On-street Parking Availability Research Articles

Trustworthiness Assessment for Crowdsourcing-Based Citywide Parking Availability Sensing via Connected and Automated Vehicles

Real-time status acquisition of parking spaces is highly valuable for an intelligent urban parking system. Crowdsourcing-based parking availability sensing via connected and automated vehicles (CAVs) provides a feasible method with the advantages of high coverage and low cost. However, data trust issues arise from incorrect detection and incomplete information. This paper proposes a trustworthiness assessment method for crowdsourced CAV data considering different impact factors, such as the distance between the CAV and the target parking space, line abrasion, scene complexity, and image sharpness. The crowdsourced CAV data are collected through extensive field experiments and PreScan simulations. The classical line detection algorithm of VPS-Net and the target detection algorithm of YOLO-v3 are applied to detect on-street parking availability. A failure probability model based on the XGBoost algorithm is then developed to establish the relationship between data trustworthiness and different impact factors. The results show that the proposed model has an average accuracy of 78.29% and can effectively assess the degrees of external influences on the trustworthiness of the crowdsourced data. This paper provides a new tool to identify the data quality and improve the sensing accuracy for a crowdsourcing-based parking availability information system.

Residual spatial-temporal graph convolutional neural network for on-street parking availability prediction

Residual spatial-temporal graph convolutional neural network for on-street parking availability prediction

Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive to changes that impact on-street parking availability. Additionally, a parking behavior change detection (PBCD) model is developed as an OSPI supplementary component to trigger potential parking map updates. The evaluation shows that the developed OSPI availability prediction model is on par with state-of-the-art models, despite having simpler but more enhanced and adaptive features. The foundational temporal and aggregated spatial parking capacity features help the most, while the PE-based features capture variances better and enable adaptivity to disruptions. The PE-based features are advantageous as data are automatically gathered daily. For the PBCD model, impacts by construction events can be detected as validation. The methodology proves that it is possible to create a reliable OSPI system with predominantly PE-based features and aggregated parking capacity features.

Residual Spatial-Temporal Graph Convolutional Neural Network for On-Street Parking Availability Prediction

Residual Spatial-Temporal Graph Convolutional Neural Network for On-Street Parking Availability Prediction

Do Safe Bike Lanes Really Slow Down Cars? A Simulation-Based Approach to Investigate the Effect of Retrofitting Safe Cycling Lanes on Vehicular Traffic

Cycling is a sustainable transportation mode that provides many health, economic and environmental benefits to society. Cities with high rates of cycling are better placed to address modern challenges of densification, carbon-neutral and connected 20-min neighbourhood goals. Despite the known benefits of cycling, participation rates in Australian cities are critically low and declining. Frequently, this low participation rate is attributed to the dangers of Australian cycle infrastructure that often necessitates the mixing of cyclists with car traffic. In addition, residents of car-dependent Australian suburbs can be resistant to the installation of cycle infrastructure where threats to traffic flow, or decreased on-street parking availability are perceived and the prohibitive cost of reconfiguration of other infrastructure maintained by the local councils to retrofit safe bike paths. This study investigates the effects on traffic behaviour of retrofitting safe, separate cycling lanes into existing residential streets in a Melbourne suburb suitable for accessing the primary neighbourhood destinations. We utilise only the widths available on the existing roadway of these streets, with minimal incursion on other facilities, such as the vehicle network and parking. Using only the existing roadway reflects the common need for municipal asset managers to minimise disruption and costs associated with street redesign. Using a traffic simulation approach, we modelled travel demand that suits suburban trips to services and shops, and we selectively applied separate cycling lanes to suitable residential streets and varied the effect of lowering speed limits. Simulations show that the selective inclusion of safe cycling lanes in some streets leads to a mere 7% increase in the average car travel times in the worst case, while requiring cyclists to increase their travel distance only marginally to avoid streets without dedicated cycling lanes. These results demonstrate that reasonable compromises are possible to make suburbs safer for cyclists and bring them closer to the 20-min neighbourhood goal. There is significant potential to enhance the result by including more street types and alternative designs. The results can inform councils in their cycle path infrastructure decisions and disprove assumptions about the influence of cyclists on car infrastructure.

