This session includes a variety of recent research in parking management policy.
Automated Vehicles and Central Business District Parking: The Effects of Drop-Off Travel on Traffic Flow and Vehicle Emissions
Huajun Chai, University of California, DavisShow Abstract
Caroline Rodier, University of California, Davis
H. Michael Zhang, University of California, Davis
Miguel Jaller, University of California, Davis
Jeffery Song, University of California, Davis
Gursewak Singh, University of California, Davis
The potential for automated vehicles (AVs) to reduce parking in central cities has generated a lot of excitement among urban planners. AVs could drop off and pick up passengers in areas where parking costs are high or where parking spots are limited. Personal AVs could return home or park in less expensive locations, and shared AVs could serve other passengers. Reduced demand for on-street and off-street parking presents numerous opportunities for redevelopment that could improve the livability of cities, e.g., additional street and sidewalk space for pedestrian and bicycle travel. However, reduced demand for parking would be accompanied by increased demand for curbside drop-off/pick-up space with related movements to enter and exit the flow of traffic. This change could be particularly challenging for traffic flows in downtown urban areas during peak hours where high volumes of drop-offs and pick-ups are likely to occur. In this paper, we simulate vehicle travel in San Francisco’s CBD to explore traffic flow effects and emissions impacts of increased demand for drop-off and pick-up travel versus parking. Then, we simulate and analyze the impact of increasing the supply of curb space designated specifically for drop-off vehicles. With more drop-off/pick-up traffic in the network, congestion and total VMT will increases while parking revenues will drop. If we increase the percentage of dedicated drop-off/pick-up space at the same time, it first reduces congestion, VMT and emission till the percentage reaches a certain point. Afterwards, more dedicated drop-off/pick-up space will contribute more congestion, VMT and emission.
Who Says There’s No Place to Park?: Evaluating Parking Sentiment Through Online Reviews
Andrew Mondschein, University of VirginiaShow Abstract
David King, Arizona State University
Christopher Hoehne, Arizona State University
Zhiqiu Jiang, University of Virginia
Mikhail Chester, Arizona State University
A common complaint against changing parking requirements is that parking is critical for businesses to survive. Such statements are generally taken as a statement of fact by planners and local officials, yet there is little empirical work to back it up. To this end, this research examines how online business reviews reflect the supply of parking in metropolitan Phoenix. Data on the supply of parking by parcel is combined with data from user-generated Yelp business reviews to assess satisfaction or frustration with parking at different types of businesses in commercial districts across the region. Results suggest that parking is mentioned in about five percent of overall reviews, and most often mentioned in reviews as a negative characteristic of the establishment. Reviewers concerned with parking give significant lower ratings to businesses, and parking sentiment is correlated with on-site parking supply, e.g. districts with fewer parking spaces per business often have more negative parking sentiment. In areas with shared parking facilities, however, parking was generally viewed more positively. These findings suggest that parking supply is part of a customer’s overall perception of a business, though not a major component, and that shared parking facilities are not associated with negative reviews. Implications for policy are that shared parking can be part of an overall package of parking reforms that satisfy businesses and customers alike. Keywords: Parking, social media, land use regulations
Spatiotemporal Classifier: A Novel Model of Citywide Parking Guidance Based on Crowdsourced Parking Events
Yan Nie, School of Sotfware EngineeringShow Abstract
Qinghao Lu, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Haoyan Chen, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Lei Peng, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
City-wide parking guidance system (CPGS) is the emerging infrastructure of intelligent transportation systems in China. Parking guidance is often designed as parking utility functions traditionally, relying on the real time data collected from parking lots. However, cases in China show it’s very hard to collect the data of all parking lots in a short term, due to some business issues. Inspired by the cases of traffic condition crowdsourced by vehicles, we consider vehicles would like to share their parking experience to help park easier by themselves and reduce the needs for the data provided by parking lots. In this paper, we install GPS devices, with capability to record parking locations, in volunteer vehicles, by which volunteers will upload the relevant data of their parking events. Then we build a classifier model based on CNN and LSTM to extract the spatiotemporal characteristics from these uploaded events. The parking guidance is not the matter of utility evaluation in our opinion, but how to classify a vehicle to a specific category, i.e. parking lot, according to its destination and driving context. The experiment shows the model can guide vehicles to the proper parking lots at/around their destinations correctly and quickly in the case without any data of parking spaces. When comparing to utility model, the classifier model shows almost the same effect, but at pretty low costs. In the long run, the classifier will achieve higher accuracy if more vehicles join and share their parking experience with others.
