Driver Parking Research Articles

Data-driven dynamic optimization for real-time parking reservation considering parking unpunctuality

This article examines a real-time parking reservation service that dynamically allocates a limited number of parking slots (which can be reused) to serve parking requests in a crowded area. The study takes into account two stochastic factors: (i) random arrivals of parking requests and (ii) driver parking unpunctuality, which are seldom simultaneously considered in the existing literature. This study formulates the dynamic parking resource allocation problem as a joint chance-constrained model to maximize the expected total revenue over a finite horizon. Due to the complex nature of the model considering the two stochastic factors, the study proposes a data-driven approach using joint chance constraint decomposition and sample average approximation to approximate the model to a deterministic mixed-integer programming model. Furthermore, a stratified ranked set sampling method is introduced to construct a high-quality sample set as the input to the deterministic model, and valid inequalities are designed to accelerate the solution process. Numerical experiments based on real-world data are conducted to validate the performance of the proposed approach under multiple parking scenarios and provide findings.

A machine learning and simulation-based dynamic parking choice model for airports

Parking choice models have been developed in previous works with certain limitations, such as fixed demand, lack of real-time knowledge of parking availability at the parking facilities, and fixed parking duration, to name a few. Furthermore, such models when developed using mathematical optimization approaches, suffer from computational complexity that limits their practical applicability. This paper proposes a machine learning and simulation-based dynamic parking choice model for airport parking. It considers real-time variable parking demand, which influences access time and parking cost to various parking lots. Drivers ultimately decide to seek a parking lot depending on: (1) their value of time; (2) parking duration, and (3) availability of a parking lot. The model uses a Random Forest classifier to predict a driver's choice of a parking lot. The results, based on some empirical data, show that parking demand is highly correlated with a driver's decision to choose a parking lot, followed by parking duration and the driver's value of time. The model's accuracy in predicting a driver's parking choice is found to be 99.6%. The model provides real-time parking occupancy and can be very useful for managing airport parking. The model can be used for seeking a parking space by connected vehicles enabled with real-time information on parking availability at various parking locations (or garages). Future works may include extending the method for autonomous vehicles parking allocation and building a user-friendly dashboard with real-time traffic information for automated ground vehicle systems.

Designing intelligent public parking locations for autonomous vehicles

Looking for parking spaces in crowded areas can be a stressful and time-consuming challenge. With the arrival of technologies like autonomous vehicles (AVs), users can park far away from their destinations. AVs can move from the city center, which has high congestion, into a parking spot outside the area, and thus have more flexibility in choosing the parking location. This study aims to provide a rent bid for the daytime parking of AVs that considers urban land use to evaluate parking strategies possibly chosen by AV users. We determine an actual parking demand function by integrating individual preferences into a p-median problem that controls user optimality. Combining all these together, a novel dynamic optimization formulation is proposed to design the location of parking facilities for AVs that examines the driver's parking preference including rent bid, waiting time for searching parking lots, and travel costs. A Lagrangian relaxation (LR) algorithm is presented to solve the stochastic parking location problem that integrates a reliability strategy to balance demand and supply for parking spaces. The average gap between the exact and the LR solution is about 6% which is very reasonable. We provide a detailed case study of our model with real data generated from daily household travel in New York City. The results show that the average parking price is reduced by 34%, and the average empty trips by AVs decrease by 22% using the proposed public parking strategy.

Study on the Psychology of Drivers Dealing with Illegal Parking Behavior

This research intends to examine and analyze how drivers' psychology and awareness variables leading an erroneous parking behavior affect illegal parking by employing the theory of planned behavior. The theory of planned behavior is explained by the relationships among ‘Attitude toward the behavior’, ‘Subjective norms’, and ‘Perceived behavioral control’ affecting ‘Intention’ and ‘Behavior’. Using the results of drivers' parking awareness survey conducted in Changwon City in 2017, variables related to each of the attitude toward the behavior, subjective norm, and perceived behavioral control for illegal parking were extracted, and the effects they would have on behavioral intentions were analyzed. Analysis of the structural equation model revealed that Attitude toward the behavior' had a positive effect on the willingness to avoid illegal parking as ‘Intention’. On the other hand, ‘Perceived behavioral control’ had a negative effect. It was also found that the latter had bigger-minus-effect on the willingness to avoid illegal parking behavior than the former. In other words, although there is a sense of norm that illegal parking is wrong behavior, which can cause harm to people including drivers and pedestrians, etc., it means that the willingness to violate the norm is up to 2.6 times higher than to follow the norm depending on driver's situation. The results of this study will be a helpful tool for policymakers to understand the driver's parking behavior and hence to develop a rational parking management policy.

