Probe Vehicles Research Articles

Analysis of single vehicle noise emissions in the frequency domain for two different motorizations

Environmental noise, primarily attributed to the road transportation system, poses a significant challenge in Europe, impacting the quality of life for millions. Therefore, a thorough characterization of noise emissions from road transportation sources is needed to stem this problem. This investigation aims to fill a gap in the literature regarding single-vehicle noise emissions in the frequency domain. The emphasis is on motorization (i.e., fuel type), with particular attention to Low-Frequency components, due to their potential impact on human health and ecosystems. Two probe vehicles, a diesel and a Liquefied Petroleum Gas-powered, were employed to collect data for noise emission curves in the frequency domain (from 63 to 8000 Hz) and compare them with those furnished in the CNOSSOS-EU, Harmonoise, and REMEL models. Moreover, data in terms of exhaust noise emissions (at the tailpipe) were also gathered and analyzed in the frequency domain.The analysis highlighted motorization's influence on noise emissions, revealing differences in frequency component contributions to the overall sound power level at different speeds. Low-frequency components were found to be predominant for both vehicles, especially at lower speeds, where the engine noise contribution dominates. This finds its endorsement in the frequency analysis on the noise emission curves provided in the examined models. The evaluation of the exhaust noise emissions revealed resonance phenomena at 63 Hz and showcased the dominance of low-frequency components in the exhaust spectrum (despite the penalization introduced by the A-weighting procedure), opening avenues for understanding and lowering noise emissions during vehicle idling and low-speed operations.

Travel Time Estimation for Urban Arterials Based on the Multi-Source Data

Accurate traffic information, such as travel time, becomes more important since it could help provide more efficient traffic management strategies. This paper presents a method for estimating the travel time of segments on urban arterials by leveraging multi-source data from loop detectors and probe vehicles. Travel time is defined into three distinct sections based on floating car trajectories, i.e., accelerating, constant speed, and decelerating. Considering the traffic flow characteristics, different methods are developed using various data for each section. The proposed methodology is validated using field data collected in Shanghai, China. The results validated the proposed method with absolute percentage errors (APEs) of approximately 5% in constrained traffic flow conditions and 10–20% in less constrained traffic flow. The results also show that the proposed method has better performance than the method with loop detector data and another data fusion model. It is expected that the proposed method could help improve traffic management efficiency, such as traffic signal control, by providing more accurate travel time information.

Using a Bike as a Probe Vehicle: Experimental Study to Determine Road Roughness with Piezoelectric Sensors

Using a Bike as a Probe Vehicle: Experimental Study to Determine Road Roughness with Piezoelectric Sensors

Bayesian inference for estimating the penetration rate of probe vehicles from interdetection times only

Data provided by floating vehicles have the same limitation as that provided by an imperfect stationary detector that undercounts vehicles: they provide data from an unknown proportion of the total flow. Since the knowledge of this proportion is critical for some applications, its value is usually estimated by complementing with data provided by other monitoring infrastructure. However, this type of infrastructure is not the norm in many cities, particularly in developing countries. This paper extends a previously proposed method to estimate this penetration solely from a series of inter-detection times. The improved method is based on Bayesian inference and supports a series of passage times coming from multilane infrastructure, albeit still requiring an uninterrupted traffic regime. The method was applied to actual RFID interdetection times from a four-lane weaving urban freeway segment in Santiago, Chile. The resulting estimated rates are consistent with those reported by the freeway operator, showing the method’s capabilities. Thus, the method is instrumental in successfully taking full advantage of technology already in place in many cities without needing new investments in monitoring infrastructure.

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Traffic state estimation from vehicle trajectories with anisotropic Gaussian processes

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations.

Development of a Length-Based Cell-State Framework Toward the Re-Creation of Large-Scale Dense Congestion Patterns

This paper presents the results and novel findings of generating a simplified version of the prevailing traffic features that existed during a major evacuation. Leveraging the underlying framework provided by the widely used cell transmission model, the desire is to reconstruct the unique characteristics of large congestion patterns that propagate under dense traffic states, where limited attempts at scaling this base model have occurred. The length-based cell-state framework presented can reproduce large spatiotemporal congestion patterns that exist, specifically from large-scale evacuations. To further simplify, the framework considers traffic state heuristics which are calibrated through oblique cumulative count and occupancy curves. As a result of this preprocessing technique, an artifact was found from the use of the cumulative curves under the lens of Newell’s two-phase traffic flow theory where three unique, separate queued regimes were identified within the fundamental diagrams. The methodology re-created a unique large-scale congestion pattern that existed during a past regional evacuation event, Hurricane Irma, the subject of this paper. To test this methodology, a large-scale congested period was analyzed, both with probe vehicle trajectory data and stationary radar detector data. Results demonstrate that traffic re-creation into state-based contours was able to be verified near a 90% level of confidence even at large spatiotemporal extents.

Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals: A combinatorial approach for nonparametric models

This study develops a combinatorial approach for nonparametric short-term queue length estimation in terms of cycle-by-cycle partially observed queues from probe vehicles (PV). The method does not assume random arrivals and does not assume any primary parameters or estimation of any parameters but uses simple algebraic expressions that only depend on signal timing. For an approach lane at a traffic intersection, the conditional queue lengths given probe vehicle location, count, time, and analysis interval (e.g., at the end of the red signal phase) are represented by a Negative Hypergeometric distribution. The simple analytical estimators obtained are compared with parametric methods from literature and highway capacity manual methods using field test data and simulation data involving probe vehicles. The analysis indicates that the nonparametric models presented in this paper match the accuracy of the parametric ones used in the field test and simulated data for estimating queue lengths.

Travel time reliability evaluation using fuzzy-possibility approach: a case study of an Indian city

ABSTRACT Due to growing traffic congestion, significant variations in travel time can be seen in Indian cities. This has led to inconsistency and uncertainty about reaching destinations on time. Therefore, average speeds cannot be the only factor in assessing traffic performance. Travel time reliability is a more crucial parameter to evaluate traffic performance considering the inconsistency and uncertainty involved in travel times. The probe vehicle technique is often employed to measure the travel time. However, this technique can yield only a small sample unless huge resources are deployed. Therefore, this paper attempts to develop a travel time reliability-consistency evaluation model (TTRC-EM) for limited and ambiguous information using a novel fuzzy-possibility approach. Two urban corridors, representing arterial and sub-arterial situations, are taken as a case study in Surat, India. The developed model can be applied to evaluate corridor traffic performance and assign a level of service under mixed traffic conditions.

A Study on Reducing Traffic Congestion in the Roadside Unit for Autonomous Vehicles Using BSM and PVD

With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety.

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

An analysis of travel behavior and willingness to pay on Express Lanes before and during the COVID-19 pandemic

In March 2020, governments worldwide implemented various social restriction measures, including lockdowns, to mitigate the spread of the COVID-19 virus. This led to a profound shift in travel behavior, accompanied by a notable reduction in congestion. This paper investigates travel demand on Express Lanes during two time periods: before the pandemic (from January 2020 to mid-March 2020) and during the pandemic (from mid-March 2020 to the end of May 2020). A large data set of trips observed on Express Lanes derived from transponders is combined with vehicle probe measures of travel times and used to estimate the willingness to pay to travel on Express Lanes. The analysis reveals that not all Express Lane assets are equal, with the longer Express Lane demonstrating better performance than the shorter one. Peak hours experienced a more significant impact than off-peak hours, reflecting the heightened flexibility in travelers' schedules during the pandemic. The proposed econometric model quantifies the increase in willingness to pay for Express Lanes, which went up to $36 and $34 per hour on the two assets from the values of $26 and $28 per hour estimated before the pandemic, which can be justified by the observed lower market shares during the pandemic.

Estimating Speeds for each movement flow using Deep-Learning-based Probe Vehicle for Signal Control

Estimating Speeds for each movement flow using Deep-Learning-based Probe Vehicle for Signal Control

Speed Data Reconstruction: Analyzing and Improving the Accuracy of MFD Estimation Using Loop Detector and Floating Car Data

Accurate MFD estimation is essential for the efficient management of urban road networks. Loop detectors and probe vehicles collect traffic data required for empirical MFD estimation. Different road network coverages of loop detectors and probe vehicles and penetration rates of probe vehicles affect the accuracy of the MFD estimation. The literature calculated the error in the MFD at different road coverage of loop detectors and penetration rates of probe vehicles. However, the error in MFD at different road coverage of probe vehicle was missing and explained the quality of FCD data as the product of probe vehicle road coverage and the square root of their penetration rate. The linear behavior leads to conflicting results and affects the accuracy of the MFD estimation. In this study, the impact of probe vehicle road coverage was initially determined, and a methodology for improving the quality of speed data was developed. Later, the study analyzed the combined effect of road coverage and penetration rate on the MFD estimation and proposed a performance factor for probe vehicle data.The results showed that the error in the MFD is a negative power function of the probe vehicle road coverage, and the intensity of the error is negatively correlated with the penetration rate. The methodology of enhancing the speed data quality improved the quality significantly, notably at lower penetration rates and road coverages of probe vehicles. Further, the proposed performance factor is consistent with the error of the MFD compared to the former approach.