Hybrid Spatial–Temporal Graph Convolutional Networks for On-Street Parking Availability Prediction

With the development of sensors and of the Internet of Things (IoT), smart cities can provide people with a variety of information for a more convenient life. Effective on-street parking availability prediction can improve parking efficiency and, at times, alleviate city congestion. Conventional methods of parking availability prediction often do not consider the spatial–temporal features of parking duration distributions. To this end, we propose a parking space prediction scheme called the hybrid spatial–temporal graph convolution networks (HST-GCNs). We use graph convolutional networks and gated linear units (GLUs) with a 1D convolutional neural network to obtain the spatial features and the temporal features, respectively. Then, we construct a spatial–temporal convolutional block to obtain the instantaneous spatial–temporal correlations. Based on the similarity of the parking duration distributions, we propose an attention mechanism called distAtt to measure the similarity of parking duration distributions. Through the distAtt mechanism, we add the long-term spatial–temporal correlations to our spatial–temporal convolutional block, and thus, we can capture complex hybrid spatial–temporal correlations to achieve a higher accuracy of parking availability prediction. Based on real-world datasets, we compare the proposed scheme with the benchmark models. The experimental results show that the proposed scheme has the best performance in predicting the parking occupancy rate.

MePark: Using Meters as Sensors for Citywide On-Street Parking Availability Prediction

Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines.

Exploiting Recurring Patterns to Improve Scalability of Parking Availability Prediction Systems

Parking Guidance and Information (PGI) systems aim at supporting drivers in finding suitable parking spaces, also by predicting the availability at driver’s Estimated Time of Arrival (ETA), leveraging information about the general parking availability situation. To do these predictions, most of the proposals in the literature dealing with on-street parking need to train a model for each road segment, with significant scalability issues when deploying a city-wide PGI. By investigating a real dataset we found that on-street parking dynamics show a high temporal auto-correlation. In this paper we present a new processing pipeline that exploits these recurring trends to improve the scalability. The proposal includes two steps to reduce both the number of required models and training examples. The effectiveness of the proposed pipeline has been empirically assessed on a real dataset of on-street parking availability from San Francisco (USA). Results show that the proposal is able to provide parking predictions whose accuracy is comparable to state-of-the-art solutions based on one model per road segment, while requiring only a fraction of training costs, thus being more likely scalable to city-wide scenarios.

Electric Free-Floating Carsharing for Sustainable Cities: Characterization of Frequent Trip Profiles Using Acquired Rental Data

Free-floating carsharing systems (FFCS) have become a new type of urban sustainable mobility, much more flexible than the previous station-based carsharing but limited by on-street parking availability and managed by municipal administrations. Literature on FFCS until now mostly relies on survey-based methodologies and simulations, and little research on FFCS has been devoted to the scientific analysis of real flows using revealed web-based data. This paper contributes to the existing literature with an analysis of FFCS trips using rental data collected directly from operators’ websites, paying special attention to the most frequent trips. The added value of this research is that it provides the first analysis of the more FFCS demanding districts in the city of Madrid. The results showed that the main origin and destinations points were concentrated in low populated and high-income districts that also had good parking availability and connectivity to the public transportation network.

Smart Parking: Using a Crowd of Taxis to Sense On-Street Parking Space Availability

Monitoring the occupancy of on-street parking spaces on a city-wide scale is still an open issue. Past research demonstrated the viability of parking crowd-sensing by means of the standard on-board sensors of probe vehicles, foreseeing the use of high-mileage vehicles, like taxis. Nevertheless, the achievable spatio-temporal sensing coverage has never been deeply investigated. In this paper, we investigate the suitability of taxi fleets of different sizes to crowd-sense on-street parking availability. We considered 579 road segments in San Francisco (USA), covered both by sensors of the SFpark project and by the GPS traces of 536 taxis. For each of these segments, we computed the taxi transit frequencies, representing the achievable coverage by vehicles equipped with sensors detecting empty parking spots. By combining these frequencies with parking occupancy data coming from SFpark, we estimated the potential quality of crowd-sensed on-street parking information for different fleet sizes. Moreover, we investigated the impact of different misdetection amounts, and Kalman filters to handle them. The results show that a total of 300 taxis can crowd-sense on-street parking availability with an error of up to ±1 stall in 86% of the cases. Moreover, the quality of the sensors is as important as the fleet size (300 taxis with 10% probability of misreadings provide availability information comparable to 486 taxis with 16% probability), while the use of Kalman filters did not lead to statistically significant improvements. In conclusion, the traffic management authorities should consider parking crowd-sensing via probe vehicles as a promising alternative to the expensive deployment of the static parking sensors.