How Are Uber and Lyft Shaping Municipal On-Street Parking Revenue?
Benjamin Clark, University of OregonShow Abstract
Anne Brown, University of Oregon
Autonomous Vehicles (AVs) will impose challenges on cities that are currently difficult to fully envision and critical to begin addressing. This research makes an incremental step toward quantifying the impacts that AVs by examining current associations between transportation network company (TNC) trips—often viewed as a harbinger of AVs—and parking revenue in Seattle, WA. Using Uber and Lyft trip data combined with parking revenue data, this research uses a mix of econometric modeling to project parking revenue in the city of Seattle. Results demonstrate that total revenue generated in each census tract will continue to increase at current rates of TNC ridership; daily parking revenue will, however, start to decline if or when trips levels are about 5.3 times higher than the average 2016 level. The results also indicate that per-space parking revenue is likely to increase by about 2.2 percent for each one thousand additional TNC trips taken if no policy changes are taken. The effects on revenue will vary quite widely by neighborhood, suggesting that a one-size-fits-all policy may not be the best path forward for cities. Instead, flexible and adaptable policies that can more quickly respond (or better yet, be proactive) to changing AV demand will be better suited at managing the changes that will affect parking revenue.
Parking Occupancy and Shared Parking: Comparative Case Studies of Parking Reduction at Transit-Oriented Developments in the United States
Reid Ewing, University of UtahShow Abstract
Keuntae Kim, University of Utah
Sadegh Sabouri, University of Utah
Fariba Siddiq, University of California, Los Angeles
This study aims at addressing the question of parking supply and demand at transit-oriented developments (TODs) through comparative case studies of six TODs in the U.S. A seventh development is more exemplary of TAD (transit-adjacent development). As far as we can determine, this is one of the first studies to estimate peak parking-generation rates for TODs. Developments are often characterized in terms of D variables—development density, land use diversity, urban design, destination accessibility and distance to transit. The six TODs studied in this project are exemplary when it comes to the Ds. At the overall peak hour, only 58.3 to 84.0 percent of parking spaces are filled. Due to limited shared parking, even these exemplary developments do not achieve their full potential. At the overall peak hour, parked cars would fill only 19.0 to 45.8 percent of parking spaces if built to ITE standards. Peak parking demand is less than one half the parking supply guideline in the ITE Parking Generation manual. A sixth D, demand management (parking management), is mixed at the TODs studied. For one thing, there is a dearth of shared parking, though opportunities abound. Another area in which parking policies are not always smart is in bundled residential parking. At some TODs, a parking space/permit comes with each apartment whether the renters want it and use it or not. Parking is effectively free. A third area in which parking policies are not always smart is in free commercial parking, the counterpart of bundled residential parking.
Macroscopic Modeling and Dynamic Control of Parking Dispatching in an Era of Autonomous Vehicles
Cong Zhao, Tongji UniversityShow Abstract
Feixiong Liao, Technische Universiteit, Eindhoven
Xinghua Li, Tongji University
Yuchuan Du, Tongji University
The advent of autonomous vehicles (AVs) provides new opportunities and challenges in urban parking management. The extra floating trips of cruising-for-parking AVs due to self-relocation from the trip destinations have adverse impacts on traffic congestion. A way of improving AV parking operations is to deploy a centralized parking dispatch system that balances vacant cruising-for-parking AVs and available parking resources. This paper presents a network-scale parking dispatch model for AVs with the consideration of mixed traffic flows and parking with conventional vehicles (CVs). The model applies the concept of the macroscopic fundamental diagram (MFD) to represent the dynamic evolution of traffic conditions incorporating the effect of cruising-for-parking in a heterogeneous, congested city, which is partitioned into multiple regions and each is represented by a well-defined MFD. A model predictive control (MPC) is suggested to control the parking dispatch system. The simulation results show that the dispatch system improves the system performance and reduces traffic congestion by regulating the network towards undersaturated conditions. The sensitivity analysis on the level of AV penetration reveals that the total system travel time gradually decreases with the increase of AV penetration and CVs benefit more from the MPC dispatch controller.
