an investigation into a range of options for generating vehicle data relevant to traffic management systems.Linking data from freight vehicles with traffic management systems stands to provide a number of benefits.These include reducing congestion,improving safety,reducing freight vehicle trip times,informing alternative routing for freight vehicles,and informing transport planning and investment decisions.This paper will explore a number of different methods to detect,classify,and track vehicles,each having strengths and weaknesses,and each with different levels of accuracy and associated costs.In terms of freight management applications,the key feature is the capability to track in real time the position of the vehicle.This can be done using a range of technologies that either are located on the vehicle such as GPS(global positioning system)trackers and RFID(Radio Frequency Identification)Tags or are part of the network infrastructure such as CCTV(Closed Circuit Television)cameras,satellites,mobile phone towers,Wi-Fi receivers and RFID readers.Technology in this space is advancing quickly having started with a focus on infrastructure based sensors and communications devices and more recently shifting to GPS and mobile devices.The paper concludes with an overview of considerations for how data from freight vehicles may interact with traffic management systems for mutual benefit.This new area of research and practice seeks to balance the needs of traffic management systems in order to better manage traffic and prevent bottlenecks and congestion while delivering tangible benefits to freight companies stands to be of great interest in the coming decade.This research has been developed with funding and support provided by Australia’s SBEnrc(Sustainable Built Environment National Research Centre)and its partners.
基金supported in part by the Science and Technology Department of Sichuan Province(No.2025ZNSFSC0427,No.2024ZDZX0035)the Open Project Fund of Vehicle Measurement,Control and Safety Key Laboratory of Sichuan Province(No.QCCK2024-004)the Industrial and Educational Integration Project of Yibin(No.YB-XHU-20240001)。
文摘The accurate prediction of battery pack capacity in electric vehicles(EVs)is crucial for ensuring safety and optimizing performance.Despite extensive research on predicting cell capacity using laboratory data,predicting the capacity of onboard battery packs from field data remains challenging due to complex operating conditions and irregular EV usage in real-world settings.Most existing methods rely on extracting health feature parameters from raw data for capacity prediction of onboard battery packs,however,selecting specific parameters often results in a loss of critical information,which reduces prediction accuracy.To this end,this paper introduces a novel framework combining deep learning and data compression techniques to accurately predict battery pack capacity onboard.The proposed data compression method converts monthly EV charging data into feature maps,which preserve essential data characteristics while reducing the volume of raw data.To address missing capacity labels in field data,a capacity labeling method is proposed,which calculates monthly battery capacity by transforming the ampere-hour integration formula and applying linear regression.Subsequently,a deep learning model is proposed to build a capacity prediction model,using feature maps from historical months to predict the battery capacity of future months,thus facilitating accurate forecasts.The proposed framework,evaluated using field data from 20 EVs,achieves a mean absolute error of 0.79 Ah,a mean absolute percentage error of 0.65%,and a root mean square error of 1.02 Ah,highlighting its potential for real-world EV applications.
基金The Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China (No. 2008-k5-14)
文摘In order to provide important parameters for schedule designing, decision-making bases for transit operation management and references for passengers traveling by bus, bus transit travel time reliability is analyzed and evaluated based on automatic vehicle location （AVL） data. Based on the statistical analysis of the bus transit travel time, six indices including the coefficient of variance, the width of travel time distribution, the mean commercial speed, the congestion frequency, the planning time index and the buffer time index are proposed. Moreover, a framework for evaluating bus transit travel time reliability is constructed. Finally, a case study on a certain bus route in Suzhou is conducted. Results show that the proposed evaluation index system is simple and intuitive, and it can effectively reflect the efficiency and stability of bus operations. And a distinguishing feature of bus transit travel time reliability is the temporal pattern. It varies across different time periods.
基金supported by the Technology Innovation Program(10083633,Development on Big Data Analysis Technology and Business Service for Connected Vehicles)funded by the Ministry of Trade,Industry&Energy(MOTIE,Korea)。
文摘In this study,we developed software for vehicle big data analysis to analyze the time-series data of connected vehicles.We designed two software modules:The rst to derive the Pearson correlation coefcients to analyze the collected data and the second to conduct exploratory data analysis of the collected vehicle data.In particular,we analyzed the dangerous driving patterns of motorists based on the safety standards of the Korea Transportation Safety Authority.We also analyzed seasonal fuel efciency(four seasons)and mileage of vehicles,and identied rapid acceleration,rapid deceleration,sudden stopping(harsh braking),quick starting,sudden left turn,sudden right turn and sudden U-turn driving patterns of vehicles.We implemented the density-based spatial clustering of applications with a noise algorithm for trajectory analysis based on GPS(Global Positioning System)data and designed a long shortterm memory algorithm and an auto-regressive integrated moving average model for time-series data analysis.In this paper,we mainly describe the development environment of the analysis software,the structure and data ow of the overall analysis platform,the conguration of the collected vehicle data,and the various algorithms used in the analysis.Finally,we present illustrative results of our analysis,such as dangerous driving patterns that were detected.
