On-street Parking Demand Research Articles

Making the trade-offs between parking and placemaking visible and actionable

ABSTRACT The space claims necessary for ensuring the liveability of public spaces often clash with on-street parking demand for private vehicles. Balancing these needs in a fair discussion has proven difficult. Demand for parking is expressed in clear-cut numbers, while liveability is a more emergent and qualitative concept. This paper seeks to (1) define the physical values of liveability; (2) translate them into generic space claims; (3) confront these claims with the demand for parking places in existing urban neighbourhoods; and (4) explore how this information could inform public debate and policy.

Evaluation of On-Street Night Parking Demand: A Case Study of Roorkee City

Nowadays, parking is a challenging issue all around the world, especially in the Central Business District (CBD) during the day and in residential areas during the night. Parking demand in CBD is increasing day by day due to insufficient off-street parking facilities and the tendency to park near the destination. The on-street night parking demand is increasing in residential areas due to the non-availability of sufficient off-street parking facilities on the premises. In this study, one residential area in Roorkee has been selected as the case study area. Socio-economic, demographic and parking characteristics were used as parameters to generate the parking demand model. The selected parameters were collected from the field using the in-out survey and questionnaire survey. Two parking demand models were developed by logistic regression analysis. The first model was developed without the parking fees, while the second model considers parking fees as a parameter. Also, the sensitivity analysis was done on both models to examine the changes in the existing parking demand.

A Short-Term Parking Demand Prediction Framework Integrating Overall and Internal Information

With the development of smart cities and smart transportation, cities can gradually provide people with more information to facilitate their life and travel, and parking is also inseparable from both of them. Accurate on-street parking demand prediction can improve parking resource utilization and parking management efficiency, as well as potentially improve urban traffic conditions. Previous parking demand prediction methods seldom consider the correlation between the parking demand of a road section and its surroundings. Therefore, in order to capture the correlation of parking demand in the temporal and spatial dimensions as carefully as possible and enrich the relevant features in the prediction model so as to achieve more accurate prediction results, we designed a parking demand prediction structure that considers different features from two perspectives: overall and internal. We used gated recurrent units (GRU) to extract demand influences in the temporal dimension. The GRU is used in combination with a graph convolutional neural network (GCN) to extract demand influencing factors in the spatial dimension. Additionally, a more detailed representation is designed to express spatial dimensional features. Then, based on the historical parking demand features extracted using encoder–decoder, we fuse the extracted spatio-temporal features with them to finally obtain an on-street parking demand prediction model combining the overall and the internal information. By combining them, we can integrate more correlation factors to achieve a more accurate parking demand prediction. The performance of the model is evaluated by real parking data in Xiufeng District of Guilin. The results show that the proposed model achieves good prediction performance compared with other baselines. In addition, we also design feature ablation experiments. Through the comparison of the results, we find that each feature considered in the proposed model is important in parking demand prediction.

Formulation of Parking Demand Estimation Model in Transit Oriented CBDs: A Review

Parking shortages are a regular occurrence in the urban Central Business District (CBD). In most Indian cities, the demand for parking outnumbers the supply. Managing parking demand is a critical aspect of transportation planning, particularly in metropolitan areas. Due to a shortage of off-street parking options in the CBD, tourists are compelled to park on the street, causing unnecessary congestion. The goals of this paper are to review existing research, identify key parameters, and provide models for forecasting on-street parking demand. To predict on-street parking demand in metropolitan CBDs with public transit (PT) service, a cost parameter (CF) model is developed. Some surveys, such as in-out surveys, license plate surveys, questionnaire surveys, video graphic survey methods, and so on, are likely to be utilized to collect data for the suggested model.

