Abstract
This literature review critically analyzes four articles that explore diverse strategies for optimizing parking availability on college campuses, highlighting the use of technology, shared parking systems, Travel Demand Management (TDM), and the impact of parking policies. Chou, Dewabharata, and Zulvia (2021) propose a dynamic shared parking system employing the Internet of Things (IoT) and recurrent neural networks (RNN) to manage parking space allocation efficiently. Huang et al. (2020) suggest leveraging unused residential parking capacities to fulfill nearby parking demands, incorporating users’ overtime parking behavior into their shared parking model. Sweet and Ferguson (2019) examine the potential of TDM in a university context, advocating for a balanced approach that enhances transportation alternatives and adjusts parking availability. Yan, Levine, and Marans (2019) assess the sensitivity of travelers to parking cost, search time, and egress time, emphasizing the synergistic effects of comprehensive parking policies. Collectively, these studies underscore the complexity of managing parking demand on campuses, the role of technological innovation, and the necessity of considering both internal and external demands in creating sustainable parking solutions. This review illuminates the multifaceted approaches to addressing parking challenges, stressing the importance of context-specific strategies and the potential benefits of integrating technology and policy measures to achieve effective parking management.
Keywords: satisfaction, demand, parking, Parking Management, Shared Parking Systems, Travel Demand Management campus(es)
Introduction
One of the many examples of student provision amongst college campuses is the supply of student parking within many facilities campus-wide. In today’s day and age, it is commonly known that, in most cases, there is a high demand for parking convenience but often little to no parking availability in many public spaces. College campuses, for example, usually need to balance the demand for parking spaces and the available supply. This imbalance creates many challenges for students, leading to frustration, dissatisfaction, and difficulty finding parking spaces, especially during peak times when the campus is bustling. Considering most campuses are located in the middle of residential areas, “The vacant parking spaces of residential areas can be efficiently utilized to meet the parking demands of those who are working nearby or come for other activities…” (Huang et al., 2020) This particular strategy opens the door to many others who work to meet the parking demands of those in need. An article published in PubMed expressed a proposal for a shared parking system to solve the parking problem in many cities. “The primary concept of the proposed shared parking system is to release parking lots that are open to specific groups for public usage without overriding personal usage.” (Chou et al., 2021) If implemented, this proposal’s success would greatly benefit many constantly busy cities and areas that require ample parking. Sweet and Ferguson (2019) highlight the importance of improving transportation alternatives and managing parking availability to influence mode share effectively. Yan, Levine, and Marans (2019) observe that parking policy adjustments lead to shifts in parking location preferences among travelers rather than mode changes. They also note that the combined implementation of pricing and time-saving measures has a more significant cumulative effect on managing parking demand than when these strategies are applied separately. However, increasing parking availability is the simple solution to such a growing problem on college campuses. However, colleges may need to assess different creative solutions or prioritize alternative transportation strategies for higher customer satisfaction. In this literature review, I will analyze four articles focused on optimizing parking availability on college campuses: Dewabharata, Chou, and Zulvia (2021); Huang et al., and He (2020); Sweet and Ferguson (2019); and Yan, Levine, and Marans (2019).
Literature Review
Article 1
The article “Dynamic Space Allocation based on Internal Demand for Optimal Release of Shared Parking” by Shuo-Yan Chou, Anindhita Dewabharata, and Ferani Eva Zulvia proposes a shared parking system wherein the Internet of Things (IoT) and recurrent neural networks (RNN) are applied to optimize the use of college parking lots. This method indeed handles the urban problem of parking deficit while not requiring any physical infrastructure expansion. The research aims to promote the current parking lot use that is achieved via a predictive model that can balance the diverse needs of individual groups and the public rather than the previous parking solutions that usually involve physical expansion or pricing strategies.
The process of inadequate parking spaces in cities, exacerbated by increased vehicles, is particularly urgent on campuses, where parking needs vary with different shifts and events. The article highlights the ineffectiveness of the parking systems that are often used and the ability of shared parking systems to overcome the chronic parking problem in the cities through support of the available literature on private residential parking sharing and the efficiency of shared parking systems.
The research hypothesis is that a shared parking system, boosted by an LSTM RNN model for demand forecasting, can improve parking space utilization without affecting stationed user groups’ comfort. The quantitative and descriptive study applies the predictive model to evaluate the effect of space allocation dynamics on parking unavailability.
For data collection, a university in Taipei City was selected and set up with a comprehensive IoT system to capture entry and exit times, vehicle numbers, and lot usage. The study’s methodology involves using natural and artificial datasets to validate the model, and comparing it with other forecasting algorithms determines its reliability. The LSTM RNN model, which relies on historical data and may need some manual adjustments, was more productive than other algorithms in predicting demand for parking spots, with no impact on the needs of the internal users.
