Probe Vehicle Data Research Articles

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.

Day-of-Week, Month, and Seasonal Demand Variations: Comparing Flow Estimates Across New Travel Data Sources

Transportation planners and engineers are increasingly interested in incorporating demand variations into travel models. Regression models are used to predict and compare variations in permanent traffic recorder (PTR) counts along Texas highways to vehicle-kilometers traveled (VKT) inferred from INRIX’s probe-vehicle data across days of the year. Results suggest INRIX data do not illuminate month-of-year variations in network use, due to random or unexpected shifts in sampling rates, but significant day-of-week differences are clear in both. Furthermore, INRIX appears to capture much more light-duty-vehicle travel than PTRs on Saturdays, but this may be due to location-based services’ over-counting of vehicles carrying multiple mobile devices and/or PTRs’ highway-site bias.

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.

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>

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.

Exploring Empty Truck Behavior Using One-Year Probe Vehicle Data

Examining empty truck behavior can be beneficial in developing an efficient and environmentally friendly logistics system. This study used probe vehicle data from April 2019 to March 2020 in Kyushu, Japan, to explore empty truck behavior. The results revealed a difference in the empty truck ratio across the day of the week, time of the day, and month and the trip distribution of empty and loaded trucks. A nonhierarchical cluster analysis was used to classify vehicles based on empty/loaded situations. These results can be potentially used to reduce the number of empty trucks and improve the efficiency of logistics systems.

Improving Supply Chain Logistics with Agent-Based Spatiotemporal Mechanistic Enumeration and Probe Vehicle Data

Improving Supply Chain Logistics with Agent-Based Spatiotemporal Mechanistic Enumeration and Probe Vehicle Data

The impact of target speed on pedestrian, bike, and speeding crash frequencies

This research aims to investigate the influence of adopting the target speed concept on different types of crashes including pedestrian, bike, and speeding-related crashes. The Target speed is the highest speed that vehicles should operate on a roadway segment in a specific context. Based on the reviewed literature, this is the first study to investigate the relationship between target speed and crash frequency. Hence, big data including probe-vehicle data, traffic characteristics, geometric features, and land use attributes were utilized to develop crash prediction models. The main contributions of this research are to quantify the impacts of target speed on traffic safety considering context categories and to conclude the potential recommendations to lower different types of crashes. The 85th percentile speed was calculated and utilized in the developed models. Three crash prediction models were developed for pedestrian, bike, and speeding-related crashes. They were used in the analysis to quantify the impact of adopting target speed on different crash types. The results showed a significant reduction in the three crash types when using the target speed. Most of the improvements took place in three context categories: C3C: Suburban Commercial Segments, C3R: Suburban Residential Segments, and C4: Urban General Segments. Hence, this research recommends adopting target speed specifically in urban and suburban areas. Further, it suggests considering some measures to lower vulnerable road users’ and speeding-related crashes. Following the recommendations of this research would help to reduce different types of crash frequency, hence, improving the mobility and safety for all users in different context classifications.

Queue Length Estimation Based on Probe Vehicle Data at Signalized Intersections

Queue length is one of the important indexes to evaluate the operation efficiency of signalized intersection and also the key parameter of intersection signal control optimization. Traditional queue length estimation models are mostly based on fixed detection equipment, and the models assumptions are too harsh; there are certain limitations. Based on the probe vehicle data, this paper establishes a model of queue length estimation for signalized intersection based on shockwave theory. First, based on the speed and location data of the probe vehicle, the vehicle density is calculated to estimate the intersection stop line. A real-time calculation method of vehicle arrival rate is proposed to improve the applicability of the model. Then, based on the shockwave theory, the meeting time of the queue forming wave and the queue discharging wave are calculated after the green light is on. Finally, the queue length is summed in sections, including the distance between the last queued probe vehicle and the stop line during the red light period, the length of the subsequent vehicles arriving during the residual red light time, and the newly increased queue length within the queue discharging time. This paper uses the VISSIM software to simulate the actual intersection. The simulation results show that when the penetration of probe vehicle is 50%, 25%, and 10%, their corresponding mean absolute relative error are 11.27%, 27.77%, and 39.12%, respectively. It can be seen that with the increase of penetration, the error gradually decreases. The average absolute relative error is within the acceptable range. After analyzing the existing similar methods, although the accuracy of the method proposed in this paper does not reach the highest level, it has the advantages of simple operation, less computation, and good real-time computation. Relevant research results can provide support for traffic control at signalized intersections.

A Data-Driven Approach to Estimate Incident-Induced Delays Using Incomplete Probe Vehicle Data: Application to Safety Service Patrol Program Evaluation

This paper presents a data-driven approach to estimate incident-induced delays (IIDs) using probe vehicle data while accounting for missing data. The proposed approach is applied to evaluate the effectiveness of a safety service patrol (SSP) program. Existing data-driven methods for IID estimation usually rely on complete data sets. The proposed approach employs a random forest-based classification model and an interpolation method to estimate IIDs when real-time data are completely or partially missing during the incident-impacted time period. It also identifies reference profiles from the closest spatial-temporal road segments to improve data availability. The case study shows that the SSP program in the Quad Cities area of Iowa reduces IIDs associated with various incidents by 15%–91%. This data-driven evaluation framework can be applied to other traffic incident management programs, allowing more accurate and objective evaluations of their effectiveness.

