Traffic Safety Improvement Research Articles

Study of urban infrastructure elements for personal mobility devices safe movement

Introduction. Despite the introduction of new amendments to the rules of the road, accidents involving personal mobility device (PMD) continue to occur on the roads of the Russian Federation. The analysis of statistical indicators makes possible to determine that more than 90% of accidents occur in populated areas, with the participation of PMDs equipped with electric motor mainly collisions with vehicles, with less powerful, not equipped with electric motor mainly collisions with pedestrians occur, which indicates certain chosen conditions for driving - the roadway and sidewalks. In addition, it was found that in these cases, one of the emerging types of accidents is tipping, associated primarily with the available elements on sections of city streets and city roads, which have a certain height above the level of roads and sidewalks. In order to determine the possibility of a overturning taking these elements into account, the analysis of the urban infrastructure was determined, the main elements that pose a risk to PMD traffic were identified, their geometric parameters were established and a mathematical calculation of the traffic conditions was carried out in the study. Methods and materials. As materials and methods for the study, the methods of statistical analysis and mathematical calculations were defined. Results. As a result of the calculation, the authors identified the elements of the urban infrastructure that are dangerous for the movement of the PMD, when interacting with which, with a high degree of probability, the device in question will overturn and injure the driver of the PMD. Conclusion. The calculations have identified the main hazardous elements located on the city streets and urban roads. It was found that the process of overturning is influenced by the geometric characteristics of the device. In view of the obtained results, the perspective directions of improvement of traffic safety in urban transport systems - development of requirements for safety PMD and creation of specialized infrastructure for the safe movement of the PMD in the conditions of modern cities were determined.

Driver Vigilance Detection Based on Limited EEG Signals

Driver vigilance detection is an important topic for traffic safety improvement and brain-computer interface design. Electroencephalography (EEG) can directly provide real-time information and is sensitive to the change of vigilance. Thus, EEG signals are widely used for driver vigilance detection. Previous studies commonly acquired EEG signals from a large number of electrode channels and utilized all the collected data for recognition tasks, which may introduce a large amount of noise and redundant data and limit their application potentials in practical systems. This paper examines the potential of using EEG signals from only one frequency band or from only a small subset of related electrode channels in recognizing driver vigilance state. The widely used SEED VIG dataset is adopt for experiments. Four candidate sets of EEG frequency bands and four candidate sets of EEG electrode channels are determined according to the statistical significance results of EEG power spectral density (PSD) features and differential entropy (DE) features. The experimental results show that the recognition accuracy is higher when using EEG signals from the selected frequency band (i.e., alpha band) or the selected electrodes (i.e., T7, TP7, CP1) than when using all the data. Specifically, the best recognition accuracy is 91.31% for the alpha band and 76.81% for the selected electrodes. These results indicate that higher driver vigilance recognition accuracy can be achieved with much less amount of data, which would facilitate the development of wearable equipment based on EEG signals.

Identifying the Local Impacts of Speed-Related Factors on Tunnel Entrance Crash Severity

According to crash statistics, freeway tunnel entrance zones have higher injury severity rates than other freeway sections. The concentration of traffic injuries is a combination of high speed and low driver alertness. The government of South Korea has deployed the variable speed limit (VSL) as a safety countermeasure to mitigate the consequences of tunnel entrance crashes. This measure has not been quantitatively reviewed, therefore, the effectiveness of implementing VSL at tunnel entrance zones is unknown. The aim of this was to predict the injury severity of crashes occurring at the tunnel entrance zone given the nature of the crash, the size of the tunnel, the roadway geometric characteristics, and environmental factors. It was anticipated that a statistical model calibrated with historical crash data and tunnel features would be able to justify the current locations of VSL implementation and prioritize new VSL implementations. A geographically weighted logistic regression was employed and benchmarked by the conventional logistic regression model, considering the varying geographic locations of freeway tunnel entrances. The geographically weighted logistic regression model presented consistent findings and a slightly better statistical goodness-of-fit than the conventional logistic model. Moreover, the geographically weighted regression captured the local impact of low-level traffic speed on crash injury severities. The findings in this study could help researchers and practitioners evaluate site-specific traffic safety improvement strategies.