An Evaluation of Temporal- and Spatial-Based Dynamic Parking Pricing for Commercial Establishments

All smart parking management systems (SPMS) have incorporated the dynamic pricing into its features and capabilities. The collection of parking fees on a corresponding spot has been dependent on either its time- or space-value, with most SPMS utilizing temporal-based parking fee collection. However, little to no study has been conducted to assess or evaluate these pricing schemes according to its friendliness and economics towards both parkers and business operators. In this work, we evaluate two current temporal- and three proposed spatiotemporal-based dynamic parking pricing methods by computing their social optimum range and economic effects to both users and parking management entities. Our extensive analysis utilizing both empirical mobility traces and driver parking duration behavior provide important insights in the current pricing setups and present necessary adjustments in improving parking fee collection that contribute to societal benefits.

Experimental Observation and Analysis of Traffic Impact on Tibetan Antelopes on the Qinghai‐Tibet Highway

Highways that cross natural reserves are an intrusion with a nonnegligible negative impact on the behavior of wild animals and have numerous and diverse ecological impacts on wildlife near road areas. Field experiments were carried out to collect traffic flow data on the Qinghai‐Tibet Highway on the Qinghai‐Tibet Plateau, China, and the behavior of the Tibetan antelope crossing the highway was observed. The relationships between the percentage of antelopes successfully crossing the highway and the different traffic flows were analyzed. The results demonstrate that the traffic volume is the main factor affecting the success rate of Tibetan antelopes when crossing the highway, displaying a nonlinear negative correlation. Furthermore, the behavioral responses of the Tibetan antelopes within 500 m of the Qinghai‐Tibet Highway before and after different parking behaviors were observed and the proportions of the different behaviors exhibited by the Tibetan antelopes affected by different driver parking behaviors were analyzed. Parking behaviors were found to have the most significant effect on Tibetan antelope behavior within 400 m of the highway, where parking with somebody getting out having the most prominent impact. The results of this study can guide engineering measures to protect wildlife in the plateau region.

Smart System to Detect a Lethargic Driver and Parking the Car to Avoid Accidents

This article describes that driving drowsily is an issue that has been troubling various countries for years, and parts of India face compounded risks due to the country's geography and driving style. Driving drowsily is especially dangerous when paired with poor road conditions in rural, mountainous areas. In this paper, fatigue detection of the driver and emergency parking to reduce the number of accidents caused due to drowsy driving, which deals with automatic drowsiness detection using image processing and emergency auto parking of the vehicle. The paper also discussed an algorithm to detect, track, and analyze driver's EAR (eye aspect ratio) associated with facial landmarks.

Energy Efficiency Analysis through Misalignment on New Design of Hexagonal Coil Array in Wireless Power Transfer

The global transportation revolution accelerates the growth of the Electric Vehicle (EV) market. Wireless Power Transfer Technology (WPT) is part of an alternative solution to replace charging by cable. In its implementation the driver's parking behavior affects Power Transfer Efficiency (PTE) due to frequently misalignment. A new coil array design proposed to optimize PTE and reduce the size of receiver coil. Receiver and transmitter circuits designed to simulate parking conditions and parking lots in small scale settings to get PTE data. Experimental results show that PTE increased by 10% in the center of the coil design and 82% during misalignment on a radius array against single loop coil. In the area of Misalignment tangential boundary, efficiency increased by 5-10% compared to circular coil arrays. The proposed novel coil series achieves a higher overall PTE than a single coil design and an increase in PTE in the tangential boundary misalignment when compared to circular coil arrays.