Analysis of the Effect of Demand-Driven Dynamic Parking Pricing on on-Street Parking Demand

The rapid increase in the number of cars has caused many problems such as “cruising for parking” and “parking illegally”. Demand-driven dynamic parking pricing mechanism is an effective method to dynamically regulate parking demand, thus reducing the parking problems. In this research, we conducted a large number of on-street parking surveys in the business districts in Beijing. A parking location choice model and the decision rules for cruising process were established. The multi-agent based on-street parking simulation was constructed to explore the effect of time-varying parking price on parking demand. It is concluded that demand-driven dynamic parking pricing can effectively regulate the distribution of parking demand and make the utilization of parking facilities into the desirable range in the business districts. Lower desirable range and higher price change for the price adjustment can cause larger fluctuations of parking demand, less time intervals within the desirable range and more price adjustment times. Higher desirable range and higher price change can bring longer cruising and driving time and lower driving speed. Considering the effectiveness and the operating costs of the pricing schemes, it is recommended that the suitable desirable rang for parking occupancy rate is 60%~80% and price change is 2 Yuan/hour. The threshold of price adjustment can be set according to the scale of the regional road network. The proposed dynamic parking pricing strategy can balance the distribution of parking demand and then reduce the parking and traffic problems. The research conclusions can also give references for the formulation of dynamic parking pricing strategies.

Citywide parking policy and traffic: Evidence from Amsterdam

We examine the effect of citywide parking policy on parking and traffic demand. Using a large increase in on-street parking prices for the city of Amsterdam, we show that the policy caused a substantial drop in on-street parking demand, which is not offset by an increase in off-street demand. The overall reduction in parking demand implies a 2% – 3% reduction in traffic, which is confirmed with traffic flow data. The decline in traffic is larger during the evening peak, which indicates that parking prices are effective in reducing congestion in the evening peak but less so in the morning peak.

Effect of On-Street parking on pedestrian flow characteristics and level of service - an Indian viewpoint

Urbanization and automobile-oriented development have resulted in a significant increase in vehicular trips towards central business district (CBD) areas, consequently increasing the demand for on-street parking. In many Indian cities, on-street parking demand is regulated by an odd–even date on-street parking scheme. Under this scheme, sidewalks are subjected to fully and partially confined boundaries, affecting pedestrian movement and the level of service (LOS). Therefore, it becomes imperative to study the influence of on-street parking on pedestrian characteristics to design efficient pedestrian facilities. Pedestrian flow data were collected at three study sites in the CBD areas of three major metropolitan cities of Gujarat, India, using a videography technique. Investigation revealed an 18% reduction in walking speed for parking sidewalk face (PSF) compared to non-parking sidewalk face (NPSF), attributed to the variation in density and presence of parked vehicles. The influence of parked vehicles probed for a similar set of pedestrian densities revealed a 14% reduction in walking speed for PSF compared to NPSF. For the same geometric attributes of the sidewalk, PSF had a deteriorated pedestrian LOS at a given average condition, promulgating the need to develop separate LOS thresholds for PSF and NPSF. Based on the developed speed-density relationships for PSF and NPSF, the Monte-Carlo simulation was performed to facilitate the development of LOS thresholds. Based on the developed LOS thresholds and cumulative percentile plots, the study recommends LOS B as the design service level for sidewalks in the CBD area characterized by on-street parking.

Impact of pricing policy change on on-street parking demand and user satisfaction: A case study in Nanning, China

Understanding the pricing policy effect is important for on-street parking management. This study investigates the impact of the on-street parking price adjustment with a case study in Nanning. Parking meter data and survey data are used to analyze the impact of the policy intervention on parking demand and user satisfaction, respectively. Regression discontinuity design (RDD), structural equation model (SEM), and binary logistic regression (BLR) models are implemented. Results show that the increase in parking pricing significantly decreases the parking volume (by around 20%) and parking duration (by around 10%). Drivers’ parking costs also increased. The influence varies across different sub-areas, days of the week, and trip purposes. After the policy adjustment, people feel that the parking price is higher, the distance of parking lots to the final destination is closer, and the parking lots are more vacant. On-street parking users after the policy intervention have higher incomes. As they are less sensitive to the price but more on quality, the overall parking satisfaction is increased.

Citywide parking policy and traffic: Evidence from Amsterdam

We examine the eff ectof citywide parking policy on parking and traffic demand. Using a large increase in on-street parking prices for the city of Amsterdam, we show that the policy caused a substantial drop in on-street parking demand, which is not off set by an increase in off -street demand. The overall reduction in parking demand implies a 2% - 3% reduction in traffic, which is con firmed with traffic flow data. The reductions in traffic are larger during the evening peak, which indicates that parking prices are effective at reducing congestion in the evening peak, but lesser in the morning peak.

