Vehicle GPS Research Articles

Vehicle security monitoring system

A vehicle left in a parking lot may be the target of a potential theft or damage. In orderfor the vehicle owner to have information about the condition of his vehicle, it is necessary to applyappropriate security measures. Therefore, a proprietary vehicle security monitoring system has beendeveloped. In order to obtain adequate information about the condition of the secured vehicle, thevehicle security system has been designed and constructed. It allows you to monitor such parametersas: vehicle inclination (related to, for example, lifting the car in order to put it on a trailer), temporaryvehicle acceleration (related to, for example, a car crash in a parking lot), GPS location (related tovehicle displacement in relation to a fixed position), temperature and humidity. In addition, themovement in the vehicle is monitored by a passive infrared detector, the door status (open/ closed)by reed switches, and the gas concentration (LPG, propane butane) by a gas sensor. Information aboutthe vehicle sensors is sent to the user in the form of an SMS message, either at the users request orin the form of an alarm. The designed vehicle security monitoring system is a solution that allowsthe user to both use and add new useful functionalities. It is also possible to add additional sensors,as well as to expand with new innovative functions (e.g. access control). This results in an increasedsense of security for the user regarding his vehicle.Keywords: security, system, vehicle, sensors

OCP: an OLAP-based bus crowdedness smart-perceiving mechanism for urban transportation

In this paper, we deal with the problem of similarity search about crowdedness for participatory-sensing buses for urban transportation. Similarity search is usually applied for measuring similarities in heterogeneous information networks. However, many models implement similarity search in a global setting, without taking object attributes into consideration. OCP, a novel OLAP-based crowdedness perception, is an attribute-enriched and meta-path-based model with machine learning to capture similarity based on the object connectivity, visibility and features. A set of common crowdedness attribute dimensions are defined across different types of objects, which can be obtained from the participatory passenger’s sensor data through deep-neural-network-based posture recognition. Accordingly, an object can be described as a series of node vectors from different dimensions. In such framework, OLAP is applied in analysing multiple resolutions and improving efficiency of similarity search. In addition, our data sources are based on participatory-sensing instead of using vehicle GPS systems. As more data be collected through participatory-sensing, more accurate crowdedness for a bus can be estimated. The experiment results further demonstrate the efficiency of our analytical approaches.

Construction and Evaluation of Construction Safety Management System Based on BIM and Internet of Things

With the continuous development and maturity of technology, the Internet of Things has gradually been applied to various fields of work and life, for example, the use of the Internet of Things and road traffic, the use of the Internet of Things smart city for vehicle identification and GPS positioning, the use of the Internet of Things smart medical smart heart pulse monitoring device, and so on. And, the construction industry bears important responsibilities for the development of national infrastructure and people’s livelihood. This article aims to study the application of BIM and Internet of Things technology in the safety management of construction sites, to drive technological development with industry needs, help expand the application fields of the Internet of Things, and promote the improvement of the national construction safety management system. This article uses BIM and Internet of Things technology to build a construction safety management system, focusing on the protection of people, through technical means, such as personnel activity control, hazard warning, major hazard monitoring, and multiple institutional measures, such as education, training, supervision, and protection, that collaborate to complete the protection and risk avoidance of operators in the process of building construction. The evaluation index of the safety management system is proposed through the analytic hierarchy process, and the dimension of the safety management system to prevent accidents is evaluated by the analytic hierarchy process. Taking the actual project as an example, the application effect of the remote monitoring system is qualitatively analyzed. On the basis of the analysis, from the three levels of policy, enterprise, and practitioners, several suggested measures that will help new technology to be applied to construction safety management are put forward. In the experiment part, the construction of the safety management system is first explained, then the appropriate system evaluation index is selected, and then the comprehensive ranking is conducted according to the relative importance weight of the highest-level elements in the same level, and the MATLAB software is used to test the consistency. The experimental results show that the construction safety management system constructed in this paper has been well perfected. The calculated weights of people and dangerous areas are as high as 0.578, so that people and dangerous areas can be controlled effectively. Fall accidents from high places can be prevented, full-time monitoring of foundation pit projects can also avoid fall and collapse accidents from high places, improve the comprehensiveness and advancement of construction safety management, and improve the level of construction safety management.

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.

