Estimating Savings in Parking Demand Using Shared Vehicles for Home–Work Commuting

Abstract

The increasing availability and adoption of shared vehicles as an alternative to personally-owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature. As a first step in this direction, this paper focuses on a type of private mobility which, although specific, is a major contributor to traffic congestion and parking needs, namely, home-work commuting. We develop a data-driven methodology for estimating commuter parking needs in different shared mobility models, including a model where self-driving vehicles are used to partially compensate flow imbalance typical of commuting, and further reduce parking infrastructure at the expense of increased traveled kilometers. We consider the city of Singapore as a case study, and produce very encouraging results showing that the gradual transition to shared mobility models will bring tangible reductions in parking infrastructure. In the future-looking, self-driving vehicle scenario, our analysis suggests that up to 50% reduction in parking needs can be achieved at the expense of increasing total traveled kilometers of less than 2%.

“Unparking”: How can Smart Mobility Reduce Parking Demand in Our Cities to the Minimum? (Beirut Case Study)

“…In the second method, "Unparking" propose a data-driven methodology for estimating commuter parking needs by focusing on home-work commuting, i.e., the most daily trips. Private cars are stationary 95% of the time; a single car usually occupies two parking spots: one at home and another at work [4]. In this work, we estimate the number of parking spaces needed (parking demand) in Beirut depending on two simulated scenarios (Sc) for commuting (Np is the number of parking spaces needed for each car):…”

“…(1) No sharing, each person uses a private car and has a private reserved parking space at their home and work location. In this case, a car needs two parking spots (Np = 2 parking spots/car) [4].…”

“…(2) Private cars, shared parking-in this scenario all parking is shared, with people taking the closet available spot to their destination at the end of each trip. In this case, 1 ≤ Np ≤ 2, depending on the number of shared parking spaces in the city (especially on-street parking) [4].…”

“…The discussion of parking is placed in the context of transport, mobility, land-use, society, technology, and planning in each of these cities. Besides, "Unparking" is a research project that aims to explore and understand a city's real need for parking spaces to reclaim land from cars with smart enabling tools such as Smart Public Transportation System, Soft Mobility, Smart Parking, and Shareable Mobility [4].…”

Crowdsourced Quantification and Visualization of Urban Mobility Space Inequality

“…Although this particular scenario is based on simulations in optimal settings, without discussing feasibility nor possible unintended consequences, the visualization provides a proof of concept how crowdsourced online platforms could be useful for accessible urban scenario planning (Peterson, Cumming, & Carpenter, 2003). Note that a more recent study using data-driven simulations in Singapore reports parking space reductions through selfdriving vehicles in the order of only 50%, at the expense of increasing total traveled kilometers by less than 2% (Kondor, Zhang, Tachet, Santi, & Ratti, 2017). As quantitative research on parking spaces is extremely sparse despite its fundamental role in city planning (Chester et al, 2015;Shoup, 2005) further research on the topic is urgently needed.…”