Assessing Minimum Parking Requirements and Parking Capacities of Residential Development in the Bangkok Metropolitan Region
Chakaphan Chullabodhi, Chulalongkorn UniversityShow Abstract
Saksith Chalermpong, Chulalongkorn University
Apiwat Ratanawaraha, Chulalongkorn University
Hironori Kato, University of Tokyo
This paper examines whether minimum parking requirements affect parking provision in condominiums and what factors determine condominiums’ parking capacities. By calculating actual, required, and excess parking capacities, the paper finds that almost 90% of the sample condominiums in Bangkok and the surrounding municipalities in the Bangkok Metropolitan Region provide more parking spaces than required by law. The parking capacities in condominiums outside Bangkok are almost as high as those in Bangkok, despite that their required minimums are half of Bangkok. Only 11% of the sample condominiums provide the required minimums. The figure has decreased to zero in recent years, likely due to soaring land prices. These results suggest that developers’ decisions to provide parking are not determined by parking requirements but by market demand.
Parking and Economic Productivity: Would Silicon Valley Be Richer with Lower Parking Requirements?
Michael Manville, University of California, Los AngelesShow Abstract
C.J. Gabbe, Santa Clara University
Taner Osman, University of California, Los Angeles
We estimate the parking supply of Silicon Valley--the seven economically most productive cities in Santa Clara County, California. Using cadastral data, data on minimum parking requirements, and validation through visual inspection, we estimate that about 14 percent of the land area in these cities is devoted to parking, and that commercial parcels, on average devote over half of their land area to parking space. This latter fact raises the possibility that binding parking supply depresses Silicon Valley’s density, and thus its productivity. In an exploratory empirical exercise we simulate a reduction in parking requirements from the year 2000 forward, and show that parking would be reduced most (and hence density increased most) in the Valley’s most productive zip codes.
Development of Joint SP-Off-RP Model for Shanghai Commute Mode Choices in Response to Parking Fee Management
Ping Zhang, Tongji UniversityShow Abstract
Xin Ye, Tongji University
Ke Wang, Tongji University
In order to cope with the increasing imbalance between supply and demand in parking and severe road traffic congestion during peak periods, this paper developed an SP-off-RP (i.e. State-Preference-off-Revealed-Preference) choice model to analyze relations between parking fee and commute mode choices based on survey data collected in Shanghai. The survey questionnaire includes daily commute information, travel choices under SP background, personal socio-economic and demographic attributes. The data of road network and public transportation network are also used for model development. The model includes three main travel modes: car, public transit and non-motorized mode. Variables that significantly influence mode choice and reasons behind are discussed, including the parking fee, the level-of-service (LOS) of three modes and socio-economic and demographic variables. Meanwhile, in the process of model development, a random sample of full-mode commute trips in Shanghai is integrated to improve model precision. The study reveals that the new random disturbance in the SP background is relatively large. The direct elasticity of parking fee is estimated at -0.8529. Women and unmarried commuters are more willing to switch to public transit. Travelers whose monthly income is less than 6K RMB Yuan or engaged in service industry are more willing to switch to non-motorized mode. The study provides references on parking pricing as an alternative policy for travel demand management.