Big-Data Driven Framework to Estimate Vehicle Volume Based on Mobile Device Location Data

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance planning, and more. Traditional methods of quantifying vehicle volume rely on manual counting, video cameras, and loop detectors at a limited number of locations. These efforts require significant labor and cost for expansions. Researchers and private sector companies have also explored alternative solutions, such as probe vehicle data, although this still suffers from a low penetration rate. In recent years, along with the technological advancement in mobile sensors and mobile networks, the quantity of mobile device location data (MDLD) has been growing dramatically in spatiotemporal coverage of the population and its mobility. This paper presents a big-data driven framework that can ingest terabytes of MDLD and estimate vehicle volume over a larger geographical area with a larger sample size. The proposed framework first employs a series of cloud-based computational algorithms to extract multimodal trajectories and trip rosters. A scalable map matching and routing algorithm is then applied to snap and route vehicle trajectories to the roadway network. The observed vehicle counts on each roadway segment are weighted and calibrated against ground truth control totals, that is, annual vehicle-miles traveled and annual average daily traffic. The proposed framework is implemented on the all-street network in the State of Maryland using MDLD for the entire year of 2019. The results demonstrate that our proposed framework produces reliable vehicle volume and also its transferability and generalization ability.

Estimating Demand Volume for Signalized Corridors Under Oversaturated Conditions Using Aggregated Probe Vehicle Speed Data

Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of this study is to develop methods that could be implemented in practice based on readily available data. To this end, two methods are proposed: an innovative method based on shockwave theory and the volume delay function adapted from the Highway Capacity Manual (7th edition). Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. Probe vehicle data are used to determine the critical times when the queue reaches the end or beginning of a road segment. From these critical times, the shockwave speed for the boundary between congested (high density) traffic and arriving (low density) traffic is estimated. The proposed methods are tested with simulation data generated in VISSIM and validated based on volume data from the field. The field data are collected from a congested arterial in Virginia Beach, VA, and include the ground truth volumes and INRIX speed data aggregated at one-minute intervals. The results show that both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data.

A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method

Connected vehicle (CV) data in this paper refer to the in-vehicle telematic data, including trajectories and driving events (e.g., hard braking) collected by vehicle manufacturers when vehicles are moving. Recently manufactured vehicles are equipped with cellular modems and Internet of Things (IoT) devices to collect vehicle data. Such data, after removing personal information, are being redistributed to third-party organizations. Compared to other probe vehicle data, the CV data has a higher penetration rate, ubiquitous coverage, and almost lane-level positioning accuracy. These features pave the road for novel transportation applications in transportation planning and traffic operations. In this paper, we represent a novel framework to estimate the regional link volumes based on the CV data and a deep neural network (DNN) model. The training data are generated according to the link volumes (targeted model output) and the corresponding CV counts (input features) at the same locations. The DNN model’s performance was compared with other estimation methods like linear regression and random forest and showed superior performance. The trained DNN model takes ubiquitous CV counts from other locations to estimate the corresponding link volumes. As a case study, the proposed DNN model was trained with a large training data set derived from CV data and time-dependent link counts collected at over 1,200 locations on freeways in the Dallas Fort Worth, Texas, area. The results reveal good accuracy and robustness.

Sparse Data Traffic Speed Prediction on a Road Network With Varying Speed Levels

Most works on graph neural networks (GNNs) for traffic speed prediction assume near-complete data and little variance of base speed levels. However, both assumptions do not necessarily hold true for network-wide probe vehicle data (PVD). Therefore, we applied two state-of-the-art GNNs to sparse PVD from a road network with highly varying speed levels and to dense motorway data for comparison. We introduce two methods to adapt preexisting GNNs for improved prediction performance: normalization of speed values with respect to the base speed levels of different roads led to significant improvements of prediction performance on both datasets. Using the number of observations supporting each speed value as an additional input feature can improve prediction performance. Furthermore, we identified characteristics of data and models encouraging the use of either method. As no fitting dataset to evaluate these approaches was found, a novel dataset derived from PVD is introduced. It features sparse speed values and underlying numbers of observations for a road network with varying speed levels.

