Fixed-location Sensors Research Articles

Estimation of traffic energy consumption based on macro-micro modelling with sparse data from Connected and Automated Vehicles

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation.

A retrospective on monitoring noise pollution with machine learning in the Sounds of New York City project

The Sounds of New York City (SONYC) project (2016–2022) was a project to monitor and mitigate urban noise pollution using a smart acoustic sensor network, citizen scientists, and collaboration with city agencies. During its lifetime, the project deployed 75 fixed-location sensors and collected over 150 × 106 10-s audio recordings. A key element of the project was the development of machine listening models to detect the sources of noise pollution rather than just the overall noise level. In this talk, we first discuss our initial approach to data collection and machine listening including sensor development and deployment, citizen-science data annotation, self-supervised audio representation learning, and downstream sound-event detection model training. We then discuss analysis results using the outputs of these models, followed by the challenges and limitations of our initial approach. Finally, we discuss our solutions to overcome those challenges, such as citizen-deployed sensors, source-specific loudness estimation, and few-shot sound-event detection.

A Methodology for Estimating Vehicle Route Choice from Sparse Flow Measurements in a Traffic Network

While traffic speed data and travel time estimates are increasingly more available from commercial vendors, they are not sufficient for proper management and performance evaluation of transportation networks. Effective traffic control and demand management requires information about volumes, which is provided by fixed location sensors, such as loop detectors or cameras, and those are sparse. This paper proposes a method for estimating route choice using sparse flow measurements and estimated speed on the road network based on compressed sensing technology widely used in image processing, where from a handful of scattered pixels, a full image is recovered. What is known includes flows at origins and at selected links of the road network, where the detection is present; speed estimates are available for all network links. We find coefficients that split origin flows among routes starting at those origins. The advantage of the proposed methodology is that it does not rely on simulation that is prone to calibration errors but only on measured data. We also show how vehicle flows can be estimated at links with no detection, which enables computing performance measures for road networks lacking complete sensor coverage. Finally, we propose a method for selecting plausible routes between origins and destinations.

Simultaneous identification of sensor faults and origin-destination matrix estimation

Efficient estimation of the origin-destination (OD) matrix is a crucial requirement for traffic monitoring and control. The OD matrix estimation problem has received significant attention over the past decades and various approaches using traffic counts from fixed location sensors have been developed and tested. A significant challenge when using information obtained from traffic sensors, is that such sensors are subject to considerable disruptions due to system errors, that affect the quality and reliability of the information provided. This paper presents a novel approach for OD matrix estimation in the presence of faulty sensors. For the purposes of this work we assume that the network under study operates under free-flow conditions and utilise a variation of the cell transmission model to capture the traffic flow dynamics in a pre-specified time window. The problem is formulated in an optimisation framework and solved by taking into account the presence of faulty measurements. We test and validate the proposed methodology on a sample network and investigate the advantage of OD matrix estimation in the presence of faulty measurements compared to OD matrix estimation when faulty sensors are not considered.

High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach.

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

Quality of location-based crowdsourced speed data on surface streets: A case study of Waze and Bluetooth speed data in Sevierville, TN

Obtaining accurate speed and travel time information is a challenge for researchers, geographers, and transportation agencies. In the past, traffic data were usually acquired and disseminated by government agencies through fixed-location sensors. High costs, infrastructure demands, and low coverage levels of these sensor devices require agencies and researchers to look beyond the traditional approaches. With the emergence of smartphones and navigation apps, location-based and crowdsourced Big Data are receiving increased attention. In this regard, location-based big data (LocBigData) collected from probe vehicles and road users can be used to provide speed and travel time information in different locations. Examining the quality of crowdsourced data is essential for researchers and agencies before using them. This study assessed the quality of Waze speed data from surface streets and conducted a case study in Sevierville, Tennessee. Typically, examining the quality of these data in surface streets and arterials is more challenging than freeways data. This research used Bluetooth speed data as the ground truth, which is independent of Waze data. In this study, three steps of methodology were used. In the first step, Waze speed data was compared to Bluetooth data in terms of accuracy, mean difference, and distribution similarity. In the second step, a k-means algorithm was used to categorize Waze data quality, and a multinomial logistics regression model was performed to explore the significant factors that impact data quality. Finally, in the third step, machine learning techniques were conducted to predict the data quality in different conditions. The result of the comparison showed a similar pattern and a slight difference between datasets, which verified the quality of Waze speed data. The statistical model indicates that that Waze speed data are more accurate in peak hours than in night hours. Also, the traffic speed, traffic volume, and segment length have a significant association on the accuracy of Waze data on surface streets. Finally, the result of machine learning prediction showed that a KNN method performed the highest prediction accuracy of 84.5% and 82.9% of the time for training and test datasets, respectively. Overall, the study results suggest that Waze speed data is a promising data source for surface streets.