ITE Guideline Versus Actual Trip and Parking Generation: Evidence from a Transit-Oriented Development in an Auto-Oriented Region
Shima Hamidi, University of Texas, ArlingtonShow Abstract
Roya Etminani, University of Texas, Arlington
Sanggyun Kang, Korea Transport Institute (KOTI)
Reid Ewing, University of Utah
The Institute of Transportation Engineers (ITE) guidelines serve as the widely used reference for trip and parking generation estimates of any new development in the U.S. However, recent empirical studies question the efficacy of ITE guidelines in forecasting trip and parking generation in transit-oriented developments (TODs). Following the methodology of seven national TODs across the U.S, this study focuses on Dallas (TX), as a more auto-oriented American city, to explore the trip and parking generation at Mockingbird TOD as compared to the ITE guidelines. We find that, the Mockingbird TOD has the lowest walk mode share (13.6%), the lowest bike mode share (0.22%), the lowest bus transit mode share (1.09%) and by far the lowest rail transit mode share (5.9%) of all other seven TODs. Mockingbird also ranks first in terms of the driving mode share with about 80% of all its daily trips generated by driving. This is almost twice as many driving trips as the average of the other six TODs. This is possibly as auto-oriented as a TOD could be as auto-trips account for about 80% of its trips mostly because it is located in an auto-oriented region where more than 96% of the commuting trips are done by automobile. Still, the total auto trip generation rate in Mockingbird is about 12% lower than the ITE estimates. Similarly, while the parking supply in Mockingbird TOD is less than 48% of the recommended ITE supply rate, its peak parking occupancy is only about 55% of the TOD supply.
Can High-Quality Public Transport Support Reduced Car Parking Requirements for New Residential Apartments?
Chris De Gruyter, RMIT UniversityShow Abstract
Long T. Truong, La Trobe University
Elizabeth Taylor, Monash University
This paper explores the extent to which high quality public transport can support reduced car parking requirements for new residential apartment buildings. Using a case study of Melbourne, the demand for car parking at residential apartment buildings in proximity to high frequency public transport is assessed, while controlling for a range of socio-demographic, urban design and demand management variables. Key findings indicate that while lower demand for car parking is associated with proximity to high quality public transport, this association is not significant when controlling for other factors that influence car ownership. Public transport service supply within 800 metres of residential apartment buildings was instead found to be significant, rather than simple distance to transit. Modelling results suggest an inelastic relationship whereby a 10% increase in public transport service supply is associated with a 0.9-1.2% reduction in car parking demand as measured by levels of car ownership. Notwithstanding broader criticisms of residential off-street parking minimums, the findings have important implications for the development of residential car parking policies, suggesting that city-wide car parking requirements should appropriately reflect the spatial distribution and quality of public transport services.
Study on Short-Term Accurate Prediction of Parking Demand
Linbo Li, Tongji UniversityShow Abstract
Yang Li, Tongji University
Yahua Zhang, University of Southern Queensland
Parking guidance system is an effective way to alleviate traffic congestion. However, despite being a key technology for the release of free parking spaces, short-term accurate prediction of parking demand has not been effectively solved. This paper firstly investigates the effect of the variation of parking demand in different working days on parking demand prediction and then introduces the Long Short-Term Memory model into short-term parking demand prediction. Here we evaluate our approach on the off-street parking data collected in Chengdu, China from July 2016 to June 2017. Results show for the first time that parking demand varies differently in different working days and then the data from March 2017 to June 2017 was divided into three groups (Monday/ Tuesday, Wednesday, Thursday/ Friday). Based on this, for each group, we used Long Short-Term Memory model to forecast respectively. Finally, the experiment results reveal that the proposed Long Short-Term Memory model provides higher accuracy than existing models and the accuracy is further improved through making group prediction.
Analyzing Drivers' Intention to Accept Parking App by Structural Equation Model
Chang Yang, Ningbo UniversityShow Abstract
Xiaofei Ye, Ningbo University
Jin Xie, Ningbo University
Xingchen Yan, Nanjing Forestry University
Lili Lu, Ningbo University
With the concept of sharing economic entering into our lives, many parking app 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’ acceptance of parking App, the conceptual framework based on technology acceptance model was developed to establish the relationships between the drivers’ intention to accept parking App, trust in parking App, perceived usefulness of parking App, perceived ease of its use. Then structural equation model was established to analyze the relationship between the various variables. The results show that the trust in parking App, perceived usefulness, perceived ease of use and parking App attributes are the main factors that determine the intention to use parking App. Through the test of direct effect, indirect effect and total effect in the model, it is found that perceived usefulness has the largest total impact on acceptance intention, with a standardized coefficient of 0.984, followed by parking App attribute (0.743), perceived ease of use (0.384) and trust in parking App (0.381).