基金supported by the Natural Science Foundation of China(No.U1811463,41975165)the National Key Research Program of China(No.2018YFB1601100)+1 种基金the Science Foundation Project of Guangdong(No.2019A1515010812)the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY221125).
文摘Estimating intercity vehicle emissions precisely would benefit collaborative control in multiple cities.Considering the variability of emissions caused by vehicles,roads,and traffic,the 24-hour change characteristics of air pollutants(CO,HC,NO_(X),PM_(2.5))on the intercity road network of Guangdong Province by vehicle categories and road links were revealed based on vehicle identity detection data in real-life traffic for each hour in July 2018.The results showed that the spatial diversity of emissions caused by the unbalanced economywas obvious.The vehicle emissions in the Pearl River Delta region(PRD)with a higher economic level were approximately 1–2 times those in the non-Pearl RiverDelta region(non-PRD).Provincial roads with high loads became potential sources of high emissions.Therefore,emission control policies must emphasize the PRD and key roads by travel guidance to achieve greater reduction.Gasoline passenger cars with a large proportion of traffic dominated morning and evening peaks in the 24-hour period and were the dominant contributors to CO and HC emissions,contributing more than 50%in the daytime(7:00–23:00)and higher than 26%at night(0:00–6:00).Diesel trucks made up 10%of traffic,but were the dominant player at night,contributed 50%–90%to NO_(X) and PM_(2.5) emissions,with amarked 24-hour change rule of more than 80%at night(23:00–5:00)and less than 60%during daytime.Therefore,targeted control measures by time-section should be set up on collaborative control.These findings provide time-varying decision support for variable vehicle emission control on a large scale.
文摘Commercially available connected vehicle (CV) probe data has been demonstrated to provide scalable and near-real-time methodologies to evaluate the performance of road networks for various applications. However, one of the major concerns of probe data for agencies is data sampling, particularly during low-volume overnight hours. This paper reports on an evaluation that looked at both connected passenger cars and connected trucks. This study analyzed 40 continuous count stations in Indiana that recorded more than 10.8 million vehicles and more than 13 million trips (3 billion records) from CV data over a 1-week period from May 9<sup>th</sup> to 15<sup>th</sup> in 2022. The average truck penetration was observed to be 3.4% during overnight hours from 1 AM to 5 AM when the connected passenger car penetration was at the lowest. When both connected trucks and connected car penetration were analyzed, the overall CV penetration was 6.32% on interstates and 5.30% on non-interstate roadways. The paper concludes by recommending that both connected car and connected truck data be used by agencies to increase penetration and reduce the hourly variation in CV penetration. This is particularly important during overnight hours.
文摘Historically, researchers and practitioners have utilized spot speeds and microscopic simulation methodologies to evaluate the operational impact of differential or uniform speed limits for trucks and passenger vehicles. This paper presents a methodology that uses connected truck data to develop a statistical characterization of both passenger car and truck speeds. These techniques were applied to three adjacent states, Illinois, Indiana and Ohio. Illinois and Ohio have 70 mph speed limits for both trucks and cars. Indiana has a differential speed limit for heavy trucks (65 mph) and passenger cars (70 mph). The statistical distribution of truck speeds was then compared among Illinois, Indiana and Ohio. These speeds were derived from over 8 million connected truck records traveling along Interstate 70 in Illinois, Indiana and Ohio during a one-week period from May 8-14, 2022. Statistical test results over selected 20-mile sections in each state showed that median truck speeds in Indiana with its differential speed limit of 65 mph were only 1 - 2 mph lesser than the neighboring states of Illinois and Ohio who observe a uniform speed limit of 70 mph for all traffic.