Automated Traffic State Optimization in the Weaving Area of Urban Expressways by a Reinforcement Learning-Based Cooperative Method of Channelization and Ramp Metering

The traffic congestion problem on urban expressways, especially in the weaving areas, has become severe. Some cooperative methods have been proven to be more effective than a separate approach in optimizing the traffic state in weaving areas on urban expressways. However, a cooperative method that combines channelization with ramp metering has not been presented and its effectiveness has not been examined yet. Thus, to fill this research gap, this study proposes a reinforcement learning-based cooperative method of channelization and ramp metering to achieve automated traffic state optimization in the weaving area. This study uses an unmanned aerial vehicle to collect the real traffic flow data, and four control strategies (i.e., two kinds of channelization methods, a ramp metering method, and a cooperative method of channelization and ramp metering) and a baseline (without controls) are designed in the simulation platform (Simulation of Urban Mobility). The speed distributions of different control strategies on each lane were obtained and analyzed in this study. The results show that the cooperative method of channelization and ramp metering is superior to other methods, with significantly higher increases in vehicle speeds. This cooperative method can increase the average vehicle speeds in lane-1, lane-2, and lane-3 by 14.51%, 14.81%, and 37.03%, respectively. Findings in this study can contribute to the improvement of traffic efficiency and safety in the weaving area of urban expressways.

A Before-and-After Study of a Collision Risk Detecting and Warning System on Local Roads

Local roads have numerous blind spots caused by complex geometry, obstacles, and narrow width. Thus, conventional proactive countermeasures, such as passive traffic signs and convex mirrors, have not always been effective in preventing local road collisions. In this paper, we present a novel proactive two-step approach for traffic safety on local roads, comprised of detection of pedestrian-to-vehicle and vehicle-to-vehicle collision risks and warning systems. First, using video surveillance and radars to eliminate blind spots, the system detects road objects, predicts their trajectories and reachable areas, and identifies a potential risk situation. Second, it provides road users such as vehicles and pedestrians with warnings through LED variable message signs, which allows them to react effectively in risky situations. We have applied the system to two local road sites in South Korea, including a university campus in Seoul City and an apartment complex in Daejeon City. The detecting system has been validated using a confusion matrix. We have assessed the warning effect through a before-and-after study and found that the proposed system contributed to the improvement of traffic safety at the case study site in that traffic conflicts decreased by 55–62%.

Behavioural Profiling of Cycling and Walking in Nine European Cities

To ensure cities’ livability, a significant modal shift from car use towards more sustainable modes of transportation, such as walking and cycling, is required. To establish such a modal shift, a better understanding is needed of the psychological components that affect people’s likelihood of shifting to active transport modes. To this end, a behavioural survey was conducted among more than 2000 respondents across nine European cities in four countries. Using factor and cluster analysis, two groups of respondents are identified that have common determinants of their variations in intentions to shift to active transport modes, i.e., a “pro-cycling” cluster (55.6% of the respondents) and a “non-pro-cycling” cluster (44.4%). The findings highlight the intrinsically different nature of walking and cycling as transport modes, underlining the importance of distinguishing walking and cycling policies. The main obstacle to cycle more frequently is perceived traffic safety. Therefore, the main priority should be the improvement of traffic safety. The most important obstacle hindering more frequent walking is time. Hence, reducing travel time, for instance, by creating shortcuts for pedestrians and denser and more diversified urban areas will be an important strategy. Future research could extend this research to cities in other countries and regions. By repeating the survey periodically, changes in people’s motivations and perceived barriers can be analysed over time.

Design and evaluation of visual guiding facilities along urban road tunnel horizontal curves based on vision and speed perception

With continuous increase in the number and length of urban road tunnels, traffic safety in the road tunnels has attracted ever greater attention. In particular, driver’s sight distance and sight zone along urban tunnel curves are constrained, and potential safety hazards are prominent. Herein, from the perspectives of active guidance, using visual guiding facilities to enable drivers to accurately perceive the orientation of the tunnel curve alignment and to control the speed reasonably have become a key research point with theoretical significance and practical application value. In this study, four types of schemes for urban road tunnel horizontal curves were constructed and evaluated by simulation experiments. Scene a was a rare scene without any visual guiding facility in practice, ordinary Scene b was a common scene in an urban road tunnel, Scene c included chevron alignment signs (additional to Scene b), and Scene d included LED arches (additional to Scene b). Glance location occupancy, mean glance duration, accuracy of speed perception and reaction time were used as driver performance indicators, in which the four types of scenes were analyzed and evaluated by visual test, speed perception test and subjective evaluation. The results showed that compared with Scenes a and b, Scene c with chevron alignment signs can improve alignment guidance along road tunnel horizontal curve, while the test results of Scene d with LED arches as proposed in this study had the best performance in all aspects. The research scheme and test results can provide reference for visual guiding facility design and traffic safety improvement along urban road tunnel horizontal curves.