A Parking Evaluation System Based on the BDS and Intensity Distance Scoring Algorithm

In this paper, a parking evaluation system based on the BDS (BeiDou Navigation Satellite System) and IDS (Intensity Distance Scoring) algorithm has been proposed. Real-time kinematic (RTK) positioning technology and an extended Kalman filter (EKF) algorithm were applied to the BDS to achieve centimeter-level positioning. A dynamic error experiment was performed to verify the positioning accuracy. The parking evaluation system was developed using the IDS algorithm to score parking trajectories. A parking experiment was carried out in a simulated field to prove the effectiveness of the parking evaluation system. With the IDS algorithm, a parking lot in the simulation was modeled using an 8-bit grayscale map image. The map contains 1 departure area, 5 obstacle areas, and 2 parking areas. The intensity of each pixel in the remaining area is inversely proportional to its shorter distance to the two parking areas. The IDS algorithm evaluates parking through three aspects, the choice of different parking trajectories, parking time, and whether an obstacle area is passed through, which can effectively judge the driver's parking technology.

Auditory Assist Method to Indicate Steering Start Timing in Reverse Parking for Improvement of Driver Performance

The development of fully automating the operation of automobiles is rapidly progressing. However, challenges related to driving skill improvement of drivers exist because of the necessity of manual operation, and these are expected to continue in the future. Despite the fact that maintaining and improving manual driving performance will be indispensable in the near future, there are few studies on parking assistance systems aimed at improving the skills of the drivers. Therefore, we focused on steering timing as a candidate factor for skill improvement in reverse parking. In this article, we developed an assist method based on auditory presentation that indicates the timing for starting of steering in order to follow the target trajectory during reverse parking. From the results of verification tests of the assistance using the driving simulator, various effects such as improvement of driver's parking accuracy and reduction of workload were confirmed.

Driver Parking Behaviour: An Observational and Experimental Intervention Study

Illegal parking has become a major problem in Malaysia especially in urban area due to the increased traffic volumes and limited parking space. When the parking facilities are limited, driver factor thus has a significant effect on the formation of traffic congestion. Extensive work has been conducted to address this issue, however most studies did not incorporate the impact of driver behaviour at limited parking condition whereas it is understood that “cruising for parking” at limited parking facilities has a very strong influence on the road traffic jams. Thus, objectives of this study are to analyse drivers’ personality traits which focus on consideration and selfishness traits; and to investigate drivers’ personality traits and their impact on driver parking behaviour in Kuching, Sarawak. Analyzation of driver parking behaviour was based on observational study of driver, vehicle, and situational variables. Experimental intervention then conducted to different age groups of drivers. They were asked to respond to questions that related to the driver parking behaviour characteristic. Analyses of the data reported in this study revealed that most of the drivers are considerate enough but they become a selfish person when confront with difficult situation at the parking space and their action sometimes are susceptible to illegal parking. The findings significantly beneficial to the traffic engineer, policy maker and researcher in enhancing parking control and management system.

ASPIRE: An Agent-Oriented Smart Parking Recommendation System for Smart Cities

An Agent-oriented Smart Parking Recommendation System for Smart Cities (ASPIRE) using Internet of Things (IoT) is proposed in which significant consideration is given to the driver's parking preferences such as type of parking sought, the maximum price that the parker is willing to pay, and the maximum time to walk from parking space to the destination that the parker can tolerate. ASPIRE is a lightweight, scalable, and adaptable system that automates the workflow of reserving the best parking spots. It employs a novel parking brokerma software agent that leverages valuable route planning information from other traffic information services, and promotes road-safety by minimizing the need for parkers to interact with their smartphones or onboard units, thus lowering distracted driving. The main contribution of this paper is the novel adoption of lightweight Analytic Hierarchy Process (AHP) in order to determine the best parking space. In addition, the parking record collected by ASPIRE can provide local governments with statistics for building future parking lots. ASPIRE's parking network and architecture, system workflow, strategy model, and simulation highlight the importance of the desirable feature such as satellite navigation, indoor navigation, event data recording, parking reference, traffic and weather information, and parking reservations.