Urban Traffic Parking System Dynamics Model with Macroscopic Properties: A Comparative Study Between Shanghai and Zurich
Biruk Mesfin, Shanghai Jiao Tong UniversityShow Abstract
Jian Sun, Shanghai Jiao Tong University
The performance of a network’s parking-traffic system integration could be evaluated with respect to the cost of cruising for parking: cruising distance and time. Interpreting and analyzing parking policies with a precise evaluation model to quantify the cruising approaches may help understand the impact of the parking system on the overall traffic network and environment, from congestion to vehicular environmental emissions. Recent studies showed that analyzing network parking system dynamics can better represent the real dynamic states and guide to analyze the actual impact of the parking system on the neighboring systems. In this paper, a model based on the macroscopic dynamic parking model proposed by Cao and Menendez (2015) further extended to quantify and analyze comparatively the impact of parking policies on two different traffic networks with different infrastructural, socio-economic and political features. The parking space, average parking duration, and parking fees policies application were analyzed in detail as a function of the resulting cruising distance and time for parking and the indirect influence on traffic emission with a low-cost approach. Empirically, the model universality is tested and nourished with the field macroscopic data of two central business districts (CBDs) in Shanghai (Xujiahui area) and Zurich (Bahnhofstrasse area). The result shows Bahnhofstrasse is more sensitive with relatively higher elasticity and showed greater responsiveness on aggregate traffic emissions compared to Xujiahui with respect to policy changes.
Parking Forcast of Railway Station Garages Based on Passenger Behavior Analysis Using LSTM Network
Songxue Gai, Tongji UniversityShow Abstract
Xiaoqing Zeng, Tongji University
Parking volume forcast is an indispensable part of the parking guidance and information System (PGIS), which is an important compenent of the intelligent transportation system (ITS). Parking forcast of railway stations garages will provide information support for garages management,and will also provid a great convenience for passengers who arrive stations by car. So, it is very valuable to study on parking volume forcast of railway station garages. Refer to parking garages of railway stations, facilities which serve passengers to arrive or depart stations by cars. These arrival or departure behaviors depends on the timetables of their trains, so the parking forcast method of railway stations we present in this paper is based on the analysis of passengers’ behaviors. And also, a novel parking forcast model based on long short-term memory (LSTM) is proposed. The proposed LSTM network make it possible for the accuate and real-time predition of arrival volumes and departure volumes which are be forcasted separately by different parking behavors. Compared with other forcast models, our parking forcast models achieve a better performance and provide more detailed information.
Parking Detection Method Using Quadtree Decomposition Analysis
Khaled Shaaban, Utah Valley UniversityShow Abstract
Houweida Tounsi, Qatar University
This study proposes a new method to automatically detect available parking spaces. The suggested system identifies empty parking spaces using grayscale images obtained from any type of video camera. The method was found to successfully identify parking availability under different conditions and scenarios. The method was tested using real-life data and achieved a high detection rate. This method can be applied in real-time in order to monitor parking availability and guide drivers to empty spaces. The method has several advantages, including simple algorithms, use of low-quality black and white images, and simple configuration. Therefore, the system can provide enormous cost savings for locations with existing black and white surveillance cameras instead of replacing existing cameras with new high-quality cameras.
Time Window–Based Dual Bin Packing Approach for Improved Sharing of Parking Resources
Pengfei Zhao, Beijing University of TechnologyShow Abstract
Hongzhi Guan, Beijing University of Technology
Heng Wei, University of Cincinnati
Shixu Liu, Fuzhou University
Sharing the unused private parking spaces with public travelers can promote the utilization of the parking resources. However, a challenge lies in the methods for maximizing the utilization efficiency of such parking resources while avoiding the conflicts with the resource owners’ needs over the time horizon. To address this challenge, this study presents a methodology aiming at searching for the optimal allocation of the shared parking problem with time-window constraints. Firstly, a framework for shared parking is proposed to guide the development of the computation algorithms. Under this framework, the developed algorithms are aimed to ensure the optimum control of the preservations for the available parking spaces under the certain period of time while minimizing the wasted spaces which can not be well allocated due to unintelligent scheduling. For this purpose, a mixed-integer nonlinear programming (MINLP) model and its solving algorithms are then developed with the optimization objective of maximizing the spatio-temporal utilization. Further, the feasibility and validity of the proposed model and algorithms are tested by empirical analysis. The results indicate that the NI-ND heuristic algorithm performs the best under both small and large scale supply and demand settings with extremely high solution efficiency. Moreover, the solutions based on the NI-ND algorithm are very close to the optimal objective. It implies that the proposed shared parking allocation method based on the dual bin-packing principle is an effective way to address the realistic demand-supply relationship of parking resources under time-window constraints.