文摘Connected vehicle data is an important assessment tool for agencies to evaluate the performance of freeways and arterials, provided there is sufficient penetration to provide statistically robust performance measures. A common concern by agencies interested in using crowd sourced probe data is the penetration rate across different types of roads, different hours of the day, and different regions. This paper describes and demonstrates a methodology that uses data from state highway performance monitoring systems in Indiana, Ohio<span style="font-family:;" "=""> </span><span style="font-family:Verdana;">and Pennsylvania. The study analyzes 54 locations over the 3 states for select Wednesdays and Saturdays in 2020 and 2021. Overall, across all locations and dates, the median penetration was approximately 4.5%. The median penetration for August 2020 for Indiana, Ohio, and Pennsylvania was 4.6%, 4.3%, and 4.0%, respectively. The median penetration for those same states in August 2020 on interstates and non-interstates was 3.9% and 4.6%, respectively. Additionally, the study conducted a longitudinal evaluation of Indiana penetration for selected months between January 2020 </span><span style="font-family:Verdana;">and</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> June 2021. Indiana penetration increased modestly between December 2020 and June 2021, perhaps due to the post-COVID rebound of passenger vehicle traffic. This pap</span><span style="font-family:Verdana;">er concludes by recommending that the techniques described in this paper</span><span style="font-family:Verdana;"> be scaled to other states so that traffic engineers can make informed decisions on the use and limitations of connected vehicle data for various use cases.</span></span>
基金National Key Research and Development Plan(2016YFB0502300)。
文摘As an independent navigation method,inertial navigation system(INS)has played a huge advantage in a lot of special conditions.But its positioning error will accumulate with time,so it is difficult to work independently for a long time.The vehicle loaded with the inertial navigation system usually drives on the road,so the high precision road data based on geographic information system(GIS)can be used as a bind of auxiliary information,which could correct INS errors by the correlation matching algorithm.The existing road matching methods rely on mathematical models,mostly for global positioning system(GPS)trajectory data,and are limited to model parameters.Therefore,based on the features of inertial navigation trajectory and road,this paper proposes a road data aided vehicle inertial navigation method based on the learning to rank and iterative closest contour point(ICCP)algorithm.Firstly,according to the geometric and directional features of inertial navigation trajectory and road,the combined feature vector is constructed as the input value;Furthermore,the scoring function and RankNet neural network based on the features of vehicle trajectory data and road data are constructed,which can learn and extract the features;Then,the nearest point of each track point and its corresponding road data set to be matched is calculated.The average translation between the two data sets is calculated by using the position relationship between each group of track points to be matched and road points;Finally,the trajectory data set is iteratively translated according to the translation amount,and the matching track point set is obtained when the trajectory error converges to complete the matching.During experiments,it is compared with other algorithms including the hidden Markov model(HMM)matching method.The experimental results show that the algorithm can effectively suppress the divergence of trajectory error.The matching accuracy is close to HMM algorithm,and the computational efficiency can meet the requirements of the traditional matching algorithm.
文摘The Chang＇e-3 （CE-3） mission is China＇s first exploration mission on the surface of the Moon that uses a lander and a rover. Eight instruments that form the scientific payloads have the following objectives： （1） investigate the morphological features and geological structures at the landing site; （2） integrated in-situ analysis of minerals and chemical compositions; （3） integrated exploration of the structure of the lunar interior; （4） exploration of the lunar-terrestrial space environment, lunar sur- face environment and acquire Moon-based ultraviolet astronomical observations. The Ground Research and Application System （GRAS） is in charge of data acquisition and pre-processing, management of the payload in orbit, and managing the data products and their applications. The Data Pre-processing Subsystem （DPS） is a part of GRAS. The task of DPS is the pre-processing of raw data from the eight instruments that are part of CE-3, including channel processing, unpacking, package sorting, calibration and correction, identification of geographical location, calculation of probe azimuth angle, probe zenith angle, solar azimuth angle, and solar zenith angle and so on, and conducting quality checks. These processes produce Level 0, Level 1 and Level 2 data. The computing platform of this subsystem is comprised of a high-performance computing cluster, including a real-time subsystem used for processing Level 0 data and a post-time subsystem for generating Level 1 and Level 2 data. This paper de- scribes the CE-3 data pre-processing method, the data pre-processing subsystem, data classification, data validity and data products that are used for scientific studies.
基金funded by the National Key Technologies R&D Program of China (Grants No. 2017YFC0505104)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation of China (Grants No. DM2016SC09)
文摘At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.
基金sponsored by the National Natural Science Foundation of China(No.52372335)the Belt and Road Cooperation Program under the 2023 Shanghai Action Plan for Science,Technology and Innovation(No.23210750500).
文摘Conflict severity results from the complex interactions between the roadway and environ-mental characteristics and the vehicle motion.Understanding how and to what extent a vehicle is influenced by roadway and surrounding road users during a conflict is helpful in analyzing the causal mechanisms of collisions,thus providing insights into roadway safety improvement countermeasures.This study utilized the NGSIM vehicle trajectory datasets to investigate the causal factors in conflicts at intersections by analyzing roadway-to-vehicle and vehicle-to-vehicle interactions.In order to remove the outliers and white noise existing in the raw data,vehicle trajectories were reconstructed by apply-ing discrete wavelet transform and Kalman filtering(KF).Generalized time-to-collision was adopted to detect and measure the severity of conflicts,by which 1127 conflict events were extracted.Path analysis(PA)models were then established to determine in exactly which ways the roadway-to-vehicle and vehicle-to-vehicle interactions were related to conflict severity.Various roadway and environmental characteristics such as traffic flow average speed,percentage of trucks,and intersection skew angle were included in the models.The results indicate that the roadway and environmental characteristics have both direct and indirect effects on conflict severity.In the indirect effects,the kinematics of conflicting vehicles such as the average and standard deviation of speed,plays an intermediate role in linking roadway factors and conflict outcome.The framework of this study can be used to assess roadway readiness for both human-driven and automated vehicles.