The behaviour of commercial broilers in response to a mobile robot

ABSTRACT 1. Modern broiler production in large sheds holding upwards of 50 000 birds uses indoor climate control based on a handful of fixed-location sensors, often well above the bird-occupied zone. Significant deviations from optimal climate conditions for the birds are common, but installing a higher-density grid of fixed sensors is not cost effective. A robotic platform, moving through the flock of birds and collecting detailed spacial information on a wide range of climate parameters at bird level, enables accurate decisions to be made to optimise the climate in large sheds being made in real time. 2. A preliminary study investigated the feasibility of running a mobile robotic platform among a flock of broiler chickens for an entire 6-week cycle. Bird behaviour in response to the robot was studied. 3. In total, 1597 Ross 308 broiler chicks were housed in a room that was set up to replicate a commercial environment. The robot was driven along a fixed route three times a day (Monday–Friday) for the whole cycle under manual control. Behaviour was studied using camera footage. 4. The birds showed very little ‘startled’ behaviour in response to the robot and were quick to fill the area behind the robot as it moved past. Activity levels increased during robot runs but to a lesser extent than during walk-through by a human stockman. The challenges of operating the robot changed as the birds grew, with many individuals coming into physical contact with the front of the robot later in the cycle. Despite this, very few birds refused to move out of the way completely, and mortality and production parameters remained acceptable throughout. 5. This study has shown that running a mobile robot among a flock of broiler chickens is possible between 3 and 37 d in a cycle and causes limited disruption to bird behaviour. Future work needs to repeat this trial on multiple flocks to see whether running a mobile robot among the broilers can be achieved in a commercial setting, with limited disruption to bird behaviour.

On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment

The new era of sharing information and “big data” has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply “drones” have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA, to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models.

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

The rapid development of connected vehicle technology and the emergence of ride-hailing services have enabled the collection of a tremendous amount of probe vehicle trajectory data. Due to the large scale, the trajectory data have become a potential substitute for the widely used fixed-location sensors in terms of the performance measures of transportation networks. Specifically, for traffic volume and queue length estimation, most of the trajectory data based methods in the existing literature either require high market penetration of the probe vehicles to identify the shockwave or require the prior information about the queue length distribution and the penetration rate, which may not be feasible in the real world. To overcome the limitations of the existing methods, this paper proposes a series of novel methods based on probability theory. By exploiting the stopping positions of the probe vehicles in the queues, the proposed methods try to establish and solve a single-variable equation for the penetration rate of the probe vehicles. Once the penetration rate is obtained, it can be used to project the total queue length and the total traffic volume. The validation results using both simulation data and real-world data show that the methods would be accurate enough for assistance in performance measures and traffic signal control at intersections, even when the penetration rate of the probe vehicles is very low.

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

ABSTRACTArterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.

Long queue estimation for signalized intersections using mobile data

Queue length is one of the key measures in assessing arterial performances. Under heavy congestion, queues are difficult to estimate from either fixed-location sensors (such as loop detectors) or mobile sensors since they may exceed the region of detection, which is defined as long queue in the literature. While the long queue problem has been successfully addressed in the past using fixed-location sensors, whether this can be done using mobile traffic sensors remains unclear. In this paper, a queue length estimation method is proposed to solve this long queue problem using short vehicle trajectories obtained from mobile sensors. The method contains vehicle trajectory reconstruction models to estimate the missing deceleration or acceleration process of a vehicle. Long queue estimation models are then developed using the reconstructed vehicle trajectories. The proposed method can provide estimates of the queue profile and the maximum queue length of a cycle. The method is tested in a field experiment with reasonable results.