Abdominal injuries in road traffic accidents – an autopsy study

Background/Aim. Traffic accident injuries are a major public health problem worldwide, with millions of people dying every year. Although the improvement of traffic safety is based on preventive action, it is necessary to conduct an analysis of victims in traffic in order to prevent future traffic accidents with the help of such findings. The aim of the study was to determine the manner of occurrence of abdominal injuries (AIs), gender differences, and the sitting position in the vehicle that affects the severity of AIs, as well as which AIs lead more often to fatalities in traffic accidents. Methods. Materials for the present study were collected from the medico-legal autopsies, showing AIs that occurred in road traffic accidents. The total number of people who died from sustained injuries or complications of the injuries after road traffic accidents was 525. Results. In the study sample, 38.3% of subjects sustained abdominal trauma. Men were more likely to have AIs, and passengers in the front seat and motorcycle drivers were identified as the most vulnerable category of these types of injuries. The liver was the most commonly injured abdominal organ. Liver destructions, liver lacerations, spleen lacerations, intestinal injuries, the simultaneous occurrence of head, chest, and AIs, or the simultaneous occurrence of head and AIs in a higher percentage led to death at the scene. Conclusion. The results of this study represent the basis for creating educational content for all traffic participants as well as the legislation in the field of traffic safety.

Characterization of a C-ITS based VRU safety system: Methodology towards scenarios selection

SAFE-UP is a research project funded by the European Commission, that aims to proactively address the novel safety challenges of future road mobility environment, by developing tools and innovative safety methods leading to improvements in road traffic safety, which are addressed by four demonstrators. This paper focuses on Demonstrator 4, which develops a safety system based on Cooperative Intelligent Transport Systems (C-ITS) technology that provides enhanced communication between vehicles, road infrastructure and vulnerable road users (VRU). This demonstrator aims at: (i) the provision of timely warnings which could support in avoiding safety-critical situations between passenger cars and VRUs, (ii) the engagement of an active safety system in cases where the reaction time is limited. This paper presents the method followed to characterize the selected scenarios for Demonstrator 4.

New Anti-Derailment System in Railway Crossings

The objective of this paper is to design a new system to reduce the risk of derailment at crossings, which are critical points in railway lines. Crossings are a common element in conventional lines of current railway systems and are the only point on the track where there is a discontinuity. Our proposal is based on adding an element to the crossing that occupies part of the crossing gap, providing a larger support surface next to the wing rail, such that the wheel does not fall into the gap. The lateral force—which is the most influential parameter in derailments—is substantially decreased, thus reducing the risk of derailment due to lifting on the rail. The proposed approach also increases the safety of the dynamic behaviour, which has a direct impact on passenger comfort and influences the service life of both the rolling stock and the track, thus reducing the cost and even increasing safety at higher speeds. It has a simple structure that is easy to assemble and does not interrupt traffic during installation. The results of simulations using this innovative solution indicate a significant reduction in lateral stresses and strains on the track, which undoubtedly produces an improvement in traffic safety; however, the results cannot be fully quantified in terms of accident reduction with only the data obtained from simulations. Therefore, it was concluded that implementation of the new crossing design provides better conditions for rolling stock to run on turnouts, increasing safety by reducing the risk of derailment. Nevertheless, it will be necessary to carry out a program of experimental tests, which we intend to make the subject of future research.

