This poster session highlights recent research on related to parking management as a transportation demand management tool. Participants will learn about modeling approaches for new parking management strategies, behavioral research results, and new methods for parking management that leverage emerging technologies.
Analysis of On-Street Parking Payment Behavior in a Large Urban Center
Behrang Assemi, Queensland University of TechnologyShow Abstract
Alexander Paz (email@example.com), Queensland University of Technology
Douglas Baker, Queensland University of Technology
Payment behaviour for on-street parking was analysed to understand patterns and improve management and operations of the facilities. Data from three parking locations in the central business district (CBD) of Brisbane, Australia, including spatiotemporal attributes, parking utiliztion and fines, and customers’ behaviour over one week were used in this analysis. Around 647 (25.8%) unpaid and 1,861 (74.2%) paid parking transactions were collected. Descriptive statistics and mixed logit modelling were used to analyse the data. In addition, elasticity and wiliness-to-pay analyses were performed. The spatio-temporal heterogeneity of parking locations throughout an urban network as well as customers’ distinct perceptions of time and illegal behaviour make payment analysis highly complex. Our analysis reveals that there are significant differences regarding perceptions of time and price. Overall, parking duration had a negative relationship with unpaid transactions. A positive effect of using a smartphone application on paying the right amount for the use of the facilities was found. A high average parking duration reduced the likelihood of unpaid transactions, while a high turnover was associated with overpay. Provision of a short free period, while enhancing enforcement activity, and encouraging customers to pay using a smartphone application can reduce illegal parking and improve the performance indicators.
Assessing Variations to Minimum Residential Parking Requirements in Melbourne
Chris De Gruyter (firstname.lastname@example.org), RMIT UniversityShow Abstract
Liam Davies, RMIT University
Long T. Truong, La Trobe University
Minimum off-street residential parking requirements are used in many cities as a way to accommodate parking demand associated with new residential development. In some cases, variations to these requirements are used in the form of reduced (or eliminated) minimums and/or maximum parking requirements to more actively manage parking demand. This paper assesses the appropriateness of such variations affecting new residential apartment development in Melbourne, known locally as parking overlays. Using household car ownership data as a proxy for off-street residential parking demand, a case-control analysis was undertaken to compare car ownership within and immediately outside areas affected by the parking overlays, while controlling for a range of built environment, public transport, demand management and socio-demographic variables. Key findings indicate that car ownership was generally lower in areas affected by parking overlays, yet this was either roughly the same or well below the actual parking requirement. Through regression modelling, the results highlighted the importance of public transport service quality, car parking requirements and demographics in influencing car ownership within and immediately outside the parking overlay areas. Despite parking overlays considered as a form of parking management, the results imply that, in Melbourne, they represent little more than a conventional supply-side approach to parking policy. The results indicate that residential off-street parking requirements could be reduced further in Melbourne, both within and outside of areas affected by parking overlays, to more actively manage parking demand.
Research on Parking App Choice Behavior Based on MNL
Chang Yang, Ningbo UniversityShow Abstract
Xiaofei Ye, Ningbo University
Xingchen Yan, Nanjing Forestry University
Lili Lu, Ningbo University
Zhen Yang, Nanjing Forestry University
Tao Wang, Guilin University of Electronic Technology
Jun Chen, Southeast University
With the concept of sharing economic entering into our lives, many parking Apps are designed for connecting the drivers and vacated parking spaces. However, there are not many drivers who use the mobile app to reserve and find available parking spaces, which is largely due to the insufficient information provided by the parking App. In order to better explain, predict and improve drivers’ choice of parking App, Multinomial Logit model was established to analyze the relationship between the drivers’ parking App choice behavior and influence factors. The influential factors include drivers’ individual characteristics and parking App attributes, which were extracted from the questionnaire and typical parking Apps currently in operation. The results show that the reservation and shared parking space, available parking space, parking charge and distance to destination are the main factors that determine the drivers’ choice of parking App. This paper provides a reference for the development of Ningbo parking Apps.