Autonomous Vehicles: Improving Cyber Security

The purpose of this paper is to address the knowledge and implementation gap in Cyber Security within the field of autonomous vehicles. As technology progresses, the utilization of autonomous vehicles will become more mainstream. With such potential for a broader use of such technology, it is crucial that security is ensured. Previous research has indicated that there are levels to vehicle autonomy which range from zero to five. Zero indicates a vehicle with no automation, while five indicates a fully automated vehicle. The potential areas which can be targeted by cyber attackers include the network which the vehicles operate within, the sensors which the vehicle uses to detect where it is going and what actions are needed to provide a safe journey, and the vehicle GPS system. The research aims to find out where the greatest weakness is within autonomous vehicles, and what software and hardware is targeted by cyber attackers, as to provide comprehensive and long lasting solutions within the field of autonomous vehicle security. As for unit of analysis, a focus will be placed on both consumers and organizations, with greater emphasis on the organizations which produce autonomous vehicles. A qualitative approach will be utilized as to streamline the process of understanding the problem through previously done research. At the current time this research aims to provide hypothetical solutions due to lack of field testing potential at this moment, however it is hoped that research and development of greater security protocols will continue to be done to evolve and provide greater security in autonomous vehicles.

Wheels Know Why You Travel

Trip purpose - i.e., why people travel - is an important yet challenging research topic in travel behavior analysis. Generally, the key to this problem is understanding the activity semantics from trip contexts. However, most existing methods rely on passengers' sensitive information - e.g., daily travel log or home address from surveys - to achieve accurate results, and could thus be hardly applied in real-life scenarios. In this paper, we aim to predict the passenger's trip purpose in the scenarios of door-to-door ride services (e.g., taxi trips) by only using the vehicle's GPS trajectory on roads, for which "wheels" is used as a metaphor. Specifically, we propose a novel dual-attention graph embedding model based on the vehicle's trajectory and public POI check-in data. Firstly, both data are aggregated to augment the activity semantics of trip contexts, including the spatiotemporal context and POI contexts at the origin and destination, which are important clues. Based on that, graph attention networks and soft-attention are employed to model the dependency of different contexts on the trip purpose, so as to obtain the trip's comprehensive activity semantics for the final prediction. Extensive experiments are conducted based on the large-scale labeled datasets in Beijing. The prediction results show a considerable improvement compared to state-of-the-arts. A case study demonstrates the feasibility of our study.

Research on GPS Vehicle Navigation System Based on Embedded Technology

GPS vehicle navigation system is an embedded system commonly used in vehicles, which can optimize the driver’s driving experience and improve the convenience of the trip process. This paper analyzes the application characteristics and architecture of embedded technology. By studying embedded Zigbee nodes, network topology design, node hardware design and node software design, the purpose is to accumulate reliable application data and provide reference for the continuous improvement of GPS vehicle navigation system.

An on-road examination of daytime and evening driving on rural roads: physiological, subjective, eye gaze, and driving performance outcomes.

Experiencing sleepiness when driving is associated with increased crash risk. An increasing number of studies have examined on-road driver sleepiness; however, these studies typically assess the effect of sleepiness during the late night or early morning hours when sleep pressure is approaching its greatest. An on-road driving study was performed to assess how a range of physiological and sleepiness measures are impacted when driving during the daytime and evening when moderate sleepiness is experienced. In total, 27 participants (14 women and 13 men) completed a driving session in a rural town lasting approximately 60 minutes, while physiological sleepiness (heart rate variability), subjective sleepiness, eye tracking data, vehicle kinematic data and GPS speed data were recorded. Daytime driving sessions began at 12:00 or 14:00, with the evening sessions beginning at 19:30 or 20:30; only a subset of participants (n = 11) completing the evening sessions (daytime and evening order counterbalanced). The results suggest reductions in the horizontal and vertical scanning ranges occurred during the initial 40 minutes of driving for both daytime and evening sessions, but with evening sessions reductions in scanning ranges occurred across the entire driving session. Moreover, during evening driving there was an increase in physiological and subjective sleepiness levels. The results demonstrate meaningful increases in sleepiness and reductions in eye scanning when driving during both the daytime and particularly in the evening. Thus, drivers need to remain vigilant when driving during the daytime and the evening.