An Adaptive Traffic-Calming Measure and Effectiveness Evaluation in a Large Urban Complex of Shanghai, China

The rapid development of the motor vehicle brings convenience to our life; however, it also increases the burden on traffic networks and the environment, especially when road space is limited. Traffic calming has proved to be an effective solution for the improvement of traffic safety and travel quality. However, most traffic-calming measures are investigated and carried out without any adaptive ability. Such measures cannot adapt to changing traffic requirements. There is a mismatch between static measures and dynamic traffic. In this study, we propose an adaptive traffic-calming measure using deep reinforcement learning. Traffic volume is controlled at intersections according to the state of dynamic traffic. Then, we take a large urban complex (the Jinding nine-rectangle-grid area) in Shanghai, China, as an example. Further, based on applied static traffic-calming measures, we consider the characteristics of the nine plots, along with traffic demand, to design traffic-calming measures. Finally, the effectiveness of the measures is evaluated in SUMO (Simulation of Urban Mobility). The experimental results show that the proposed measure can increase driving speed under the speed limit and reduce traffic volume in a peak period. The results indicate that the proposed measure is an effective and novel solution for traffic calming in the large urban complex.

A Temporal–Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals

Distracted driving has been recognized as a major challenge to traffic safety improvement. This article presents a novel driving distraction detection method that is based on a new deep network. Unlike traditional methods, the proposed method uses both temporal information and spatial information of electroencephalography (EEG) signals as model inputs. Convolutional techniques and gated recurrent units were adopted to map the relationship between drivers’ distraction status and EEG signals in the time domain. A driving simulation experiment was conducted to examine the effectiveness of the proposed method. Twenty-four healthy volunteers participated and three types of secondary tasks (i.e., cellphone operation task, clock task, and 2-back task) were used to induce distraction during driving. Drivers’ EEG responses were measured using a 32-channel electrode cap, and the EEG signals were preprocessed to remove artifacts and then split into short EEG sequences. The proposed deep-network-based distraction detection method was trained and tested on the collected EEG data. To evaluate its effectiveness, it was also compared with the networks using temporal or spatial information alone. The results showed that our proposed distraction detection method achieved an overall binary (distraction versus nondistraction) classification accuracy of 0.92. In terms of task-specific distraction detection, its accuracy was 0.88. Further analysis on the individual difference in detection performance showed that drivers’ EEG performance differed across individuals, which suggests that adaptive learning for each individual driver would be needed when developing in-vehicle distraction detection applications. Note to Practitioners—Driver distraction detection is crucial for safety enhancement to avoid crashes caused by nondriving-related activities, such as calling and texting while driving. Related previous studies mainly focus on detection by monitoring head and eye movement using computer vision technologies or by extracting indicators from driving performance measures for driver state inference. However, complex traffic environments (e.g., dynamically changing light distribution on driver’s face and nighttime driving with low illumination) strongly limit the effectiveness of computer vision technologies, and the driving performance characteristics may also be caused by factors other than distraction (e.g., fatigue). To solve these problems, this article seeks to develop a deep learning-based approach to map the unique relationship between driver distraction and the bioelectric electroencephalography (EEG) signals that are not affected by traffic environments. The proposed method can be integrated into the driver assistance systems and autonomous vehicles to deal with emergency situations that need drivers to handle. The timely detection of distraction by our method will significantly facilitate its practical applications in collision avoidance or danger mitigation in the handover process.

A Short-Term Hybrid TCN-GRU Prediction Model of Bike-Sharing Demand Based on Travel Characteristics Mining

This paper proposes an accurate short-term prediction model of bike-sharing demand with the hybrid TCN-GRU method. The emergence of shared bicycles has provided people with a low-carbon, green and healthy way of transportation. However, the explosive growth and free-form development of bike-sharing has also brought about a series of problems in the area of urban governance, creating a new opportunity and challenge in the use of a large amount of historical data for regional bike-sharing traffic flow predictions. In this study, we built an accurate short-term prediction model of bike-sharing demand with the bike-sharing dataset from 2015 to 2017 in London. First, we conducted a multidimensional bike-sharing travel characteristics analysis based on explanatory variables such as weather, temperature, and humidity. This will help us to understand the travel characteristics of local people, will facilitate traffic management and, to a certain extent, improve traffic congestion. Then, the explanatory variables that help predict the demand for bike-sharing were obtained using the Granger causality with the entropy theory-based MIC method to verify each other. The Multivariate Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) model were integrated to build the prediction model, and this is abbreviated as the TCN-GRU model. The fitted coefficient of determination R2 and explainable variance score (EVar) of the dataset reached 98.42% and 98.49%, respectively. Meanwhile, the mean absolute error (MAE) and root mean square error (RMSE) were at least 1.98% and 2.4% lower than those in other models. The results show that the TCN-GRU model has strong efficiency and robustness. The model can be used to make short-term accurate predictions of bike-sharing demand in the region, so as to provide decision support for intelligent dispatching and urban traffic safety improvement, which will help to promote the development of green and low-carbon mobility in the future.