基金supported by the National Key Research and Development Program of China(grant No.2022YFC2205203)the National Natural Science Foundation of China(grant No.42241118)+1 种基金the Strategic Pilot Study Program of the Chinese Academy of Sciences(grant No.XDB0800103)the Key Incubation Project of Shanghai Astronomical Observatory,CAS.
文摘For some space radio telescopes,the orbit determination accuracy is not high enough,the time synchronization accuracy provided by the satellite platforms is low,and GNSS devices are not available.As a result,a traditional method that relies on GNSS devices to obtain an initial clock offset followed by performing correlation with the calibration source may fail to obtain fringes.Moreover,a brutal force search across the 2D clock offset and fringe rate search plane is computationally expensive.In light of these challenges,we propose a novel time synchronization method that utilizes the spacecraft’s telemetry tone signal.This method employs frequency polynomials derived from Doppler tracking for fringe rotation during the correlation process.By aligning the frequency of the target station precisely with that of the reference station,it is only necessary to split the clock offset search range into multiple time windows,perform correlation for each window,and identify the window with the highest signal-to-noise ratio(SNR).The precise clock offset is determined by combining the residual delay with the initial offset.To validate the method,we observe the Tianwen-1 telemetry signal with the 4.5 m small telescope in the Tianma campus of Shanghai Astronomical Observatory and 40 m telescope in Kunming.The results demonstrate that our method can accurately determine clock offset for a time range as wide as±10 ms,with an SNR slightly higher than that achieved with the delay model.This method is suitable for wide-range time synchronization for space Very Long Baseline Interferometry observations,especially in scenarios involving small antennas with low sensitivity and poor orbit determination accuracy.
文摘The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since winter weather varies considerably, allocation of snow removal and deicing resources are highly decentralized to facilitate agile response. Historically, real-time two-way radio communication with drivers has been the primary monitoring system, but with 6 districts, 29 subdistricts, and over one hundred units it does not scale well for systematic data collection. Emerging technology such as real-time truck telematics, hi-resolution NOAA data, dash camera imagery, and crowdsourced traffic speeds can now be fused into dashboards. These real-time dashboards can be used for systematic monitoring and allocation of resources during critical weather events. This paper reports on dashboards used during the 2020-2021 winter season derived from that data. Nearly 13 million location records and 11 million dash camera images were collected from telematics onboard 1105 trucks. Peak impact of nearly 1570 congested miles and 610 trucks deployed was observed for a winter storm on February 15<sup><span style="font-family:Verdana;">th</span></sup><span style="font-family:Verdana;">, 2021 chosen for further analysis. In addition to tactical adjustments of resources during storms, this system-wide collection of resources allows agencies to monitor multiple seasons and make long</span><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">term strategic asset allocation decisions. Also, from a public information perspective, these resources were found to be very useful to agencies that interface with the media (and social media) during large storms to provide real-time visual updates on conditions throughout the state from pre-treatment, through cleanup.</span>
文摘Data acquisition and preprocessing is a core course on digital intelligence at Wuhan University that is designed to cultivate students’understanding of data sources and improve preprocessing methods.The course aims at fostering digital thinking and literacy and enhancing intelligent computing skills.This study examined digital intelligence education and reform practices integrated into the data acquisition and preprocessing course,which covered web data,social sensing data,remote sensing data,sensor network data,unmanned aerial vehicle data,and 3D data.Moreover,the study explored the development and implementation of the course’s teaching platform,which was based on the open geospatial engine.
文摘Many studies have been carried out using vehicle trajectory to analyze traffic conditions, for instance, identifying traffic congestion. However, there is a lack of a systematic study on the appropriate number of probe vehicles and their sampling interval in order to identify traffic congestion accurately. Moreover, most of related studies ignore the streaming feature of trajectory data. This paper first represents a novel method of identifying traffic congestion considering the stream feature of vehicle trajectories. Instead of processing the whole data stream, a series of snapshots are extracted. Congested road segments can be identified by analyzing the clusters＇ evolution among a series of adjacent snapshots. We then calculated a series of parameters and their corresponding congestion identification accuracy. The results have implications for related probe vehicle deployment and traffic analysis; for example, when 5% of probe vehicles are available, 85% identification accuracy can be reached if the sampling time interval is 10 s.