Machine-Learning based Tire-Road Friction Prediction for Ground Vehicles

The presence of adverse road conditions like water, snow, and ice is known to largely increase the chances of road vehicle crashes due to reduced vehicle performance. Providing useful information about future tire-road friction characteristics that will influence vehicle traction and braking capability are essential for safe operation under such weather conditions. This paper describes an approach for using a low-cost camera, vehicle GPS and other basic vehicle sensors on a scaled autonomous vehicle to build a Neural Network for slip-slope prediction. A Recursive Least Squares Filter is used to estimate the longitudinal friction slip-slope. As an alternative to surface classification which requires a labelled data set, this method uses an easily obtained slip-slope to infer tire-road friction levels. A preliminary experiment is conducted which indicates the neural networks effectiveness to distinguish slip-slope values based on previewed road images captured by the vehicle camera.

A Method of Optimal Control for Class 8 Vehicle Platoons Over Hilly Terrain

Abstract This work develops and implements a nonlinear model predictive control (NMPC) control system to facilitate fuel-optimal platooning of class 8 vehicles over challenging terrain. Prior research has shown that cooperative adaptive cruise control (CACC), which allows multiple class 8 vehicles to follow in close succession, can save between 3% and 8% in overall fuel consumption on flat terrain. However, on more challenging terrain, e.g., rolling hills, platooning vehicles can experience diminished fuel savings, and, in some cases, an increase in fuel consumption relative to individual vehicle operation. This research explores the use of NMPC with predefined route grade profiles to allow platooning vehicles to generate an optimal velocity trajectory with respect to fuel consumption. In order to successfully implement the NMPC system, a model relating vehicle velocity to fuel consumption was generated and validated using experimental data. Additionally, the predefined route grade profiles were created by using the vehicle's GPS velocity over the desired terrain. The real-time NMPC system was then implemented on a two-truck platoon operating over challenging terrain, with a reference vehicle running individually. The results from NMPC platooning are compared against fuel results from a classical proportional-integral-derivative (PID) headway control method. This comparison yields the comparative fuel savings and energy efficiency benefit of the NMPC system. In the final analysis, significant fuel savings of greater than 14 and 20% were seen for the lead and following vehicles relative to their respective traditional cruise control and platooning architectures.

Interference-Aware Transmission Scheduling for Internet of Vehicles

Next-generation Intelligent Transportation Systems (ITS) use wirelessly connected vehicles for safety and non-safety applications such as autonomous driving, cooperative awareness, route guidance and multimedia transmissions. This network known as the Internet of Vehicles (IoVs) suffers from many challenges such as collisions due to hidden terminals, and interference from simultaneously transmitting vehicles. Moreover, the packet reception ratio of transmissions between vehicles is significantly reduced at high vehicle densities and severe fading scenarios. As safety applications require periodic broadcast of safety messages from each vehicle to all other vehicles in the neighborhood, the development of an efficient medium access technique is a key challenge. In this paper, we propose an interference-aware medium access protocol for vehicular networks that allocate time slots to the vehicles such that interference due to hidden nodes is minimized. We first provide an analytical model to compute the interference range of a vehicle in the presence of simultaneously transmitting vehicles. We also propose a scheduling algorithm that uses vehicle GPS positions and reuse the same time slot only at a distance greater than the interference range. Simulation results show that the proposed technique improves the packet reception ratio by 70% as compared to the standard IEEE 802.11p based CSMA/CA protocol.

The Simulation of Traffic Signal Preemption using GPS and Dijkstra Algorithm for Emergency Fire Handling at Makassar City Fire Service

The Makassar City Fire Department often faces obstacles in handling fires. Problems that often hinder such as congestion at crossroads, panic residents, and others. The result of this research is a system that can assist firefighters when handling fire cases in terms of accelerating the firefighting team to the location of the fire. Dijkstra's algorithm will be used to find the shortest path to the fire location and the travel time. Then the traffic signal preemption simulation adjusts the color of the lights when the GPS vehicle approaches the traffic lights on the path to be traversed. The simulation results show that the use of traffic signal preemption in collaboration with Dijkstra's algorithm and GPS can help the performance of the Makassar City Fire Department, especially for handling fires that require fast time.

Impact Assessing of Traffic Lights via GPS Vehicle Trajectories

The adaptability of traffic lights in the control of vehicle traffic heavily affects the trafficability of vehicles and the travel efficiency of traffic participants in busy urban areas. Existing studies mainly have focused on the presence of traffic lights, but rarely evaluate the impact of traffic lights by analyzing traffic data, thus there is no solution for practicably and precisely self-regulating traffic lights. To address these issues, we propose a low-cost and fast traffic signal detection and impact assessment framework, which detects traffic lights from GPS trajectories and intersection features in a supervised way, and analyzes the impact range and time of traffic lights from intersection track data segments. The experimental results show that our approach gains the best AUC value of 0.95 under the ROC standard classification and indicates that the impact pattern of traffic lights at intersections is high related to the travel rule of traffic participants.