The authors propose that the project be tested in different urban areas, and available real-time data should be processed to make the model more accurate. Therefore, this plan reveals that this scheme can solve parking issues on college campuses and beyond by providing green solutions to parking problems without needing infrastructure expansion.
Article 2
The research article by Xin Huang, Xueqin Long, Jianjun Wang, and Lan He (2020) deals with parking scarcity in cities by developing the mechanism of shared parking, which uses unnecessary parking in residential areas to satisfy office employees’ requirements. Through investigation of vehicle arrival times and days stayed, the authors found a model that enables parking cars overtime when necessary. Through such a strategy, parking supply and demand are balanced innovatively, which sets it apart from the standard approach primarily aimed at infrastructure development or maximizing operator revenues.
The qualitative, explanatory study, based on Xi’an’s shopping mall, Joy City, will utilize empirical research data on parking behaviors to propose an intelligent parking allocation system. This model aims to handle parking time windows correctly and provide reserved shared spaces to solve conflicts arising from overtime parking. The model validation through numerical simulations investigates the relationship among parking revenues, acceptance rates, and shared space utilization, indicating the optimal allocation of resources that help achieve the most significant benefit.
Recognizing the separate consideration of parking duration and arrival traits, the research is prepared to proceed further with the dynamic allocation and to apply shared parking in reality. The data indicates that a prudent mix of reserved parking may curtail the amount of overtime parking, thereby increasing the lot’s value and the residents’ satisfaction. Future research proposes a dynamic and holistic approach to shared parking strategies, including real-time data and scenario analysis, to improve model accuracy and enhance applicability.
In the final analysis, the article contributes to the discourse on urban parking solutions, systematically suggesting the shared parking model, which is optimized and flexible in considering various user behaviors. The model suggests economizing on some of the existing infrastructure by well-balancing between reserved and spillover parking, which is economically beneficial. The recommendations for future research point out the need to consider a more dynamic approach to the utilization of shared parking spaces, one that relies on real-time data and scenario analysis to come up with practical solutions to urban parking challenges
Article 3
The article by Sweet and Ferguson titled “Parking Demand Management in a Relatively Uncongested University Setting” focuses on Travel Demand Management (TDM) in a university environment and McMaster University in Hamilton, Ontario, as a case example. This study is intended to investigate the effect of TDM tools availability on auto trip reduction, keeping in mind the specific problems and prospects of the university environment for the promotion of greener travel patterns. Unlike other studies that concentrate just on urban areas with high levels of congestion, this study covers a less congested environment, making a unique point on parking demand management.
The main issue to be addressed in this research study is the disproportionate ratio between parking demand and supply in the university setting, which is more critical given the emergence of more private cars. The study explores whether improving alternatives to driving—such as enhancing pedestrian, bicycling, and transit services—or implementing measures to raise parking costs and reduce parking availability can effectively shift the driving mode share. This research question stands out by considering both “pull” (improving alternatives) and “push” (making driving less attractive) strategies within a relatively uncongested city, providing a comprehensive look at potential behavioral shifts in mode choice.
The research employs a quantitative, explanatory approach by collecting primary survey data from students, faculty, and staff and using discrete choice models to explore alternative future parking demand scenarios. The survey sample includes 1,480 students and 729 faculty and staff, providing a broad base to analyze mode choice responses to various transportation services, demand management, and price interventions. This methodological choice allows the study to generate specific, actionable insights into how different TDM measures could impact parking demand and mode shares.
A notable limitation of the study is its reliance on survey data and stated preferences, which may not perfectly capture actual behavioral responses to implemented TDM strategies. Additionally, the study’s context—McMaster University in a medium-sized city with relatively low congestion—may limit the generalizability of its findings to other university settings, particularly those in larger, more congested urban areas.
Findings from this study emphasize the measures of improving non-auto travel services. However, the most critical shifts in mode shares need measures to address parking supply constraints, increase parking costs, and decrease transit fares for faculty and staff. The courses of action will be very controversial, but it will require the political will to alter the current policy. This conclusion highlights that the policy of promoting alternatives to driving and implementing the things that directly affect the commutes and cost of driving should be balanced. The article’s authors also urge further studies examining the application of TDM policies in more diverse university campuses and heavily congested cities. Such a proposition emphasizes the need for more thoughtful analyses of how location-based strategies can be used to restrain overcrowding and give rise to environmentally friendly modes of transportation in the college neighborhood.