Applying SimPark for Parking Policy Analysis in Amsterdam
Jan Vuurstaek, Universiteit HasseltShow Abstract
Milou Bisseling, Universiteit van Amsterdam
Luk Knapen, UHasselt
Elenna Dugundji, Vrije Universiteit, Amsterdam
Jeroen Schmidt, Universiteit van Amsterdam
Jullian van Kampen, Centrum Wiskunde en Informatica
Bas Schotten, Municipality of Amsterdam
Tom Bellemens, Universiteit Hasselt
The application of the agent-based parking model SimPark as a parking policy evaluation tool is explored. The model is applied to the Frans Halsbuurt in Amsterdam, The Netherlands. This is a neighborhood with a high parking demand where recently most on-street parking places were replaced by a newly built parking garage. This transition from on-street to off-street parking went through different phases, starting from a situation with 567 on-street parking places. After the opening of the parking garage in May 2018, 600 off-street parking places became available resulting in a total of 1167 parking places in the neighborhood. Finally, several months after the opening of the parking garage, most on-street parking places were removed. These different phases were simulated using SimPark which is developed to simulate parking policies and to capture the effects on traffic flow and parking (behavior). The simulation results are compared to real world data collected by parked vehicle scan cars in order to assess SimPark as a parking policy evaluation tool.
Development of Multi-Nomial Logit Model for Shanghai Auto Drivers’ Non-Work Mode Choices in Response to Parking Fee Management
Ping Zhang, Tongji UniversityShow Abstract
Xin Ye, Tongji University
Ke Wang, Tongji University
In order to have a comprehensive understanding of non-work travel behavior, this paper takes into account five dimensions of travel pattern: mode, departure date, departure time, trip destination, and the number of companions. A multinomial logit model is developed to analyze relations between parking fee and non-work mode choice based on a survey conducted in Shanghai. The questionnaire includes the latest non-work trip information, the five-dimensional choices under stated preference (SP) scenarios designed based on revealed preference (RP) parking charges and travelers’ arrival time, personal socioeconomic and demographic attributes. The data of road and transit networks are used for model development. Variables that significantly influence mode choice and reasons behind are discussed, including the parking fee, the level-of-service (LOS) of all modes and personal attributes. The results show that as the parking fee increase in the SP scenarios, 39.79% of travelers are willing to change their mode choice to get a lower cost, 16.17% of travelers may change departure time, 12.13% of travelers may change the number of companions, less than 10% of travelers show tendency to change departure date or destination, 6.38% of travelers may cancel the trip, 18.72% of respondents may change both mode choice and departure time, 20.32% of respondents would like to change their mode choice and the number of companions at the same time. The elasticities of parking fee are -0.4882 and -0.2285 for SOV and 2-person HOV respectively, which means that SOV drivers are more sensitive to parking fee increase than 2-person HOV passengers.
Assessment of Students’ Campus Parking in Rural Areas
Jaser Mahasneh, Jordan University of Science and TechnologyShow Abstract
Doaa Al-Alawneh, Jordan University of Science and Technology
Anne Gharaibeh, Jordan University of Science and Technology
This study investigates the most crucial factors affecting parking behaviors among students in a rural campus settled within car-dominant context. A random sample of 1252 students is collected through a web-based survey. The contribution of this study lies in trying to fill the literature gap of parking demand at rural campuses in developing countries suffering from unorganized public transport options and poor parking management practices. This study performs quantitative analysis to examine the major variables that affect driving alone and parking behaviors among students in rural campuses. This study was applied to Jordan University of Science and Technology (JUST) student campus parking. Results from the survey and cross-tabulation analysis indicate that driving alone decisions for students in rural campuses are determined by a suit of socio-economic, demographic, and psychological variables. Socio-economic and demographic variables include; gender, marital status, age, residential location, travel cost of public transport, and number of private vehicles per household. Psychological motives of car use related to speed, shorter travel times, convenience, comfort and hygiene have the highest magnitude of predicting driving alone decisions. Most importantly, the ability to find a parking space is heavily influenced by students’ daily parking habits comprising of; number of parking days per week, arrival time to students’ parking, time spent searching for a parking space, being late to class while looking for a parking space, and trip chaining. This study recommends strategies that may be used to supplement future planning efforts in promoting multimodal commute among students of other rural campuses.