Research on on-street temporary parking effects based on cellular automaton model under the framework of Kerner’s three-phase traffic theory

In this paper, the on-street temporary parking behavior is studied under the framework of Kerner’s three-phase traffic theory based on an improved Kerner–Klenov–Schreckeneberg–Wolf (KKSW) cellular automation (CA) model named KKSW-OSTP (on-street temporary parking) model. The complete parking process is divided into four steps and the microscopic effects of these four steps are analyzed. The parking behavior has significant negative impacts on the traffic flow, while having limited positive impacts on the left lane. The congestion pattern caused by on-street temporary parking is analyzed. Localized synchronized flow pattern (LSP) is observed in low-density traffic condition. Moving synchronized flow pattern (MSP) and General Pattern (GP) are observed in high-density traffic condition. The macroscopic effects of on-street temporary parking on road traffic conditions are analyzed by introducing parking rate into the model. It is found that more on-street temporary parking demand results in higher probability of traffic breakdown and higher congestion level. However, with the increase of parking rate and leave-parking rate, the frequency of rear-end conflicts reduces.

Data monitoring for the assessment of on-street parking demand in CBD areas of developing countries

On-street parking is considered as one of the essential and critical components of the urban transportation system. Despite urban sprawl, central business district (CBD) areas continue to attract significant number of trips, especially for shopping, trade and commerce purposes. Most of the cities in developing countries lack the mechanisms to monitor on-street parking demand. Further, the absence of unambiguous on-street parking policy has led to the phenomenon of largely disorganized on-street parking, especially in these cities. Therefore, to formulate parking management measures, it becomes imperative to assess existing on-street parking demand in a rational manner. To comprehend the objective, on-street parking survey using the license plate method was carried out for three days covering a normal working day, weekend and an event day at data monitoring interval of 10 min during peak parking hours for two diverse land-use types namely commercial and hospital in four metropolitan cities of Gujarat, India. Investigations revealed that the data monitoring interval significantly affected recorded parking demand. Recorded parking demand was observed to be insignificant after 30 min and 50 min data monitoring interval for commercial and hospital land-use respectively. The behaviour of parkers was probed by extracting percentage unique of parked vehicles (PUPV). PUPV revealed uniform parking behaviour in different cities, with progressively increasing trend as the data monitoring interval increased. Turnover (E2W/hr/bay) was evaluated for different data monitoring intervals and revealed a decaying trend with an increase in data monitoring interval. The consistency of PUPV (in form of the coefficient of variation (CV)) was observed at 30 min and 50 min data monitoring interval for commercial and hospital land-use respectively. Therefore, to capture all possible variation in on-street parking demand, the study proposes to perform on-street parking inventory and on-street parking evaluation at 30 min and 50 min data monitoring interval for commercial and hospital land-use respectively. The study provides a fundamental insight into the assessment of on-street parking demand using different data monitoring intervals and can accordingly facilitate planners and policymakers to formulate on-street parking demand management measures in developing countries.

Analyzing on-Street Parking Duration and Demand in a Metropolitan City of a Developing Country: A Case Study of Yogyakarta City, Indonesia

On-street parking is an urgent issue to address in a fast-growing city of a developing country, such as Yogyakarta City in Indonesia. However, this issue has not been satisfactorily studied due to a lack of relevant parking data. Using a sample of 21 street segments that are currently used for on-street parking in the central district of the city, this study analyzes how the parking duration and demand are differentiated by street and land use characteristics. The characteristics are evaluated through a field survey, which is supplemented by remote sensing and GIS. Specifically, QuickBird imagery is used to roughly examine the length and angle of the street segments and GIS data to calculate parking capacity and demand as well as to confirm the street length and angle. Regression models find that the parking duration is affected by the street length, parking volume, and commercial type of land use, while the street length also differentiates the parking demand. Although the model for the parking demand has only one significant variable—street length—its variation is better accounted for by the same set of variables than the variation in the parking duration. Regarding the street length, it is found to be the only significant variable in the demand model, but it becomes the weakest among those significant in the duration model, where the land use type has the highest magnitude.