Article 4
The work by Xiang Yan, Jonathan Levine, and Robert Marans (2019) closely examines the implementation of parking policies in the university setting of Ann Arbor, Michigan. It explores their capabilities to alleviate parking demand pressure and car usage. This quantitative, explanatory research is marked by its comprehensive methodology, which consists of monetary and time-related parking costs. Through a critical review of the literature, the authors find a niche by simultaneously modeling parking and mode choice, an approach that needs to be better studied in the current literature, hence providing a detailed view of how the policy on parking can shape travelers’ decision process.
The main issue addressed by this analysis is that traditional parking system changes do not prompt vehicle ownership reduction, and parking demand decreases notably, particularly in the urban center. Unlike other research that primarily addresses the monetary aspects of parking, this study would also focus on the different elements that may affect parking search time and exit time. The study aims to find the sensitivity of travelers to these measures and determine the cumulative impact of parking policies that tackle both cost and time.
The study uses a hierarchical model of travel mode and parking location selection by using information from a revealed preference survey from commuters to the University of Michigan. This approach allows for a detailed examination of the influence of crucial policy variables: parking expense, search time, and exit time. For this study, the selected population involves faculty and staff members of the University from the University of Michigan sustainability culture indicators (SCIP) surveys for 2012 – 2015. The instrument, an online survey, maintains the credibility of the data because it is designed to record the actual travel behaviors and preferences of respondents.
One limitation acknowledged by the authors is the specificity of the study’s context to the University of Michigan, suggesting that while the findings offer valuable insights, they may not be directly transferable to other settings without consideration of local conditions such as parking enforcement, alternative travel mode availability, and population characteristics.
The study’s findings highlight several critical insights: Travelers are significantly more sensitive to changes in egress time compared to parking cost and search time, indicating that improvements in the connectivity between parking locations and final destinations could effectively encourage the use of remote parking lots. Furthermore, the study reveals that travelers tend to respond to parking policies more by relocating their parking rather than changing their mode of travel. This behavior underscores the necessity of implementing parking policies as a bundle, combining both “carrot” (e.g., improving alternative travel modes) and “stick” (e.g., increasing parking costs) approaches to achieve desired outcomes in parking demand management and car use reduction.
Authors recommend that future research investigate how parking and choice of transport modes interact in different situations. Pricing and non-pricing factors in parking policy should all be taken into account. This advice coincides with the research findings that an efficient parking policy needs specialized comprehension of travelers’ sentiments and behaviors, which can be obtained only by complex and context-specific studies.
Discussion/Conclusion
Cumulatively, the four articles demonstrate the multi-sided character of the parking demand on college campuses, and distinct methodologies and insights are brought in each article. The combination of IoT and RNN by Chou, Dewabharata, and Zulvia introduces a new direction for the dynamic allocation of space, highlighting the potential of technology in optimizing shared parking systems without adding any physical space and differing significantly from Huang and co-authors’ work concerning the joint parking strategies in residential areas following commercial centers, user behaviors consideration is needed in the allocation methods, especially when it comes to overtime parking. Sweet and Ferguson’s research primarily focuses on the narrower university campus setting, where TDM can be implemented through a multi-pronged approach. That would include the increase of alternatives for the car and the introduction of physical restrictions on parking. Yan, Levine, and Marans go further by employing a micro-study of price and non-price factors at the University of Michigan to show that the quality of connectivity between parking locations and destinations could significantly impact parking behaviors. The studies indeed verify how important strategic parking management is in addressing the problem of campus parking, and they do so by stating the complex nature of mitigating the demands between internal and external parking and how technological solutions can be the key to achieving a more sustainable parking ecosystem. Nevertheless, the issue of increasing the scale of these solutions in a wide variety of unique urban and University environments and the long-term effects on transportation behaviors are still essential to consider. These articles add priceless information to the current dialogue on parking policy by fusing technical advances, policy measures, and behavioral aspects.
References
Chou, S. Y., Dewabharata, A., & Zulvia, F. E. (2021). Dynamic Space Allocation Based on Internal Demand for Optimizing Release of Shared Parking. Sensors (Basel, Switzerland), 22(1), 235. https://doi.org/10.3390/s22010235
Huang, X., Long, X., Wang, J., & He, L. (2020). Research on parking sharing strategies considering user overtime parking. PloS one, 15(6), e0233772. https://doi.org/10.1371/journal.pone.0233772
Sweet, M. N., & Ferguson, M. R. (2019). Parking demand management in a relatively uncongested university setting. Case Studies on Transport Policy, 7(2), 453–462. https://www.sciencedirect.com/science/article/pii/S2213624X18302980
Yan, X., Levine, J., & Marans, R. (2019). The effectiveness of parking policies to reduce parking demand pressure and car use. Transport Policy, 73, 41-50. https://www.sciencedirect.com/science/article/pii/S0967070X18304402
write