Optimization-Based Traffic Safety Improvement Strategy for Autonomous Vehicle Driving at Level Crossings

Railway level crossings pose a serious threat to the safety and mobility of drivers traversing them. Globally, a significant number of traffic violations occur annually at level crossings. This study proposes an optimization-based autonomous vehicle (AV) driving strategy to improve the traffic safety of AVs at level crossings. First, an optimization model considering the operational constraints is developed. The cost function of the proposed optimization-based driving strategy is the time difference between the time required by the train to reach the level crossing and the time taken by the AV to complete the whole crossing maneuver. Second, a MATLAB simulation model is developed to validate the proposed driving strategy. Simulation results show that the proposed driving strategy delivers optimized AV driving without stoppage. The comparative study also shows that the proposed method improves driving performance. For instance, when a train is 1 m away from the light signal while the AV’s initial speed is 36 km/h, the time required for the AV to reach the speed limit sign is about 0.44 s, while the time required by the train to reach the car block area is 0.13 s. Thus, the AV is forced to stop and then go after the train leaves the level crossing. Contrary to this, the proposed method allows a safe passage of the vehicle within 1.66 s without stoppage. Most importantly, the driving strategy is a collision avoidance strategy without unnecessary enforcement of stop-and-go when AVs have the chance of crossing safely. Consequently, the optimization-based driving strategy both improves traffic safety and reduces traveling time.

Impacts of advanced driver assistance systems on commercial truck driver behaviour performance using naturalistic data

Traffic crashes tend to be more serious when commercial trucks are involved. And an increasing number of commercial vehicles are equipped with advanced driver assistance systems (ADAS) for traffic safety improvement. To this end, this study investigated the impacts of ADAS with respect to forward collision warning (FCW), urban forward collision warning (UFCW), lane departure warning (LDW), headway monitoring and warning (HMW), and speed limit indicator (SLI) on commercial truck drivers' behaviours using naturalistic data. Participants experienced two different test scenarios with two different ADAS warning configurations: Visual and audio alerts in the inactive state and in the active state. After activating alerts, 62%, 35%, 69%, 81% and 73% of drivers received less warnings in terms of FCW, UFCW, LDW, HMW and SLI respectively; the average of FCW, UFCW, LDW, HMW and SLI respectively declined 22%, 13%, 28%, 45% and 15%, but median changed little except for LDW and HMW. Then, the further analysis results by the one-way analysis of variance (ANOVA) method revealed that all warnings except UFCW had positive effect on commercial truck drivers' behaviour performance. The findings provide important references for commercial truck driver training and supervision in the age of advanced technology.

Model for Sustainable Evaluation of the Impact of the Total Number of Centers for Technical Inspections of Motor Vehicles on the Occurrence and Consequences of Traffic Accidents in an Area

Along with the development of capacities for the improvement of traffic safety, this work creates a model that analyzes the impact of the total number of centers for technical inspections of motor vehicles on the occurrence and consequences of accidents in a selected area. By using the statistical program SPSS26 and applying standard multiple regression, an analysis of the statistical correlations between a continuous dependent variable (number of accidents caused by technical malfunctions of vehicles) and two independent variables (number of centers for technical inspections of vehicles and number of vehicles registered by police jurisdictions) was performed. The aim of this work was to determine the influence of a part of the dependent variable’s variance that was explained by the variance of independent variables. The research results showed that the total number of technical inspections in relation to the number of registered vehicles in the selected area included a much larger part of the dependent variable’s variance, which referred to the total number of accidents caused by vehicles’ technical inspections. The results also showed a negative correlation value, i.e., that a larger number of technical inspections in relation to the number of registered vehicles did not have a positive impact on the increase in the number of accidents and consequences where the cause was technical malfunction, as well as that the number of centers and the number of registered vehicles had different influences on the occurrence of accidents and their consequences.