Developing a measure of traffic calming associated with elementary school students’ active transport

The objective of this study is to develop a measure of traffic calming with nationally available GIS data from NAVTEQ and to validate the traffic calming index with the percentage of children reported by school administrators as walking or biking to school, using data from a nationally representative sample of elementary schools in 2006–2010. Specific models, with and without correlated errors, examined associations of objective GIS measures of the built environment, nationally available from NAVTEQ, with the latent construct of traffic calming. The best fit model for the latent traffic calming construct was determined to be a five factor model including objective measures of intersection density, count of medians/dividers, count of low mobility streets, count of roundabouts, and count of on-street parking availability, with no correlated errors among items. This construct also proved to be a good fit for the full measurement model when the outcome measure of percentage of students walking or biking to school was added to the model. The traffic calming measure was strongly, significantly, and positively correlated with the percentage of students reported as walking or biking to school. Applicability of results to public health and transportation policies and practices are discussed.

Theory versus implementation in congestion-priced parking: An evaluation of SFpark, 2011–2012

Shortages of street parking can cause cruising, a major source of urban congestion. We used SFpark, a federally funded experiment in market-priced parking in San Francisco, to study how changes in meter prices influenced on-street parking availability. We supervised observations of more than 13,400 vehicles parked on a subset of dynamically priced and control blocks at three points in time during 2011 and 2012. Repeated-observation, change-on-change regressions show that when prices rose, the block-level occupancy of parking fell, suggesting that SFpark worked as intended. But blocks where prices rose showed no discernible improvement in parking availability—the share of time at least one space on a block face was vacant. Price increases also had no association with other factors we would expect to be influenced by price, including parking duration, vehicle turnover, and carpooling. These relationships were robust to controlling for the parking zone, the previous price level, nearby employment, and the weather. A price system designed to improve average occupancy may not improve parking availability, and thus may not reduce cruising. Cities trying to reduce cruising may need to adjust prices based on minimum vacancy, and price changes may need to be larger in many cases.

Does TOD Need the T?

Problem, research strategy, and findings: Transit-oriented developments (TODs) often consist of new housing near rail stations. Channeling urban growth into such developments is intended in part to reduce the climate change, pollution, and congestion caused by driving. But new housing might be expected to attract more affluent households that drive more, and rail access might have smaller effects on auto ownership and use than housing tenure and size, parking availability, and the neighborhood and subregional built environments. I surveyed households in northern New Jersey living within two miles of 10 rail stations about their housing age and type, access to off-street parking, work and non-work travel patterns, demographics, and reasons for choosing their neighborhoods. The survey data were geocoded and joined to on-street parking data from a field survey, along with neighborhood and subregional built environment measures. I analyzed how these factors were correlated with automobile ownership and use as reported in the survey. Auto ownership, commuting, and grocery trip frequency were substantially lower among households living in new housing near rail stations compared to those in new households farther away. But rail access does little to explain this fact. Housing type and tenure, local and subregional density, bus service, and particularly off- and on-street parking availability, play a much more important role. Takeaway for practice: Transportation and land use planners should broaden their efforts to develop dense, mixed-use, low-parking housing beyond rail station areas. This could be both more influential and less expensive than a development policy oriented around rail. Research support: Data collection and initial research were funded under contract with the New Jersey Department of Transportation.

Parking in the City

This paper provides insight into the magnitude of the freight parking problem in large urban areas and the effectiveness of alternative solution strategies. The paper estimates the demand for parking through the use of freight trip generation estimates and estimates the supply of parking on the basis of curb space. The paper discusses strategies for the management of freight parking demand developed by governmental agencies and other organizations. In addition, the paper proposes an approximate methodology to quantify freight parking demand and on-street parking availability. Parking demand is expressed as a function of the freight trip generation of individual establishments, and parking availability is estimated to be a function of curb space dimensions and commercial vehicle characteristics. Empirical findings are provided. New York City is used as a case study. From the analyses and results, the paper provides a set of policy recommendations to help mitigate the identified issues.

Does residential parking supply affect household car ownership? The case of New York City

This paper investigates the impact of residential parking supply on private car ownership, the relationship at the heart of the debate on whether residential parking regulations could be used as a demand management strategy to influence travel behavior. However, no empirical studies have sufficiently answered the question. Many believe that parking has little or no effect on car ownership, while others disagree. The paper analyzes 770 households randomly selected from a household travel survey in the New York City region, and measures their complete parking supply, including garage size, driveway spaces, and on-street parking availability, using Google Streetviews and Bing Maps. Results from a nested logit model show that parking supply can significantly determine household car ownership decisions, even after controlling for the endogeneity between the two. Their influence actually outperforms household income and demographic characteristics, the often-assumed dominant determinants of car ownership. Different parking types also behave differently: driveway spaces are more important to car ownership than garages probably because many residents in the study region do not use a garage for car storage. On-street parking is also important to households with off-street parking. Implications for residential parking policies like the maximum off-street parking standard, resident parking permit, and street cleaning are also discussed.