Deep Spatiotemporal Networks for Shared Parking Supply and its Corresponding Shareable Duration Prediction
yonghong liu, Southwest Jiaotong UniversityShow Abstract
luo xia, Southwest Jiaotong University
Xin chen, Southwest Jiaotong University
Shared parking supply and its corresponding shareable parking duration prediction is one of the fundamental and practical issues in shared parking systems, which plays a vital role in various tasks such as shared parking spaces allocation and shared parking pricing. Such predictions are very challenging, as the shared parking supply usually shows nonlinearities and complex spatiotemporal dependencies. In this paper, we propose a deep-learning-based network, which consists of two components for modeling Graph Convolutional Network (GCN) module and Long Short-Term Memory (LSTM) module to predict the shared parking supply and its corresponding shareable parking duration in the shared parking system. The GCN is used to capture the spatial dependencies, then the LSTM is leveraged to extract the temporal features. Moreover, in order to capture the periodically shifted correlations, we divided the input into three portions-recent, daily, and weekly. The proposed model is evaluated by real-word shared parking dataset in Chengdu, China. Experimentation shows that our method outperforms other five well-known baselines methods within an acceptable time frame. Extensive additional experiments and evaluations conducted to investigate the sensitivity of our model. All the results demonstrate the effectiveness of the proposed method.
Long-Term Forecasting of Off-Street Parking Occupancy for Smart Cities
Elisabeth Fokker, Vrije Universiteit, AmsterdamShow Abstract
Thomas Koch, Vrije Universiteit
Elenna Dugundji, Vrije Universiteit, Amsterdam
Recent developments in the field of parking can be enhanced with smart city alternatives. One of these alternatives is the monitoring of parking sensory data. In this paper, this data is used to propose a Decision Support System (DSS) that supports the decision-making of the municipality of Amsterdam on parking. The DSS provides insight into the six months ahead parking occupancy for 57 off-street parking locations in Amsterdam. An effect analysis has been conducted into factors that influence the off-street parking occupancy, and five forecast models are compared to predict the parking occupancy. For the effect analysis, weather and event variables are highlighted. It is observed that the most influential factors on parking occupancy are sunshine, temperature, relative humidity and event factor 'match', that indicates whether or not a soccer match is taking place. The forecasting algorithms compared are Seasonal Naive Model as a benchmark approach, Box-Jenkins Seasonal Autoregressive Integrated Moving Average with and without exogenous regressors (SARIMAX and SARIMA, respectively), exponential smoothing models, and Long Short-Term Memory neural network. Based on the effect analysis study, the exogenous regressors of the SARIMAX model are included per parking location. This model also outperforms the other algorithms according to the lowest Root Mean Squared Error. Especially the event factor is important for the parking occupancy forecasts. Future studies can focus on the addition of more event variables, the extension into an online model based on real-time parking sensory data and the effect analysis on changes, such as public transit networks on parking.
Investigating city parking policies for connected autonomous vehicles and their effect on transportation network performance
Md Mehedi Hasnat (email@example.com), North Carolina State UniversityShow Abstract
Eleni Bardaka, North Carolina State University
George List, North Carolina State University
Nagui Rouphail, North Carolina State University
Billy Williams, North Carolina State University
Access to driverless autonomous vehicles (AVs) and connected-autonomous vehicles (CAVs) could provide several parking options to the owners, for instance, sending the vehicle back home, ﬁnding cheaper or free parking spots, or to relocate somewhere outside the busy central business district (CBD) areas. This study explores a number of parking relocation scenarios and policies to better understand their regional and local impacts on travel demand. The study focuses on the North Carolina (NC) Triangle Region which includes three major employment centers (Raleigh, Durham, and Chapel Hill). We use the Triangle Regional Model (TRM), which is the four-step travel demand forecasting model for the Triangle Region to simulate parking scenarios with 75% market penetration rate of privately owned CAVs for the year 2045. Our results indicate that a single CAV could travel as much as 10.5 miles back to home if the areas outside the CBD do not allow on-street parking of non-resident vehicles, leading to an increase of daily vehicle-miles traveled (VMT), vehicle-hours traveled (VHT) and delay by 3.6%, 10.2% and 43.6%, respectively. On the contrary, providing subsidized parking facilities outside the CBD areas could provide parking for 145,927 vehicles and result in an increase of 1.6%, 7.2%, and 35.3% in daily VMT, VHT, and delays. The results of this study are useful to urban and transportation planners and of particular interest to polycentric regions similar to the study area.
Private parking space owners’ decision in response to shared parking schemes under uncertainty: results of a stated choice experiment
qianqian yan, Technische Universiteit, EindhovenShow Abstract
Tao Feng, Eindhoven University
Harry Timmermans, Technische Universiteit, Eindhoven
To develop effective strategies for the supply of shared parking and study various theoretical choice models under uncertainty, this paper investigates private parking space owners’ propensity to engage in shared parking schemes using a stated choice experiment that involves an uncertain key attribute. First, a hybrid expected utility-regret model is specified to explore private parking space owners' propensity to participate in shared parking. Next, another emotion - rejoice - is added to the hybrid function. Finally, equivalent models considering the perception of attribute differences are estimated. Results show that socio-demographic characteristics, social influence, government's role, media attention, platform fee, and revenues are all important factors explaining private parking owners’ propensity to engage in shared parking schemes. The model incorporating all these components produces the best results.
