Vehicle GPS Data Research Articles

Dynamic risk analysis of flammable liquid road tanker based on fuzzy Bayesian network

AbstractDue to environmental variability of combustible liquid road transportation, it is difficult to analyze risk accurately. This article proposes a method for real‐time analysis of the risk of flammable liquid road transportation using a fuzzy Bayesian network. This method combines the binary logistic regression model with the bow‐tie model to analyze the influencing factors and accident evolution. Then, a Bayesian network framework is established based on the fault tree model. In the case of limited historical accident statistics, the fuzzy set theory and Leaky Noisy‐OR theory are used to determine the prior probability and conditional probability of Bayesian network nodes, and ALOHA software is used to simulate the dangerous area of the accident to define further the safe evacuation range and emergency rescue material demand. Taking the road transportation of a tanker in Beijing as an example, the accident probability is updated in real time by monitoring the data of transportation nodes, and the change rule of risk level with internal and external conditions at different times is analyzed. The research shows that this method can dynamically describe the road transportation accidents of hazardous chemicals with the GPS data of vehicles.

Understanding the impact of vehicle dynamics, geometric and non-geometric roadway attributes on surrogate safety measure using connected vehicle data

Traditional safety research mostly relies on accident data to analyze the precedents to a crash. Alternatively, surrogate safety measures have the potential to proactively evaluate safety events. The era of connected vehicles and smart sensing has brought about tremendous innovations in safety research. GPS data from such vehicles form a useful case of big data analytics where surrogate safety measures have largely been unexplored. In this paper, we propose time to collision estimation from connected vehicle GPS data. The vehicle dynamics such as speed, acceleration, yaw rate, etc. are then coupled with geometric and non-geometric roadway attributes to understand the contributing factors for a traffic conflict. The dataset contains 2,568,421 GPS points from 14,753 unique journeys. 1:4 ratio of conflict to non-conflict events was used to select 15,258 samples with 28 independent vehicle dynamics, geometric, and non-geometric variables. Binary logit model was used to investigate the relationship of these variables with conflicts. Model results showed that out of 28 independent variables, 6 independent variables and 7 interaction variables were found significant. The results showed some interesting and unique relations of these variables with conflicts. Based on these significant variables, k-means clustering was performed to understand the threshold for the significant values for which the number of conflicts is significantly increased. Results from k-means clustering and two sample binomial proportion t-tests revealed that when absolute acceleration crossed 0.8 m/s2, conflict probability increased by 8 percentage points.​ Moreover, when the yaw rate crossed 8 degrees/s, the conflict probability doubled. Besides, vehicles traveling at more than 140% of the recommended speed limit increased conflict probability by 7 percentage points.

Traj2Traj: A road network constrained spatiotemporal interpolation model for traffic trajectory restoration

AbstractIn transportation, the trajectory data generated by various mobile vehicles equipped with GPS modules are essential for traffic information mining. However, collecting trajectory data is susceptible to various factors, resulting in the lack and even error of the data. Missing trajectory data could not correctly reflect the actual situation and also affect the subsequent research work related to the trajectory. Although increasing efforts are paid to restore missing trajectory data, it still faces many challenges: (1) the difficulty of data restoration because traffic trajectories are unstructured spatiotemporal data and show complex patterns; and (2) the difficulty of improving trajectory restoration efficiency because traditional trajectory interpolation is computationally arduous. To address these issues, a novel road network constrained spatiotemporal interpolation model, namely Traj2Traj, is proposed in this work to restore the missing traffic trajectory data. The model is constructed with a seq2seq network and integrates a potential factor module to extend environmental factors. Significantly, the model uses a spatiotemporal attention mechanism with the road network constraint to mine the latent information in time and space dimensions from massive trajectory data. The Traj2Traj model completes the road‐level restoration according to the entire trajectory information. We present the first attempt to omit the map‐matching task when the trajectory is restored to solve the time‐consuming problem of map matching. Extensive experiments conducted on the provincial vehicle GPS data sets from April 2018 to June 2018 provided by the Fujian Provincial Department of Transportation show that the Traj2Traj model outperforms the state‐of‐the‐art models.

Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety

The causes of traffic crashes are complex and uncertain, among which the risky driving behaviors of drivers and the types of road sections in high-crash areas are all critical influencing factors. We used ArcGIS software to draw traffic heat maps under different thresholds to prevent the occurrence of traffic crashes accurately and effectively according to the vehicle GPS data of urban expressways in Jinan City, Shandong Province. This paper studied the relationship between risky driving behaviors (rapid acceleration, rapid deceleration, and overspeed) and road types with traffic crashes. The traffic safety evaluation model of urban expressways based on ordered logistic was established to predict the safety level of the urban expressway. The model’s accuracy was 85.71%, and the applicability was good. The research results showed that rapid deceleration was the most significant influencing factor of crashes on urban expressways. When the vehicle deceleration was less than or equal to −4 m/s2, the probability of a crash was 22.737 times greater than when the vehicle deceleration was at −2 to −2.5 m/s2; when the vehicle acceleration was greater than or equal to 3 m/s2, the probability of a crash was 19.453 times greater than when the vehicle acceleration was at 1 to 1.5 m/s2. The likelihood of a crash at a road section with a ramp opening was 8.723 times greater than that of a crash at a non-ramp opening; the crash probability of a speeding vehicle was 7.925 times greater than that of a non-speeding vehicle; the likelihood of a crash on a curve was 6.147 times greater than that on a straight. The research results can provide adequate technical support for identifying high-risk sections of expressways and active early warning of traffic crashes.

Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers’ clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles’ abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle’s actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations.

In utero exposure to near-roadway air pollution and autism spectrum disorder in children

ImportancePrevious studies have reported associations between in utero exposure to regional air pollution and autism spectrum disorders (ASD). In utero exposure to components of near-roadway air pollution (NRAP) has been linked to adverse neurodevelopment in animal models, but few studies have investigated NRAP association with ASD risk. ObjectiveTo identify ASD risk associated with in utero exposure to NRAP in a large, representative birth cohort. Design, Setting, and ParticipantsThis retrospective pregnancy cohort study included 314,391 mother–child pairs of singletons born between 2001 and 2014 at Kaiser Permanente Southern California (KPSC) hospitals. Maternal and child data were extracted from KPSC electronic medical records. Children were followed until: clinical diagnosis of ASD, non-KPSC membership, death, or December 31, 2019, whichever came first. Exposure to the complex NRAP mixture during pregnancy was assessed using line-source dispersion models to estimate fresh vehicle emissions from freeway and non-freeway sources at maternal addresses during pregnancy. Vehicular traffic load exposure was characterized using advanced telematic models combining traditional traffic counts and travel-demand models with cell phone and vehicle GPS data. Cox proportional-hazard models estimated hazard ratios (HR) of ASD associated with near-roadway traffic load and dispersion-modeled NRAP during pregnancy, adjusted for covariates. Non-freeway NRAP was analyzed using quintile distribution due to nonlinear associations with ASD. ExposuresAverage NRAP and traffic load exposure during pregnancy at maternal residential addresses. Main OutcomesClinical diagnosis of ASD. ResultsA total of 6,291 children (5,114 boys, 1,177 girls) were diagnosed with ASD. The risk of ASD was associated with pregnancy-average exposure to total NRAP [HR(95% CI): 1.03(1.00,1.05) per 5 ppb increase in dispersion-modeled NOx] and to non-freeway NRAP [HR(95% CI) comparing the highest to the lowest quintile: 1.19(1.11, 1.27)]. Total NRAP had a stronger association in boys than in girls, but the association with non-freeway NRAP did not differ by sex. The association of freeway NRAP with ASD risk was not statistically significant. Non-freeway traffic load exposure demonstrated associations with ASD consistent with those of NRAP and ASD. ConclusionsIn utero exposure to near-roadway air pollution, particularly from non-freeway sources, may increase ASD risk in children.

Estimating the influence of disruption on highway networks using GPS data

Incidents, such as natural disasters, public events, and holidays, often cause problems to highways, even paralyze the operation of the whole networks, leading to a serious threat to travel efficiency and safety of the public. To provide better transport management and plans for emergencies, it is important to quickly and accurately identify such incidents and estimate their disruptive effects on the networks. To this end, a novel approach has been proposed in this paper, which is based on the Bayesian theory and thrice-standard-error principle while utilizing vehicle GPS data. Two important indicators, including traffic flows and congestion indexes, along with their change ratios, are built to detect the incidents and evaluate the extent of the disruption. The specific disrupted and detour roads are further determined. The proposed method has been tested using two real-world events in China, and the potential and effectiveness of this technique are demonstrated. With more and more vehicles being equipped with GPS devices worldwide, the designed method can be easily transferable to other countries, paving a way for the adoption of the approach for a more spatial–temporal sensitive highway network disruption analysis method that supports the establishment of a more resilient transport system for emergencies.

Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen

Online ride-hailing has gradually become a popular travel choice worldwide, while it also brought policy challenges to balance the traditional taxi industry and online ride-hailing services. Understanding the operation patterns of urban online ride-hailing services is essential for government policy-making. However, insufficient attention has been paid to the operating characteristics of online ride-hailing vehicles due to limited empirical data. This paper proposes a cluster analysis framework for the identification of different operation patterns of urban online ride-hailing. The customer order and GPS data of online ride-hailing vehicles and traditional taxis in Xiamen, China is used in this study. The k-means++ clustering algorithm is used based on the proposed intensity and stability indices of ride-hailing vehicle operating characteristics. The results show that there are three types of online ride-hailing operation patterns, namely full-time (which accounts for 52.801%), part-time (29.502%), and occasional (17.697%). The operation pattern of full-time ride-hailing vehicles is similar to that of traditional taxis, but with lower intensity and stability due to a reduced workload and flexible time schedule. Part-time ride-hailing vehicles are operated unsteadily and irregularly in the drivers’ spare time, and the working time periods are mainly concentrated in the morning and evening peak hours. Occasional ride-hailing vehicles provide very limited service. Finally, several policy suggestions for online ride-hailing from the perspective of government management, e.g., the number of licenses and operation places and time periods, are proposed based on the results.

EXPLORATION OF MUNICIPAL MOBILITY USING SMARTPHONE GPS DATA

Abstract. Due to the advances in location-acquisition techniques, smartphone GPS location data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study human behaviour through exploring patterns from these data. In this paper, we use smartphone GPS location data to investigate municipal mobility. Kernel density estimation and emerging hot spot analysis are used to the GPS dataset to demonstrate GPS user (point) distribution across space and time. Flow maps are capable of tracking clustering behaviours and direction maps drawn upon the orientation of vectors can precisely identify location of the events. Case study with Indianapolis metropolitan area traffic rush hour verifies the effectiveness of these methods. Furthermore, we identify smartphone GPS data of vehicles and develop a concise and effective method for traffic volume estimation for county highway network. It is shown that the developed smartphone GPS data analytics is powerful for predicting reliable annual average daily traffic estimation. The study showcases the capability of GPS location data in identifying municipal mobility patterns for both citizens and vehicles.

Research on Geographic Location Prediction Algorithm Based on Improved Teaching and Learning Optimization ELM

With the rapid development of vehicle-mounted communication technology, GPS data is an effective method to predict the current road vehicle track based on vehicle-mounted data. GPS-oriented vehicle-mounted data position prediction method is currently a hot research work and an effective method to realize intelligent transportation. In this paper, an improvement scheme is proposed based on the problem of falling into local optimization existing in the basic algorithm of teaching and learning optimization algorithm. An interference operator is used to disturb teachers to enhance the kinetic energy of the population to jump out of local optimization. By comparing the performance of GA, PSO, TLBO and ITLBO algorithms with four test functions, the results show that ITLBO has efficient optimization effect and generalization ability. Finally, the ITLBO-ELM algorithm has the best prediction effect by comparing the vehicle GPS data and comparing the experimental algorithms.

Intelligent Group Prediction Algorithm of GPS Trajectory Based on Vehicle Communication

With the rapid development of in-vehicle communication technology and the integration of big data intelligent technology, intelligent algorithms for vehicle communication used to predict traffic flow and location information have been widely used. Aiming at the problem that the gravitational algorithm is difficult to minimize the complex function and easily fall into the local optimum, this paper proposes an improved IGSA algorithm. First, a gridding algorithm is introduced to initialize the population, and under the premise of ensuring the randomness of the initial individuals, improving the ergodicity of the population is conducive to improving the quality of the solution; then, an adaptive location-based update strategy of decreasing inertia weights is proposed. this strategy inherits the advantages of linearly decreasing weights, and adaptively adjusts the weights according to the fitness value to further improve the optimization performance. The optimization simulation of 8 classic test functions shows that the IGSA algorithm is an effective algorithm for solving complex optimization problems. Finally, the IGSA algorithm is used to predict the geographic location problem in the vehicle GPS data. The IGSA algorithm is used to optimize the extreme learning method to optimize the hyperparameters and establish a vehicle GPS data prediction model. Simulation results verify the feasibility of the method.