An Agent-Based Simulation Model for Evaluation of Parking Policies in the Era of Autonomous Vehicles
Sina Bahrami, University of Michigan, Ann ArborShow Abstract
Matthew J Roorda, University of Toronto
Autonomous Vehicles ( AVs ) eliminate the burden of finding a parking spot upon arrival to the destination. AVs can park at a strategic location or cruise until summoned by their users. In this study, we investigate where AVs park considering cost and time constraints. Since each traveller choice has impacts on the network travel time and others' choices, we use an agent-based simulation model. Results show that travellers consider sending their vehicles to park at home if they have to pay anything for the parking. Also, our analysis for downtown Toronto shows that AVs will travel on average 12 minutes and a maximum of 47 minutes to park in cheaper parking lots. While the same parking price across all the locations would exacerbate the congestion by motiving more AVs to cruise, a toll for zero-occupant AVs would decrease the VKT by 3.5%.
Optimal Dedicated Locations for Curbside Pickup and Drop-Off Ridesourcing and Ridesharing Autonomous Vehicles
Xidong Pi, Carnegie Mellon UniversityShow Abstract
Susu Xu, Carnegie Mellon University
Sean Qian, Carnegie Mellon University
Ridesourcing and ridesharing with autonomous vehicles will be important travel modes in future intelligent transportation systems (ITS). Their development and popularization is likely to lead to a decline in urban curbside parking demand from private vehicles, but may substantially increase the demand for curbside pick-ups/drop-offs from shared autonomous vehicles (SAV). The excessive short-term curb use can lead to not only the shortage of pick-up/drop-off space, but also negative societal externalities on both SAV services and the through traffic. One possible way to mitigate this impact is to have dedicated pick-up/drop-off curb spaces for ridesourcing and ridesharing vehicles, in order to ensure service efficiency, mobility, and safety. In this study, from the perspective of a ridesourcing service provider, we formulate the problem of choosing dedicated curbside pick-up/drop-off locations to optimize service efficiency. The problem is cast into a non-linear bounded knapsack problem and solved through a pragmatic and scalable heuristic solution algorithm. Numerical experiments based on a real-world transportation network show our model and algorithm can provide solutions close to the global optimum in a highly computational efficient manner. Another case study based on a grid network also provides policy insights for choosing dedicated curbside pick-up/drop-off locations under different design parameters, e.g. pricing schemes and ride-request demand patterns.
Curb Management in Business Districts: Current Trends and Future Options
Juan Matute, University of California, Los AngelesShow Abstract
Yu Hong Hwang, University of California, Los Angeles
This paper describes curb management approaches in business districts, generally, explores state and city curb regulations in California and Washington and documents the current state of curb management in three business districts in those states. Existing curb regulations prioritize safety from hazards, the free flow of traffic, and provision of access, in that order. Curb regulations that provide for access focus on provision of zones for passenger loading and the pickup and drop-off of bulk goods. Trends in new mobility have led to an increase in demand for pickup and dropoff of passengers and prepared foods or other perishable goods. These trends have been most pronounced in business districts, where the combination of insufficient supply of curbspace allocated to such high-turnover activities leads to double parking, causing safety hazards, impeding traffic flow, interfering with the operation of bus stops, and even reducing public parking revenues. We explore current trends for courier network services create an acute demand for curb space at restaurants around mealtimes. These services are also leading to changes in land use and restaurant business models. We present two case studies of how Southern California cities are reallocating curb space in response to the demands of courier network services, with a preliminary evaluation from a pilot project in Downtown Santa Monica.
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