Parking Management of Automated Vehicles in Downtown Areas
Sina Bahrami (firstname.lastname@example.org), University of Michigan, Ann ArborShow Abstract
Yafeng Yin, University of Michigan, Ann Arbor
Daniel Vignon, University of Michigan, Ann Arbor
Ken Laberteaux, Toyota Motor Corporation
Automated Vehicles (AVs) eliminate the burden of finding a parking spot upon arrival to the destination, because they can park at a strategic location or cruise until summoned by their users. In this study, we investigate where AVs park in a downtown area considering the cost and time constraints of their users. Since each user's choice has impacts on another via cruising-incurred traffic congestion and parking competition, we model the parking choice problem of AVs as a Nash equilibrium of a large game in which each user cannot further reduce their parking cost by unilaterally changing their choice. Results show that AVs cruise and travel at low speed to decrease their parking costs, which calls for a zero-occupant congestion toll to discourage them from cruising for a long period.
PARKING SPACES DYNAMIC ALLOCATION MODEL IN COMPLEX PARKINGLOT
Guang Yang, Southeast UnivesityShow Abstract
Jun Chen, Southeast University
Kuan Lu, Southeast University
There are significant differences in the utilization efficiency of parking spaces in different spatial locations in the complex parking lots, which reduces the utilization efficiency of parking resources. In view of the above problem, a parking spaces supply-demand characteristics indexes system from the perspective of “parking space level” was constructed. The Metro City complex parking lot was taken as an example, the demand utilization characteristics of parking spaces in different spatial locations were analyzed, and the parking spaces utilization problems were judged accurately. On this basis, a parking spaces dynamic allocation model was constructed to improve the satisfaction of drivers and balance the parking spaces occupancy rate in different zones of parking lot. The parking spaces dynamic allocation simulation is written in C + +, the results show that after implementing parking spaces dynamic allocation, the difference of turnover number of parking zones A and B is reduced from 2.24 times to 0.03 times, the difference of total parking time of parking zones A and B is reduced from 1.31hours to 0.52hours, the difference of average parking time of parking zones A and B is reduced from 2.2h to 0.17h, and the average interval time of parking spaces is small and evenly distributed. It can be seen that the parking spaces dynamic allocation model can effectively improve the utilization efficiency of the overall parking spaces of the complex parking lot and drivers’ parking satisfaction.
Willingness-to-Relocate: Analyzing Travelers’ Parking Preferences for Private Autonomous Vehicles
Wenjian Jia, University of VirginiaShow Abstract
T. Donna Chen (email@example.com), University of Virginia
Wenwen Zhang, Virginia Polytechnic Institute and State University (Virginia Tech)
Linda Lim, University of Virginia
Kaidi Wang, Virginia Polytechnic Institute and State University (Virginia Tech)
Mirla Abi Aad, Virginia Polytechnic Institute and State University (Virginia Tech)
Private autonomous vehicles (PAVs) can relocate themselves elsewhere after arriving at a traveler’s destination, which may induce empty vehicle miles traveled (VMT) and impact parking patterns. However, few behavioral studies have explored travelers’ preferences for PAV relocation. This study fills this gap by designing stated choice experiments which include three alternatives: park at destination, relocate PAV to park at other places with a lower parking cost, and send the vehicles for use by other household members. The survey is distributed in Kansas City and Seattle in 2020, with 406 and 633 complete responses from the two cities, respectively. For each city, respondents are randomly divided into a control group (without displaying relocation fuel cost) and a treatment group (explicitly displaying relocation fuel cost). Mixed logit model results suggest that, for both cities, travelers with shopping/errands purposes or from low-income households are more open to the relocating options. This paper develops the notion of willingness-to-relocate (WTR), which represents the travel time travelers would relocate their PAVs to save $1 in parking cost. The treatment group from Kansas City and Seattle show WTR values of 5.64 and 4.98 minutes, which are 23% and 34% lower than the control group from the two cities, respectively. Findings of this paper are highly relevant for policy-making for AVs. For example, a VMT fee, which heightens travelers’ awareness of costs associated with vehicle relocation, could be effective in curbing the induced VMT from PAV relocation.
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