Role of High-Speed Roads and Vehicle Ownership on Traffic Fatalities in India

The development of higher quality road infrastructure in developing countries improves ride quality but also enables greater driving speeds. Similarly, a growing middle class allows more people to afford personal vehicles but increases the number of drivers on the road. Improved mobility has historically been associated with economic growth, and its impact on traffic safety has been explored in high-income countries. However, the behavior of road users and vehicle ownership characteristics in middle-income countries are substantively different than those in high-income countries. This study explores the relationship between mobility and traffic safety at a region-wide level in India, a middle-income country. The results show that increasing lengths of National Highways are associated with an improvement in traffic safety while increasing lengths of all other types of roads and total number of motor vehicles are both associated with a deterioration in traffic safety. This study shows that safe roadway infrastructure has a huge role in enhancing overall safety even in countries with high vehicle heterogeneity, lack of driver education, and weak enforcement. This study’s contribution should guide decision-makers in other middle-income countries to invest in traffic safety measures alongside any investments in higher quality road infrastructure.

Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support

Millions of car crashes occur annually in the US, leaving tens of thousands of deaths and many more severe injuries. Thus, understanding the most impactful contributors to severe injuries in automobile crashes and mitigating their effects are of great importance in traffic safety improvement. This paper develops a hybrid framework involving predictive analytics, explainable AI, and heuristic optimization techniques to investigate and explain the injury severity risk factors in automobile crashes. First, our framework examines various machine learning models to identify the one with the best prediction performance as the base model. Then, it utilizes two popular state-of-the-art explainable AI techniques from the literature (i.e., leave-one-covariate-out and TreeExplainer) and our proposed explanation method based on the variable neighborhood search procedure to construe the importance of the variables. Finally, by applying an information fusion technique, our approach identifies a unified ranking list of the most important variables contributing to severe car crash injuries. Transportation safety planners and policymakers can use our findings to reduce the severity of car accidents, improve traffic safety, and save many lives.

Traffic Safety Improvement via Optimizing Light Environment in Highway Tunnels

Driving in tunnel areas depends more heavily on light conditions than that on open roadways. Traditional lighting systems in highway tunnels adjust lighting parameters only caring about outside light luminance, and focus is usually on energy conservation; however, little concern is about drivers’ actual physical and psychological needs. How to leverage the enormous research progress of traffic safety, light environment, human factors engineering, and modern lighting sources to create an ideal tunnel light environment that aids with ensuring driving safety and lower interference effects caused by the change of light environment will greatly improve safety level and reduce adverse influence on drivers’ visual health in a tunnel area. An intelligent lighting control system designed with multiple influence factors are systematically considered. Based on sensor data from outside natural light conditions, target lighting parameters are determined per each lighting zone requires; then, lighting commands will be transferred and parsed by adaptive lighting controllers and modules, eventually LED lighting properties are altered step by step. This system helps a lot with optimizing tunnel lighting quality and improving drivers’ visual performance; as a result, it contributes to lower the fluctuation of drivers’ workload and get a smooth traffic flow, and ultimately this technically ensures physical and mental health of drivers in a tunnel area.

Smart Application for Every Car (SAEC). (AR Mobile Application)

Technology is continuously evolving at an exponential rate. Fast technological advances are being made, especially in the field of smart phones, that facilitate the conduct of our daily activities in many areas such as driving. The ever-increasing number of vehicles on roads increases the likelihood of traffic accidents, resulting in higher number of deaths and serious injuries to drivers, passengers, and pedestrians. Among the main causes of road accidents are over speeding, unsafe lane jumping, and failure to keep a safe distance between vehicles, to name a few. In an attempt to contribute to the improvement of road traffic safety, we have developed an Augmented Reality-based Smart Vehicle Driver Assistance application. The application is designed to enhance vehicle driver’s safety, in particular, but is also considered to lead to incremental improvement of safety of road traffic. The application can run on both Android and iOS platforms and incorporates several beneficial features required by a vehicle driver such as monitoring of vehicle speed, warning the driver in case of lane deviation, detection of road signs, and to alert the driver if the vehicle is not being driven at a safe distance from the vehicle in front. In addition to providing information to improve safe driving, the application also helps the vehicle driver save parking location of the vehicle in order to efficiently identify the parking location when retrieving the vehicle. This feature is very useful at large and unfamiliar parking areas, such as at airports or one-off large public gatherings, especially in inclement weather. The application also includes other useful functions such as the payment of parking fees, storage of information regarding vehicle maintenance, and keeping the vehicle legal document up to date. The application uses the stored information to display reminders of the appropriate action that needs to be taken before it becomes overdue.