Energy-Efficient and Adaptive Design for Wireless Power Transfer in Electric Vehicles

Wireless power transfer (WPT) could revolutionize global transportation and accelerate growth in the electric vehicle (EV) market, offering an attractive alternative to cabled charging. Coil misalignment is inevitable due to driver parking behavior and has a detrimental effect on power transfer efficiency (PTE). This paper proposes a novel coil design and adaptive hardware to improve PTE in magnetic resonant coupling WPT and mitigate coil misalignment, a crucial roadblock in the acceptance of WPT for EVs. The new design was verified using ADS, providing a good match to theoretical analysis. Custom designed receiver and transmitter circuitry was used to simulate vehicle and parking bay conditions and obtain PTE data in a small-scale setup. Experimental results showed that PTE can be improved by 30% at the array's center, and an impressive 90% when misaligned by three-fourths of the array's radius. The proposed novel coil array achieves overall higher PTE compared to the benchmark single coil design.

Kajian Kapasitas, Kebutuhan, dan Efektivitas Pakir di Bandar Udara El Tari Kupang

Undisciplined and difficulty to find parking space are problems at El Tari Airport Kupang. The purpose of research to determine the capacity and demand of parking space and the effectiveness of parking services. The methods used include analysis of parking characteristics, linear regression, method of Importance Performance Analysis (IPA). The results showed that the demand parking space for car at El Tari Airport in existing condition is already exceeding capacity, while for motorcycle still sufficient. For the next 5 years the demand of parking space is 573 parking spaces (SRP) for cars and 599 SRP for motorcycles. The relationship between parking space demand and the number of passengers are y = 1.463x - 6203.6 for car and y = 3.3449x - 14565 for motorcycles. For parking service performance with IPA method, there are 7 variables as the main priority improvement, that is the ability of the parking operator to direct the vehicle to the parking lot, arrange the traffic in the parking area, direct the driver in and out of the parking lot, supervision of parking offenders, parking attendant behavior on the driver, circulation arrangement and vehicle parking position expressed by parking signs and markings, and parking patterns in order.

The optimal searching strategies of curbside parking with schedule delay penalties

ABSTRACTThis paper investigates the drivers’ microscopic curbside parking behaviours under random distribution of vacant parking spaces. For each trip, the driver has a preferred arrival time at the destination and may incur schedule delay cost due to the uncertainty of searching process. The expected trip costs include the in-transit travel cost, the cruising cost, the walking cost, the schedule delay cost and the parking fee. To minimize the expected trip cost, there are two decisions require the driver to make, namely how long before the specified arrival time to initiate the trip and how far away ahead of the destination to search for a vacant parking position. Two possible searching state of the driver are illustrated. The ‘fastest parker’ refers to the possible state of the driver parking successfully when just starts cruising with zero searching distance, and the ‘luckiest parker’ refers to the possible state that driver parks exactly on her/his destination and incurs zero walking distance. Based on the schedule delay states of being the fastest parker and the luckiest parker, three searching strategies are distinguished. The ‘Early-park-early-arrival’ strategy refers to the driver has the expectation that arrives at the destination early when being the fastest parker; the ‘Early-park-late-arrival’ strategy refers to the driver has the expectation that arrives at the destination late when being the fastest parker and arrives early when being the luckiest parker; the ‘Never-early’ strategy refers to the driver has the expectation of late arrival whatever being the fastest or the luckiest parker. The analytical proofs reach some interesting findings: the ‘Never-early’ strategy can never be optimal; if the unit time cost of early arrival is larger than that of late arrival, the optimal parking strategy is ‘Early-park-late-arrival’; if the ‘Early-park-early-arrival’ strategy is optimal, the driver’s possibility of parking before the destination is larger than 50%. Several numerical results are illustrated to verify the analytical propositions and show that the ‘Early-park-early-arrival’ strategy is preferred to the ‘Early-park-late-arrival’ strategy under higher parking charge and higher unit time cost of walking.