Sequence-to-Sequence Recurrent Graph Convolutional Networks for Traffic Estimation and Prediction Using Connected Probe Vehicle Data

Traffic estimation is imperative for conducting fundamental transportation engineering tasks such as transportation planning and traffic safety studies. Additionally, traffic prediction is vital for many data-driven intelligent transportation system applications. Most traffic estimation and prediction methods rely on infrastructure-based sensors to collect traffic parameters. However, infrastructure-based data collection can be costly and time consuming to set up and maintain. Additionally, the spatial distribution of the collected traffic data is limited by the location of the deployed hardware sensors. Probe vehicle data can be used to overcome these limitations. Traffic modeling for estimation and prediction is a complex task due to the stochastic nonlinear spatiotemporal dependencies exhibited by traffic parameters. In this paper, a deep learning-based sequence-to-sequence architecture called Seq2seq GCN-LSTM was proposed to estimate and predict network-wide traffic volume and speed. The proposed method utilizes short-term historical traffic data collected from a low-penetration rate probe vehicle fleet to estimate and predict traffic parameters up to 60-minutes ahead. The method utilizes Graph Convolutional Networks for spatial dependency extraction and Long Short-Term Memory networks to model temporal dependencies. The proposed method generated superior traffic results compared to the baseline models. Additionally, the model demonstrated robustness against perturbations caused by the low rate. Furthermore, the probe vehicle penetration rate was varied to test its effect on the proposed method’s modeling capability. The model was able to maintain traffic volume and speed estimation and prediction performance within a reasonable margin of error using penetration rates as low as 1.5% and 0.5%, respectively.

Measuring congestion at road-diet reductions using probe vehicle data

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

How is traffic safety affected by changes in traffic speeds following speed limit increases? An evaluation with probe vehicle data

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

Temporal Aggregated Analysis of GPS Trajectory Data Using Two-Fluid Model

The market for on-demand mobility services is growing worldwide. These services include, for example, ride-hailing, ride-sharing, and car-sharing. Large-scale fleets of such services collect GPS trajectory (probe vehicle) data constantly everywhere in the network. At a certain penetration rate, this data becomes representative of the entire road network. It can give valuable insights into traffic dynamics and the evolution of congestion. In this paper, we use such GPS trajectory data from Chengdu, China, to investigate the stability and recurrence of macroscopic traffic patterns. Using the two-fluid theory, we find that the two-fluid coefficients are robust on between-day variation, not only supporting the theory itself but also emphasizing that the general evolution of traffic is a robust pattern. We investigate the deviations from the model using time series analysis of the residuals of the two-fluid model. Here, we find evidence for daily and weekly seasonality in the residuals, indicating that congestion patterns are convincingly recurring. These patterns can be used for network-wide traffic state prediction. We conclude that GPS trajectory data from large on-demand mobility fleets is a promising data source for observing traffic patterns in urban road networks once the data becomes representative.

Integrated macro-micro modelling for individual vehicle trajectory reconstruction using fixed and mobile sensor data

Vehicle trajectories can provide a clear picture of the traffic flow that plays a pivotal role in traffic management and control. Two types of traffic sensors, i.e., fixed and mobile sensors, are widely adopted to collect road traffic information. However, fully-sampled vehicle trajectories are far from obtainable through those sensors due to their fixed-location measurements or low penetration rates. To overcome these issues, this study proposes an integrated macro–micro framework to reconstruct individual vehicle trajectories on freeways under a multi-source data environment, in which fixed-location sensor data that collect positions and velocities of vehicles in a certain place and a small fraction of probe vehicle (PV) data that yield continuous trajectories are available. The framework consists of three modules. First, at the macro level, we apply shockwave theory and fundamental diagram to expand the partially detected information to the whole space–time diagram, and then establish a velocity baseline for evaluation of the trajectory fusion thereafter. Second, at the micro level, we develop two trajectory estimation algorithms based on extended car-following models to generate individual trajectories between any two consecutive PVs, in which two candidate trajectories will be generated for each non-probe vehicle. Third, we tailor a trajectory fusion algorithm to integrate the macro- and micro-level models, and apply dynamic programming to solve the optimal trajectories as per the established velocity baseline. The proposed framework is finally tested with NGSIM and HighD datasets. The results show that our integrated method attains a notable improvement in accuracies and smoothness of reconstructed trajectories compared to the non-integrated and the classical Variational Theory-based methods respectively.

Routes Alternatives with Reduced Emissions: Large-Scale Statistical Analysis of Probe Vehicle Data in Lyon

Vehicle air pollution is a significant problem for health and climate change that can be solved by several approaches. The route is one of the many components to be considered. In this work, we propose a statistical analysis of a large FCD database in November 2017 in Lyon (France) in order to find alternative sustainable trips and evaluate potential emission reductions (CO2, NOx, PM10). To this end, an innovative framework was built. First, we assessed vehicle speeds for each network section and the fifteen-minute period, when this information was reachable. Then, we used a regression random forest (RF) algorithm to fill in the missing data. This dynamical speed map allowed us to search for fewer pollutant trips, for the first ten days of November. By using COPERT emission factors (EFs) and the time-dependent Dijkstra algorithm, we successfully identified between 51% and 72% of alternative sustainable paths, depending on the engine technology and the pollutant. We investigated the influence of vehicle technology. In all cases, the number of alternative trips found tends to be the same as soon as the emission savings exceed 5%. Moreover, about 400 trips out of 11,000 have the potential to mitigate about 20% of emissions.