Street parking spots for automobiles are a scarce commodity in most urban environments. The heterogeneity of car sizes makes it inefficient to rigidly define fixed-sized spots. Instead, unmarked streets in cities like New York leave placement decisions to individual drivers, who have no direct incentive to maximize street utilization. In this paper, we explore the effectiveness of two different behavioral interventions designed to encourage better parking, namely (1) educational campaigns to encourage parkers to "kiss the bumper" and reduce the distance between themselves and their neighbors, or (2) painting appropriately-spaced markings on the street and urging drivers to "hit the line". Through analysis and simulation, we establish that the greatest densities are achieved when lines are painted to create spots roughly twice the length of average-sized cars. Kiss-the-bumper campaigns are in principle more effective than hit-the-line for equal degrees of compliance, although we believe that the visual cues of painted lines induce better parking behavior.
Perhaps the most related paper in terms of our research is Cassady and Korbza @cite_5 , who study the performance of two different driver search strategies in minimizing (1) walking time, (2) driving time, and (3) combined time in reaching their destination in a typical parking lot layout. Like us, they are interested in analyzing the impact of drivers parking strategies. However, we are concerned with maximizing the utilization of scarce street parking in an urban setting.
@cite_5
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Upon entering the parking lot of a facility, a driver must select a parking space. This paper proposes and evaluates strategies which are intended to find a "good" parking space. Three performance measures are defined for evaluating the "goodness" of a selected parking space. One of these is based on the amount of walking required of the customer, another is based on the amount of driving required, and the third is a combination of the first two and represents the time required for the customer to reach the front door of the facility. Two strategies are defined for selecting a parking space. The first is simple and somewhat conservative, whereas the second strategy is more complicated and aggressive. A probabilistic approach is used to evaluate the three performance measures for each strategy using a parking lot of somewhat "typical" configuration. This approach treats driver decisions and parking space availability as random experiments, and uses conditional probability to evaluate the performance of the strategies. This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting humanmobility fromdiscovering patterns of in the number of passenger pick-ups quantity (PUQ) from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale realworld data set of 4 000 taxis' GPS traces over one year shows a prediction error of only 5.8 . We also explore the application of the prediction approach to help drivers find their next passengers. The simulation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next passenger, by 37.1 and 6.4 , respectively. This paper systematically explores the efficiency of uncoordinated parking space allocation in urban environments with two types of parking facilities. Drivers decide whether to go for inexpensive but limited on-street parking spots or for expensive yet overdimensioned parking lots, incurring an additional cruising cost when they decide for on-street parking spots but fail to actually acquire one. Their decisions are made under perfect knowledge of the total parking supply and costs and different levels of information about the parking demand, i.e., complete probabilistic information and uncertainty. We take a game-theoretic approach and analyze the parking-space allocation process in each case as resource selection game instances. We derive their equilibria, compute the related price-of-anarchy (PoA) values, and study the impact of pricing on them. It is shown that, under typical pricing policies on the two types of parking facilities, drivers tend to overcompete for the on-street parking space, giving rise to redundant cruising cost. However, this inefficiency can be alleviated through systematic manipulation of the information that is announced to the drivers. In particular, counterintuitive less-is-more effects emerge regarding the way that information availability modulates the resulting efficiency of the process, which underpin general competitive service provision settings. Two friends have become separated in a building or shopping mall and and wish to meet as quickly as possible. There are n possible locations where they might meet. However, the locations are identical and there has been no prior agreement where to meet or how to search. Hence they must use identical strategies and must treat all locations in a symmetrical fashion. Suppose their search proceeds in discrete time. Since they wish to avoid the possibility of never meeting, they will wish to use some randomizing strategy. If each person searches one of the n locations at random at each step, then rendezvous will require n steps on average. It is possible to do better than this: although the optimal strategy is difficult to characterize for general n, there is a strategy with an expected time until rendezvous of less than 0.829 n for large enough n. For n = 2 and 3 the optimal strategy can be established and on average 2 and 8 3 steps are required respectively. There are many tantalizing variations on this problem, which we discuss with some conjectures. DYNAMIC PROGRAMMING; SEARCH PROBLEMS