Mining Travel Time of Airport Ferry Network Based on Historical Trajectory Data

An airport ferry vehicle is a ground service vehicle used to transfer passengers between the far apron and the terminal. The travel time of ferry tasks in the airport ferry network is an important decision-making basis for ferry vehicle scheduling. This paper presents a graph-based method to mine the travel time between nodes in the airport ferry network. Firstly, combined with map and trajectory information, the method takes the terminal boarding gates, parking lots, and remote stands as road network nodes to build a complete airport ferry road network. Then, this paper uses big data processing technology to identify the travel time between regional connection nodes by data fusion through the temporal and spatial relationship between flight schedule and ferry vehicle GPS travel trajectory. Finally, the Floyd shortest path algorithm in graph theory is used to obtain the shortest path and travel time of all OD points. The experimental results show that all the ferry times calculated by the method proposed in this paper can better reflect the actual driving situation. This method saves the manpower, material resources, and time cost of on-site investigation and lays a foundation for the scheduling of ferry vehicles.

Detecting and Rectifying Vehicle Malicious Misbehavior for Intersection Movement Assist: A Sensor-Based Misbehavior Detection Study

Recent developments in wireless communication technologies have led to the evolution of connectivity between vehicles. Maintaining connectivity between vehicles increases a vehicle’s awareness of other nearby vehicles, which can be used in safety applications. Identification of malicious misbehaving vehicles plays an important role in road safety. This research establishes the minimum detectable error (MDE) boundary for relative position between the observer and status vehicles (SV) using vehicle sensor and GPS error profile from field tests and established minimum standards. The results demonstrated that the MDE increases in the lateral direction (side-to-side) with the increase in relative distance between the observer and status vehicles (OV and SV) while remaining the same in the longitudinal direction (front-to-back). This research effort explores the use of Sensor-Based Misbehavior Detection (SBMD) with current specifications and the defined MDE boundary for implementation in the Intersection Movement Assist (IMA) safety application to rectify false positive and false negative hazard messages propagated by a malicious misbehaving vehicle. The simulation approach used in this research quantifies the total number of false positive/negative hazard detections received by a third-party vehicle (TPV) using the IMA safety application and assesses the capability of the OV equipped with SBMD to rectify the false positive/negative hazard detection. In cases where there was no hazard, SBMD produced an 83% to 90% improvement in the reduction of false positive hazard detections. In the cases with hazard scenario, where the SV is in the not-safe-to-cross zone, SBMD produced an 80% to 99% improvement in application performance.

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.

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.

IoT-Based Vehicle Tracking and Monitoring using Raspberry Pi

The inspection of the powered vehicle and the following mechanisms is given and checked as an embedded board and the Android software for the tracking of school vehicles from every area A to area B. The system suggested would allow a significant use of creativity in the area of the smart phone android smart phone programme. The proposed framework utilizes the internet of things (IoT) and GPS, where IoT sends information to any guardian or owner regarding the vehicle status and GPS sends the current area of the vehicle.

Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

A Mixed Geographically Weighted Regression (GWR) model is applied to explore the effects of shared mobility trips on taxi and public transit ridership at the macro-level. Several essential variables, including socioeconomic, transportation, network, and land use data, are set as the causal factors. The experiment is conducted using the smart card data, vehicle GPS trajectories, and vehicle order data collected in Shenzhen City, China. We show that the Mixed GWR outperforms the basic GWR in model fitting and capturing the unobserved heterogeneity. The spatial analysis reveals that bike-sharing addresses the “last-mile” and “first-mile” problems to bus and metro in the urban periphery. It substitutes the bus and taxis in short-distance journeys in the city center. However, the over-placement of bike-sharing in some regions limits the flexibility of bike-sharing connections to the metro. In the city center, ride-hailing fills the gaps in bus coverage and competes with the metro. In the peripheral areas, ride-hailing replaces buses and improves the accessibility to metro stations. The transportation policy increases the cooperation between ride-hailing and taxis citywide, although competitions in few regions need to be solved. The abovementioned results provide policy suggestions to optimize the allocation of local transportation resources.