Optimizing Urban Traffic Management: Investigating the Impact of Probe Vehicle Road Coverage, Penetration Rates, and Speed Data Quality in Macroscopic Fundamental Diagram Estimation

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.

Evaluating the environmental impact on connected vehicles during freeway accidents using VISSIM with probe vehicle data

<abstract> <p>We evaluated emissions as an environmental effect resulting from connected vehicles (CVs) during freeway accidents. The CVs were used to determine the CV driving characteristics, whose values were used to implement the CV driving pattern using a microscopic traffic simulation. The environmental effect of implementation of CV was evaluated using the vehicle trajectory data derived from the simulation results. Implementation of CV effectively minimized the vehicle emissions regardless of the market penetration rate (MPR). In terms of vehicle type, the emissions reduction rate of passenger cars was the highest at a maximum of 33.4%. In the case of pollutants, the reduction rate of CO based on all vehicles was the highest at a maximum of 28.8%. Overall, we found that the implementation of CV positively affected vehicle emissions reductions, and an MPR of 60% could maximize the vehicle emissions reduction effect.</p> </abstract>

Assessment of floating car data quality for emission estimation

This paper presents a method for assessing the quality of floating car data and estimating emissions of the road network. The authors use a fundamental diagram from real probe vehicles to determine the categorization of the road network. These data are complemented by the widely used emission factors of the COPERT model to investigate the quality of the floating vehicle data to use them for the determination of emissions on individual road classes with a detailed spatiotemporal resolution. From the same data, an estimate of the emission rate of the categories is determined and it is assessed whether similar differences are exhibited. The hypothesis is that the quality of data is quite sufficient on highways and high-class roads, but unusable and insufficient on low-class roads. The results show that differences in emission estimates can be observed for the road categories of the highway network and low-class road network. For some pollutants, the urban network can also be identified. The offered categorization could lead to further possibilities of using floating vehicle data, such as the assumption of the possibility to detect atypical traffic phenomena (traffic jams, accidents, or impassable roads due to bad weather conditions).

Arterial Signal Timing Based on Probe Vehicle Trajectories Under Cyclic Stochastic Demand

Arterial Signal Timing Based on Probe Vehicle Trajectories Under Cyclic Stochastic Demand

Evaluation of the Versatility of a Traffic Anomaly Detection Method during Heavy Rainfall

AbstractDuring heavy rains, traffic monitoring is limited, as the affected areas are monitored by reporting and patrolling. In this study, a method for detecting traffic anomalies during heavy rainfall events was established, and a model that uses probe vehicle data to detect traffic anomalies during a disaster (an event in which vehicles make U-turns in front of a damaged area) was proposed. In addition, a parameter calibration method was developed for the model using past disaster-related data. The generalizability of the calibrated model was evaluated by applying it to other disasters. According to the results, the proposed model exhibited good generalizability.

A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City

The rapid urbanization of cities often brings about complex mobility issues, such as traffic congestion that, when unplanned, results in decreased productivity and quality of life. While many cities have adopted smart city initiatives to capture and monitor mobility, applying these in a developing country context remains a challenge when infrastructure and high-resolution spatial and temporal data are lacking. In this work, we use GPS data obtained from probe vehicles (a mix of public and private transport vehicles) within the city of Baguio, The Philippines, to develop and propose the Zone-based Speed Index (ZSI), a mobility index based on the speed clusters observed in this city. The ZSI dynamically infers monthly speed thresholds to classify zones as fast or slow and successfully captures the decrease in vehicle mobility associated with the impact of typhoons and holidays. Thus, it can be used to characterize urban vehicle mobility with high (hourly) resolution. Insights from the use of our dynamic mobility index are useful in the development and optimization of transportation systems, in monitoring the ease of vehicle mobility, and in the performance assessment of smart city initiatives, which are much needed in tourism hotspots.