Level of service for on-street parking

Parking of vehicle is one of the most important issues of the urban transportation system in any Central Business District (CBD). A sharp rise in vehicular ownership and insufficient transit system give rise to the huge on-street parking demand leading to a reduction in effective carriageway width, flow speed, creating unnecessary congestion, etc. The problems can be controlled by providing an efficient parking system. The present study aims to estimate the Level of Service (LOS) of on-street parking. LOS is calculated based on three parking selection criteria, viz. Parking Characteristic (PC), Safety Characteristic (SC) and Design Characteristic (DC). Four CBDs of Kolkata Metropolitan Area (KMA) are selected as a case study area. The overall LOS equation is formulated by Analytic Hierarchy Process (AHP). Cluster analysis and Zero dimension process are used to standardize the variables to designated LOS. Various surveys are carried out for data collection. The data are analyzed using SPSS. Finally, the overall LOS for on-street parking is estimated for all the selected CBDs.

On-Street Parking Demand Estimation in Urban CBD using FI and CF Model: A Case Study – Kolkata, India

Objectives:To estimate the on-street parking demand in the urban Central Business Districts (CBDs). Methods/Statistical Analysis: To achieve the goal, the study formulates two parking demand estimation models i.e., the fee index (FI) model and the cost factor (CF ) model, based on regression analysis using SPSSStatistical Package for the Social Science. FI Model estimates the on-street parking demand where the transit system is absent. On the other hand CF model estimates the demand by considering the mode shift from the private vehicle (PV) users to the public transit (PT ). Findings: Priority wise requirements for selecting PT are found out in this survey. The existing demand in the both selected CBDs of Kolkata, viz. Dalhousie and Gariahat is found to be much higher than the present parking supply. FI Model shows that, the demand will satisfy the existing supply if unit FI can be achieved. CF model explain that, the transit fare need to be increased by 52% and 26% for Dalhousie and Gariahat area respectively to meet the demand with the existing supply. It is also found out that, the on-street demand is less in transit oriented CBDs. The forecasted demand is reduced by 69% and 71% and by 63% and 59% than the present demand using CF model and the FI model respectively. In this study, it has been attempted to evaluate the on-street parking demand and such type of works has not been found out by the authors particularly in India which make it a pioneer study for others. Application/Improvements: The users need to be shifted from PV to PT immediately and the government must take necessary actions to introduce sufficient transit service to counter the on-street parking problem.

The sensitivity of on-street parking demand in response to price changes: A case study in Seattle, WA

This study investigates the sensitivity of on-street parking demand using the automatic transaction data from parking pay stations obtained before and after a parking rate change that was implemented in Seattle in early 2011. The parking rate implementation is based on performance-based pricing where rates are increased, decreased, or not changed in neighborhoods with occupancy levels higher than, lower than, or within the desired level. We calculated the price elasticity of on-street parking demand, or the percentage change in block-level occupancy in response to a change in pricing, modified by time of day and neighborhood characteristics. This study is the first one that calculates price elasticity by time of day for on-street parking demand on a block level in the U.S. context. This study is also the first one that empirically derives how neighborhood characteristics affect on-street parking demand in response to pricing. Moreover, this study looks into how pricing results in changes in parking turnover rates, parking duration and total revenue generated.Results confirm our hypotheses—price elasticity of the parking occupancy is inelastic and varies by time of day and neighborhood characteristics. In addition, the results showed that the pricing also affects parking duration: motorists park for a shorter time on average during the day in neighborhoods with increased rates and longer in neighborhoods with decreased rates. Performance based pricing policy seems to be lowering the turnover rates on average during the day in neighborhoods with increased rates, however, it appears working ideally in the peak-hours when the parking demand is the greatest, allowing more motorists to park. The decreased rate seemed to be increasing the parking occupancy during peak-hours when motorists see the benefit in paying lower fees.The study demonstrated that the estimated elasticities can be used to determine the optimal parking rate to achieve a desired level of parking occupancy on every block in the study area. This method can be easily applied in every city that has similar parking pay stations.