Reservation based optimal parking lot recommendation model in Internet of Vehicle environment

In order to solve the problem that the drivers can't find the optimal parking lot timely, a reservation based optimal parking lot recommendation model in Internet of Vehicle (IoV) environment is designed. Based on the users oriented parking information recommendation system, the model considers subjective demands of drivers comprehensively, makes a deeply analysis of the evaluation indicators. This recommendation model uses a phased selection method to calculate the optimal objective parking lot. The first stage is screening which based on the users' subjective parking demands; the second stage is processing the candidate parking lots through multiple attribute decision making. Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot, save the driver's parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.

Semi-autonomous advanced parking assistants: do they really have to be learned if steering is automated?

Several studies have demonstrated positive effects of advanced parking assistants (APA) on driver comfort and parking performance. However, learning effects while handling the APA system and possible transfer effects on manual parking have not yet been discussed. In this study, N=18 subjects parked parallel in a test area (26 manoeuvres) and in real traffic (nine manoeuvres). One half of the manoeuvres was done without a parking assistant, one half with a semi-autonomous APA system that utilises automatic steering. The APA system did not control speed by accelerating or braking. Parking performance and glance behaviour in selected manoeuvres were analysed as well as drivers' judgements and observations by an in-vehicle experimenter. Consistent with earlier studies, the APA system facilitates parking. Learning effects particularly appear in glance behaviour and maximum velocity during the first parking motion as also do the number of errors while handling the system. Using the APA system repeatedly might influence parking without an assistant as well: the more manoeuvres are carried out with the APA system, the more often the drivers look into the display during manual parking. The implications of this study are discussed.

Evaluating Urban Parking Policies with Agent-Based Model of Driver Parking Behavior

This paper presents an explicit agent-based model of parking search in a city. In the model, “drivers” drive toward their destination, search for parking, park, remain at the parking place, and leave. The city's infrastructure is represented by a high-resolution geographic information system (GIS) of the street network and parking lots; information is included on traffic directions and permitted turns, on-street parking permissions, and layers of off-street parking places and lots. Destinations are presented by layers of dwellings and public places. Driver agents belong to one of four categories: residents and guests with dwellings as destinations and employees and customers with public places as destinations. Each agent has its own destination, willingness to pay, time of arrival, and duration of stay. In the model, driver agents are “landed” at a distance of approximately 250 m from their destination, that is, close to the area in which drivers start searching for parking. First, a driver estimates the parking situation in the area and then starts to search for a parking place. During the search, a driver agent accounts for the availability of parking places, differences in pricing, and parking enforcement efforts. The model outputs include distributions of (a) search time, (b) distance between parking place and destination, (c) fees paid by the drivers, and (d) parking revenues for the proprietor of paid parking places (whether local authority or private operator). The model is implemented as an ArcGIS application and applied to analyze parking dynamics in an inner city neighborhood in Tel Aviv, Israel, during the course of a regular weekday.

Quantifying the potential savings in travel time resulting from parking guidance systems — a simulation case study

Parking Guidance and Information (PGI) signs are thought to enable a more efficient use of the available parking stock. Despite the installation of PGI systems in many cities and their operation for a number of years, there is a lack of reliable evidence of the size of the benefits that these systems can achieve. This paper describes the development of driver parking choice models (both during the journey and pre-trip) and the implementation of these models in the existing network traffic simulation model RGCONTRAM. Besides quantifying the effects of the PGI system on both the drivers seeking suitable parking spaces and the parking stock itself, this also enables quantification of the impact of parking choice on the wider network. Factors influencing PGI effectiveness are described and conclusions are drawn that illustrate the potential of PGI to induce the demand to spread more efficiently across the parking stock.

Towards a behavioural approach to city centre parking

An understanding of the demand for city centre parking is an important input to policy making. This study emphasizes the importance and benefits of analysing the demand for parking at the level of individual users rather than basing analyses on traffic and parking counts. A survey of drivers' parking behaviour in Jerusalem's business centre was used. A structure is presented for analysing the driver's parking decision process and policy implications that can be drawn from disaggregate data are suggested.