An Unmanned Intelligent Transportation Scheduling System for Open-Pit Mine Vehicles Based on 5G and Big Data

With the maturity of the Internet of Things, 5G communication, big data and artificial intelligence technologies, open-pit mine intelligent transportation systems based on unmanned vehicles has become a trend in smart mine construction. Traditional open-pit mine transportation systems rely on human power for command, which often causes vehicle delay and congestion. The operation of unmanned vehicles in an open pit mine relies on many sensors. Using big data from the sensors, we optimize vehicle paths and build an efficient intelligent transportation system. Based on large amounts of data, such as unmanned vehicle GPS data, vehicle equipment information, production plan data, etc., with the goal of reducing vehicle transportation costs, total unmanned vehicle delay time, and ore content fluctuation rate, a multi-objective intelligent scheduling model for open-pit mine unmanned vehicles was established, and it is aligned with actual open pit mine production. Next, we use artificial intelligence algorithms to solve the scheduling problem. To improve the convergence, distribution and diversity of the classical fast non-dominated sorting genetic algorithm (NSGA-II) to solve constrained high-dimensional multi-objective problems, we propose a decomposition-based constrained dominance principle genetic algorithm (DBCDP-NSGA-II), retaining feasible and non-feasible solutions in sparse areas, and compare it with four other commonly-used multi-objective optimization algorithms. Simulation analysis shows our algorithm provides the best overall performance results of the multi-objective models. Furthermore, we apply intelligent scheduling models and optimization algorithms to mining practice and obtain new truck operation routes and schedules, reducing truck operation costs by 18.2%, truck waiting time by 55.5%, and ore content fluctuation by 40.3%. For open-pit mine unmanned transportation, the approach provides a variety of optimized solutions for minimum transportation costs, minimum waiting time, minimum ore content fluctuation rate, and a balance of the three indicators. Through an artificial intelligence algorithm, this study realizes intelligent unmanned vehicle path planning and improves the operation efficiency of open-pit mine intelligent transportation systems.

Route Recommendations for Intelligent Transportation Services

The accumulated large amount of mobility data and the ability to track moving people or objects have enabled us to develop advanced mobile recommendations, which are essential to recommend a sequence of locations to an individual user on the move. In this paper, we study a particular case of mobile recommendations, route recommendations to drivers, by utilizing vehicle GPS data. Specifically, we formulate a new Route Recommendation with Relaxed Assumptions (RR-RA) problem, the goal of which is to recommend a sequence of locations to a driver based on his current location in order to maximize his business success. To make our recommendation practical and scalable for real practice, we need to produce recommendation results in a timely fashion once a request emerges. Therefore, we propose an efficient algorithm to efficiently generate recommendations. Furthermore, we identify and address a destination-oriented route recommendation (DORR) problem. Without solving DORR problem, RR-RA alone does not work well in practice because drivers may encounter the destination constraint on a daily basis. We develop a dedicated and efficient algorithm for solving DORR problem. The package of solutions for both RR-RA and DORR problems provide a comprehensive approach for route recommendations to drivers. We evaluate our methods using both real-world GPS data and synthetic data, and demonstrate the effectiveness and efficiency of proposed methods with different evaluation metrics.

Travel time prediction in transport and logistics

PurposeMany transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions.Design/methodology/approachThe paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case.FindingsThe paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper.Research limitations/implicationsThe findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances.Practical implicationsThe paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions.Originality/valueThis paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.

Cooperative collision avoidance algorithm for multi-vehicle based on vehicle networking technology

This paper addresses the problem of low success rate of traditional algorithm collision avoidance and poor control of parking distance. The vehicle network system and the vehicle kinematics model are established. The paper describes accurate estimation of the vehicle driving state in tunnel intranet based on vehicle sensor and vehicle GPS data. Constraints are established for fast response disturbances, rapid stabilisation, reduced braking acceleration and deceleration, and safe distances. From the energy point of view, we consider the relative kinetic energy of any two vehicles under the vehicle network, measure the collision risk, establish the objective function, and analyse the relative kinetic energy of all vehicles approaching the preceding vehicle. In this way, a multi-vehicle cooperative collision avoidance method based on vehicle network technology is realised. The experimental results show that the algorithm has strong collision avoidance performance, and the parking distance control is better, which can ensure safety.

Efficient algorithms to match GPS data on a map

Estimating the distribution of travel times on a transportation network from vehicle GPS data requires finding the closest path on the network to a trajectory of GPS points. In this work, we develop: 1) an efficient algorithm (MOE) to find such a path and able to detect the presence of cycles; 2) a faster but less accurate heuristic (MMH) unable to detect cycles. We present computational results that compare these algorithms, for different sampling rates and GPS sensitivities, using GPS trajectories of three networks: a grid graph and street networks of Santiago and Seattle. We show that MOE (MMH) returns in seconds (hundredths of second) paths where on average 93% (91%) of the edges are within a corridor of one metre from the real path.