Development of an Oversaturated Speed–Flow Model Based on the Highway Capacity Manual

Oversaturated speed, flow, and density relationships are of key importance to studies of freeway operations. The Highway Capacity Manual (HCM) oversaturated model, which is defined by a linear transition from the flow and the density at capacity to a zero flow at jam density in the flow–density space, provides a reasonable representation of this relationship but does not provide an unbiased representation for all freeway facilities with different road conditions or driver behavior. This study proposes a method for fitting the HCM model to oversaturated flow and density. Fifteen-minute aggregated flow rate and speed data were collected in 2010 from Traffic.com fixed-location sensors at three sites on North Carolina urban freeways. Density was calculated as the flow rate divided by the speed. The fitted models for these sites were compared with the default HCM model. A set of thresholds was defined to identify eligible sensor observations that represented the steady-state congested traffic conditions. The results revealed that data observations during inclement weather, lane closures, or incidents biased the model-fitting results and therefore needed to be filtered out. The steady-state congestion data identified in the manner proposed in this study fit well with the HCM-based linear flow–density oversaturated model. This method avoids possible bias caused by capacity and jam density differences between the default HCM model and the site-specific models; therefore, the fitted models represented the actual traffic characteristics relationships better than the default HCM models did. Fitting a site-specific HCM-based model is recommended for sites with sufficient speed and flow data.

Use of Commercial Data Loggers to Develop Process Understanding in Pharmaceutical Unit Operations

Due to the growing emphasis on quality by design during pharmaceutical development, accurate process data collection has become even more crucial. While process analytical technology has emphasized complex spectroscopic techniques, univariate measurements can provide data to develop further process understanding, such as assessing the impact of operating conditions on downstream product attributes or spatially mapping the equipment environment for building detailed process models. Since pharmaceutical scientists have historically had little ability to influence equipment manufacturers on the number, type, or location of sensors intended to measure processing conditions, additional univariate measurements have been obtained using robust temperature/humidity sensors in a number of unit operations. The small size of the sensors allows them to be placed anywhere they are not subject to mechanical damage, while the 21-CFR-11-compliant database provides for traceability for regulatory submissions. This work presents data from fixed-location and portable sensors for two widely used pieces of pharmaceutical equipment, film coaters and fluid bed systems. Portable sensors are also demonstrated as a viable means to obtain process data not available by fixed-location sensors, such as the tablet bed of a film coater. These sensors have been used for a range of process scales, from early-stage products with minimal drug substance to commercial-scale production.

Identification of oversaturated intersections using high-resolution traffic signal data

Conceptually, an oversaturated traffic intersection is defined as one where traffic demand exceeds the capacity. Such a definition, however, cannot be applied directly to identify oversaturated intersections because measuring traffic demand under congested conditions is not an easy task, particularly with fixed-location sensors. In this paper, we circumvent this issue by quantifying the detrimental effects of oversaturation on signal operations, both temporally and spatially. The detrimental effect is characterized temporally by a residual queue at the end of a cycle, which will require a portion of green time in the next cycle; or spatially by a spill-over from downstream traffic whereby usable green time is reduced because of the downstream blockage. The oversaturation severity index (OSI), in either the temporal dimension (T-OSI) or the spatial dimension (S-OSI) can then be measured using high-resolution traffic signal data by calculating the ratio between the unusable green time due to detrimental effects and the total available green time in a cycle. To quantify the T-OSI, in this paper, we adopt a shockwave-based queue estimation algorithm to estimate the residual queue length. S-OSI can be identified by a phenomenon denoted as “Queue-Over-Detector (QOD)”, which is the condition when high occupancy on a detector is caused by downstream congestion. We believe that the persistence duration and the spatial extent with OSI greater than zero provide an important indicator for measuring traffic network performance so that corresponding congestion mitigation strategies can be prepared. The proposed algorithms for identifying oversaturated intersections and quantifying the oversaturation severity index have been field-tested using traffic signal data from a major arterial in the Twin Cities of Minnesota.

Toward acoustic tomography in a shallow estuary.

There is presently much interest in understanding the hydrophysical parameters in a shallow estuary. The importance of predicting the currents in Hudson River was demonstrated during rescue and recovery of the USAirways aircraft crashed in the river on January 15, 2009. Usually hydrophysical parameters are measured by fixed location sensors that cannot provide high spatial resolution. Acoustic tomography, on the other hand, can map the hydrophysical parameters. Recently, Stevens Institute of Technology using their fully equipped underwater facility including two surface vessels has conducted the first feasibility test on acoustic tomography. The test was conducted in the Hudson River, a shallow estuary that is connected to New York Harbor. The test used an estimate of the differential time of flight of an acoustical signal in opposite directions between two nodes. One node was at a fixed location, while the other was on a research vessel drifting on the other side of the river. An application of a frequency sweep signal (30–90 kHz) and a cross‐correlation technique allowed measurements of the difference in time of flight of the acoustic signals propagated in opposite directions that is proportional to the current in the river. The average current speed was estimated and correlated well with current measurements available from the Stevens Observing and Prediction System. [This work was supported by ONR Project #N00014‐05‐1‐0632: Na.]