- Doctoral Thesis
Rights / licenseIn Copyright - Non-Commercial Use Permitted
Parking policies and their interactions with the urban traffic and parking systems can have significant impacts on the traffic performance and the congestion in an urban area. These impacts have a long-term component affecting the travel demand and the travelers’ preferences, and a short-term component affecting the traffic and parking operations. This dissertation studies multiple parking policies focusing on pricing and occupancy aspects and analyzes their short-term impacts on the parking searchers and the performance of the traffic and parking systems, which, in turn, might impact the efficiency of the parking policies themselves. In other words, we investigate the interdependencies between different parking policies and parking-caused traffic issues. In particular, we evaluate the influences on the searching-for-parking traffic, the congestion in the network, the total driven distance, and the revenue created by parking, park and ride (P+R) fees, and/or congestion tolls for the city. We show the results for the different parking policies in some case studies of a central area within the city of Zurich, Switzerland. Our easy to implement model uses a dynamic macroscopic framework which saves on data collection efforts and reduces the computational costs significantly as all values correspond to aggregations at the network level over time. Our work clusters the parking policies into two types. First, we study static and dynamic parking pricing strategies and second, we investigate parking occupancy related strategies. i. At the beginning of this dissertation, we focus on a macroscopic on-street and garage parking framework which allows us to model the drivers’ decision between searching for an on-street parking space or driving to a parking garage instead. Different static on-street and garage parking fee ratios are analyzed with respect to the impacts on the traffic system and the parking search model over time. Our framework shows how traffic performance issues might influence the drivers’ decision between on-street and garage parking in the short-term. This decision is faced by multiple user groups with respect to their value of time (VOT). We study the impacts of different parking policies, including the availability of real-time garage usage information, and the conversion of on-street parking to garage parking spaces. The recovered on-street curb can then be used for other activities (e.g., bike lanes) in order to improve the quality of life for the city’s residents. Another strategy for cities might be to establish a P+R facility outside the city in order to reduce the searching-for-parking traffic in the central area. We analyze a P+R policy with static fares and compare it to a congestion pricing scenario and/or parking pricing policy in the network. In case the area consists of a high number of public parking spaces, parking pricing could be considered as a viable alternative to congestion pricing in terms of improving the performance of the traffic and parking system (i.e., traffic performance, parking availability, revenue for the city, etc.). Different parking fees or traffic conditions might, however, affect the drivers’ decision between entering the network by car or using P+R instead. We propose a decision model with respect to the drivers’ VOT and integrate it into a multimodal macroscopic traffic and parking framework focusing on parking and congestion pricing. We evaluate the distributional effects of our heterogenous VOT model on the drivers’ decision of which mode (P+R or car) to use when entering the area. Additionally, the proposed methodology can be used by city councils to find the trade-offs between the parking fee and the congestion toll when looking to reduce the average cruising time in the network, or increase the total revenue for the city. Moreover, we study a dynamic responsive parking pricing scheme which takes the parking search phenomenon and the parking occupancy into account. This macroscopic pricing policy maximizes the parking revenue for a city while minimizing the searching-for-parking time simultaneously. In different words, our pricing algorithm changes in response to the parking occupancy rate and the number of searching vehicles on the network. It checks whether the cost of paying the current parking fee is lower than the cost of keep on searching for another available parking space depending on the drivers’ VOT. The latter cost includes paying the predicted parking fee for the next available parking space at a future time slice under consideration of the driving and penalty costs to get there. We show the short-term impacts of the proposed dynamic parking pricing scheme on the urban traffic and parking systems, including the financial benefits of the pricing scheme and the benefits (or disbenefits) for the traffic performance in the area. ii. As the second type of parking policies, we study parking occupancy strategies in this dissertation. Here, we model the optimal parking occupancy rate over, e.g., the peak hours of the day, to guarantee an optimal trade-off between an efficient usage of the parking infrastructure and a high likelihood of finding parking to improve the traffic performance in a central area. In other words, our framework tries to find the optimal equilibrium between a high occupancy rate and a low average searching time in the network. It is based on the same macroscopic traffic and parking model that we used in the first part of the thesis. We extend it to include multiple vehicle types allowing us to generate insights about the parking occupancy’s dependency on specific vehicle types (e.g., fuel and electric vehicles). We evaluate a differentiated and a hierarchical parking policy for parking supply with and without battery chargers, and compare the results to a parking scheme without any parking differentiation. Our optimal parking occupancy strategy allows local governments to evaluate how to react towards a constantly varying parking demand (e.g., a modal shift towards electric vehicles), and how much parking supply to dedicate to electric vehicles in order to have the best balance between traffic performance, optimal parking occupancies, social impacts, and a high parking revenue for the city. Additionally, we provide cities a tool to analyze the influences on the optimal parking occupancy rate caused by a change in parking demand, supply, or parking duration in the area. In general, we discuss various parking policies in this dissertation and develop the tools for cities to evaluate the short-term impacts on the traffic and parking system when applying such policies. We show how to evaluate them macroscopically with the minimum amount of data requirements and costs, as our algorithms can easily be implemented with a simple numerical solver. Parking planners, traffic managers, consultants, practitioners, and local authorities can then use the new insights about these parking policies to develop the best fit for their city. Show more
External linksSearch print copy at ETH Library
SubjectParking; Parking policies; Parking pricing; Parking strategy; Parking search; Parking garage; Parking-related traffic state; Parking occupancy
Organisational unit03521 - Axhausen, Kay W. / Axhausen, Kay W.
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