Efficient algorithms to match GPS data on a map

Estimating the distribution of travel times on a transportation network from vehicle GPS data requires finding the closest path on the network to a trajectory of GPS points. In this work, we develop: 1) an efficient algorithm (MOE) to find such a path and able to detect the presence of cycles; 2) a faster but less accurate heuristic (MMH) unable to detect cycles. We present computational results that compare these algorithms, for different sampling rates and GPS sensitivities, using GPS trajectories of three networks: a grid graph and street networks of Santiago and Seattle. We show that MOE (MMH) returns in seconds (hundredths of second) paths where on average 93% (91%) of the edges are within a corridor of one metre from the real path.

Carsharing Trip Characteristic Analysis: Do Users Choose Carsharing Rather Than Taxi to Economize?

Carsharing and taxi are both shared or public car services in urban transportation systems, which means they have a lot in common. In particular, the narrow price gap between carsharing and taxi or public transit in China makes them similar. Trip characteristics analysis and market segmentation are needed, to investigate why and when travelers choose carsharing rather than taxi. In this study, vehicle GPS data and operation order data of a round-trip carsharing system in Hangzhou, China, is used to obtain information on 13,338 valid trips. The trips are divided into three groups based on the travel cost comparison with taxi. Then, an artificial neural network model is developed to analyze group characteristics. The trip characteristics concluded from the model, and analysis on typical service stations, reveal that carsharing has its price advantage on simple long-distance trips during off-peak hours, which is called the regular market. Carsharing’s extended market, in which travel cost is higher than by taxi, covers two types of trips: One involves short driving distance (around 20 km) and long stopping time, and tends to occur in areas in which it is difficult to hail a taxi during peak hours; the other involves very short driving distance (around 10 km), more activity spots and very low travel cost (around 25 CNY). The results of this study can help carsharing operators to extend and adjust their business. Also, these results can contribute to help city administrators to reach better decisions.

Hybrid-Trip-Model-Based Energy Management of a PHEV With Computation-Optimized Dynamic Programming

Plug-in hybrid electric vehicles (PHEVs) with fuel and electricity have demonstrated the capability to reduce fuel consumption and emissions by adopting appropriate energy management strategies. In the existing energy management strategies, the dynamic programming (DP)-based energy management strategy (EMS) can realize the global optimization of the fuel consumption if the global vehicle-speed trajectory is known in advance. The global vehicle-speed trajectory can be obtained by applying GPS data of vehicles when the trip path is determined. However, for a trip path without GPS data, the global vehicle-speed trajectory is difficult to be gained. In this case, the DP-based EMS cannot be utilized to achieve the globally optimal fuel consumption, which is the issue discussed in this paper. This paper makes the following two contributions to solve this issue. First of all, the cell transmission model of the road traffic flow and the vehicle kinematics are introduced to obtain the traffic speeds of road segments and the accelerations of the PHEV. On this basis, a hybrid trip model is presented to obtain the vehicle-speed trajectory for the trip path without GPS data. Next, a DP-based EMS with prediction horizon is proposed, and moreover, in order to improve its real-time implementation, a search range optimization algorithm of the state of charge (SOC) is designed to reduce the computational load of DP. In summary, we propose a computation-optimized DP-based EMS through applying the hybrid trip model. Finally, a simulation study is conducted for applying the proposed EMS to a practical trip path in Beijing road network. The results show that the hybrid trip model can effectively construct the vehicle-speed trajectory online, and the average accuracy of the vehicle-speed trajectory is more than 78%. In addition, compared with the existing optimization algorithm for DP calculation, the SOC search range optimization algorithm can further reduce the calculation load of DP. More importantly, compared to the globally optimal DP-based EMS, although the proposed EMS makes the fuel consumption grow less than 5.36%, it can be implemented in real time. Moreover, compared with the existing real-time strategies, it can further reduce the fuel consumption and emissions. Thus, the proposed EMS can offer an effective solution for the PHEV applying it online in the trip path without GPS data.

Identification of workstations in earthwork operations from vehicle GPS data

Abstract The paper proposes a methodology for the identification of workstations in earthwork operations based on GPS traces from construction vehicles. The model incorporates relevant information extracted from the GPS data to infer locations of different workstations as probability distributions over the environment. Monitoring of workstation locations may support map inference for generating and continuously updating the layout and road network topology of the construction environment. A case study is conducted at a complex earthwork site in Sweden. The workstation identification methodology is used to infer the locations of loading stations based on vehicle speeds and interactions between vehicles, and the locations of dumping stations based on vehicle turning patterns. The results show that the proposed method is able to identify workstations in the earthwork environment efficiently and in sufficient detail.